Ch. 1 - An Overview of Business Intelligence, Analytics, and Data Science

Because Big Data is data that cannot be store or processed easily using traditional tools/means, Google developed:

- "Hadoop Distributed File System (HDFS)" --> for storing large amounts of data in a distributed way - "MapReduce processing algorithm" --> for pushing computation to the data, instead of pushing data to a computing node

List some capabilities of information systems that can facilitate managerial decision making?

- Ability to perform functions that allow for better communication and information capture - Better storage and recall of data - Vastly improved analytical models that can be more voluminous or precise

List three of the terms that have been predecessors of analytics.

- Data support systems (DSS) - Operations research (OR) models - Expert systems (ES)

What was the primary difference between the systems called MIS, DSS, and Executive Support Systems?

- MIS provided reports on various aspects of business functions using captured information - DSS added the ability to use data with models to address unstructured problems - ESS added ability to capture understanding from experts and integrating it into systems via if-then-else rules or heuristics

These things accelerated the growth and subsequent need of real-time BI:

- RFID - Web services - Intelligent agents

Business Intelligence (BI)

- [broad definition] an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies - [narrow definition] descriptive analytics tools and techniques (i.e., reporting tools)

Prescriptive analytics:

- aims to determine the best possible decision - uses both descriptive and predictive to create the alternatives, and then determines the best one QUESTIONS: - What should I do? - Why should I do it? ENABLERS: - optimization - simulation - decision modeling (multi-criteria) - expert systems - heuristic programming OUTCOMES: - best possible business decisions and actions

Predictive analytics:

- aims to determine what is likely to happen in the future (foreseeing the future events) by looking at past data QUESTIONS: - What will happen? - Why will it happen? ENABLERS: - data mining - text mining - web/media mining - forecasting (i.e., time series) OUTCOMES: - accurate projections of future events and outcomes

Process of BI

- based on the transformation of data to information, then to decisions, and finally to actions - mantra -- "right information at the right time and in the right place"

What is "Big Data"?

- data that cannot be stored or processed easily using traditional tools/means - typically refers to data that comes in many different forms: large, structured, unstructured, continuous (3Vs - Volume, Variety, Velocity) - major sources of such data are clickstreams from Web sites, postings on social media sites such as Facebook, or data from traffic, sensors, or weather

Online Analytical Processing (OLAP)

- data warehouses --> a distinct system that provides storage for data from multiple sources that will be made use of in analysis - Ex: sales analysis report, fraud report, stock market predictions - Goal: decision support

3 types of Analytics:

- descriptive - predictive - prescriptive

Analytics is also known as:

- data science - deep learning and the Internet of Things

Online Transaction Processing (OLTP)

- operational databases - handle a company's routine ongoing business - Ex: ATM withdrawals, bank transaction, cash register scans - Goal: data capture - The very design that makes the OLTP system efficient for transaction processing makes it inefficient for end-user ad hoc reports, queries, and analysis

BI shells

- pre-programmed tools allowing you to insert your number

Descriptive (or reporting) analytics:

- retrospective analysis of historic data QUESTIONS: - What happened? - What is happening? ENABLERS: - business reporting - dashboards (data visualization) - scorecards (data visualization) - data warehousing - OLAP/DW OUTCOMES: - well-defined business problems and opportunities

The Google __________________ is an example of Big Data in that it has to search and index billions of __________________ in fractions of a second for each search.

- search engine - Web pages

How does Amazon.com use predictive analytics to respond to product searches by the customer?

