DSCI 4330 Exam 1

What is the meaning of and motivation for balance in BSC?

In BSC, the term "balance" arises because the combined set of measures is supposed to encompass indicators that are financial and nonfinancial, leading and lagging, internal and external, quantitative and qualitative, and both short term and long term.

Why is it important for IRS and for U.S. state governments to use data warehousing and business intelligence (BI) tools in managing state revenues?

Revenues are complex and have many sources. This variety and detail make understanding the data difficult, hampering efficiency. The use of BI tools allows for better analysis, understanding, and governance.

What issues should be considered when deciding which architecture to use in developing a data warehouse? List the 10 most important factors.

1. Information interdependence between organizational units 2. Upper management's information needs 3. Urgency of need for a data warehouse 4. Nature of end-user tasks 5. Constraints on resources 6. Strategic view of the data warehouse prior to implementation 7. Compatibility with existing systems 8. Perceived ability of the in-house IT staff 9. Technical issues 10. Social/political factors

What is a business report?

a written document that contains information regarding business matters.

What are the main characteristics of a good business report?

should include clarity, brevity, completeness and correctness.

What is the difference between information visualization and visual analytics?

Visual analytics is the combination of visualization and predictive analytics. While information visualization is aimed at answering "what happened" and "what is happening" and is closely associated with business intelligence (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, and correlation analysis).

Look at Gartner's Magic Quadrant for Business Intelligence and Analytics Platforms. What do you see? Discuss and justify your observations.

What we can see from Gartner's Magic Quadrant is that the vast majority of the "challengers" in the visual analytics space are considered to be "niche players," while all of the "leaders" are "visionaries." Most of the leaders are either relatively recently founded information visualization companies (e.g., Tableau Software, QlikTech, or Tibco Spotfire) or are well-established, large analytics companies (e.g., SAS, IBM, Microsoft, SAP, or MicroStrategy) that are increasingly focusing their efforts in information visualization and visual analytics. This chart, and the Gartner report from which it comes, shows that data discovery/visualization has become a mainstream architecture, perhaps surpassing the OLAP/semantic offerings of traditional BI vendors. This is further evidenced by the change of name of the magic quadrant from "BI" to "BI and Analytics."

Describe Dashboard- type reports within the three major categories of business reports.

a range of performance indicators on one page, with both static/predefined elements and customizable widgets and views.

What is a report?

any communication artifact prepared with the specific intention of conveying information in a presentable form to whoever needs it, whenever and wherever they may need it.

Describe Metric management reports within the three major categories of business reports.

involve outcome-oriented metrics based on service level agreements and/or key performance indicators.

Describe Balanced scorecard reports within the three major categories of business reports.

present an integrated view of a company's health and include financial, customer, business process, and learning/growth perspectives.

What are the three key components of a BPM system?

According to Colbert (2009), a BPM encompasses three key components. The first is a set of integrated, closed-loop management and analytic processes (supported by technology) that addresses financial as well as operational activities. The second involves tools for businesses to define strategic goals and then measure and manage performance against those goals. And the third component involves a core set of processes, including financial and operational planning, consolidation and reporting, modeling, analysis, and monitoring of key performance indicators (KPIs), linked to organizational strategy.

Describe the three steps of the ETL process.

Extraction: selecting data from one or more sources and reading the selected data. Transformation: converting data from their original form to whatever form the DW needs. This step often also includes cleansing of the data to remove as many errors as possible. Load: putting the converted (transformed) data into the DW.

Carefully analyze Charles Joseph Minard's graphical portrayal of Napoleon's march. Identify and comment on all of the information dimensions captured in this ancient diagram.

In this graphic Minard managed to simultaneously represent several data dimensions, including the size of the army, direction of movement, geographic locations, outside temperature, etc. He did this in an artistic and informative manner. The background of the image is a map depicting the location of battles. There is a thick lighter band that shows the size of Napoleon's army at each position, and a dark lower one that depicts the retreat. A line at the bottom depicts temperatures at each position in time and space.

List and describe the three layers of information portrayed on dashboards.

The three layers of information found in most dashboards are: 1. Monitoring. Graphical, abstracted data to monitor key performance metrics. 2. Analysis. Summarized dimensional data to analyze the root cause of problems. 3. Management. Detailed operational data that identify what actions to take to resolve a problem.

