MISY 5360 Exam One Review

Sentiment analysis applications, VOC, VOM, and VOE,

Sentiment Analysis Applications Compared to traditional sentiment analysis methods, which were survey based or focus group centered, costly, and time consuming (and therefore driven from a small sample of participants), the new face of text analytics-based sentiment analysis is a limit breaker. Current solutions automate very large-scale data collection, filtering, classification, and clustering methods via NLP and data mining technologies that handle both factual and subjective information. Sentiment analysis is perhaps the most popular application of text analytics, tapping into data sources like tweets, Facebook posts, online communities, discussion boards, Web logs, product reviews, call center logs and recordings, product rating sites, chat rooms, price comparison portals, search engine logs, and newsgroups. The following applications of sentiment analysis are meant to illustrate the power and the widespread coverage of this technology. Voice of t he Cu stomer (VOC ) Voice of the customer (VOC) is an integral part of analytic CRM and customer experience management systems. As the enabler of VOC, sentiment analysis can access a company's product and service reviews (either continuously or periodically) to better understand and better manage customer complaints and praises. For instance, a motion picture advertising/marketing company may detect negative sentiments toward a movie that is about to open in theatres (based on its trailers) and quickly change the composition of trailers and advertising strategy (on all media outlets) to mitigate the negative impact. Similarly, a software company may detect the negative buzz regarding the bugs found in their newly released product early enough to release patches and quick fixes to alleviate the situation. Often, the focus of VOC is individual customers, their service- and support-related needs, wants, and issues. VOC draws data from the full set of customer touch points, including e-mails, surveys, call center notes/recordings, and social media postings, and matches customer voices to transactions (inquiries, purchases, returns) and individual customer profiles captured in enterprise operational systems. VOC, mostly driven by sentiment analysis, is a key element of customer experience management initiatives, where the goal is to create an intimate relationship with the customer. Voice of the M arket (VOM ) VOM is about understanding aggregate opinions and trends. It's about knowing what stakeholders—customers, potential customers, influencers, whoever—are saying about your (and your competitors') products and services. A well-done VOM analysis helps companies with competitive intelligence and product development and positioning. Voice of t he Employee (VOE) Traditionally, VOE has been limited to employee satisfaction surveys. Text analytics in general (and sentiment analysis in particular) is a huge enabler of assessing the VOE. Using rich, opinionated textual data is an effective and efficient way to listen to what employees are saying. As we all know, happy employees empower customer experience efforts and improve customer satisfaction.

Examples of data mining applications,

Data mining has become a popular tool in addressing many complex businesses Problems and opportunities. It has been proven to be very successful and helpful in many areas, some of which are shown by the following representative examples. The goal of many of these business data mining applications is to solve a pressing problem or to explore an emerging business opportunity to create a sustainable competitive advantage. • Customer relationship management. Customer relationship management (CRM) is the extension of traditional marketing. The goal of CRM is to create one-on-one relationships with customers by developing an intimate understanding of their needs and wants. As businesses build relationships with their customers over time through a variety of interactions (e.g., product inquiries, sales, service requests, warranty calls, product reviews, social media connections), they accumulate tremendous amounts of data. When combined with demographic and socioeconomic attributes, this information-rich data can be used to (1) identify most likely responders/buyers of new products/services (i.e., customer profiling); (2) understand the root causes of customer attrition to improve customer retention (i.e., churn analysis); (3) discover time-variant associations between products and services to maximize sales and customer value; and (4) identify the most profitable customers and their preferential needs to strengthen relationships and to maximize sales. • Banking. Data mining can help banks with the following: (1) automating the loan application process by accurately predicting the most probable defaulters, (2) detecting fraudulent credit card and online banking transactions, (3) identifying ways to maximize customer value by selling them products and services that they are most likely to buy, and (4) optimizing the cash return by accurately forecasting the cash flow on banking entities (e.g., ATM machines, banking branches). • Retailing and logistics. In the retailing industry, data mining can be used to (1) predict accurate sales volumes at specific retail locations to determine correct inventory levels; (2) identify sales relationships between different products (with market-basket analysis) to improve the store layout and optimize sales promotions; (3) forecast consumption levels of different product types (based on seasonal and environmental conditions) to optimize logistics and, hence, maximize sales; and (4) discover interesting patterns in the movement of products (especially for the products that have a limited shelf life because they are prone to expiration, perishability, and contamination) in a supply chain by analyzing sensory and radiofrequency identification (RFID) data. • Manufacturing and production. Manufacturers can use data mining to (1) predict machinery failures before they occur through the use of sensory data (enabling what is called condition-based maintenance); (2) identify anomalies and commonalities in production systems to optimize manufacturing capacity; and (3) discover novel patterns to identify and improve product quality. • Brokerage and securities trading. Brokers and traders use data mining to (1) predict when and how much certain bond prices will change; (2) forecast the range and direction of stock fluctuations; (3) assess the effect of particular issues and events on overall market movements; and (4) identify and prevent fraudulent activities in securities trading. • Insurance. The insurance industry uses data mining techniques to (1) forecast claim amounts for property and medical coverage costs for better business planning, (2) determine optimal rate plans based on the analysis of claims and customer data, (3) predict which customers are more likely to buy new policies with special features, and (4) identify and prevent incorrect claim payments and fraudulent activities. • Computer hardware and software. Data mining can be used to (1) predict disk drive failures well before they actually occur, (2) identify and filter unwanted Web content and e-mail messages, (3) detect and prevent computer network security breaches and (4) identify potentially unsecure software products. • Government and defense. Data mining also has a number of military applications. It can be used to (1) forecast the cost of moving military personnel and equipment; (2) predict an adversary's moves and, hence, develop more successful strategies for military engagements; (3) predict resource consumption for better planning and budgeting; and (4) identify classes of unique experiences, strategies, and lessons learned from military operations for better knowledge sharing throughout the organization. • Travel industry (airlines, hotels/resorts, rental car companies). Data mining has a variety of uses in the travel industry. It is successfully used to (1) predict sales of different services (seat types in airplanes, room types in hotels/resorts, car types in rental car companies) in order to optimally price services to maximize revenues as a function of time-varying transactions (commonly referred to as yield management); (2) forecast demand at different locations to better allocate limited organizational resources; (3) identify the most profitable customers and provide them with personalized services to maintain their repeat business; and (4) retain valuable employees by identifying and acting on the root causes for attrition. • Healthcare. Data mining has a number of healthcare applications. It can be used to (1) identify people without health insurance and the factors underlying this undesired phenomenon, (2) identify novel cost-benefit relationships between different treatments to develop more effective strategies, (3) forecast the level and the time of demand at different service locations to optimally allocate organizational resources, and (4) understand the underlying reasons for customer and employee attrition. • Medicine. Use of data mining in medicine should be viewed as an invaluable complement to traditional medical research, which is mainly clinical and biological in nature. Data mining analyses can (1) identify novel patterns to improve survivability of patients with cancer, (2) predict success rates of organ transplantation patients to develop better organ donor matching policies, (3) identify the functions of different genes in the human chromosome (known as genomics), and (4) discover the relationships between symptoms and illnesses (as well as illnesses and successful treatments) to help medical professionals make informed and correct decisions in a timely manner. • Entertainment industry. Data mining is successfully used by the entertainment industry to (1) analyze viewer data to decide what programs to show during prime time and how to maximize returns by knowing where to insert advertisements, (2) predict the financial success of movies before they are produced to make investment decisions and to optimize the returns, (3) forecast the demand at different locations and different times to better schedule entertainment events and to optimally allocate resources, and (4) develop optimal pricing policies to maximize revenues. • Homeland security and law enforcement. Data mining has a number of homeland security and law enforcement applications. Data mining is often used to (1) identify patterns of terrorist behaviors (see Application Case 4.3 for an example of the use of data mining to track funding of terrorists' activities); (2) discover crime patterns (e.g., locations, timings, criminal behaviors, and other related attributes) to help solve criminal cases in a timely manner; (3) predict and eliminate potential biological and chemical attacks to the nation's critical infrastructure by analyzing special-purpose sensory data; and (4) identify and stop malicious attacks on critical information infrastructures (often called information warfare). • Sports. Data mining was used to improve the performance of National Basketball Association (NBA) teams in the United States. Major League Baseball teams are into predictive analytics and data mining to optimally utilize their limited resources for a winning season (see Moneyball article in Chapter 1). In fact, most, if not all, professional sports nowadays employ data crunchers and use data mining to increase their chances of winning. Data mining applications are not limited to professional sports. In a 2012 article, Delen, Cogdell, and Kasap (2012) developed data mining models to predict National Collegiate Athletic Association (NCAA) Bowl Game outcomes using a wide range of variables about the two opposing teams' previous games statistics (more details about this case study are provided in Chapter 2). Wright (2012) used a variety of predictors for examination of the NCAA men's basketball championship bracket (a.k.a. March Madness).

Characteristics that define the readiness level of data for analytics

Following are some of the characteristics (metrics) that define the readiness level of data for an analytics study (Delen, 2015; Kock, McQueen, & Corner, 1997). • Data source reliability refers to the originality and appropriateness of the storage medium where the data is obtained—answering the question of "Do we have the right confidence and belief in this data source?" If it all possible, one should always look for the original source/creator of the data to eliminate/mitigate the possibilities of data misrepresentation and data transformation caused by the mishandling of the data as it moved from the source to destination through one or more steps and stops along the way. Every move of the data creates a chance to unintentionally drop or reformat data items, which limits the integrity and perhaps true accuracy of the data set. • Data content accuracy means that data are correct and are a good match for the analytics problem—answering the question of "Do we have the right data for the job?" The data should represent what was intended or defined by the original source of the data. For example, the customer's contact information recorded in a record within a database should be the same as what the patient said it was. Data accuracy will be covered in more detail in the following subsection. • Data accessibility means that the data are easily and readily obtainable—answering the question of "Can we easily get to the data when we need to?" Access to data may be tricky, especially if the data is stored in more than one location and storage medium and need to be merged/transformed while accessing and obtaining it. As the traditional relational database management systems leave their place (or coexist with) a new generation of data storage mediums like data lakes and Hadoop infrastructure, the importance/criticality of data accessibility is also increasing. • Data security and data privacy means that the data is secured to only allow those people who have the authority and the need to access it and to prevent anyone else from reaching it. Increasing popularity in educational degrees and certificate programs for Information Assurance is an evidence to the criticality and the increasing urgency of this data quality metric. Any organization that maintains health records for individual patients must have systems in place that not only safeguard the data from unauthorized access (which is mandated by federal laws like Health Insurance Portability and Accountability Act [HIPPA]) but also accurately identifies each patient to allow proper and timely access to records by authorized users (Annas, 2003). • Data richness means that all the required data elements are included in the data set. In essence, richness (or comprehensiveness) means that the available variables portray a rich enough dimensionality of the underlying subject matter for an accurate and worthy analytics study. It also means that the information content is complete (or near complete) to build a predictive and/or prescriptive analytics model. • Data consistency means that the data are accurately collected and combined/ merged. Consistent data represent the dimensional information (variables of interest) coming from potentially disparate sources but pertaining to the same subject. If the data integration/merging is not done properly, some of the variables of different subjects may find themselves in the same record—having two different patient records mixed up—for instance, it may happen while merging the demographic and clinical test result data records. • Data currency/data timeliness means that the data should be up-to-date (or as recent/new as it needs to be) for a given analytics model. It also means that the data is recorded at or near the time of the event or observation so that the time-delay related misrepresentation (incorrectly remembering and encoding) of the data is prevented. Because accurate analytics rely on accurate and timely data, an essential characteristic of analytics-ready data is the timeliness of the creation and access to data elements. • Data granularity requires that the variables and data values be defined at the lowest (or as low as required) level of detail for the intended use of the data. If the data is aggregated, it may not contain the level of detail needed for an analytics algorithm to learn how to discern different records/cases from one another. For example, in a medical setting, numerical values for laboratory results should be recorded to the appropriate decimal place as required for the meaningful interpretation of test results and proper use of those values within an analytics algorithm. Similarly, in the collection of demographic data, data elements should be defined at a granular level to determine the differences in outcomes of care among various subpopulations. One thing to remember is that the data that is aggregated cannot be disaggregated (without access to the original source), but it can easily be aggregated from its granular representation. • Data validity is the term used to describe a match/mismatch between the actual and expected data values of a given variable. As part of data definition, the acceptable values or value ranges for each data element must be defined. For example, a valid data definition related to gender would include three values: male, female, and unknown. • Data relevancy means that the variables in the data set are all relevant to the study being conducted. Relevancy is not a dichotomous measure (whether a variable is relevant or not); rather, it has a spectrum of relevancy from least relevant to most relevant. Based on the analytics algorithms being used, one may choose to include only the most relevant information (i.e., variables) or if the algorithm is capable enough to sort them out, may choose to include all the relevant ones, regardless of their relevancy level. One thing that analytics studies should avoid is to include totally irrelevant data into the model building, as this may contaminate the information for the algorithm, resulting in inaccurate and misleading results. Although these are perhaps the most prevailing metrics to keep up with, the true data quality and excellent analytics readiness for a specific application domain would require different levels of emphasis paid on these metric dimensions and perhaps add more specific ones to this collection. The following section will dive into the nature of data from a taxonomical perspective to list and define different data types as they relate to different analytics projects.

