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Chapter9-BusinessIntelligenceandAnalytics.pptxPrinciples of Information Systems, Thirteenth Edition
Chapter 9
Business Intelligence and Analytics
1
Principles of Information Systems, Thirteenth Edition
Chapter 9
Business Intelligence and Analytics
Objectives
After completing this chapter, you will be able to:
Define the terms business intelligence (BI) and analytics
Provide several real-world examples of BI and analytics being used to improve decision making
Identify the key components that must be in place for an organization to get real value from its BI and analytics efforts
Identify several BI techniques and discuss how they are used
Objectives
After completing this chapter, you will be able to:
Define the terms business intelligence (BI) and analytics
Provide several real-world examples of BI and analytics being used to improve decision making
Identify the key components that must be in place for an organization to get real value from its BI and analytics efforts
Identify several BI techniques and discuss how they are used
2
Objectives
After completing this chapter, you will be able to (cont’d):
Identify several BI tools
Define the term self-service analytics and discuss its pros and cons
Objectives
After completing this chapter, you will be able to (cont’d):
Identify several BI tools
Define the term self-service analytics and discuss its pros and cons
3
What Are Analytics and Business Intelligence
Business analytics
The extensive use of data and quantitative analysis to support fact-based decision making within organizations
Business analytics can be used to:
Gain a better understanding of current business performance
Reveal new business patterns and relationships
Explain why certain results occurred
Optimize current operations
Forecast future business results
What Are Analytics and Business Intelligence
Business analytics
The extensive use of data and quantitative analysis to support fact-based decision making within organizations
Business analytics can be used to:
Gain a better understanding of current business performance
Reveal new business patterns and relationships
Explain why certain results occurred
Optimize current operations
Forecast future business results
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What Are Analytics and Business Intelligence
Business intelligence (BI)
Includes a wide range of applications, practices, and technologies for the extraction, transformation, integration, visualization, analysis interpretation, and presentation of data to support improved decision making
Data used in BI is often pulled from multiple sources and may come from sources internal and external to the organization
Data can be used to build large collections of data called data warehouses, data marts, and data lakes
What Are Analytics and Business Intelligence
Business intelligence (BI)
Includes a wide range of applications, practices, and technologies for the extraction, transformation, integration, visualization, analysis interpretation, and presentation of data to support improved decision making
Data used in BI is often pulled from multiple sources and may come from sources internal and external to the organization
Data can be used to build large collections of data called data warehouses, data marts, and data lakes
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Benefits Achieved from BI and Analytics
BI and analytics are used to achieve a number of benefits:
Detect fraud
Improve forecasting
Increase sales
Optimize operations
Reduce costs
Benefits Achieved from BI and Analytics
BI and analytics are used to achieve a number of benefits:
Detect fraud
Improve forecasting
Increase sales
Optimize operations
Reduce costs
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The Role of a Data Scientist
Data scientists are individuals who combine:
Strong business acumen
A deep understanding of analytics
A healthy appreciation of the limitations of their data, tools, and techniques to deliver real improvements
Data scientists
View a situation from many angles
Determine what data and tools can help further an understanding of the situation
Often work in a team setting with business managers and specialists
Are highly inquisitive
The Role of a Data Scientist
Data scientists are individuals who combine:
Strong business acumen
A deep understanding of analytics
A healthy appreciation of the limitations of their data, tools, and techniques to deliver real improvements
Data scientists
View a situation from many angles
Determine what data and tools can help further an understanding of the situation
Often work in a team setting with business managers and specialists
Are highly inquisitive
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The Role of a Data Scientist
Educational requirements for being a data scientist are quite rigorous
Requires a mastery of statistics, math, and computer programming
Positions may require an advanced degree
Many schools offer career-focused courses, degrees, and certificates in analytical-related disciplines such as
Database management
Predictive analytics
BI
Big data analysis
Data mining
Job outlook for data scientists is extremely bright
The Role of a Data Scientist
Educational requirements for being a data scientist are quite rigorous
Requires a mastery of statistics, math, and computer programming
Positions may require an advanced degree
Many schools offer career-focused courses, degrees, and certificates in analytical-related disciplines such as
Database management
Predictive analytics
BI
Big data analysis
Data mining
Job outlook for data scientists is extremely bright
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Components Required for Effective BI and Analytics
Components include:
Existence of a solid data management program, including data governance
Data governance defines the roles, responsibilities, and processes for ensuring that data can be trusted and used by the entire organization
Creative data scientists
Management team
Must have a strong commitment to data-driven decision making
Components Required for Effective BI and Analytics
Components include:
Existence of a solid data management program, including data governance
Data governance defines the roles, responsibilities, and processes for ensuring that data can be trusted and used by the entire organization
Creative data scientists
Management team
Must have a strong commitment to data-driven decision making