What is a time series?

The use of mathematical modeling to predict future values of the variable of interest based on previously observed values.

What are the differences and commonalities between dashboards and scorecards?

These terms are often used interchangeably, and they share many common features. The main difference is that scorecards are used by executives, managers, and staff to monitor strategic alignment and success with strategic objectives and targets. By contrast, dashboards are used at the operational and tactical levels. Managers, supervisors, and operators use operational dashboards to monitor detailed operational performance on a weekly, daily, or even hourly basis.

List the three major categories of business reports.

Metric management reports, dashboard-type reports, and balanced scorecard-type reports.

Find two more kinds of charts that are not covered in this section and comment on their usability.

A concept map is a diagram that shows relationships between concepts, usually showing specific ideas and information as boxes and using arrows to connect them. Concept maps are often used by designers and engineers to organize ideas. Another type of chart is an organization chart (or org-chart). This is a hierarchical, tree-structured chart that shows how an organization is structured and how its parts and jobs are related. A motion chart is like a bubble chart in that it depicts data on dimensions of the x-axis, y-axis, size, and color of bubbles. In addition, however, it is also animated, so that bubbles move and resize themselves over time.

What is a data warehouse?

A data warehouse is defined in this section as "a pool of data produced to support decision making." This focuses on the essentials, leaving out characteristics that may vary from one DW to another but are not essential to the basic concept. The same paragraph gives another definition: "a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management's decision-making process." This definition adds more specifics, but in every case appropriately: it is hard, if not impossible, to conceive of a data warehouse that would not be subject-oriented, integrated, etc.

What is a high-powered visual analytics environment? Why do we need it?

A high-powered visualization environment is one in which high-performance, in-memory solutions are applied to exploring massive amounts of data in a very short time (almost instantaneously). Due to the increasing demand for visual analytics coupled with fast-growing data volumes, there is an ever-growing need to invest in highly efficient visualization systems. SAS Visual Analytics is an example of such an environment. These systems help to empower larger numbers of users, solve complex problems more quickly, and improve collaboration and information sharing. By enabling end-users, IT staff are freed up. In addition, these tools allow for growth at a self-determined pace.

What is a performance measurement system? How does it work?

A performance measurement system is one component of a performance management system. The most popular performance measurement systems in use are some variant of Kaplan and Norton's balanced scorecard (BSC).

What are the common characteristics of dashboards and other information visuals?

All well-designed dashboards share some common characteristics. They use visual components (e.g., charts, performance bars, sparklines, gauges, meters, stoplights) to highlight, at a glance, the data and exceptions that require action. They are transparent to the user, meaning that they require minimal training and are extremely easy to use. They combine data from a variety of systems into a single, summarized, unified view of the business. They enable drill-down or drill-through to underlying data sources or reports, providing more detail about the underlying comparative and evaluative context. They present a dynamic, real-world view with timely data refreshes, enabling the end user to stay up to date with any recent changes in the business. And they require little, if any, customized coding to implement, deploy, and maintain.

Differentiate among a DM, an ODS, and an EDW.

An ODS (Operational Data Store) is the database from which a business operates on an ongoing basis. Both an EDW and a data mart (DM) are data warehouses. An EDW (Enterprise Data Warehouse) is an all-encompassing DW that covers all subject areas of interest to the entire organization. A data mart is a smaller DW designed around one problem, organizational function, topic, or other suitable focus area.

Why do we need to define separate objectives, measures, targets, and initiatives for each of these four BSC perspectives?

BSC is designed to overcome the limitations of systems that are financially focused. An organization's vision and strategy should recognize the interrelation between financial and nonfinancial objectives, measures, targets, and initiatives. Therefore, nonfinancial objectives form a simple causal chain with "learning and growth" driving "internal business process" change, which produces "customer" outcomes that are responsible for reaching a company's "financial" objectives.

Compare BSC and Six Sigma as two competing performance measurement systems.

BSC is focused on improving overall strategy, whereas Six Sigma is focused on improving processes. BSC gives a longer-term view of the organization, whereas Six Sigma gives a snapshot at a particular point in time of its operational effectiveness. BSC focuses on long-term growth, whereas Six Sigma emphasizes current profitability. These are a few of the differences between the two. Some companies choose to give a more holistic performance assessment by combining elements of both approaches.