Arithmetic mean, median, mode,

The arithmetic mean (or simply mean or average) is the sum of all the values/observations divided by the number of observations in the data set. It is by far the most popular and most commonly used measure of central tendency. It is used with continuous or discrete numeric data. For a given variable x, if we happen to have n values/observations (x1, x2, . . . , xn), we can write the arithmetic mean of the data sample (x, pronounced as x-bar) as follows: The mean has several unique characteristics. For instance, the sum of the absolute deviations (differences between the mean and the observations) above the mean are the same as the sum of the deviations below the mean, balancing the values on either side of it. That said, it does not suggest, however, that half the observations are above and the other half are below the mean (a common misconception among those who do not know basic statistics). Also, the mean is unique for every data set and is meaningful and calculable for both interval- and ratio-type numeric data. One major downside is that the mean can be affected by outliers (observations that are considerably larger or smaller than the rest of the data points). Outliers can pull the mean toward their direction and, hence, bias the centrality representation. Therefore, if there are outliers or if the data is erratically dispersed and skewed, one should either avoid using mean as the measure of centrality or augment it with other central tendency measures, such as median and mode. Median The median is the measure of center value in a given data set. It is the number in the middle of a given set of data that has been arranged/sorted in order of magnitude (either ascending or descending). If the number of observation is an odd number, identifying the median is very easy—just sort the observations based on their values and pick the value right in the middle. If the number of observations is an even number, then identify the two middle values, and then take the simple average of these two values. The median is meaningful and calculable for ratio, interval, and ordinal data types. Once determined, half the data points in the data are above and the other half are below the median. In contrary to the mean, the median is not affected by outliers or skewed data. Mode The mode is the observation that occurs most frequently (the most frequent value in our data set). On a histogram it represents the highest bar in a bar chart, and hence, it may be considered as being the most popular option/value. The mode is most useful for data sets that contain a relatively small number of unique values. That is, it may be useless if the data have too many unique values (as is the case in many engineering measurements that capture high precision with a large number of decimal places), rendering each value having either one or a very small number representing its frequency. Although it is a useful measure (especially for nominal data), mode is not a very good representation of centrality, and therefore, it should not be used as the only measure of central tendency for a given data set. In summary, which central tendency measure is the best? Although there is not a clear answer to this question, here are a few hints—use the mean when the data is not prone to outliers and there is no significant level of skewness; use the median when the data has outliers and/or it is ordinal in nature; use the mode when the data is nominal. Perhaps the best practice is to use all three together so that the central tendency of the data set can be captured and represented from three perspectives. Mostly because "average" is a very familiar and highly used concept to everyone in regular daily activities, managers (as well as some scientists and journalists) often use the centrality measures (especially mean) inappropriately when other statistical information should be considered along with the centrality. It is a better practice to present descriptive statistics as a package—a combination of centrality and dispersion measures—as opposed to a single measure like mean.

Different types of charts and graphs,

What follows are the basic charts and graphs that are commonly used for information visualization. Line Chart Line charts are perhaps the most frequently used graphical visuals for time series data. Line charts (or a line graphs) show the relationship between two variables; they are most often used to track changes or trends over time (having one of the variables set to time on the x-axis). Line charts sequentially connect individual data points to help infer changing trends over a period of time. Line charts are often used to show time-dependent changes in the values of some measure, such as changes on a specific stock price over a 5-year period or changes on the number of daily customer service calls over a month. Bar Chart Bar charts are among the most basic visuals used for data representation. Bar charts are effective when you have nominal data or numerical data that splits nicely into different categories so you can quickly see comparative results and trends within your data. Bar charts are often used to compare data across multiple categories such as percent of advertising spending by departments or by product categories. Bar charts can be vertically or horizontally oriented. They can also be stacked on top of each other to show multiple dimensions in a single chart. Pie Chart Pie charts are visually appealing, as the name implies, pie-looking charts. Because they are so visually attractive, they are often incorrectly used. Pie charts should only be used to illustrate relative proportions of a specific measure. For instance, they can be used to show the relative percentage of an advertising budget spent on different product lines, or they can show relative proportions of majors declared by college students in their sophomore year. If the number of categories to show is more than just a few (say more than four), one should seriously consider using a bar chart instead ofa pie chart. Scatter Pl ot Scatter plots are often used to explore the relationship between two or three variables (in 2-D or 2-D visuals). Because they are visual exploration tools, having more than three variables, translating them into more than three dimensions is not easily achievable. Scatter plots are an effective way to explore the existence of trends, concentrations, and outliers. For instance, in a two-variable (two-axis) graph, a scatter plot can be used to illustrate the corelationship between age and weight of heart disease patients or it can illustrate the relationship between the number of customer care representatives and the number of open customer service claims. Often, a trend line is superimposed on a two-dimensional scatter plot to illustrate the nature of the relationship. Bubble Chart Bubble charts are often enhanced versions of scatter plots. Bubble charts, though, are not a new visualization type; instead, they should be viewed as a technique to enrich data illustrated in scatter plots (or even geographic maps). By varying the size and/or color of the circles, one can add additional data dimensions, offering more enriched meaning about the data. For instance, a bubble chart can be used to show a competitive view of college-level class attendance by major and by time of the day, or it can be used to show profit margin by product type and by geographic region. Specialized Charts and Graphs The graphs and charts that we review in this section are either derived from the basic charts as special cases or they are relatively new and are specific to a problem type and/or an application area. Hi stogra m Graphically speaking, a histogram looks just like a bar chart. The difference between histograms and generic bar charts is the information that is portrayed. Histograms are used to show the frequency distribution of a variable or several variables. In a histogram, the x-axis is often used to show the categories or ranges, and the y-axis is used to show the measures/values/frequencies. Histograms show the distributional shape of the data. That way, one can visually examine if the data is normally or exponentially distributed. For instance, one can use a histogram to illustrate the exam performance of a class, where distribution of the grades as well as comparative analysis of individual results can be shown, or one can use a histogram to show age distribution of the customer base. Gantt Chart Gantt charts are a special case of horizontal bar charts that are used to portray project timelines, project tasks/activity durations, and overlap among the tasks/activities. By showing start and end dates/times of tasks/activities and the overlapping relationships, Gantt charts provide an invaluable aid for management and control of projects. For instance, Gantt charts are often used to show project timelines, task overlaps, relative task completions (a partial bar illustrating the completion percentage inside a bar that shows the actual task duration), resources assigned to each task, milestones, and deliverables. PERT Chart PERT charts (also called network diagrams) are developed primarily to simplify the planning and scheduling of large and complex projects. They show precedence relationships among the project activities/tasks. A PERT chart is composed of nodes (represented as circles or rectangles) and edges (represented with directed arrows). Based on the selected PERT chart convention, either nodes or the edges may be used to represent the project activities/tasks (activity-on-node versus activity-on-arrow representation schema). Geogra phic Ma p When the data set includes any kind of location data (e.g., physical addresses, postal codes, state names or abbreviations, country names, latitude/longitude, or some type of custom geographic encoding), it is better and more informative to see the data on a map. Maps usually are used in conjunction with other charts and graphs, as opposed to by themselves. For instance, one can use maps to show distribution of customer service requests by product type (depicted in pie charts) by geographic locations. Often a large variety of information (e.g., age distribution, income distribution, education, economic growth, or population changes) can be portrayed in a geographic map to help decide where to open a new restaurant or a new service station. These types of systems are often called geographic information systems (GIS). Bullet Bullet graphs are often used to show progress toward a goal. A bullet graph is essentially a variation of a bar chart. Often they are used in place of gauges, meters, and thermometers in a dashboard to more intuitively convey the meaning within a much smaller space. Bullet graphs compare a primary measure (e.g., year-to-date revenue) to one or more other measures (e.g., annual revenue target) and present this in the context of defined performance metrics (e.g., sales quotas). A bullet graph can intuitively illustrate how the primary measure is performing against overall goals (e.g., how close a sales representative is to achieving his/her annual quota). Heat Ma p Heat maps are great visuals to illustrate the comparison of continuous values across two categories using color. The goal is to help the user quickly see where the intersection of the categories is strongest and weakest in terms of numerical values of the measure being analyzed. For instance, one can use heat maps to show segmentation analysis of target markets where the measure (color gradient would be the purchase amount) and the dimensions would be age and income distribution. Hig hlig ht Table Highlight tables are intended to take heat maps one step further. In addition to showing how data intersects by using color, highlight tables add a number on top to provide additional detail. That is, they are two-dimensional tables with cells populated with numerical values and gradients of colors. For instance, one can show sales representatives' performance by product type and by sales volume. Tree Ma p Tree maps display hierarchical (tree-structured) data as a set of nested rectangles. Each branch of the tree is given a rectangle, which is then tiled with smaller rectangles representing subbranches. A leaf node's rectangle has an area proportional to a specified dimension on the data. Often the leaf nodes are colored to show a separate dimension of the data. When the color and size dimensions are correlated in some way with the tree structure, one can often easily see patterns that would be difficult to spot in other ways, such as if a certain color is particularly relevant. A second advantage of tree maps is that, by construction, they make efficient use of space. As a result, they can legibly display thousands of items on the screen simultaneously.

The fundamental challenge of a dashboard design

"The fundamental challenge of dashboard design is to display all the required information on a single screen, clearly and without distraction, in a manner that can be assimilated quickly."

data warehouse (DW)

A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format.

Tokenizing,

A token is a categorized block of text in a sentence. The block of text corresponding to the token is categorized according to the function it performs. This assignment of meaning to blocks of text is known as tokenizing. A token can look like anything; it just needs to be a useful part of the structured text.

OLAP (online analytical processing)

An information system that enables the user, while at a PC, to query the system, conduct an analysis, and so on. The result is generated in seconds.

What is big data (analytics, sources, characteristics, and processing technique),

Any book on analytics and data science has to include significant coverage of what is called Big Data analytics. We will cover it in Chapter 7 but here is a very brief introduction. Our brains work extremely quickly and efficiently and are versatile in processing large amounts of all kinds of data: images, text, sounds, smells, and video. We process all different forms of data relatively easily. Computers, on the other hand, are still finding it hard to keep up with the pace at which data is generated, let alone analyze it fast. This is why we have the problem of Big Data. So, what is Big Data? Simply put, Big Data is data that cannot be stored in a single storage unit. Big Data typically refers to data that comes in many different forms: structured, unstructured, in a stream, and so forth. Major sources of such data are clickstreams from Web sites, postings on social media sites such as Facebook, and data from traffic, sensors, or weather. A Web search engine like Google needs to search and index billions of Web pages to give you relevant search results in a fraction of a second. Although this is not done in real time, generating an index of all the Web pages on the Internet is not an easy task. Luckily for Google, it was able to solve this problem. Among other tools, it has employed Big Data analytical techniques. There are two aspects to managing data on this scale: storing and processing. If we could purchase an extremely expensive storage solution to store all this at one place on one unit, making this unit fault tolerant would involve a major expense. An ingenious solution was proposed that involved storing this data in chunks on different machines connected by a network—putting a copy or two of this chunk in different locations on the network, both logically and physically. It was originally used at Google (then called the Google File System) and later developed and released as an Apache project as the Hadoop Distributed File System (HDFS). However, storing this data is only half the problem. Data is worthless if it does not provide business value, and for it to provide business value, it has to be analyzed. How can such vast amounts of data be analyzed? Passing all computation to one powerful computer does not work; this scale would create a huge overhead on such a powerful computer. Another ingenious solution was proposed: Push computation to the data, instead of pushing data to a computing node. This was a new paradigm and gave rise to a whole new way of processing data. This is what we know today as the MapReduce programming paradigm, which made processing Big Data a reality. MapReduce was originally developed at Google, and a subsequent version was released by the Apache project called Hadoop MapReduce. Today, when we talk about storing, processing, or analyzing Big Data, HDFS and MapReduce are involved at some level. Other relevant standards and software solutions have been proposed. Although the major toolkit is available as an open source, several companies have been launched to provide training or specialized analytical hardware or software services in this space. Some examples are HortonWorks, Cloudera, and Teradata Aster. Over the past few years, what was called Big Data changed more and more as Big Data applications appeared. The need to process data coming in at a rapid rate added velocity to the equation. An example of fast data processing is algorithmic trading. This uses electronic platforms based on algorithms for trading shares on the financial market, which operates in microseconds. The need to process different kinds of data added variety to the equation. Another example of a wide variety of data is sentiment analysis, which uses various forms of data from social media platforms and customer responses to gauge sentiments. Today, Big Data is associated with almost any kind of large data that has the characteristics of volume, velocity, and variety.

Parallel processing for data mining,

Because of the large amounts of data and massive search efforts, it is sometimes necessary to use parallel processing for data mining.

Dashboard present information in three different levels

Information can be presented in three layers depending on the granularity of the information: the visual dashboard level, the static report level, and the self-service cube level.

Management feature of a dashboard

Management: Detailed operational data that identify what actions to take to resolve a problem.