1. "clustering algorithms" to segment customers into different clusters to be able o target specific promotions to them 2. "association mining techniques" to estimate relationships between different purchasing behaviors 3. predictive analytics helps Amazon recommend or promote related products

Shopper Insight

1. by customer segmentation, the business owner can create personalized offers resulting in better customer experience and retention of the customer

Inventory Optimization

1. forecast the consumption of fast-moving products and order them with sufficient inventory to avoid a stock-out scenario 2. perform fast inventory turnover of slow-moving products by combining them with one in high demand

Video Analytics

1. in-store promotions and events can be planned based on the demography of incoming traffic 2. targeted customer engagement and instant discount enhances the customer experience resulting in higher retention

Price Elasticity

1. markdown prices for each product can be optimized to reduce the margin dollar loss 2. optimized price for the bundle of products is identified to save the margin dollar

Channel Analysis

1. marketing budget can be optimized based on insight for better return on investment

Market Basket Analysis

1. the affinity analysis identifies the hidden correlations between the products, which can help in following values: a) strategize the product bundle offering based on focus on inventory or margin b) increase cross-sell or up-sell by creating bundle from different categories or the same categories, respectively

Store Layout

1. understand the association of products to decide store layout and better alignment with customer needs 2. workforce deployment can be planned for better customer interactivity and thus satisfying customer experience

What is a DW? How can data warehousing technology help to enable analytics?

A data warehouse, introduced in Section 1.7, is the component of a BI system that contains the source data. As described in this section, developing a data warehouse usually includes development of the data infrastructure for descriptive analytics—that is, consolidation of data sources and making relevant data available in a form that enables appropriate reporting and analysis. A data warehouse serves as the basis for developing appropriate reports, queries, alerts, and trends.

Why would a health insurance company invest in analytics beyond fraud detection? Why is it in their best interest to predict the likelihood of falls by patients?

An insurance company would potentially want to evaluate analytics to both quantify the risk of a potential incident category (like falls) and to help identify subgroups of the population that are at-risk for this type of injury. With this type of information, the company can address clients who might be at-risk, and attempt to intervene with less expensive preventative measures.

List three of the terms that have been predecessors of analytics.

Analytics has evolved from other systems over time including data support systems (DSS), operations research (OR) models, and expert systems (ES).

Define modeling from the analytics perspective

As Application Case 1.6 illustrates, analytics uses descriptive data to create models of how people, equipment, or other variables operate in the real world. These models can be used in predictive and prescriptive analytics to develop forecasts, recommendations, and decisions.

How can analytics aid in objective decision making?

As noted in the analysis of Application Case 1.4, problem solving in organizations has tended to be subjective, and decision makers tend to rely on familiar processes. The result is that future decisions are no better than past decisions. Analytics builds on historical data and takes into account changing conditions to arrive at fact-based solutions that decision makers might not have considered.

What processing technique is applied to process Big Data?

One computer, even a powerful one, could not handle the scale of Big Data. The solution is to push computation to the data, using the MapReduce programming paradigm.

What are the 4 major components of a Business Intelligence (BI) system:

1. DW - a data warehouse, with its source data (a distinct system that provides storage for data from multiple sources that will be made use of in analysis) 2. BA - business analytics, a collection of tools for manipulating, mining, and analyzing the data in the data warehouse 3. BPM - business performance management, for monitoring and analyzing performance and for strategy 4. UI - user interface (e.g., browser, portal, dashboard), for data visualization

New Store Analysis

1. best practices of other locations and channels can be used to get a jump start 2. comparison with competitor data can help to create a differentiator/USP factor to attract the new customers

Customer Churn Analysis

1. businesses can identify the customer and product relationships that are not working and show high churn, thus can have better focus on product quality and reason for that churn 2. based on the customer lifetime value (LTV), the business can do targeted marketing resulting in retention of the customer

Is it a good idea to follow a hierarchy of descriptive and predictive analytics before applying prescriptive analytics?

As noted in the analysis of Application Case 1.5, it is important in any analytics project to understand the business domain and current state of the business problem. This requires analysis of historical data, or descriptive analytics. Although the chapter does not discuss a hierarchy of analytics, students may observe that testing a model with predictive analytics could logically improve prescriptive use of the model.

List and describe the major components of BI.

BI systems have four major components: the data warehouse (with its source data), business analytics (a collection of tools for manipulating, mining, and analyzing the data in the data warehouse), business performance management (for monitoring and analyzing performance), and the user interface (e.g., a dashboard).

Which retail stores that you know of employ some of the analytics applications identified in this section?