What is a balanced scorecard? Where did it come from?

Balanced Scorecard was first developed by Kaplan and Norton in their 1992 Harvard Business Review article, "The Balanced Scorecard: Measures That Drive Performance." It is a performance management system whose key feature is that it does not rely solely on financial measures of success. Over the past few years, BSC has become a generic term that is used to represent virtually every type of scorecard application and implementation, but it is intended to emphasize a strategic focus.

What are the best practices in dashboard design?

Benchmark key performance indicators with industry standards. • Wrap the dashboard metrics with contextual metadata (e.g. data source, data currency, refresh schedule). • Prioritize and rank alerts/exceptions streamed to the dashboard. • Enrich the dashboard with business users' comments. • Present information in three different levels (visual dashboard, static report, and self-service cube). • Pick the right visual construct using dashboard design principles. • Provide for guided analytics.

What are the key similarities and differences between a two-tiered architecture and a three-tiered architecture?

Both provide the same user visibility through a client system that accesses a DSS/BI application remotely. The difference is behind the scenes and is invisible to the user: in a two-tiered architecture, the application and data warehouse reside on the same machine; in a three-tiered architecture, they are on separate machines.

What is business performance management? How does it relate to BI?

Business performance management (BPM) refers to the business processes, methodologies, metrics, and technologies used by enterprises to measure, monitor, and manage business performance. It is also known as corporate. performance management (CPM), enterprise performance management (EPM), and strategic enterprise management (SEM). It can be considered to be a type of BI tool/technique. The most significant differentiator of BPM from any other BI tools and practices is its strategy focus. BPM encompasses a closed-loop set of processes that link strategy to execution in order to optimize business performance.

What is DMAIC? List and briefly describe the steps involved in DMAIC.

DMAIC is a closed loop performance improvement model that involves the following steps: define, measure, analyze, improve, and control. First, you define the goals, objectives, and boundaries of the improvement activity. Next, you measure the existing system, in order to monitor its performance against the goals. Then, you analyze the system to identify ways to eliminate the gap between the current performance of the system or process and the desired goal. This leads to improvement, which involves initiating actions to reduce these gaps. Finally, control involves modifying compensation and incentive systems, policies, procedures, manufacturing resource planning, budgets, operation instructions, or other management systems.

What are the graphical widgets commonly used in dashboards? Why?

Dashboards can include many kinds of visual widgets, including charts, performance bars, sparklines, gauges, meters, stoplights, geographic maps, etc. These help to highlight, at a glance, the data and exceptions that require action. A picture tells a thousand words, and through the use of many graphical widgets, a dashboard can convey a wealth of information to decision makers in a short time.

Describe data integration.

Data integration is an umbrella term that covers three processes that combine to move data from multiple sources into a data warehouse: accessing the data, combining different views of the data, and capturing changes to the data.

What is OLAP and how does it differ from OLTP?

Data stored in a data warehouse can be analyzed using techniques referred to as OLAP. OLAP is one of the most commonly used data analysis techniques in data warehouses. OLAP is an approach to quickly answer ad hoc questions that require data analysis. OLTP is concerned with the capture and storage of data. OLAP is concerned with the analysis of that data.

What is data visualization?Why is it needed?

Data visualization, perhaps more appropriately called "information visualization," is the use of visual representations to explore, make sense of, and communicate data. It is closely related to the fields of information graphics, scientific visualization, and statistical graphics. What is portrayed in visualizations is the information (aggregations, summarizations, and contextualization) and not the data. Companies and individuals increasingly rely on data to make good decisions. Because data is so voluminous, there is a need for visual tools that help people understand it.

Why do you think there are many different types of charts and graphs?

Different types of charts are appropriate for conveying different types of information. Line graphs are good for time-series data. Bar charts are good for depicting nominal or numerical data that can be easily categorized. Pie charts should be used for depicting proportions. Scatter plots and bubble charts are good for illustrating relationships between two or three variables (bubble charts add a dimension via the size of the dot). Histograms are like bar charts, except they depict frequency distributions. Gantt charts and PERT charts are good at illustrating project timelines and task dependencies. Geographic maps, of course, show geographic information. Bullet graphs show progress toward a goal. Heat maps and highlight tables illustrate the comparison of continuous values across two categories using color. Tree maps are good for showing hierarchical information. Even though these charts and graphs cover a major part of what is commonly used in information visualization, they by no means cover it all. Nowadays, one can find many other specialized graphs and charts that serve a specific purpose.