Business vs data understanding

Step 1: Business Understanding The key element of any data mining study is to know what the study is for. Answering such a question begins with a thorough understanding of the managerial need for new knowledge and an explicit specification of the business objective regarding the study to be conducted. Specific goals such as "What are the common characteristics of the customers we have lost to our competitors recently?" or "What are typical profiles of our customers, and how much value does each of them provide to us?" are needed. Then a project plan for finding such knowledge is developed that specifies the people responsible for collecting the data, analyzing the data, and reporting the findings. At this early stage, a budget to support the study should also be established, at least at a high level with rough numbers. Step 2: Data Understanding A data mining study is specific to addressing a well-defined business task, and different business tasks require different sets of data. Following the business understanding, the main activity of the data mining process is to identify the relevant data from many available databases. Some key points must be considered in the data identification and selection phase. First and foremost, the analyst should be clear and concise about the description of the data mining task so that the most relevant data can be identified. For example, a retail data mining project may seek to identify spending behaviors of female shoppers who purchase seasonal clothes based on their demographics, credit card transactions, and socioeconomic attributes. Furthermore, the analyst should build an intimate understanding of the data sources (e.g., where the relevant data are stored and in what form; what the process of collecting the data is—automated versus manual; who the collectors of the data are and how often the data are updated) and the variables (e.g., What are the most relevant variables? Are there any synonymous and/or homonymous variables? Are the variables independent of each other—do they stand as a complete information source without overlapping or conflicting information?). To better understand the data, the analyst often uses a variety of statistical and graphical techniques, such as simple statistical summaries of each variable (e.g., for numeric variables the average, minimum/maximum, median, and standard deviation are among the calculated measures, whereas for categorical variables the mode and frequency tables are calculated), correlation analysis, scatterplots, histograms, and box plots. A careful identification and selection of data sources and the most relevant variables can make it easier for data mining algorithms to quickly discover useful knowledge patterns. Data sources for data selection can vary. Traditionally, data sources for business applications include demographic data (such as income, education, number of households, and age), sociographic data (such as hobby, club membership, and entertainment), transactional data (sales record, credit card spending, issued checks), and so on. Nowadays, data sources also use external (open or commercial) data repositories, social media, and machine-generated data. Data can be categorized as quantitative and qualitative. Quantitative data is measured using numeric values, or numeric data. It can be discrete (such as integers) or continuous (such as real numbers). Qualitative data, also known as categorical data, contains both nominal and ordinal data. Nominal data has finite nonordered values (e.g., gender data, which has two values: male and female). Ordinal data has finite ordered values. For example, customer credit ratings are considered ordinal data because the ratings can be excellent, fair, and bad. A simple taxonomy of data (i.e., the nature of data) is provided in Chapter 2. Quantitative data can be readily represented by some sort of probability distribution. A probability distribution describes how the data is dispersed and shaped. For instance, normally distributed data is symmetric and is commonly referred to as being a bell-shaped curve. Qualitative data may be coded to numbers and then described by frequency distributions. Once the relevant data are selected according to the data mining business objective, data preprocessing should be pursued.

BPM,

The term business performance management (BPM) refers to the business processes, methodologies, metrics, and technologies used by enterprises to measure, monitor, and manage business performance.

Data warehouse

data warehouse (DW) is a pool of data produced to support decision making; it is also a repository of current and historical data of potential interest to managers throughout the organization. Data are usually structured to be available in a form ready for analytical processing activities (i.e., online analytical processing [OLAP], data mining, querying, reporting, and other decision support applications). A data warehouse is a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management's decision-making process

Business intelligence (BI)

is a content-free expression, so it means different things to different people. BI's major objective is to enable interactive access (sometimes in real time) to data. By analyzing historical and current data, decision makers get valuable insights that enable them to make better decisions.

Analytics tools (descriptive, social network, advanced),

• Descriptive analytics: Uses simple statistics to identify activity characteristics and trends, such as how many followers you have, how many reviews were generated on Facebook, and which channels are being used most often. • Social network analysis: Follows the links between friends, fans, and followers to identify connections of influence as well as the biggest sources of influence. • Advanced analytics: Includes predictive analytics and text analytics that examine the content in online conversations to identify themes, sentiments, and connections that would not be revealed by casual surveillance.

Social media analytics,

Social media refers to the enabling technologies of social interactions among people in which they create, share, and exchange information, ideas, and opinions in virtual communities and networks. It is a group of Internet-based software applications that build on the ideological and technological foundations of Web 2.0 and that allow the creation and exchange of user-generated content (Kaplan & Haenlein, 2010). Social media depends on mobile and other Web-based technologies to create highly interactive platforms for individuals and communities to share, co-create, discuss, and modify user-generated content. It introduces substantial changes to communication among organizations, communities, and individuals. Since their emergence in the early 1990s, Web-based social media technologies have seen a significant improvement in both quality and quantity. These technologies take on many different forms, including online magazines, Internet forums, Web logs, social blogs, microblogging, wikis, social networks, podcasts, pictures, video, and product/service evaluations/ ratings. By applying a set of theories in the field of media research (social presence, media richness) and social processes (self-presentation, self-disclosure), Kaplan and Haenlein (2010) created a classification scheme with six different types of social media: collaborative projects (e.g., Wikipedia), blogs and microblogs (e.g., Twitter), content communities (e.g., YouTube), social networking sites (e.g., Facebook), virtual game worlds (e.g., World of Warcraft), and virtual social worlds (e.g. Second Life). Web-based social media are different from traditional/industrial media, such as newspapers, television, and film, as they are comparatively inexpensive and accessible to enable anyone (even private individuals) to publish or access/consume information. Industrial media generally require significant resources to publish information, as in most cases the articles (or books) go through many revisions before being published (as was the case in the publication of this very book). Here are some of the most prevailing characteristics that help differentiate between social and industrial media (Morgan, Jones, & Hodges, 2010): Quality: In industrial publishing—mediated by a publisher—the typical range of quality is substantially narrower than in niche, unmediated markets. The main challenge posed by content in social media sites is the fact that the distribution of quality has high variance: from very high-quality items to low-quality, sometimes abusive, content. Reach: Both industrial and social media technologies provide scale and are capable of reaching a global audience. Industrial media, however, typically use a centralized framework for organization, production, and dissemination, whereas social media are by their very nature more decentralized, less hierarchical, and distinguished by multiple points of production and utility. Frequency: Compared to industrial media, updating and reposting on social media platforms is easier, faster, and cheaper, and therefore practiced more frequently, resulting in fresher content. Accessibility: The means of production for industrial media are typically government and/or corporate (privately owned) and are costly, whereas social media tools are generally available to the public at little or no cost. Usability: Industrial media production typically requires specialized skills and training. Conversely, most social media production requires only modest reinterpretation of existing skills; in theory, anyone with access can operate the means of social media production. Immediacy: The time lag between communications produced by industrial media can be long (weeks, months, or even years) compared to social media (which can be capable of virtually instantaneous responses). Updatability: Industrial media, once created, cannot be altered (once a magazine article is printed and distributed, changes cannot be made to that same article), whereas social media can be altered almost instantaneously by comments or editing.

Data mining myths,

Data mining is a powerful analytical tool that enables business executives to advance from describing the nature of the past (looking at a rearview mirror) to predicting the future (looking ahead) to better manage their business operations (making accurate and timely decisions). Data mining helps marketers find patterns that unlock the mysteries of customer behavior. The results of data mining can be used to increase revenue and reduce cost by identifying fraud and discovering business opportunities, offering a whole new realm of competitive advantage. As an evolving and maturing field, data mining is often associated with a number of myths, including those listed in Table 4.6 (Delen, 2014; Zaima, 2003). Data mining visionaries have gained enormous competitive advantage by understanding that these myths are just that: myths. Although the value proposition and therefore the necessity of it is obvious to anyone, those who carry out data mining projects (from novice to seasoned data scientist) sometimes make mistakes that result in projects with less-than-desirable outcomes. The following 16 data mining mistakes (also called blunders, pitfalls, or bloopers) are often made in practice (Nesbit et al., 2009 Shultz, 2004; Skalak, 2001), and data scientists should be aware of them, and to the extent that is possible, do their best to avoid them: 1. Selecting the wrong problem for data mining. Not every business problem can be solved with data mining (i.e., the magic bullet syndrome). When there is no representative data (large and feature rich), there cannot be a practicable data mining project. 2. Ignoring what your sponsor thinks data mining is and what it really can and cannot do. Expectation management is the key for successful data mining projects. 3. Beginning without the end in mind. Although data mining is a process of knowledge discovery, one should have a goal/objective (a stated business problem) in mind to succeed. Because, as the saying goes, "if you don't know where you are going, you will never get there." 4. Define the project around a foundation that your data can't support. Data mining is all about data; that is, the biggest constraint that you have in a data mining project is the richness of the data. Knowing what the limitations of data are help you craft feasible projects that deliver results and meet expectations. 5. Leaving insufficient time for data preparation. It takes more effort than is generally understood. The common knowledge suggests that up to a third of the total project time is spent on data acquisition, understanding, and preparation tasks. To succeed, avoid proceeding into modeling until after your data is properly processed (aggregated, cleaned, and transformed). 6. Looking only at aggregated results and not at individual records. Data mining is at its best when the data is at a granular representation. Try to avoid unnecessarily aggregating and overly simplifying data to help data mining algorithms—they don't really need your help; they are more than capable of figuring it out themselves. 7. Being sloppy about keeping track of the data mining procedure and results. Because it is a discovery process that involves many iterations and experimentations, it is highly likely to lose track of the findings. Success requires a systematic and orderly planning, execution, and tracking/recording of all data mining tasks. 8. Using data from the future to predict the future. Because of the lack of description and understanding of the data, oftentimes analysts include variables that are unknown at the time when the prediction is supposed to be made. By doing so, their prediction models produce unbelievable accurate results (a phenomenon that is often called "fool's gold"). If your prediction results are too good to be true, they usually are; in that case, the first thing that you need to look for is the incorrect use of a variable from the future. 9. Ignoring suspicious findings and quickly moving on. The unexpected findings are often the indicators of real novelties in data mining projects. Proper investigation of such oddities can lead to surprisingly pleasing discoveries. 10. Starting with a high-profile complex project that will make you a superstar. Data mining projects often fail if they are not thought out carefully from start to end. Success often comes with a systematic and orderly progression of projects from smaller/simpler to larger/complex ones. The goal should be to show incremental and continuous value added, as opposed to taking on a large project that will consume resources without producing any valuable outcomes. 11. Running data mining algorithms repeatedly and blindly. Although today's data mining tools are capable of consuming data and setting algorithmic parameters to produce results, one should know how to transform the data and set the proper parameter values to obtain the best possible results. Each algorithm has its own unique way of processing data, and knowing that is necessary to get the most out of each model type. 12. Ignore the subject matter experts. Understanding the problem domain and the related data requires a highly involved collaboration between the data mining and the domain experts. Working together helps the data mining expert to go beyond the syntactic representation and also obtain semantic nature (i.e., the true meaning of the variables) of the data. 13. Believing everything you are told about the data. Although it is necessary to talk to domain experts to better understand the data and the business problem, the data scientist should not take anything for granted. Validation and verification through a critical analysis is the key to intimate understanding and processing of the data. 14. Assuming that the keepers of the data will be fully on board with cooperation. Many data mining projects fail because the data mining expert did not know/understand the organizational politics. One of the biggest obstacles in data mining projects can be the people who own and control the data. Understanding and managing the politics is a key to identify, access, and properly understand the data to produce a successful data mining project. 15. Measuring your results differently from the way your sponsor measures them. The results should talk/appeal to the end user (manager/decision maker) who will be using them. Therefore, producing the results in a measure and format that appeals to the end user tremendously increases the likelihood of true understanding and proper use of the data mining outcomes. 16. If you build it, they will come: don't worry about how to serve it up. Usually, data mining experts think they are done once they build models that meet and hopefully exceed the needs/wants/expectations of the end user (i.e., the customer). Without a proper deployment, the value deliverance of data mining outcomes is rather limited. Therefore, deployment is a necessary last step in the data mining process where models are integrated into the organizational decision support infrastructure for enablement of better and faster decision making.

Document indexer,

Document Indexer As the documents are found and fetched by the crawler, they are stored in a temporary staging area for the document indexer to grab and process. The document indexer is responsible for processing the documents (Web pages or document files) and placing them into the document database. To convert the documents/pages into the desired, easily searchable format, the document indexer performs the following tasks.

Descriptive analytics,

· Descriptive (or reporting) analytics refers to knowing what is happening in the organization and understanding some underlying trends and causes of such occurrences. First, this involves the consolidation of data sources and availability of all relevant data in a form that enables appropriate reporting and analysis. Usually, the development of this data infrastructure is part of DWs. From this data infrastructure we can develop appropriate reports, queries, alerts, and trends using various reporting tools and techniques. A significant technology that has become a key player in this area is visualization. Using the latest isualization tools in the marketplace, we can now develop powerful insights in the operations of our organization.

Mantra for modern approaches to BI,

· Managers need the right information at the right time and in the right place.

Meaning of the key terms (process, nontrivial, etc.),

• Process implies that data mining comprises many iterative steps. • Nontrivial means that some experimentation-type search or inference is involved; that is, it is not as straightforward as a computation of predefined quantities. • Valid means that the discovered patterns should hold true on new data with a sufficient degree of certainty. • Novel means that the patterns are not previously known to the user within the context of the system being analyzed. • Potentially useful means that the discovered patterns should lead to some benefit to the user or task. • Ultimately understandable means that the pattern should make business sense that leads to the user saying, "Mm! It makes sense; why didn't I think of that," if not immediately, at least after some post processing.