Based on the retail establishments they are familiar with and the applications used at the time.

Define BI.

Business Intelligence (BI) is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies. Its major objective is to enable interactive access (sometimes in real time) to data, enable manipulation of these data, and provide business managers and analysts the ability to conduct appropriate analysis.

How can a computer help overcome the cognitive limits of humans?

Computer-based systems are not limited in many of the ways people are, and this lack of limits allows unique abilities to evaluate data. Examples include being able to store large amounts of data, being able to run extensive numbers of scenarios and analyses, and the ability to spot trends in vast datasets or models

How can a computer help overcome the cognitive limits of humans?

Computer-based systems are not limited in many of the ways people are, and this lack of limits allows unique abilities to evaluate data. Examples of abilities include being able to store huge amounts of data, being able to run extensive numbers of scenarios and analyses, and the ability to spot trends in vast datasets or models.

What other applications similar to prediction of falls can you envision?

Could include a number of other medical conditions or types of accidents.

Did DSS evolve into BI or vice versa?

DSS evolved into BI in the 2000s with the addition of data warehousing capabilities and began to be referred to as Business Information (BI) systems

Did DSS evolve into BI or vice versa?

DSS systems became more advanced in the 2000s with the addition of data warehousing capabilities and began to be referred to as Business Information (BI) systems.

How is descriptive analytics different from traditional reporting?

Descriptive analytics gathers more data, often automatically. It makes results available in real time and allows reports to be customized.

What is descriptive analytics? What are the various tools that are employed in descriptive analytics?

Descriptive analytics refers to knowing what is happening in the organization and understanding some underlying trends and causes of such occurrences. Tools used in descriptive analytics include data warehouses and visualization applications.

How would you convince a new health insurance customer to adopt healthier lifestyles (Humana Example 3)?

Focus on improved customer education that is targeted at specific risk factors as well as financial or benefit inducements tied to positive changes in lifestyle.

List some of the implementation topics addressed by Gartner's report.

Gartner's framework decomposes planning and execution into business, organization, functionality, and infrastructure components. At the business and organizational levels, strategic and operational objectives must be defined while considering the available organizational skills to achieve those objectives. Issues of organizational culture surrounding BI initiatives and building enthusiasm for those initiatives and procedures for the intra-organizational sharing of BI best practices must be considered by upper management—with plans in place to prepare the organization for change.

What are some of the key system-oriented trends that have fostered IS-supported decision making to a new level?

Improvements and innovation in systems in many areas have facilitated the growth of decision-making systems. These areas include: • Group communication and collaboration software and systems • Improved data management applications and techniques • Data warehouses and Big Data for information collection • Analytical support systems • Growth in processing and storing formation storage capabilities • Knowledge management systems • Support of all of these systems that is always available

What are some of the key system-oriented trends that have fostered IS-supported decision making to a new level?

Improvements and innovation in systems in many areas have facilitated the growth of decision-making systems. These areas include: • Group communication and collaboration software and systems • Improved data management applications and techniques • Data warehouses and Big Data for information collection • Analytical support systems • Growth in processing and storing formation storage capabilities • Knowledge management systems • Support of all of these systems that is always available

List some capabilities of information systems that can facilitate managerial decision making.

Information systems can aid decision making because they have the ability to perform functions that allow for better communication and information capture, better storage and recall of data, and vastly improved analytical models that can be more voluminous or more precise

Which companies are dominant in more than one category?

It appears that several larger IT companies have products and services in several of these areas. Examples include IBM, Microsoft, SAS, Dell, and SAP.

Is it better to be the strongest player in one category or be active in multiple categories?

It can be argued that cross-discipline strength provides better integration and insight, or that that domination in multiple areas reduces completion or innovation.

What are the sources of Big Data?

Major sources include clickstreams from Web sites, postings on social media, and data from traffic, sensors, and the weather.

Identify at least three other opportunities for applying analytics in the retail value chain beyond those covered in this section.

Many potential opportunities exist, and student responses will vary based on their experiences.