List the benefits of data warehouses.

Direct benefits include: • Allowing end users to perform extensive analysis in numerous ways. • A consolidated view of corporate data (i.e., a single version of the truth). • Better and more timely information. A data warehouse permits information processing to be offloaded from costly operational systems onto low-cost servers; therefore, end-user information requests can be processed more quickly. • Enhanced system performance. A data warehouse frees production processing because some operational system reporting requirements are moved to DSS. • Simplification of data access. Indirect benefits arise when end users take advantage of these direct benefits.

Who is Edward Tufte? Why do you think we should know about his work?

Edward Tufte is a statistician whose website chronicles many historical data visualizations, including Minard's graphic of Napoleon's defeat. His work can bring insights into how to follow best practices for information visualization.

What are the ingredients for an effective performance management system?

Effective performance management/measurement should focus on key factors. It should mix past, present, and future. Also, it should balance the needs of shareholders, employees, partners, suppliers, and other stakeholders. Performance measures should start at the top and flow to the bottom, and should involve targets that are based on research and reality rather than arbitrary.

What steps can an organization take to ensure the security and confidentiality of customer data in its data warehouse?

Effective security in a data warehouse should focus on four main areas: Step 1. Establishing effective corporate and security policies and procedures. An effective security policy should start at the top and be communicated to everyone in the organization. Step 2. Implementing logical security procedures and techniques to restrict access. This includes user authentication, access controls, and encryption. Step 3. Limiting physical access to the data center environment. Step 4. Establishing an effective internal control review process for security and privacy.

What recent technologies may shape the future of data warehousing? Why?

Following are some of the recently popularized concepts and technologies that will play a significant role in defining the future of data warehousing. Sourcing: Acquisition of data from diverse and dispersed sources • Web, social media, and Big Data • Open source software • SaaS (software as a service) "The Extended ASP Model" • Cloud computing Infrastructure: Architectural—hardware and software—enhancements • Columnar (a new way to score and access data in the database) • Real-time data warehousing • Data warehouse appliances (all-in-one solutions to DW) • Data management technologies and practices • In-database processing technology (putting the algorithms where the data is) • In-memory storage technology (moving the data in the memory for faster processing) • New database management systems • Advanced analytics As the world of business becomes more global and complex, the need for business intelligence and data warehousing tools also becomes more prominent. The fast improving information technology tools and techniques seem to be moving in the right direction to address the needs of the future business intelligence systems.

Why would you use a geographic map? What other types of charts can be combined with a geographic map?

Geographic maps are useful when the data set includes any kind of location data, including addresses, postal codes, state names or abbreviations, country names, latitude/longitude, or some type of custom geographic encoding. Maps can be used in conjunction with other charts and graphs. For instance, one can use maps to show distribution of customer service requests by product type (depicted in pie charts) by geographic locations.

What is an information dashboard? Why are they so popular for BI software tools?

Information dashboards provide visual displays of important information that is consolidated and arranged on a single screen so that information can be digested at a single glance and easily drilled in and further explored. They are common components of most, if not all, performance management systems, performance measurement systems, BPM software suites, and BI platforms. Dashboards pack a lot of information into a single screen, which is one reason for their popularity. 2.

What are reports used for?

It is usually a document that collects data driven information and personal experiences organized in a narrative, graphic, and/or tabular form, prepared periodically (recurring) or on an as-required basis, referring to specific time periods, events, occurrences, or subjects.

What are the main differences among line, bar, and pie charts? When should you use one over the others?

Line graphs are good for time-series data. Bar charts are good for depicting nominal or numerical data that can be easily categorized. Pie charts should be used for depicting proportions. You shouldn't use pie charts if the number of categories is very large.

chp3. Explain the importance of metadata.

Metadata, "data about data," are the means through which applications and users access the content of a data warehouse, through which its security is managed, and through which organizational management manages, in the true sense of the word, its information assets. Most database management systems would be unable to function without at least some metadata. Indeed, the use of metadata, which enable data access through names and logical relationships rather than physical locations, is fundamental to the very concept of a DBMS. Metadata are essential to any database, not just a data warehouse. (See answer to Review Question 2 of this section above.)