Three tier data warehousing architecture

1. The data warehouse itself, which contains the data and associated software 2. Data acquisition (back-end) software, which extracts data from legacy systems and external sources, consolidates and summarizes them, and loads them into the data warehouse 3. Client (front-end) software, which allows users to access and analyze data from the warehouse (a DSS/BI/business analytics [BA] engine)

Oper marts

An operational data store (ODS) provides a fairly recent form of customer information file. This type of database is often used as an interim staging area for a data warehouse.Unlike the static contents of a data warehouse, the contents of an ODS are updated throughout the course of business operations. An ODS is used for short-term decisions involving mission-critical applications rather than for the medium- and long-term decisions associated with an EDW. An ODS is similar to short-term memory in that it stores only very recent information. In comparison, a data warehouse is like long-term memory because it stores permanent information. An ODS consolidates data from multiple source systems and provides a near-real-time, integrated view of volatile, current data. The exchange, transfer, and load (ETL) processes (discussed later in this chapter) for an ODS are identical to those for a data warehouse. Finally, oper marts (see Imhoff, 2001) are created when operational data needs to be analyzed multidimensionally. The data for an oper mart come from an ODS.

Association rule mining,

Association rule mining (also known as affinity analysis or market-basket analysis) is a popular data mining method that is commonly used as an example to explain what data mining is and what it can do to a technologically less-savvy audience. Most of you might have heard the famous (or infamous, depending on how you look at it) relationship discovered between the sales of beer and diapers at grocery stores. As the story goes, a large supermarket chain (maybe Walmart, maybe not; there is no consensus on which supermarket chain it was) did an analysis of customers' buying habits and found a statistically significant correlation between purchases of beer and purchases of diapers. It was theorized that the reason for this was that fathers (presumably young men) were stopping off at the supermarket to buy diapers for their babies (especially on Thursdays), and because they could no longer go down to the sports bar as often, would buy beer as well. As a result of this finding, the supermarket chain is alleged to have placed the diapers next to the beer, resulting in increased sales of both. In essence, association rule mining aims to find interesting relationships (affinities) between variables (items) in large databases. Because of its successful application to retail business problems, it is commonly called market-basket analysis. The main idea in marketbasket analysis is to identify strong relationships among different products (or services) that are usually purchased together (show up in the same basket together, either a physical basket at a grocery store or a virtual basket at an e-commerce Web site). For example, 65% of those who buy comprehensive automobile insurance also buy health insurance; 80% of those who buy books online also buy music online; 60% of those who have high blood pressure and are overweight have high cholesterol; 70% of the customers who buy a laptop computer and virus protection software also buy extended service plans.

ETL,

At the heart of the technical side of the data warehousing process is extraction, transformation, and load (ETL). ETL technologies, which have existed for some time, are instrumental in the process and use of data warehouses. The ETL process is an integral component in any data-centric project. IT managers are often faced with challenges because the ETL process typically consumes 70% of the time in a data-centric project. The ETL process consists of extraction (i.e., reading data from one or more databases), transformation (i.e., converting the extracted data from its previous form into the form in which it needs to be so that it can be placed into a data warehouse or simply another database), and load (i.e., putting the data into the data warehouse). Transformation occurs by using rules or lookup tables or by combining the data with other data. The three database functions are integrated into one tool to pull data out of one or more databases and place them into another, consolidated database or a data warehouse. ETL tools also transport data between sources and targets, document how data elements (e.g., metadata) change as they move between source and target, exchange metadata with other applications as needed, and administer all runtime processes and operations (e.g., scheduling, error management, audit logs, statistics). ETL is extremely important for data integration as well as for data warehousing. The purpose of the ETL process is to load the warehouse with integrated and cleansed data. The data used in ETL processes can come from any source: a mainframe application, an ERP application, a CRM tool, a flat file, an Excel spreadsheet, or even a message queue. In Figure 3.9, we outline the ETL process.

Web site usability

Beginning with your Web site, let's take a look at how well it works for your visitors. This is where you can learn how "user friendly" it really is or whether or not you are providing the right content. 1. Page views. The most basic of measurements, this metric is usually presented as the "average page views per visitor." If people come to your Web site and don't view many pages, then your Web site may have issues with its design or structure. Another explanation for low page views is a disconnect in the marketing messages that brought them to the site and the content that is actually available. 2. Time on site. Similar to page views, it's a fundamental measurement of a visitor's interaction with your Web site. Generally, the longer a person spends on your Web site, the better it is. That could mean they're carefully reviewing your content, utilizing interactive components you have available, and building toward an informed decision to buy, respond, or take the next step you've provided. On the contrary, the time on site also needs to be examined against the number of pages viewed to make sure the visitor isn't spending his or her time trying to locate content that should be more readily accessible. 3. Downloads. This includes PDFs, videos, and other resources you make available to your visitors. Consider how accessible these items are as well as how well they're promoted. If your Web statistics, for example, reveal that 60% of the individuals who watch a demo video also make a purchase, then you'll want to strategize to increase viewership of that video. 4. Click map. Most analytics programs can show you the percentage of clicks each item on your Web page received. This includes clickable photos, text links in your copy, downloads, and, of course, any navigation you may have on the page. Are they clicking the most important items? 5. Click paths. Although an assessment of click paths is more involved, it can quickly reveal where you might be losing visitors in a specific process. A well-designed Web site uses a combination of graphics and information architecture to encourage visitors to follow "predefined" paths through your Web site. These are not rigid pathways but rather intuitive steps that align with the various processes you've built into the Web site. One process might be that of "educating" a visitor who has minimum understanding of your product or service. Another might be a process of "motivating" a returning visitor to consider an upgrade or repurchase. A third process might be structured around items you market online. You'll have as many process pathways in your Web site as you have target audiences, products, and services. Each can be measured through Web analytics to determine how effective it is.

Classification, study with examples,

Classification Classification, or supervised induction, is perhaps the most common of all data mining tasks. The objective of classification is to analyze the historical data stored in a database and automatically generate a model that can predict future behavior. This induced model consists of generalizations over the records of a training data set, which help distinguish predefined classes. The hope is that the model can then be used to predict the classes of other unclassified records and, more important, to accurately predict actual future events. Common classification tools include neural networks and decision trees (from machine learning), logistic regression and discriminant analysis (from traditional statistics), and emerging tools such as rough sets, support vector machines (SVMs), and genetic algorithms. Statistics-based classification techniques (e.g., logistic regression and discriminant analysis) have received their share of criticism—that they make unrealistic assumptions about the data, such as independence and normality—which limit their use in classification-type data mining projects.

Prediction problems with numeric value (regression),

Classification is perhaps the most frequently used data mining method for real-world problems. As a popular member of the machine-learning family of techniques, classification learns patterns from past data (a set of information—traits, variables, features—on characteristics of the previously labeled items, objects, or events) to place new instances (with unknown labels) into their respective groups or classes. For example, one could use classification to predict whether the weather on a particular day will be "sunny," "rainy," or "cloudy." Popular classification tasks include credit approval (i.e., good or bad credit risk), store location (e.g., good, moderate, bad), target marketing (e.g., likely customer, no hope), fraud detection (i.e., yes/no), and telecommunication (e.g., likely to turn to another phone company, yes/no). If what is being predicted is a class label (e.g., "sunny," "rainy," or "cloudy"), the prediction problem is called a classification, whereas if it is a numeric value (e.g., temperature, such as 68°F), the prediction problem is called a regression. Even though clustering (another popular data mining method) can also be used to determine groups (or class memberships) of things, there is a significant difference between the two. Classification learns the function between the characteristics of things (i.e., independent variables) and their membership (i.e., output variable) through a supervised learning process where both types (input and output) of variables are presented to the algorithm; in clustering, the membership of the objects is learned through an unsupervised learning process where only the input variables are presented to the algorithm. Unlike classification, clustering does not have a supervising (or controlling) mechanism that enforces the learning process; instead, clustering algorithms use one or more heuristics (e.g., multidimensional distance measure) to discover natural groupings of objects. The most common two-step methodology of classification-type prediction involves model development/training and model testing/deployment. In the model development phase, a collection of input data, including the actual class labels, is used. After a model has been trained, the model is tested against the holdout sample for accuracy assessment and eventually deployed for actual use where it is to predict classes of new data instances (where the class label is unknown). Several factors are considered in assessing the model, including the following.

Conversion statistics,

Each organization will define a "conversion" according to its specific marketing objectives. Some Web analytics programs use the term goal to benchmark certain Web site objectives, whether that be a certain number of visitors to a page, a completed registration form, or an online purchase. 1. New visitors. If you're working to increase visibility, you'll want to study the trends in your new visitors data. Analytics identifies all visitors as either new or returning. 2. Returning visitors. If you're involved in loyalty programs or offer a product that has a long purchase cycle, then your returning visitors data will help you measure progress in this area. 3. Leads. Once a form is submitted and a thank-you page is generated, you have created a lead. Web analytics will permit you to calculate a completion rate (or abandonment rate) by dividing the number of completed forms by the number of Web visitors that came to your page. A low completion percentage would indicate a page that needs attention. 4. Sales/conversions. Depending on the intent of your Web site, you can define a "sale" by an online purchase, a completed registration, an online submission, or any number of other Web activities. Monitoring these figures will alert you to any changes (or successes!) that occur further upstream. 5. Abandonment/exit rates. Just as important as those moving through your Web site are those who began a process and quit or came to your Web site and left after a page or two. In the first case, you'll want to analyze where the visitor terminated the process and whether there are a number of visitors quitting at the same place. Then, investigate the situation for resolution. In the latter case, a high exit rate on a Web site or a specific page generally indicates an issue with expectations. Visitors click to your Web site based on some message contained in an advertisement, a presentation, and so on, and expect some continuity in that message. Make sure you're advertising a message that your Web site can reinforce and deliver. Within each of these items are metrics that can be established for your specific organization. You can create a weekly dashboard that includes specific numbers or percentages that will indicate where you're succeeding—or highlight a marketing challenge that should be addressed. When these metrics are evaluated consistently and used in conjunction with other available marketing data, they can lead you to a highly quantified marketing program. Figure 5.14 shows a Web analytics dashboard created with freely available Google Analytics tools.

Enterprise application integration,

Enterprise application integration (EAI) provides a vehicle for pushing data from source systems into the data warehouse. It involves integrating application functionality and is focused on sharing functionality (rather than data) across systems, thereby enabling flexibility and reuse. Traditionally, EAI solutions have focused on enabling application reuse at the application programming interface level. Recently, EAI is accomplished by using SOA coarse-grained services (a collection of business processes or functions) that are well defined and documented. Using Web services is a specialized way of implementing an SOA. EAI can be used to facilitate data acquisition directly into a near-realtime data warehouse or to deliver decisions to the OLTP systems. There are many different approaches to and tools for EAI implementation.

Enterprise information integration,

Enterprise information integration (EII) is an evolving tool space that promises real-time data integration from a variety of sources, such as relational databases, Web services, and multidimensional databases. It is a mechanism for pulling data from source systems to satisfy a request for information. EII tools use predefined metadata to populate views that make integrated data appear relational to end users. XML may be the most important aspect of EII because XML allows data to be tagged either at creation time or later. These tags can be extended and modified to accommodate almost any area of knowledge (see Kay, 2005). Physical data integration has conventionally been the main mechanism for creating an integrated view with data warehouses and DMs. With the advent of EII tools (see Kay, 2005), new virtual data integration patterns are feasible. Manglik and Mehra (2005) discussed the benefits and constraints of new data integration patterns that can expand traditional physical methodologies to present a comprehensive view for the enterprise.

In-database processing technology

In-database processing technology (putting the algorithms where the data is). In-database processing (also called in-database analytics) refers to the integration of the algorithmic extent of data analytics into data warehousing. By doing so, the data and the analytics that work off the data live within the same environment. Having the two in close proximity increases the efficiency of the computationally intensive analytics procedures. Today, many large database-driven decision support systems, such as those used for credit card fraud detection and investment bank risk management, use this technology because it provides significant performance improvements over traditional methods in a decision environment where time is of the essence. In-database processing is a complex endeavor compared to the traditional way of conducting analytics, where the data is moved out of the database (often in a flat-file format that consists of rows and columns) into a separate analytics environment (such as SAS Enterprise Modeler, Statistica Data Miner, or IBM SPSS Modeler) for processing. In-database processing makes more sense for high-throughput, real-time application environments, including fraud detection, credit scoring, risk management, transaction processing, pricing and margin analysis, usage-based micro-segmenting, behavioral ad targeting, and recommendation engines, such as those used by customer service organizations to determine next-best actions. In-database processing is performed and promoted as a feature by many of the major data warehousing vendors, including Teradata (integrating SAS analytics capabilities into the data warehouse appliances), IBM Netezza, EMC Greenplum, and Sybase, among others.

