ISM Chapter 1 Review

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

- 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

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

-Can Better Store Data

-Manage large amounts of information that can be retrieved for decision making support

-Perform Simulations to understand the impact of a decision

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

By storing and analyzing large amounts of data and information.

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

1.) Expert Systems (ES)

2.) Management Information Systems (MIS)

3.) Decision Support Systems (DSS)

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

- MIS: variety of reports to better understand and address changing needs and challenges of the business

- DSS: computer-based support system for management decision makers who deal with semistructured problems

- ESS: DSSs designed specifically for executives and their decision-making needs

Did DSS evolve into BI or vice versa?

DSS evolved into BI because DSSs became larger and more powerful to allow for vast amounts of data to be analyzed and stored, thus creating the need for BI tools.

List and describe the major components of BI

1.) Data Warehouse: A physical repository where relational data are specially organized to provide enterprisewide, cleansed data in a standardized format.

2.) Business Analytics: A collection of tools for manipulating, mining, and analyzing the data in the data warehouse

3.) Business Performance Management: for monitoring and analyzing performance

4.) User Interface: ex, dashboard

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

-Strategic & Operational Objectives: Defining clear business goals based on current staff skills.

-Organizational Culture: Managing company culture and building team excitement for Business Intelligence (BI) initiatives.

-Best Practices Sharing: Creating procedures for sharing BI knowledge across different parts of the organization.

-Change Management: Planning ahead to prepare the organization and its employees for transitions.

List some other success factors of BI.

-Demonstrate how BI is clearly linked to strategy and execution of strategy.

-Serve to encourage interaction between the potential business user communities and the IS organization.

-Serve as a repository and disseminator of best BI practices between

-Standards of excellence in BI practices 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.

-Business user community and IS organization can better understand why the data warehouse platform must be flexible enough to provide for changing business requirements.

-Can help important stakeholders like high-level executives see how BI can play an important role.

-Facilitate a real-time, on-demand agile environment.

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

Data warehouses and Visualization applications.

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. Traditional reporting focuses on structuring and summarizing past data—often financial metrics (snapshot of specific period).

How can data warehousing technology help to enable analytics?

A data warehouse serves as the basis for developing appropriate reports, queries, alerts, and trends. Consolidation of data sources and making relevant data available in a form that enables appropriate reporting and analysis from data infrastructure.

How can organizations employ 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

-Whether a customer is creditworthy.

-Sports teams have used predictive analytics to identify the players most likely to contribute to a team's success.

Define modeling from the analytics perspective.

It uses descriptive data to create designs/visuals/graphs 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.

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

Testing a model with predictive analytics could logically improve prescriptive use of the model. Understanding the business domain and current state of the business problem requires analysis of historical data, or descriptive analytics. (Yes)

How can analytics aid in objective decision making?

It builds on historical data and takes into account changing conditions to arrive at fact-based solutions that decision makers might not have considered.

What are the sources of Big Data?

-Clickstreams from Websites

-Postings on Social Media

-Data from traffic, sensors, and the weather

What are the characteristics of Big Data?

Any kind of large data that has the elements of volume, velocity, and variety.

Examples:

-the Billions of Web pages searched by Google

-Data about Financial Trading (operates in microseconds)

-Data about consumer opinions measured from postings in social media.

What processing technique is applied to process Big Data?

MapReduce programming paradigm to push computation to the data to handle the scale tremendously large data.

Analytics

Developing actionable decisions or recommendations based upon insights generated from historical data. (descriptive, predictive, and prescriptive)

Business Intelligence (BI)

A conceptual framework for managerial decision support. It combines architecture, databases (or data warehouses), analytical tools, and applications.

Dashboard

A visual presentation of critical data for executives to view. It allows executives to see hot spots in seconds and explore the situation.

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.

Decision or Normative Analytics

a type of analytics modeling, also known as prescriptive analytics, that focuses on identifying the best possible decision from a range of alternatives.

Descriptive (or reporting) Analytics

an earlier phase in the continuum that focuses on describing historical data, answering questions about what has happened and why it occurred.

Intelligent Agents ()

an autonomous, relatively small computer software program that observes and acts upon changes in its environment by running specific tasks autonomously.

Online Analytical Processing (OLAP)

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.

Online Transaction Processing (OLTP)

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

Predictive Analytics

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

Prescriptive Analytics

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

Web Services

refers to a standardized way of integrating-based applications using open standards over an internet protocol backbone. They enable different applications from various sources to communicate with each other without custom coding.

In football analytics, what tool was used to visualize passing tendencies across zones?