Identify and discuss the role of middleware tools.

Middleware tools enable access to the data warehouse. Power users such as analysts may write their own SQL queries. Others may access data through a managed query environment. There are many front-end applications that business users can use to interact with data stored in the data repositories, including data mining, OLAP, reporting tools, and data visualization tools. All these have their own data access requirements. Those may not match with how a given data warehouse must be accessed. Middleware translates between the two.

What is Six Sigma? How is it used as a performance measurement system?

Most companies use Six Sigma as a process improvement methodology that enables them to scrutinize their processes, pinpoint problems, and apply remedies. It's not used much as a performance management or measurement methodology. As a performance tool, it is aimed at reducing the number of defects in a business process to as close to zero DPMO (defects per million opportunities) as possible.

What are the main components of a business reporting system?

One is the online transaction processing system (ERP, POS, etc.) that records transactions. A second is a data supply that takes recorded events and transactions and delivers them to the reporting system. Next comes an ETL component that ensures quality and performs necessary transformations prior to loading the data into a data store. Then there is the data storage itself (such as a data warehouse). Business logic converts the data into the reporting outputs. Publication distributes or hosts the reports for end users. And finally assurance provides a quality control check on the reports and their dissemination.

What is an ODS?

Operational Data Store is the database from which a business operates on an on-going basis.

What are the historical roots of data visualization?

Predecessors to data visualization date back to the second century AD. Today's most popular visual forms date back a few centuries. Geographical exploration, mathematics, and popularized history spurred the creation of early maps, graphs, and timelines as far back as the 1600s. The now familiar line and bar charts date back to the late 1700s. Charles Joseph Minard used visualizations to graphically portray the losses suffered by Napoleon's army in the Russian campaign of 1812. The 1900s saw the rise of a more formal, empirical attitude toward visualization, which tended to focus on aspects such as color, value scales, and labeling. In the 2000s the Internet has emerged as a new medium for visualization, and added interactivity to previously static graphics.

How has the Web influenced data warehouse design?

Primarily by making Web-based data warehousing possible.

What are the major DW implementation tasks that can be performed in parallel?

Reeves (2009) and Solomon (2005) provided some guidelines regarding the critical questions that must be asked, some risks that should be weighted, and some processes that can be followed to help ensure a successful data warehouse implementation. They compiled a list of 11 major tasks that could be performed in parallel: Establishment of service-level agreements and data-refresh requirements Identification of data sources and their governance policies Data quality planning Data model design ETL tool selection Relational database software and platform selection. Data transport Data conversion Reconciliation process Purge and archive planning End-user support

What is scalability? How does it apply to DW?

Scalability refers to the degree to which a system can adjust to changes in demand without major additional changes or investments. DW scalability issues are the amount of data in the warehouse, how quickly the warehouse is expected to grow, the number of concurrent users, and the complexity of user queries. A data warehouse must scale both horizontally and vertically. The warehouse will grow as a function of data growth and the need to expand the warehouse to support new business functionality. Data growth may be a result of the addition of current cycle data (e.g., this month's results) and/or historical data.

What other problems and challenges do you think federal and state governments are having that can benefit from BI and data warehousing?

Responses will vary but could include ideas relating to voter fraud, medical use, and other tax issues.

Which data warehousing architecture is the best? Why?

See Table 3.1 Average Assessment Scores for the Success of the Architectures. What is interesting is the similarity of the averages for the bus, hub-and-spoke, and centralized architectures. The differences are sufficiently small that no claims can be made for a particular architecture's superiority over the others, at least based on a simple comparison of these success measures.

Why is the ETL process so important for data warehousing efforts?

Since ETL is the process through which data are loaded into a data warehouse, a DW could not exist without it. The ETL process also contributes to the quality of the data in a DW.

List several criteria for selecting a data warehouse vendor, and describe why they are important.