KPIs, dashboard and balanced score card,

Metric Ma nage ment Report s In many organizations, business performance is managed through outcome-oriented metrics. For external groups, these are service-level agreements. For internal management, they are key performance indicators (KPIs). Typically, there are enterprise-wide agreed targets to be tracked against over a period of time. They may be used as part of other management strategies such as Six Sigma or Total Quality Management. Dashboard -Type Report s A popular idea in business reporting in recent years has been to present a range of different performance indicators on one page, like a dashboard in a car. Typically, dashboard vendors would provide a set of predefined reports with static elements and fixed structure, but also allow for customization of the dashboard widgets, views, and set targets for various metrics. It's common to have color-coded traffic lights defined for performance (red, orange, green) to draw management's attention to particular areas. A more detailed description of dashboards can be found in later part of this chapter. Balanced Scorecard -Type Report s This is a method developed by Kaplan and Norton that attempts to present an integrated view of success in an organization. In addition to financial performance, balanced scorecard-type reports also include customer, business process, and learning and growth perspectives. More details on balanced scorecards are provided later in this chapter. Application Case 2.5 is an example to illustrate the power and the utility of automated report generation for a large (and, at a time of natural crisis, somewhat chaotic) organization like FEMA.

NLP,

Natural language processing (NLP) is an important component of text mining and is a subfield of artificial intelligence and computational linguistics. It studies the problem of "understanding" the natural human language, with the view of converting depictions of human language (such as textual documents) into more formal representations (in the form of numeric and symbolic data) that are easier for computer programs to manipulate. The goal of NLP is to move beyond syntax-driven text manipulation (which is often called "word counting") to a true understanding and processing of natural language that considers grammatical and semantic constraints as well as the context. The definition and scope of the word understanding is one of the major discussion topics in NLP. Considering that the natural human language is vague and that a true understanding of meaning requires extensive knowledge of a topic (beyond what is in the words, sentences, and paragraphs), will computers ever be able to understand natural language the same way and with the same accuracy that humans do? Probably not! NLP has come a long way from the days of simple word counting, but it has an even longer way to go to really understanding natural human language.

OLTPOnline Transaction Processing

Online Transaction ProcessingTransaction system that is primarily responsible for capturing and storing data related to day-to-day business functions.

Predictive analytics,

Predictive analytics aims to determine what is likely to happen in the future. This analysis is based on statistical techniques as well as other more recently developed techniques that fall under the general category of data mining. The goal of these techniques is to be able to predict if the customer is likely to switch to a competitor ("churn"), what the customer would likely buy next and how much, what promotions a customer would respond to, whether this customer is a creditworthy risk, and so forth. A number of techniques are used in developing predictive analytical applications, including various classification algorithms. For example, as described in Chapters 4 and 5, we can use classification techniques such as logistic regression, decision tree models, and neural networks to predict how well a motion picture will do at the box office. We can also use clustering algorithms for segmenting customers into different clusters to be able to target specific promotions to them. Finally, we can use association mining techniques to estimate relationships between different purchasing behaviors. That is, if a customer buys one product, what else is the customer likely to purchase? Such analysis can assist a retailer in recommending or promoting related products. For example, any product search on Amazon.com results in the retailer also suggesting other similar products that a customer may be interested in.

Real time data warehousing,

Real-time data warehousing. Real-time data warehousing (RDW) implies that the refresh cycle of an existing data warehouse to update the data is more frequent (almost at the same time as the data becomes available at operational databases). These RDW systems can achieve near real-time updates of data, where the data latency typically is in the range from minutes to hours. As the latency gets smaller, the cost of data updates seems to be increasing exponentially. Future advancements in many technological fronts (ranging from automatic data acquisition to intelligent software agents) are needed to make RDW a reality with an affordable price tag.

Search engine optimization

Search engine optimization (SEO) is the intentional activity of affecting the visibility of an e-commerce site or a Web site in a search engine's natural (unpaid or organic) search results. In general, the higher ranked on the search results page, and the more frequently a site appears in the search results list, the more visitors it will receive from the search engine's users. As an Internet marketing strategy, SEO considers how search engines work, what people search for, the actual search terms or keywords typed into search engines, and which search engines are preferred by their targeted audience. Optimizing a Web site may involve editing its content, HTML, and associated coding to both increase its relevance to specific keywords and to remove barriers to the indexing activities of search engines. Promoting a site to increase the number of backlinks, or inbound links, is another SEO tactic. In the early days, in order to be indexed, all Webmasters needed to do was to submit the address of a page, or URL, to the various engines, which would then send a "spider" to "crawl" that page, extract links to other pages from it, and return information found on the page to the server for indexing. The process, as explained before, involves a search engine spider downloading a page and storing it on the search engine's own server, where a second program, known as an indexer, extracts various information about the page, such as the words it contains and where these are located, as well as any weight for specific words, and all links the page contains, which are then placed into a scheduler for crawling at a later date. Nowadays, search engines are no longer relying on Webmasters submitting URLs (even though they still can); instead, they are proactively and continuously crawling the Web and finding, fetching, and indexing everything about it. Being indexed by search engines like Google, Bing, and Yahoo! is not good enough for businesses. Getting ranked on the most widely used search engines (see Technology Insights 5.3 for a list of most widely used search engines) and getting ranked higher than your competitors are what make the difference. A variety of methods can increase the ranking of a Web page within the search results. Cross-linking between pages of the same Web site to provide more links to the most important pages may improve its visibility. Writing content that includes frequently searched keyword phrases, so as to be relevant to a wide variety of search queries, will tend to increase traffic. Updating content to keep search engines crawling back frequently can give additional weight to a site. Adding relevant keywords to a Web page's metadata, including the title tag and metadescription, will tend to improve the relevancy of a site's search listings, thus increasing traffic. URL normalization of Web pages so that they are accessible via multiple URLs and using canonical link elements, and redirects can help make sure links to different versions of the URL all count toward the page's link popularity score.

Sentiment flavors: explicit and implicit,

Sentiment that appears in text comes in two flavors: explicit, where the subjective sentence directly expresses an opinion ("It's a wonderful day"), and implicit, where the text implies an opinion ("The handle breaks too easily"). Most of the earlier work done in sentiment analysis focused on the first kind of sentiment because it is easier to analyze. Current trends are to implement analytical methods to consider both implicit and explicit sentiments. Sentiment polarity is a particular feature of text that sentiment analysis primarily focuses on. It is usually dichotomized into two—positive and negative—but polarity can also be thought of as a range. A document containing several opinionated statements will have a mixed polarity overall, which is different from not having a polarity at all (being objective; Mejova, 2009). Timely collection and analysis of textual data, which may be coming from a variety of sources—ranging from customer call center transcripts to social media postings—is a crucial part of the capabilities of proactive and customer focused companies, nowadays. These real-time analyses of textual data are often visualized in easy-to-understand dashboards. Application Case 5.6 provides a customer success story, where a collection of analytics solutions is collectively used to enhance viewers' experience at the Wimbledon tennis tournament.

Six sigma,

Sigma, , is a letter in the Greek alphabet that statisticians use to measure the variability in a process. In the quality arena, variability is synonymous with the number of defects. Generally, companies have accepted a great deal of variability in their business processes. In numeric terms, the norm has been 6,200 to 67,000 defects per million opportunities (DPMO). For instance, if an insurance company handles 1 million claims, then under normal operating procedures 6,200 to 67,000 of those claims would be defective (e.g., mishandled, have errors in the forms). This level of variability represents a three- to four-sigma level of performance. To achieve a Six Sigma level of performance, the company would have to reduce the number of defects to no more than 3.4 DPMO. Therefore, Six Sigma is a performance management methodology aimed at reducing the number of defects in a business process to as close to zero DPMO as possible.

Benefits of SAS Visual Analytics,

Some of the key benefits proposed by SAS analytics are the following: • Empowers all users with data exploration techniques and approachable analytics to drive improved decision making. SAS Visual Analytics enables different types of users to conduct fast, thorough explorations on all available data. Sampling to reduce the data is not required and not preferred. • Easy-to-use, interactive Web interfaces broaden the audience for analytics, enabling everyone to glean new insights. Users can look at more options, make more precise decisions, and drive success even faster than before. • Answer complex questions faster, enhancing the contributions from your analytic talent. SAS Visual Analytics augments the data discovery and exploration process by providing extremely fast results to enable better, more focused analysis. Analytically savvy users can identify areas of opportunity or concern from vast amounts of data so further investigation can take place quickly. • Improves information sharing and collaboration. Large numbers of users, including those with limited analytical skills, can quickly view and interact with reports and charts via the Web, Adobe PDF files, and iPad mobile devices, while IT maintains control of the underlying data and security. SAS Visual Analytics provides the right information to the right person at the right time to improve productivity and organizational knowledge. • Liberates IT by giving users a new way to access the information they need. Frees IT from the constant barrage of demands from users who need access to different amounts of data, different data views, ad hoc reports, and one-off requests for information. SAS Visual Analytics enables IT to easily load and prepare data for multiple users. Once data is loaded and available, users can dynamically explore data, create reports, and share information on their own. • Provides room to grow at a self-determined pace. SAS Visual Analytics provides the option of using commodity hardware or database appliances from EMC Greenplum and Teradata. It is designed from the ground up for performance optimization and scalability to meet the needs of any size organization.

Box and whiskers plot,

The box-and-whiskers plot (or simply a box plot) is a graphical illustration of several descriptive statistics about a given data set. They can be either horizontal or vertical, but vertical is the most common representation, especially in modern-day analytics software products. It is known to be first created and presented by John W. Tukey in 1969. Box plot is often used to illustrate both centrality and dispersion of a given data set (i.e., the distribution of the sample data) in an easy-to-understand graphical notation. Figure 2.8 shows a couple of box plots side by side, sharing the same y-axis. As shown therein, a single chart can have one or more box plots for visual comparison purposes. In such cases, the y-axis would be the common measure of magnitude (the numerical value of the variable), with the x-axis showing different classes/subsets such as different time dimensions (e.g., descriptive statistics for annual Medicare expenses in 2015 versus 2016) or different categories (e.g., descriptive statistics for marketing expenses versus total sales). Although, historically speaking, the box plot was not used widely and often enough (especially in areas outside of statistics), with the emerging popularity of business analytics, it is gaining fame in less-technical areas of the business world. Its information richness and ease of understanding are largely to credit for its recent popularity. The box plot shows the centrality (median and sometimes also mean) as well as the dispersion (the density of the data within the middle half—drawn as a box between the first and third quartile), the minimum and maximum ranges (shown as extended lines from the box, looking like whiskers, that are calculated as 1.5 times the upper or lower end of the quartile box) along with the outliers that are larger than the limits of the whiskers. A box plot also shows whether the data is symmetrically distributed with respect to the mean or it sways one way or another. The relative position of the median versus mean and the lengths of the whiskers on both side of the box give a good indication of the potential skewness in the data.

The future of data warehousing,

The field of data warehousing has been a vibrant area in IT in the last couple of decades, and the evidence in the BI/BA and Big Data world shows that the importance of the field will only get even more interesting. Following are some of the recently popularized concepts and technologies that will play a significant role in defining the future of data warehousing. Sourcing (mechanisms for acquisition of data from diverse and dispersed sources): • Web, social media, and Big Data. The recent upsurge in the use of the Web for personal as well as business purposes coupled with the tremendous interest in social media creates opportunities for analysts to tap into very rich data sources. Because of the sheer volume, velocity, and variety of the data, a new term, "Big Data," has been coined to name the phenomenon. Taking advantage of Big Data requires development of new and dramatically improved BI/BA technologies, which will result in a revolutionized data warehousing world. • Open source software. Use of open source software tools is increasing at an unprecedented level in warehousing, BI, and data integration. There are good reasons for the upswing of open source software used in data warehousing (Russom, 2009): (1) the recession has driven up interest in low-cost open source software, (2) open source tools are coming into a new level of maturity, and (3) open source software augments traditional enterprise software without replacing it. • SaaS (software as a service), "The Extended ASP Model." SaaS is a creative way of deploying information systems applications where the provider licenses its applications to customers for use as a service on demand (usually over the Internet). SaaS software vendors may host the application on their own servers or upload the application to the consumer site. In essence, SaaS is the new and improved version of the ASP model. For data warehouse customers, finding SaaS-based software applications and resources that meet specific needs and requirements can be challenging. As these software offerings become more agile, the appeal and the actual use of SaaS as the choice of data warehousing platform will also increase.

Challenges associated with Natural Language Processing,

The following are just a few of the challenges commonly associated with the implementation of NLP: • Part-of-speech tagging. It is difficult to mark up terms in a text as corresponding to a particular part of speech (such as nouns, verbs, adjectives, or adverbs) because the part of speech depends not only on the definition of the term but also on the context within which it is used. • Text segmentation. Some written languages, such as Chinese, Japanese, and Thai, do not have single-word boundaries. In these instances, the text-parsing task requires the identification of word boundaries, which is often a difficult task. Similar challenges in speech segmentation emerge when analyzing spoken language because sounds representing successive letters and words blend into each other. • Word sense disambiguation. Many words have more than one meaning. Selecting the meaning that makes the most sense can only be accomplished by taking into account the context within which the word is used. • Syntactic ambiguity. The grammar for natural languages is ambiguous; that is, multiple possible sentence structures often need to be considered. Choosing the most appropriate structure usually requires a fusion of semantic and contextual information. • Imperfect or irregular input. Foreign or regional accents and vocal impediments in speech and typographical or grammatical errors in texts make the processing of the language an even more difficult task. • Speech acts. A sentence can often be considered an action by the speaker. The sentence structure alone may not contain enough information to define this action. For example, "Can you pass the class?" requests a simple yes/no answer, whereas "Can you pass the salt?" is a request for a physical action to be performed.