What was the primary difference between the systems called MIS, DSS, and Executive Support Systems?

Many systems have been used in the past and present to provide analytics. Management information systems (MIS) provided reports on various aspects of business functions using captured information while decision support systems (DSS) added the ability to use data with models to address unstructured problems. Executive support systems (ESS) added to these abilities by capturing understanding from experts and integrating it into systems via if-then-else rules or heuristics.

Define OLAP

OLAP (online analytical processing) is processing for end-user ad hoc reports, queries, and analysis. Separating the OLTP from analysis and decision support provided by OLAP enables the benefits of BI that were described earlier and provides for competitive intelligence and advantage as described next.

Define OLTP

OLTP (online transaction processing) 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.

What is predictive analytics? How can organizations employ predictive analytics?

Predictive analytics is the use of statistical techniques and data mining to determine what is likely to happen in the future. Businesses use predictive analytics to forecast whether customers are likely to switch to a competitor, what customers are likely to buy, how likely customers are to respond to a promotion, and whether a customer is creditworthy. Sports teams have used predictive analytics to identify the players most likely to contribute to a team's success.

What is prescriptive analytics? What kind of problems can be solved by prescriptive analytics?

Prescriptive analytics is a set of techniques that use descriptive data and forecasts to identify the decisions most likely to result in the best performance. Usually, an organization uses prescriptive analytics to identify the decisions or actions that will optimize the performance of a system. Organizations have used prescriptive analytics to set prices, create production plans, and identify the best locations for facilities such as bank branches.

What is Big Data analytics?

The term Big Data refers to data that cannot be stored in a single storage unit. Typically, the data is arriving in many different forms, be they structured, unstructured, or in a stream. Big Data analytics is analytics on a large enough scale, with fast enough processing, to handle this kind of data.

Define analytics.

The term replaces terminology referring to individual components of a decision support system with one broad word referring to business intelligence. More precisely, analytics is the process of developing actionable decisions or recommendations for actions based upon insights generated from historical data. Students may also refer to the eight levels of analytics and this simpler descriptive language: "looking at all the data to understand what is happening, what will happen, and how to make the best of it."

What are the characteristics of Big Data?

Today Big Data refers to almost any kind of large data that has the characteristics of volume, velocity, and variety. Examples include data about Web searches, such as the billions of Web pages searched by Google; data about financial trading, which operates in the order of microseconds; and data about consumer opinions measured from postings in social media.

Prescriptive analytics

a branch of business analytics that deals with finding the best possible solution alternative for a given problem

Predictive analytics

a business analytical approach toward forecasting (e.g., demand, problems, opportunities) that is used instead of simply reporting data as they occur

Analytics ecosystem

a classification of sectors, technology/solution providers, and industry participants for analytics

Business intelligence (BI)

a conceptual framework for managerial decision support; it combines architecture, databases (or data warehouses), analytical tools, and applications

Data mining

a process that uses statistical, mathematical, artificial intelligence, and machine-learning techniques to extract and identify useful information and subsequent knowledge from large databases

Dashboard

a visual presentation of critical data for executives to view; it allows executives to see hot spots in seconds and explore the situation

Decision or normative analytics

also called prescriptive analytics, is a type of analytics modeling that aims at identifying the best possible decision from a large set of alternatives

Web services

an architecture that enables assembly of distributed applications from software services and ties them together

Descriptive (or reporting) analytics

an earlier phase in analytics continuum that deals with describing the data - answering the question of what happened and why did it happen

Intelligent agents

an expert or knowledge-based system embedded in computer-based information systems (or their components) to make them smarter

Online analytical processing (OLAP)

an information system that enables the user, while at a PC, to query the system, conduct an analysis, and so one; the result is generated in seconds

Big data analytics

data that is characterized by its volume, variety, and velocity that exceeds the reach of commonly used hardware environments and/or capabilities of software tools and process

What are the key players in analytics industry?

different types of players are identified and described in the "analytics ecosystem"

The analytics ecosystem can be viewed as a collection of _________________, ___________________, and ___________________.

providers, users, and facilitators

Analytics represents:

the combination of "computer technology, management science techniques, and statistics to solve real problems"

Analytics is:

the process of developing "actionable decisions" or recommendations for actions based on insights generated from historical data

Analytics

the science of analysis

Online transaction processing (OLTP)

transaction system that is primarily responsible for capturing and storing data related to day-to-day business functions

DSS (Decision Support Systems)

use data, models, and sometimes knowledge management to find solutions for semi-structured and some unstructured problems

List the 11 categories of players in the analytics ecosystem.