Six important criteria are: financial strength, ERP linkages, qualified consultants, market share, industry experience, and established partnerships. These are important to indicate that a vendor is likely to be in business for the long term, to have the support capabilities its customers need, and to provide products that interoperate with other products the potential user has or may obtain. One could add others, such as product functionality (Does it do what we need?), vendor strategic vision (Does their direction make sense for our future plans and/or is it consistent with industry trends?) and quality of customer references (What do their existing customers think of them?).

Why is strategy the most important part of a BPM implementation?

Strategy is the art and the science of crafting decisions that help businesses achieve their goals. More specifically, it is the process of identifying and stating the organization's mission, vision, and objectives. Business strategy provides an overall direction to the enterprise, which is why it is so important.

How does a data warehouse differ from a database?

Technically a data warehouse is a database, albeit with certain characteristics to facilitate its role in decision support. Specifically, however, it is (see previous question) an "integrated, time-variant, nonvolatile, subject-oriented repository of detail and summary data used for decision support and business analytics within an organization." These characteristics, which are discussed further in the section just after the definition, are not necessarily true of databases in general—though each could apply individually to a given one. As a practical matter most databases are highly normalized, in part to avoid update anomalies. Data warehouses are highly denormalized for performance reasons. This is acceptable because their content is never updated, just added to. Historical data are static.

List and briefly describe the four phases of the BPM cycle.

The BPM cycle contains four main phases. First is to strategize. This involves answering the question, "Where do we want to go?", and involves a high-level, long-term plan. Missions, visions, and objectives are key components of this phase. The second phase is to plan, which answers the question, "How do we get there?" Key elements here are a detailed operational plan and a financial plan including budget. The next phase is to monitor and analyze, which answers the question, "How are we doing?" Here is where KPIs, dashboards, reporting, and analytics are helpful. Finally come action and adjustment, based on comparing our analysis results against our plans. Sometimes this means changing the way we operate, and sometimes it means adjusting our strategy.

What is a performance management system? Why do we need one?

The purpose of a performance management system is to (a) identify and articulate the strategic mission, goals, and objectives of an organization, and (b) assist managers in tracking the implementations of business strategy by comparing actual results against these strategic goals and objectives. The latter task is accomplished by a performance measurement system, which can be considered a. subset of the overall performance management system. A performance measurement system typically comprises systematic methods of setting business goals together with periodic feedback reports that indicate progress against goals. This is a key and necessary element of the BPM process.

Why should storytelling be a part of your reporting and data visualization?

The central idea of business reporting is to tell a story. Everyone who has data to analyze has stories to tell, whether it's diagnosing the reasons for manufacturing defects, selling a new idea in a way that captures the imagination of your target audience, or informing colleagues about a particular customer service improvement program. Stories bring life to data and facts. They can help you make sense and order out of a disparate collection of facts. They make it easier to remember key points and can paint a vivid picture of what the future can look like. Stories also create interactivity—people put themselves into stories and can relate to the situation. People will be much more engaged and receptive if information is presented to them in a story format.

Describe the data warehousing process.

The data warehousing process consists of the following steps: 1. Data are imported from various internal and external sources 2. Data are cleansed and organized consistently with the organization's needs 3. a. Data are loaded into the enterprise data warehouse, or b. Data are loaded into data marts. 4. a. If desired, data marts are created as subsets of the EDW, or b. The data marts are consolidated into the EDW 5. Analyses are performed as needed

What was the solution they adopted? Do you agree with their approach? Why?

The state implemented a data warehouse from Teradata that allowed them to examine data and identify/flag traits that were consistent with fraudulent return.

What were the challenges the state of Maryland was facing with regard to tax fraud?

The state was facing tax fraud from fraudulent returns as other states were, and the process of detecting and investigating potential fraud was time consuming.

What are the four perspectives that BSC suggests to view organizational performance?

The four perspectives are: customer, financial, internal business processes, and learning and growth. If customers are not satisfied, they will eventually find other suppliers that will meet their needs. Poor performance from this perspective is thus a leading indicator of future decline, even though the current financial picture may look good. Timely and accurate funding data will always be a priority, and managers will do whatever is necessary to provide it. This should include risk analysis. In the current climate of rapid technological change, it is becoming necessary for knowledge workers to be in a continuous learning and growing mode. Metrics based on this perspective allow the managers to know how well their internal business processes and functions are running, and whether the outcomes of these processes (i.e., products and services) meet and exceed the customer requirements (the mission).