OLAP operations,

The main operational structure in OLAP is based on a concept called cube. A cube in OLAP is a multidimensional data structure (actual or virtual) that allows fast analysis of data. It can also be defined as the capability of efficiently manipulating and analyzing data from multiple perspectives. The arrangement of data into cubes aims to overcome a limitation of relational databases: Relational databases are not well suited for near instantaneous analysis of large amounts of data. Instead, they are better suited for manipulating records (adding, deleting, and updating data) that represent a series of transactions. Although many report-writing tools exist for relational databases, these tools are slow when a multidimensional query that encompasses many database tables needs to be executed. Using OLAP, an analyst can navigate through the database and screen for a particular subset of the data (and its progression over time) by changing the data's orientations and defining analytical calculations. These types of user-initiated navigation of data through the specification of slices (via rotations) and drill down/up (via aggregation and disaggregation) is sometimes called "slice and dice." Commonly used OLAP operations include slice and dice, drill down, roll-up, and pivot. • Slice. A slice is a subset of a multidimensional array (usually a two-dimensional representation) corresponding to a single value set for one (or more) of the dimensions not in the subset. A simple slicing operation on a three-dimensional cube is shown in Figure 3.11. • Dice. The dice operation is a slice on more than two dimensions of a data cube. • Drill Down/Up. Drilling down or up is a specific OLAP technique whereby the user navigates among levels of data ranging from the most summarized (up) to the most detailed (down). • Roll-up. A roll-up involves computing all the data relationships for one or more dimensions. To do this, a computational relationship or formula might be defined. • Pivot. This is used to change the dimensional orientation of a report or ad hoc query-page display.

Star schema,

The star schema (sometimes referenced as star join schema) is the most commonly used and the simplest style of dimensional modeling. A star schema contains a central fact table surrounded by and connected to several dimension tables (Adamson, 2009). The fact table contains a large number of rows that correspond to observed facts and external links (i.e., foreign keys). A fact table contains the descriptive attributes needed to perform decision analysis and query reporting, and foreign keys are used to link to dimension tables. The decision analysis attributes consist of performance measures, operational metrics, aggregated measures (e.g., sales volumes, customer retention rates, profit margins, production costs, scrap rate), and all the other metrics needed to analyze the organization's performance. In other words, the fact table primarily addresses what the data warehouse supports for decision analysis. Surrounding the central fact tables (and linked via foreign keys) are dimension tables. The dimension tables contain classification and aggregation information about the central fact rows. Dimension tables contain attributes that describe the data contained within the fact table; they address how data will be analyzed and summarized. Dimension tables have a one-to-many relationship with rows in the central fact table. In querying, the dimensions are used to slice and dice the numerical values in the fact table to address the requirements of an ad hoc information need. The star schema is designed to provide fast query-response time, simplicity, and ease of maintenance for read-only database structures. A simple star schema is shown in Figure 3.10a. The star schema is considered a special case of the snowflake schema. The snowflake schema is a logical arrangement of tables in a multidimensional database in such a way that the entity-relationship diagram resembles a snowflake in shape. Closely related to the star schema, the snowflake schema is represented by centralized fact tables (usually only one), which are connected to multiple dimensions. In the snowflake schema, however, dimensions are normalized into multiple related tables, whereas the star schema's dimensions are denormalized, with each dimension being represented by a single table. A simple snowflake schema is shown in Figure 3.10b.

Prescriptive analytics,

The third category of analytics is termed prescriptive analytics. The goal of prescriptive analytics is to recognize what is going on as well as the likely forecast and make decisions to achieve the best performance possible. This group of techniques has historically been studied under the umbrella of OR or management sciences and are generally aimed at optimizing the performance of a system. The goal here is to provide a decision or a recommendation for a specific action. These recommendations can be in the form of a specific yes/no decision for a problem, a specific amount (say, price for a specific item or airfare to charge), or a complete set of production plans. The decisions may be presented to a decision maker in a report or may be used directly in an automated decision rules system (e.g., in airline pricing systems). Thus, these types of analytics can also be termed decision or normative analytics

Performance measurement system,

There is a difference between a performance measurement system and a performance management system. The latter encompasses the former. That is, any performance management system has a performance measurement system but not the other way around. If you were to ask, most companies today would claim that they have a performance measurement system but not necessarily a performance management system, even Though a performance measurement system has very little, if any, use without the overarching structure of the performance management system. The most popular performance measurement systems in use are some variant of Kaplan and Norton's balanced scorecard (BSC). Various surveys and benchmarking studies indicate that anywhere from 50 to over 90% of all companies have implemented some form of a BSC at one time or another. Although there seems to be some confusion about what constitutes "balance," there is no doubt about the originators of the BSC, Kaplan and Norton (1996): "Central to the BSC methodology is a holistic vision of a measurement system tied to the strategic direction of the organization. It is based on a four-perspective view of the world, with financial measures supported by customer, internal, and learning and growth metrics."

Issues to consider when deciding which data warehousing architecture to use,

Which database management system (DBMS) should be used? Most data warehouses are built using RDBMS. Oracle (Oracle Corporation, oracle.com), SQL Server (Microsoft Corporation, microsoft.com/sql), and DB2 (IBM Corporation, http:// www-01.ibm.com/software/data/db2) are the ones most commonly used. Each of these products supports both client/server and Web-based architectures. • Will parallel processing and/or partitioning be used? Parallel processing enables multiple central processing units (CPUs) to process data warehouse query requests simultaneously and provides scalability. Data warehouse designers need to decide whether the database tables will be partitioned (i.e., split into smaller tables) for access efficiency and what the criteria will be. This is an important consideration that is necessitated by the large amounts of data contained in a typical data warehouse. A recent survey on parallel and distributed data warehouses can be found in Furtado (2009). Teradata (teradata.com) has successfully adopted and is often commended on its novel implementation of this approach. • Will data migration tools be used to load the data warehouse? Moving data from an existing system into a data warehouse is a tedious and laborious task. Depending on the diversity and the location of the data assets, migration may be a relatively simple procedure or (on the contrary) a months-long project. The results of a thorough assessment of the existing data assets should be used to determine whether to use migration tools, and if so, what capabilities to seek in those commercial tools. • What tools will be used to support data retrieval and analysis? Often it is necessary to use specialized tools to periodically locate, access, analyze, extract, transform, and load necessary data into a data warehouse. A decision has to be made on (1) developing the migration tools in-house, (2) purchasing them from a third-party provider, or (3) using the ones provided with the data warehouse system. Overly complex, real-time migrations warrant specialized third-party ETL tools.

Traffic sources

Your Web analytics program is an incredible tool for identifying where your Web traffic originates. Basic categories such as search engines, referral Web sites, and visits from bookmarked pages (i.e., direct) are compiled with little involvement by the marketer. With a little effort, however, you can also identify Web traffic that was generated by your various offline or online advertising campaigns. 1. Referral Web sites. Other Web sites that contain links that send visitors directly to your Web site are considered referral Web sites. Your analytics program will identify each referral site your traffic comes from, and a deeper analysis will help you determine which referrals produce the greatest volume, the highest conversions, the most new visitors, and so on. 2. Search engines. Data in the search engine category is divided between paid search and organic (or natural) search. You can review the top keywords that generated Web traffic to your site and see if they are representative of your products and services. Depending upon your business, you might want to have hundreds (or thousands) of keywords that draw potential customers. Even the simplest product search can have multiple variations based on how the individual phrases the search query. 3. Direct. Direct searches are attributed to two sources. An individual who bookmarks one of your Web pages in their favorites and clicks that link will be recorded as a direct search. Another source occurs when someone types your URL directly into their browser. This happens when someone retrieves your URL from a business card, brochure, print ad, radio commercial, and so on. That's why it's a good strategy to use coded URLs. 4. Offline campaigns. If you utilize advertising options other than Web-based campaigns, your Web analytics program can capture performance data if you include a mechanism for sending them to your Web site. Typically, this is a dedicated URL that you include in your advertisement (i.e., "www.mycompany.com/offer50") that delivers those visitors to a specific landing page. You now have data on how many responded to that ad by visiting your Web site. 5. Online campaigns. If you are running a banner ad campaign, search engine advertising campaign, or even e-mail campaign, you can measure individual campaign effectiveness by simply using a dedicated URL similar to the offline campaign strategy.

Transaction processing vs analytic processing (OLTP, OLAP, data warehouse),

DWs are intended to work with informational data used for online analytical processing (OLAP) systems. Most operational data in ERP systems is stored in an OLTP system, which responds immediately to user requests. An OLTP system makes it inefficient for end-user ad hoc reports, queries, and analysis. In the 1980s, many business users referred to their mainframes as "black holes " because all the information went into them, but none ever came back. To resolve these issues, notions of DW and BI were created.

Data visualization,

Data visualization (or more appropriately, information visualization) has been defined as "the use of visual representations to explore, make sense of, and communicate data"(Few, 2007). Although the name that is commonly used is data visualization, usually what is meant by this is information visualization. Because information is the aggregation, summarization, and contextualization of data (raw facts), what is portrayed in visualizations is the information and not the data. However, because the two terms data visualization and information visualization are used interchangeably and synonymously, in this chapter we will follow suit. Data visualization is closely related to the fields of information graphics, information visualization, scientific visualization, and statistical graphics. Until recently, the major forms of data visualization available in both BI applications have included charts and graphs, as well as the other types of visual elements used to create scorecards and dashboards. To better understand the current and future trends in the field of data visualization, it helps to begin with some historical context.

Tasks by document indexer

Ste p 1: Pre processing t he D ocuments Because the documents fetched by the crawler may all be in different formats, for the ease of processing them further, in this step they all are converted to some type of standard representation. For instance, different content types (text, hyperlink, image, etc.) may be separated from each other, formatted (if necessary), and stored in a place for further processing. Ste p 2: P arsing t he D ocuments This step is essentially the application of text mining (i.e., computational linguistic, NLP) tools and techniques to a collection of documents/pages. In this step, first the standardized documents are parsed into components to identify index-worthy words/terms. Then, using a set of rules, the words/terms areindexed. More specifically, using tokenization rules, the words/terms/entities are extracted from the sentences in these documents. Using proper lexicons, the spelling errors and other anomalies in these words/terms are corrected. Not all the terms are discriminators. The nondiscriminating words/terms (also known as stop words) are eliminated from the list of index-worthy words/terms. Because the same word/term can be in many different forms, stemming is applied to reduce the words/terms to their root forms. Again, using lexicons and other language-specific resources (e.g., WordNet), synonyms and homonyms are identified, and the word/term collection is processed before moving into the indexing phase. Ste p 3: Cre ati ng t he Ter m-by-Document M atri x In this step, the relationships between the words/terms and documents/pages are identified. The weight can be as simple as assigning 1 for presence or 0 for absence of the word/term in the document/page. Usually more sophisticated weight schemas are used. For instance, as opposed to binary, one may choose to assign frequency of occurrence (number of times the same word/term is found in a document) as a weight. As we have seen early in this chapter, text mining research and practice have clearly indicated that the best weighting may come from the use of term frequency divided by inverse document frequency (TF/IDF). This algorithm measures the frequency of occurrence of each word/term within a document and then compares that frequency against the frequency of occurrence in the document collection. As we all know, not all high-frequency words/term are good document discriminators, and a good document discriminator in a domain may not be one in another domain. Once the weighing schema is determined, the weights are calculated and the term-by-document index file is created.

Nominal, ordinal data,

Nominal data has finite nonordered values (e.g., gender data, which has two values: male and female). Ordinal data has finite ordered values. For example, customer credit ratings are considered ordinal data because the ratings can be excellent, fair, and bad.

