ISDS Chapter 1 Quiz

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.

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).

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 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.

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.

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 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.

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.

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 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.

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.

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 two techniques that football teams can use to do opponent analysis?

In the example provided, opponent analytics was evaluated using the coach's annotated game film to produce an analysis evaluating whether to build a cascaded decision tree model on play prediction, heat maps of passing offenses, and time series analytics on explosive plays

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 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 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 are three factors that might be part of a PM for season ticket renewals?

The case provides several examples of data that may be used as a part of this analysis. Data factors may include survey responses, pricing models, and customer tweets.

How can wearables improve player health and safety? What kinds of new analytics can trainers use?

The case provides several examples of how wearables can be used to improve player health. Wearables can help to identify levels and variation in core body strength, mobile devices worn during play can record data on hits to assist in concussion protocols, and sleeps sensors can identify how rested players are.

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."

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.