What do you think is the "next big thing" in data visualization?

The future of data/information visualization is very hard to predict. We can only extrapolate from what has already been invented: more three-dimensional visualization, more immersive experience with multidimensional data in a virtual reality environment, and holographic visualization of information. There is a pretty good chance that we will see something that we have never seen in the information visualization realm invented before the end of this decade.

What are the main reasons for the recent emergence of visual analytics?

The growth of visual analytics correlates with the growth of analytics in general. More BI and analytics vendors are becoming aware that their customers require quick and preferably interactive visualizations, not just for their normal reporting systems, but also to illustrate predictive and prescriptive decision-making information. Many of the information visualization vendors are adding the capabilities to call themselves visual analytics solution providers. Conversely, analytics solution providers such as SAS are embedding their analytics capabilities into a high-performance data visualization environment that they call visual analytics.

What are the distinguishing features of KPIs?

The key features described in the book are strategy, targets, ranges, encodings, time frames, and benchmarks. KPIs embody strategic objectives and measure performance against specific targets, based on specified ranges of values. Encodings provide visual cues (e.g., color) to indicate how close or far from a target we are on a particular metric. Benchmarks provide something to compare against.

What is a cube? What do drill down, roll up, and slice and dice mean?

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. 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) are 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. • 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).

What were the results that they obtained? Did the investment in BI and data warehousing pay off?

The team was able to flag a smaller number of potentially fraudulent returns, but those that they did identify were significantly more likely to be false. This allowed the state to recover $7 million more, making the investment pay off.

Describe the cyclic process of management and comment on the role of business reports.

data acquisition leads to information generation which leads to decision making which leads to business process management. Another key part of this process is reporting- converting data from different sources into useful information.

List and briefly define the four most commonly cited operational areas for KPIs.

• 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.

Describe the major components of a data warehouse.

• Data sources. Data are sourced from operational systems and possibly from external data sources. • Data extraction and transformation. Data are extracted and properly transformed using custom-written or commercial software called ETL. 4 Copyright © 2018 Pearson Education, Inc. • Data loading. Data are loaded into a staging area, where they are transformed and cleansed. The data are then ready to load into the data warehouse. • Comprehensive database. This is the EDW that supports decision analysis by providing relevant summarized and detailed information. • Metadata. Metadata are maintained for access by IT personnel and users. Metadata include rules for organizing data summaries that are easy to index and search. • Middleware tools. Middleware tools enable access to the data warehouse from a variety of front-end applications.

What skills should a DWA possess? Why?

• Familiarity with high-performance hardware, software, and networking technologies, since the data warehouse is based on those • Solid business insight, to understand the purpose of the DW and its business justification • Familiarity with business decision-making processes to understand how the DW will be used • Excellent communication skills, to communicate with the rest of the organization

List the alternative data warehousing architectures discussed in this section.

• Independent data marts architecture • Data mart bus architecture with linked dimensional data marts • Hub-and-spoke architecture (corporate information factory) • Centralized data warehouse architecture • Federated architecture

List and discuss the most pronounced DW implementation guidelines.

• Senior management must support development of the data warehouse. The DW needs a project champion at a high position in the organization chart. Benefits of a DW project may be difficult to measure, so management support makes it more likely the project will receive funding. • Web-based data warehouses may need special security requirements. These ensure that only authorized users have access to the data. • Users should participate in the development process. Their participation is essential for data modeling and access modeling. User participation ensures that the DW includes the needed data and that decision makers can retrieve the data they need. • DW implementation requires certain skills from members of the development team: in-depth knowledge of database technology and the development tools used.

When developing a successful data warehouse, what are the most important risks and issues to consider and potentially avoid?

• Starting with the wrong sponsorship chain • Setting expectations that you cannot meet • Engaging in politically naive behavior • Loading the data warehouse with information just because it is available • Believing that data warehousing database design is the same as transactional database design • Choosing a data warehouse manager who is technology oriented rather than user oriented • Focusing on traditional internal record-oriented data and ignoring the value of external data and of text, images, and, perhaps, sound and video • Delivering data with overlapping and confusing definitions. • Believing promises of performance, capacity, and scalability • Believing that your problems are over when the data warehouse is up and running • Focusing on ad hoc data mining and periodic reporting instead of alerts