Alternative data warehousing architectures

At the highest level, data warehouse architecture design viewpoints can be categorized into enterprise-wide data warehouse (EDW) design and DM design (Golfarelli & Rizzi, 2009). In Figure 3.8a-e, we show some alternatives to the basic architectural design types that are neither pure EDW nor pure DM, but in between or beyond the traditional architectural structures. Notable new ones include hub-and-spoke and federated architectures. The five architectures shown in Figure 3.8a-e, are proposed by Ariyachandra and Watson (2005, 2006a,b). Previously, in an extensive study, Sen and Sinha (2005) identified 15 different data warehousing methodologies. The sources of these methodologies are classified into three broad categories: core-technology vendors, infrastructure vendors, and information-modeling companies. a. Independent data marts. This is arguably the simplest and the least costly architecture alternative. The DMs are developed to operate independent of each other to serve the needs of individual organizational units. Because of their independence, they may have inconsistent data definitions and different dimensions and measures, making it difficult to analyze data across the DMs (i.e., it is difficult, if not impossible, to get to the "one version of the truth"). b. Data mart bus architecture. This architecture is a viable alternative to the independent DMs where the individual marts are linked to each other via some kind of middleware. Because the data are linked among the individual marts, there is a better chance of maintaining data consistency across the enterprise (at least at the metadata level). Even though it allows for complex data queries across DMs, the performance of these types of analysis may not be at a satisfactory level. c. Hub-and-spoke architecture. This is perhaps the most famous data warehousing architecture today. Here the attention is focused on building a scalable and maintainable infrastructure (often developed in an iterative way, subject area by subject area) that includes a centralized data warehouse and several dependent DMs (each for an organizational unit). This architecture allows for easy customization of user interfaces and reports. On the negative side, this architecture lacks the holistic enterprise view and may lead to data redundancy and data latency. d. Centralized data warehouse. The centralized data warehouse architecture is similar to the hub-and-spoke architecture except that there are no dependent DMs; instead, there is a gigantic EDW that serves the needs of all organizational units. This centralized approach provides users with access to all data in the data warehouse instead of limiting them to DMs. In addition, it reduces the amount of data the technical team has to transfer or change, therefore simplifying data management and administration. If designed and implemented properly, this architecture provides a timely and holistic view of the enterprise to whoever, whenever, and wherever they may be within the organization. e. Federated data warehouse. The federated approach is a concession to the natural forces that undermine the best plans for developing a perfect system. It uses all possible means to integrate analytical resources from multiple sources to meet changing needs or business conditions. Essentially, the federated approach involves integrating disparate systems. In a federated architecture, existing decision support structures are left in place, and data are accessed from those sources as needed. The federated approach is supported by middleware vendors that propose distributed query and join capabilities. These eXtensible Markup Language (XML)-based tools offer users a global view of distributed data sources, including data warehouses, DMs, Web sites, documents, and operational systems. When users choose query objects from this view and press the submit button, the tool automatically queries the distributed sources, joins the results, and presents them to the user. Because of performance and data quality issues, most experts agree that federated approaches work well to supplement data warehouses, not replace them (see Eckerson, 2005).

Data mining concepts, understanding the customer,

Data mining, a new and exciting technology of only a few years ago, has become a common practice for a vast majority of organizations. In an interview with Computerworld magazine in January 1999, Dr. Arno Penzias (Nobel laureate and former chief scientist of Bell Labs) identified data mining from organizational databases as a key application for corporations of the near future. In response to Computerworld's age-old question of "What will be the killer applications in the corporation?" Dr. Penzias replied: "Data mining." He then added, "Data mining will become much more important and companies will throw away nothing about their customers because it will be so valuable. If you're not doing this, you're out of business." Similarly, in an article in Harvard Business Review, Thomas Davenport (2006) argued that the latest strategic weapon for companies is analytical decision making, providing examples of companies such as Amazon.com, Capital One, Marriott International, and others that have used analytics to better understand their customers and optimize their extended supply chains to maximize their returns on investment while providing the best customer service. This level of success is highly dependent on a company understanding its customers, vendors, business processes, and the extended supply chain very well. A large portion of "understanding the customer" can come from analyzing the vast amount of data that a company collects. The cost of storing and processing data has decreased dramatically in the recent past, and, as a result, the amount of data stored in electronic form has grown at an explosive rate. With the creation of large databases, the possibility of analyzing the data stored in them has emerged. The term data mining was originally used to describe the process through which previously unknown patterns in data were discovered. This definition has since been stretched beyond those limits by some software vendors to include most forms of data analysis in order to increase sales with the popularity of the data mining label. In this chapter, we accept the original definition of data mining. Although the term data mining is relatively new, the ideas behind it are not. Many of the techniques used in data mining have their roots in traditional statistical analysis and artificial intelligence work done since the early part of the 1980s. Why, then, has it suddenly gained the attention of the business world? Following are some of most pronounced reasons: • More intense competition at the global scale driven by customers' ever-changing needs and wants in an increasingly saturated marketplace. • General recognition of the untapped value hidden in large data sources. • Consolidation and integration of database records, which enables a single view of customers, vendors, transactions, and so on. • Consolidation of databases and other data repositories into a single location in the form of a data warehouse. • The exponential increase in data processing and storage technologies. • Significant reduction in the cost of hardware and software for data storage and processing. • Movement toward the demassification (conversion of information resources into nonphysical form) of business practices. Data generated by the Internet is increasing rapidly in both volume and complexity. Large amounts of genomic data are being generated and accumulated all over the World. Disciplines such as astronomy and nuclear physics create huge quantities of data on a regular basis. Medical and pharmaceutical researchers constantly generate and store data that can then be used in data mining applications to identify better ways to accurately diagnose and treat illnesses and to discover new and improved drugs. On the commercial side, perhaps the most common use of data mining has been in the finance, retail, and healthcare sectors. Data mining is used to detect and reduce fraudulent activities, especially in insurance claims and credit card use (Chan et al., 1999); to identify customer buying patterns (Hoffman, 1999); to reclaim profitable customers (Hoffman, 1998); to identify trading rules from historical data; and to aid in increased profitability using market-basket analysis. Data mining is already widely used to better target clients, and with the widespread development of e-commerce, this can only become more imperative with time.

Developments that have contributed to the growth of decision support and analytics

· Group communication and collaboration. · Improved data management. · Managing giant data warehouses and Big Data. · Analytical Support. · Overcoming cognitive limits in processing and storing information. · Knowledge Management. · Anywhere, Anytime Support.

· Evolution of decision support, BI, and analytics, pages 13-15, fig. 1.8

- In the 1970s, management information systems (MIS) focused on providing structured, periodic reports that a manager could use for decision making. DSSs are computer-based systems that help decision makers utilize data and models to solve unstructured problems. There is no universally accepted definition of DSS; it means different things to different people. -In the late 1970s and early 1980s, a new line of models emerged that promised to capture experts' knowledge in a format that computers could process. Rules-based expert systems (ESs) allowed scarce expertise to be made available where and when needed, using an "intelligent" DSS. -The 1980s saw a significant change in the way organizations captured businessrelated data. The old mostly sequential and nonstandardized data representation schemas were replaced by relational database management (RDBM) systems. Data integrity and consistency became an issue, significantly hindering the effectiveness of business practices. -In the 1990s, executive information systems (EISs) were designed for executives and their decision-making needs. These systems were designed as graphical dashboards and scorecards so that they could serve as visually appealing displays. By doing so, they were not hindering the efficiency of the business transaction systems. - 2000s As the amount of data accumulated in DWs increased, so did the capabilities of hardware and software. Because the data in a DW is updated periodically, it does not reflect the latest information in a timely manner. Vendors developed systems to update the data more frequently, leading to the term real-time data warehousing. -In the 2010s, we are seeing yet another paradigm shift in the way that data is processed and used. The term Big Data has been coined to highlight the challenges that these new data streams have brought on us. Many advancements in both hardware and software have been developed to address the challenges of Big Data. -· Future, Data-driven insights are more accessible to business professionals than ever before. More companies are now preparing their employees with the know-how of business analytics. Analytics can increase revenue while decreasing costs by building better products, improving customer experience, and catching fraud before it happens.

Four major patterns identified by data mining, associations, predictions, clusters, and sequential relationships, study with examples of applications,

1. Associations find the commonly co-occurring groupings of things, such as beer and diapers going together in market-basket analysis. 2. Predictions tell the nature of future occurrences of certain events based on what has happened in the past, such as predicting the winner of the Super Bowl or forecasting the absolute temperature of a particular day. 3. Clusters identify natural groupings of things based on their known characteristics, such as assigning customers in different segments based on their demographics and past purchase behaviors. 4. Sequential relationships discover time-ordered events, such as predicting that an existing banking customer who already has a checking account will open a savings account followed by an investment account within a year. These types of patterns have been manually extracted from data by humans for centuries, but the increasing volume of data in modern times has created a need for more automatic approaches. As data sets have grown in size and complexity, direct manual data analysis has increasingly been augmented with indirect, automatic data processing tools that use sophisticated methodologies, methods, and algorithms. The manifestation of such evolution of automated and semiautomated means of processing large data sets is now commonly referred to as data mining. Generally speaking, data mining tasks can be classified into three main categories: prediction, association, and clustering. Based on the way in which the patterns are extracted from the historical data, the learning algorithms of data mining methods can be classified as either supervised or unsupervised. With supervised learning algorithms, the training data includes both the descriptive attributes (i.e., independent variables or decision variables) as well as the class attribute (i.e., output variable or result variable). In contrast, with unsupervised learning the training data includes only the descriptive attributes. Figure 4.2 shows a simple taxonomy for data mining tasks, along with the learning methods and popular algorithms for each of the data mining tasks. Prediction Prediction is commonly referred to as the act of telling about the future. It differs from simple guessing by taking into account the experiences, opinions, and other relevant information in conducting the task of foretelling. A term that is commonly associated with prediction is forecasting. Even though many believe that these two terms are synonymous, there is a subtle but critical difference between the two. Whereas prediction is largely experience and opinion based, forecasting is data and model based. That is, in order of increasing reliability, one might list the relevant terms as guessing, predicting, and forecasting, respectively. In data mining terminology, prediction and forecasting are used synonymously, and the term prediction is used as the common representation of the act. Depending on the nature of what is being predicted, prediction can be named more specifically as classification (where the predicted thing, such as tomorrow's forecast, is a class label such as "rainy" or "sunny") or regression (where the predicted thing, such as tomorrow's temperature, is a real number, such as "65°F").

KPI,

There is a difference between a "run of the mill" metric and a "strategically aligned" metric. The term key performance indicator (KPI) is often used to denote the latter. A KPI represents a strategic objective and measures performance against a goal. According to Eckerson (2009), KPIs are multidimensional. Loosely translated, this means that KPIs have a variety of distinguishing features, including • Strategy. KPIs embody a strategic objective. • Targets. KPIs measure performance against specific targets. Targets are defined in strategy, planning, or budget sessions and can take different forms (e.g., achievement targets, reduction targets, absolute targets). • Ranges. Targets have performance ranges (e.g., above, on, or below target). • Encodings. Ranges are encoded in software, enabling the visual display of performance (e.g., green, yellow, red). Encodings can be based on percentages or more complex rules. • Time frames. Targets are assigned time frames by which they must be accomplished. A time frame is often divided into smaller intervals to provide performance mileposts. • Benchmarks. Targets are measured against a baseline or benchmark. The previous year's results often serve as a benchmark, but arbitrary numbers or external benchmarks may also be used. A distinction is sometimes made between KPIs that are "outcomes" and those that are "drivers." Outcome KPIs—sometimes known as lagging indicators—measure the output of past activity (e.g., revenues). They are often financial in nature, but not always. Driver KPIs—sometimes known as leading indicators or value drivers—measure activities that have a significant impact on outcome KPIs (e.g., sales leads). In some circles, driver KPIs are sometimes called operational KPIs, which is a bit of an oxymoron (Hatch, 2008). Most organizations collect a wide range of operational metrics. As the name implies, these metrics deal with the operational activities and performance of a company. The following list of examples illustrates the variety of operational areas covered by these metrics: • Customer performance. Metrics for customer satisfaction, speed and accuracy of issue resolution, and customer retention. • Service performance. Metrics for service-call resolution rates, service renewal rates, service level agreements, delivery performance, and return rates. • Sales operations. New pipeline accounts, sales meetings secured, conversion of inquiries to leads, and average call closure time. • Sales plan/forecast. Metrics for price-to-purchase accuracy, purchase order-to fulfillment ratio, quantity earned, forecast-to-plan ratio, and total closed contracts. Whether an operational metric is strategic or not depends on the company and its use of the measure. In many instances, these metrics represent critical drivers of strategic outcomes. For instance, Hatch (2008) recalls the case of a midtier wine distributor that was being squeezed upstream by the consolidation of suppliers and downstream by the consolidation of retailers. In response, it decided to focus on four operational measures: on-hand/on-time inventory availability, outstanding "open" order value, net-new accounts, and promotion costs and return on marketing investment. The net result of its efforts was a 12% increase in revenues in 1 year. Obviously, these operational metrics were key drivers. However, as described in the following section, in many cases, companies simply measure what is convenient with minimal consideration of why the data are being collected. The result is a significant waste of time, effort, and money.

· Changing business environments and evolving needs for decision support and analytics,

Managers must have high-speed, networked, cloud-based information systems to assist them with their most important decisions. Analytics and BI tools such as data mining, online analytical processing (OLAP), and decision support are the cornerstones of today's modern management.

Standard deviation, variance,

The standard deviation is also a measure of the spread of values within a set of data. The standard deviation is calculated by simply taking the square root of the variations. The following formula shows the calculation of standard deviation from a given sample of data points.

OLAP vs OLTP, page 158

OLTP (online transaction processing system) is a term used for a transaction system that is primarily responsible for capturing and storing data related to day-to-day business functions such as ERP, CRM, SCM, POS, and so forth. An OLTP system addresses a critical business need, automating daily business transactions, and running real-time reports and routine analysis. But these systems are not designed for ad hoc analysis and complex queries that deal with a number of data items. OLAP, on the other hand, is designed to address this need by providing ad hoc analysis of organizational data much more effectively and efficiently. OLAP and OLTP rely heavily on each other: OLAP uses the data captured by OLTP, and OLTP automates the business processes that are managed by decisions supported by OLAP. Table 3.5 provides a multicriteria comparison between OLTP and OLAP

Contextual metadata

Often when a report or a visual dashboard/scorecard is presented to business users, questions remain unanswered. The following are some examples: • Where did you source this data from? • While loading the data warehouse, what percentage of the data got rejected/encountered data quality problems? • Is the dashboard presenting "fresh" information or "stale" information? • When was the data warehouse last refreshed? • When is it going to be refreshed next? • Were any high-value transactions that would skew the overall trends rejected as a part of the loading process?