• Data Generation Infrastructure Providers • Data Management Infrastructure Providers • Data Warehouse Providers • Middleware Providers • Data Service Providers • Analytics Focused Software Developers • Application Developers: Industry Specific or General • Analytics Industry Analysts and Influencers • Academic Institutions and Certification Agencies • Regulators and Policy Makers • Analytics User Organizations

Give examples of companies in each of the 11 types of players.

• Data Generation Infrastructure Providers (Sports Sensors, Zepp, Shockbox, Advantech B+B SmartWorx, Garmin, and Sensys Network, Intel, Microsoft, Google, IBM, Cisco, Smartbin, SIKO Products, Omega Engineering, Apple, and SAP) • Data Management Infrastructure Providers (Dell NetApp, IBM, Oracle, Teradata, Microsoft, Amazon (Amazon Web Services), IBM (Bluemix), Salesforce.com, Hadoop clusters, MapReduce, NoSQL, Spark, Kafka, Flume) • Data Warehouse Providers (IBM, Oracle, Teradata, Snowflake, Redshift, SAS, Tableau) • Middleware Providers (Microstrategy, Plum, Oracle, SAP, IBM, SAS, Tableau, and many more) • Data Service Providers (Nielsen, Experian, Omniture, Comscore, Google, Equifax, TransUnion, Acxiom, Merkle, Epsilon, Avention, ESRI.org) • Analytics Focused Software Developers (Microsoft, Tableau, SAS, Gephi, IBM, KXEN, Dell, Salford Systems, Revolution Analytics, Alteryx, RapidMiner, KNIME, Rulequest, NeuroDimensions, FICO, AIIMS, AMPL, Frontline, GAMS, Gurobi, Lindo Systems, Maximal, NGData, Ayata, Rockwell, Simio, Palisade, Frontline, Exsys, XpertRule, Teradata, Apache, Tibco, Informatica, SAP, Hitachi) • Application Developers: Industry Specific or General (IBM, SAS, Teradata, Nike, Sportsvision, Acxiom, FICO, Experian, YP.com, Towerdata, Qualia, Simulmedia, Shazam, Soundhound, Musixmatch, Waze, Apple, Google, Amazon, Uber, Lyft, Curb, Ola, Facebook, Twitter, LinkedIn, Unmetric, Smartbin) • Analytics Industry Analysts and Influencers (Gartner Group, The Data Warehousing Institute, Forrester, McKinsey, INFORMS, AIS, Teradata, SAS) • Academic Institutions and Certification Agencies (IBM, Microsoft, Microstrategy, Oracle, SAS, Tableau, Teradata, INFORMS • Regulators and Policy Makers (Federal Communications Commission, Federal Trade Commission, International Telecommunication Union, National Institute of Standards and Technology) • Analytics User Organizations (many topic-specific and local groups)

List some other success factors of BI.

• The center can demonstrate how BI is clearly linked to strategy and execution of strategy. • A center can serve to encourage interaction between the potential business user communities and the IS organization. • The center can serve as a repository and disseminator of best BI practices between and among the different lines of business. • Standards of excellence in BI practices can be advocated and encouraged throughout the company. • The IS organization can learn a great deal through interaction with the user communities, such as knowledge about the variety of types of analytical tools that are needed. • The business user community and IS organization can better understand why the data warehouse platform must be flexible enough to provide for changing business requirements. • It can help important stakeholders like high-level executives see how BI can play an important role. Another important success factor of BI is its ability to facilitate a real-time, on-demand agile environment.