Heat Map

What type of analysis was used by the NCAA tournament committee to optimize team assignments to regions?

Genetic algorithm

In cricket analytics, what technique was used to extract structured data from commentator blogs?

Text mining

What is the benefit of using Monte Carlo simulations in football coaching decisions?

Simulate potential game outcomes under tactical changes.

The book Moneyball was the first to popularize the use of analytics in sports (True or False)

False

Dynamic pricing in ticket sales does not consider external factors like weather or traffic. (T or F)

False

Heat maps and Sankey diagrams are used in football to understand player behavior and play outcomes. (T or F)

True

Sports analytics is only useful for coaches and has no role in business operations like merchandising or pricing. (T or F)

False

Which of the following is not a benefit of modern decision support systems?

Enhancing intuition

What is the role of knowledge management systems (KMS) in decision-making?

Supports decisions through structured and unstructured communications

Decision-making in organizations has remained largely unaffected by advances in information technology. (T or F)

False

Simon's theory indicates that humans have limited cognitive capacity to process and store information. (T or F)

True

Collaboration technologies became less relevant after the COVID-19 pandemic. (True or False)

False

One of the key features of modern decision-making support is the use of dashboards and online analytical processing (OLAP). (True or False)

True

What is the primary focus of the intelligence phase in Simon's model?

Identifying and defining the problem

What is the purpose of sensitivity analysis in the choice phase?

To assess how robust a solution is to parameter changes

In modeling, simplifying assumptions are made to:

Balance cost and representativeness

What was the real problem behind elevator complaints in Analytics in Action 1.1?

Perceived waiting time

Real-world problems can often be mistaken for symptoms unless properly analyzed. (T or F)

True

A model must replicate reality exactly to be effective (T or F)

False

Resistance to change is a common challenge during the implementation phase. (T or F)

True

Problem decomposition is useful because some subproblems may be more structured than the main problem. (T or F)

True

The term goal seeking refers to generating multiple alternatives for decision making. (T or F)

False

Which of the following terms emerged in the 1990s to describe executive-focused dashboards and scorecards?

Executive Information Systems (EIS)

Which of the following technologies enabled the processing of Big Data in the 2010s?

Hadoop and MapReduce

DSSs were first introduced in the 1970s as tools for solving structured problems only. (T or F)

False

ERP systems help organizations create a consistent view of enterprise-wide data. (T or F)

True

Data warehouses (DWs) are typically updated in real time and reflect the latest transactional data. (T or F)

False

Expert systems use a series of if-then-else rules to replicate human decision-making. (T or F)

True

The development of social media has led to easier and more structured forms of business data. (T or F)

False

The popularity of analytics has grown due to better tools, cloud storage, and data availability. (T or F)

True

What does a Business Intelligence (BI) system typically include as a core component?

Data warehouse (DW)

Which system is designed for handling transactional data such as sales or ATM withdrawals?

OLTP

Business Intelligence (BI) and Decision Support Systems (DSS) are the same in current practice. (T or F)

False

Data Warehouses are optimized for analysis and decision support rather than transaction processing.(T or F)

True

A major benefit of BI is helping managers access key information at the right time and place. (T or F)

True

BI systems cannot support real-time data updates or alerts. (T or F)

False

BI initiatives must be aligned with an organization's business strategy for maximum effectiveness. (T or F)

True

What is the goal of descriptive analytics?

To understand what is happening and why

In the Silvaris case study, what technology was used to generate real-time dashboards and visualizations?

Tableau

What type of analytics was used by Purina to predict cattle weight after 60 days of feeding?

Predictive analytics

Which analytics type focuses on recommending actions to optimize outcomes?

Prescriptive

Prescriptive analytics helps determine the best course of action based on forecasts and existing conditions. (T or F)

True

Predictive analytics cannot be used to identify customer churn or likely product purchases. (T or F)

False

In the analytics maturity model, issuing automatic alerts based on data is part of basic reporting. (T or F)

False

Descriptive analytics typically includes tools such as OLAP, dashboards, and ad hoc query systems. (T or F)

True

In retail analytics, what does market basket analysis help determine?

Product bundling strategies

Which advanced analytics platform did Gulfstream Park Casino implement to optimize slot machine placement?

Gaminganalytics.ai

Market basket analysis is primarily used to identify employee churn in retail analytics. (T or F)

False

Gulfstream Park Casino saved time and money by using AI to test and move slot machines based on player behavior data. (T or F)

True

The New Member Predictive Model at Humana significantly increased early identification of high-risk members. (T or F)

True

In the COVID-19 analytics study, cities with mask mandates experienced a statistically significant drop in daily case growth. (T or F)

True