Direct and indirect benefits of data warehouses

• End users can perform extensive analysis in numerous ways. • A consolidated view of corporate data (i.e., a single version of the truth) is possible. • Better and more timely information is possible. A data warehouse permits information processing to be relieved from costly operational systems onto low-cost servers; therefore, many more end-user information requests can be processed more quickly. • Enhanced system performance can result. A data warehouse frees production processing because some operational system reporting requirements are moved to DSS. • Data access is simplified.

Regression, correlation

Correlati on ver sus Regre ssion Because regression analysis originated from correlation studies, and because both methods attempt to describe the association between two (or more) variables, these two terms are often confused by professionals and even by scientists. Correlation makes no a priori assumption of whether one variable is dependent on the other(s) and is not concerned with the relationship between variables; instead it gives an estimate on the degree of association between the variables. On the other hand, regression attempts to describe the dependence of a response variable on one (or more) explanatory variables where it implicitly assumes that there is a one-way causal effect from the explanatory variable(s) to the response variable, regardless of whether the path of effect is direct or indirect. Also, although correlation is interested in the low-level relationships between two variables, regression is concerned with the relationships between all explanatory variables and the response variable.

Visual analytics,

Visual analytics is a recently coined term that is often used loosely to mean nothing more than information visualization. What is meant by visual analytics is the combination of visualization and predictive analytics. Whereas information visualization is aimed at answering, "What happened?" and "What is happening?" and is closely associated with BI (routine reports, scorecards, and dashboards), visual analytics is aimed at answering, "Why is it happening?" "What is more likely to happen?" and is usually associated with business analytics (forecasting, segmentation, correlation analysis). Many of the information visualization vendors are adding the capabilities to call themselves visual analytics solution providers. One of the top, long-time analytics solution providers, SAS Institute, is approaching it from another direction. They are embedding their analytics capabilities into a high-performance data visualization environment that they call visual analytics. Visual or not visual, automated or manual, online or paper based, business reporting is not much different than telling a story.

Cloud computing, data lakes,

• Cloud computing. Cloud computing is perhaps the newest and the most innovative platform choice to come along in years. Numerous hardware and software resources are pooled and virtualized, so that they can be freely allocated to applications and software platforms as resources are needed. This enables information systems applications to dynamically scale up as workloads increase. Although cloud computing and similar virtualization techniques are fairly well established for operational applications today, they are just now starting to be used as data warehouse platforms of choice. The dynamic allocation of a cloud is particularly useful when the data volume of the warehouse varies unpredictably, making capacity planning difficult. • Data lakes. With the emergence of Big Data, there came a new data platform: data lake, which is a large storage location that can hold vast quantities of data (mostly unstructured) in its native/raw format for future/potential analytics consumption. Traditionally speaking, whereas a data warehouse stores structured data, a data lake stores all kinds of data. While they are both data storage mechanisms, a data warehouse is all about structured/tabular data and a data lake is about all types of data. Although much has been said and written about the relationship between the two (some of which suggests that data lake is the future name of data warehouses), as it stands, a data lake is not a replacement for a data warehouse; rather, they are complementary to one another. Technology Insight 3.2 digs deeper into explaining data lakes and their role in the worlds of data warehousing and business analytics.

Benefits of hosted data warehouses,

• Requires minimal investment in infrastructure • Frees up capacity on in-house systems • Frees up cash flow • Makes powerful solutions affordable • Enables powerful solutions that provide for growth • Offers better-quality equipment and software • Provides faster connections • Enables users to access data from remote locations • Allows a company to focus on core business • Meets storage needs for large volumes of data

Speed, robustness, scalability, and interpretability,

• Speed. The computational costs involved in generating and using the model, where faster is deemed to be better. • Robustness. The model's ability to make reasonably accurate predictions, given noisy data or data with missing and erroneous values. • Scalability. The ability to construct a prediction model efficiently given a rather large amount of data. • Interpretability. The level of understanding and insight provided by the model (e.g., how and/or what the model concludes on certain predictions).

ERP vs SCM,

· Most operational data in enterprise resources planning (ERP) systems—and in its complementary siblings like supply chain management (SCM) or CRM—are stored in an OLTP system, which is a type of computer processing where the computer responds immediately to user requests. Each request is considered to be a transaction, which is a computerized record of a discrete event, such as the receipt of inventory or a customer order. In other words, a transaction requires a set of two or more database updates that must be completed in an all-or-nothing fashion. The very design that makes an OLTP system efficient for transaction processing makes it inefficient for end-user ad hoc reports, queries, and analysis. In the 1980s, many business users referred to their mainframes as "black holes " because all the information went into them, but none ever came back. All requests for reports had to be programmed by the IT staff, whereas only "precanned" reports could be generated on a scheduled basis, and ad hoc real-time querying was virtually impossible. Although the client/serverbased ERP systems of the 1990s were somewhat more report-friendly, it has still been a far cry from a desired usability by regular, nontechnical, end users for things such as operational reporting, interactive analysis, and so on. To resolve these issues, the notions of DW and BI were created

· Developing or acquiring BI systems, shells, pages 21-22

· Today, many vendors offer diversified tools, some of which are completely preprogrammed (called shells); all you have to do is insert your numbers. These tools can be purchased or leased. For a list of products, demos, white papers, and more current product information, see product directories at tdwi.org. Free user registration is required. Almost all BI applications are constructed with shells provided by vendors who may themselves create a custom solution for a client or work with another outsourcing provider. The issue that companies face is which alternative to select: purchase, lease, or build. Each of these alternatives has several options. One of the major criteria for making the decision is justification and cost-benefit analysis.

Using a lexicon,

A lexicon is essentially the catalog of words, their synonyms, and their meanings for a given language. In addition to lexicons for many other languages, there are several general-purpose lexicons created for English. Often general-purpose lexicons are used to create a variety of special-purpose lexicons for use in sentiment analysis projects. Perhaps the most popular general-purpose lexicon is WordNet, created at Princeton University, which has been extended and used by many researchers and practitioners for sentiment analysis purposes. As described on the WordNet Web site (wordnet.princeton.edu), it is a large lexical database of English, including nouns, verbs, adjectives, and adverbs grouped into sets of cognitive synonyms (i.e., synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations.

Data mining privacy issues,

Data that is collected, stored, and analyzed in data mining often contains information about real people. Such information may include identification data (name, address, Social Security number, driver's license number, employee number, etc.), demographic data (e.g., age, sex, ethnicity, marital status, number of children), financial data (e.g., salary, gross family income, checking or savings account balance, home ownership, mortgage or loan account specifics, credit card limits and balances, investment account specifics), purchase history (i.e., what is bought from where and when—either from vendor's transaction records or from credit card transaction specifics), and other personal data (e.g., anniversary, pregnancy, illness, loss in the family, bankruptcy filings). Most of these data can be accessed through some third-party data providers. The main question here is the privacy of the person to whom the data belongs. To maintain the privacy and protection of individuals' rights, data mining professionals have ethical (and often legal) obligations. One way to accomplish this is the process of de-identification of the customer records prior to applying data mining applications, so that the records cannot be traced to an individual. Many publicly available data sources (e.g., CDC data, SEER data, UNOS data) are already de-identified. Prior to accessing these data sources, users are often asked to consent that, under no circumstances, will they try to identify the individuals behind those figures. There have been a number of instances in the recent past where the companies shared their customer data with others without seeking the explicit consent of their customers. For instance, as most of you might recall, in 2003, JetBlue Airlines provided more than a million passenger records of their customers to Torch Concepts, a U.S. government contractor. Torch then subsequently augmented the passenger data with additional information such as family sizes and Social Security numbers—information purchased from a data broker called Acxiom. The consolidated personal database was intended to be used for a data mining project to develop potential terrorist profiles. All of this was done without notification or consent of passengers. When news of the activities got out, however, dozens of privacy lawsuits were filed against JetBlue, Torch, and Acxiom, and several U.S. senators called for an investigation into the incident (Wald, 2004). Similar, but not as dramatic, privacy-related news came out in the recent past about the popular social network companies, which allegedly were selling customer-specific data to other companies for personalized target marketing. There was another peculiar story about privacy concerns that made it to the headlines in 2012. In this instance, the company did not even use any private and/or personal data. Legally speaking, there was no violation of any laws.

Data Marts, dependent and independent

Whereas a data warehouse combines databases across an entire enterprise, a data mart (DM) is usually smaller and focuses on a particular subject or department. A DM is a subset of a data warehouse, typically consisting of a single subject area (e.g., marketing, operations). A DM can be either dependent or independent. A dependent data mart is a subset that is created directly from the data warehouse. It has the advantages of using a consistent data model and providing quality data. Dependent DMs support the concept of a single enterprise-wide data model, but the data warehouse must be constructed first. A dependent DM ensures that the end user is viewing the same version of the data that is accessed by all other data warehouse users. The high cost of data warehouses limits their use to large companies. As an alternative, many firms use a lower-cost, scaled-down version of a data warehouse referred to as an independent DM. An independent data mart is a small warehouse designed for a strategic business unit or a department, but its source is not an EDW

Two main types of web analytics,

off-site and on-site. Off-site Web analytics refers to Web measurement and analysis about you and your products that takes place outside your Web site. It includes the measurement of a Web site's potential audience (prospect or opportunity), share of voice (visibility or word-of-mouth), and buzz (comments or opinions) that is happening on the Internet. What is more mainstream has been on-site Web analytics. Historically, Web analytics has referred to on-site visitor measurement. However, in recent years this has blurred, mainly because vendors are producing tools that span both categories. On-site Web analytics measure visitors' behavior once they are on your Web site. This includes its drivers and conversions—for example, the degree to which different landing pages are associated with online purchases. On-site Web analytics measure the performance of your Web site in a commercial context. The data collected on the Web site is then compared against key performance indicators for performance and used to improve a Web site's or marketing campaign's audience response. Even though Google Analytics is the most widely used on-site Web analytics service, others are provided by Yahoo! and Microsoft, and newer and better tools are emerging constantly that provide additional layers of information. For on-site Web analytics, there are two technical ways of collecting the data. The first and more traditional method is the server log file analysis, where the Web server records file requests made by browsers. The second method is page tagging, which uses JavaScript embedded in the site page code to make image requests to a third-party analytics-dedicated server whenever a page is rendered by a Web browser (or when a mouse click occurs). Both collect data that can be processed to produce Web traffic reports. In addition to these two main streams, other data sources may also be added to augment Web site behavior data. These other sources may include e-mail, direct mail campaign data, sales and lead history, or social media-originated data.

Real time, on demand BI,

· The demand for instant, on-demand access to dispersed information has grown as the need to close the gap between the operational data and strategic objectives has become more pressing. As a result, a category of products called real-time BI applications has emerged. The introduction of new data-generating technologies, such as RFID and other sensors is only accelerating this growth and the subsequent need for real-time BI. Traditional BI systems use a large volume of static data that has been extracted, cleansed, and loaded into a DW to produce reports and analyses. However, the need is not just reporting because users need business monitoring, performance analysis, and an understanding of why things are happening. These can assist users, who need to know (virtually in real time) about changes in data or the availability of relevant reports, alerts, and notifications regarding events and emerging trends in social media applications. In addition, business applications can be programmed to act on what these real-time BI systems discover. For example, an SCM application might automatically place an order for more "widgets" when real-time inventory falls below a certain threshold or when a CRM application automatically triggers a customer service representative and credit control clerk to check a customer who has placed an online order larger than $10,000. One approach to real-time BI uses the DW model of traditional BI systems. In this case, products from innovative BI platform providers provide a service-oriented, nearreal- time solution that populates the DW much faster than the typical nightly extract/transfer/load batch update does (see Chapter 3). A second approach, commonly called business activity management (BAM), is adopted by pure-play BAM and/or hybrid BAMmiddleware providers (such as Savvion, Iteration Software, Vitria, webMethods, Quantive, Tibco, or Vineyard Software). It bypasses the DW entirely and uses Web services or other monitoring means to discover key business events. These software monitors (or intelligent agents) can be placed on a separate server in the network or on the transactional application databases themselves, and they can use event- and process-based approaches to proactively and intelligently measure and monitor operational processes.

· Integration of systems and applications, page 22

· With the exception of some small applications, all BI applications must be integrated with other systems such as databases, legacy systems, enterprise systems (particularly ERP and CRM), e-commerce (sell side, buy side), and many more. In addition, BI applications are usually connected to the Internet and many times to information systems of business partners. Furthermore, BI tools sometimes need to be integrated among themselves, creating synergy. The need for integration pushed software vendors to continuously add capabilities to their products. Customers who buy an all-in-one software package deal with only one vendor and do not have to deal with system connectivity. But, they may lose the advantage of creating systems composed from the "best-of-breed" components.