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Business Intelligence and Analytics Tools
Spreadsheets
Reporting and querying tools
Data visualization tools
Online analytical processing (OLAP)
Drill-down analysis
Linear regression
Data Mining
Dashboards
Business Intelligence and Analytics Tools
Spreadsheets
Reporting and querying tools
Data visualization tools
Online analytical processing (OLAP)
Drill-down analysis
Linear regression
Data Mining
Dashboards
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Spreadsheets
Business managers often import data into a spreadsheet program
Can be used to perform operations on the data based on formulas created by the end user
Spreadsheets are also used to create reports and graphs based on that data
Excel Scenario Manager
Used to perform “what-if” analysis to evaluate various alternatives
Spreadsheets
Business managers often import data into a spreadsheet program
Can be used to perform operations on the data based on formulas created by the end user
Spreadsheets are also used to create reports and graphs based on that data
Excel Scenario Manager
Used to perform “what-if” analysis to evaluate various alternatives
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Spreadsheets
Spreadsheets
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Reporting and Querying Tools
Reporting and querying tools can present data in an easy-to-understand fashion via:
Formatted data
Graphs
Charts
Many tools enable users to make their own data requests and format the results without the need for additional help from the IT organizations
Reporting and Querying Tools
Reporting and querying tools can present data in an easy-to-understand fashion via:
Formatted data
Graphs
Charts
Many tools enable users to make their own data requests and format the results without the need for additional help from the IT organizations
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Data Visualization Tools
Data visualization
The presentation of data in a pictorial or graphical format
Representing data in visual form brings immediate impact to dull and boring numbers
Word cloud
A visual depiction of a set of words that have been grouped together because of the frequency of their occurrence
Conversation funnel
A graphical representation that summarizes the steps a consumer takes in making the decision to buy a product and become a customer
Data Visualization Tools
Data visualization
The presentation of data in a pictorial or graphical format
Representing data in visual form brings immediate impact to dull and boring numbers
Word cloud
A visual depiction of a set of words that have been grouped together because of the frequency of their occurrence
Conversation funnel
A graphical representation that summarizes the steps a consumer takes in making the decision to buy a product and become a customer
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Data Visualization Tools
Data Visualization Tools
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Data Visualization Tools
Data Visualization Tools
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Data Visualization Tools
Data Visualization Tools
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Online Analytical Processing
Online Analytical Processing (OLAP)
A method to analyze multidimensional data from many different perspectives
OLAP enables users to identify issues and opportunities and perform trend analysis
Data cubes
Contain numeric facts called measures, which are categorized by dimensions, such as time and geography
Can be built to summarize unit sales of a specific item on a specific day for a specific store
Online Analytical Processing
Online Analytical Processing (OLAP)
A method to analyze multidimensional data from many different perspectives
OLAP enables users to identify issues and opportunities and perform trend analysis
Data cubes
Contain numeric facts called measures, which are categorized by dimensions, such as time and geography
Can be built to summarize unit sales of a specific item on a specific day for a specific store
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Online Analytical Processing
Online Analytical Processing
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Drill-Down Analysis
Drill-down analysis
Involves the interactive examination of high-level summary data in increasing detail to gain insight into certain elements
Example: in reviewing the worldwide sales for the past quarter, the VP of sales might want to drill down to view the sales for each country
Further drilling could be done to view the sales for a specific country for the last quarter
A third level of drill-down analysis could be done to see the sales for a specific country for a specific month of the quarter
A fourth level of analysis could be accomplished by drilling down to sales by product line for a particular country by month
Drill-Down Analysis
Drill-down analysis
Involves the interactive examination of high-level summary data in increasing detail to gain insight into certain elements
Example: in reviewing the worldwide sales for the past quarter, the VP of sales might want to drill down to view the sales for each country
Further drilling could be done to view the sales for a specific country for the last quarter
A third level of drill-down analysis could be done to see the sales for a specific country for a specific month of the quarter
A fourth level of analysis could be accomplished by drilling down to sales by product line for a particular country by month
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Linear Regression
Linear regression
A mathematical technique for predicting the value of a dependent variable based on a single independent variable and the linear relationship between the two
Consists of finding the best-fitting straight line through a set of observations of the dependent and independent variables
Linear Regression
Linear regression
A mathematical technique for predicting the value of a dependent variable based on a single independent variable and the linear relationship between the two
Consists of finding the best-fitting straight line through a set of observations of the dependent and independent variables
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Linear Regression
The following key assumptions must be satisfied when using linear regression on a set of data:
A linear relationship between the independent (X) and dependent (Y)variables must exist
Errors in the prediction of the value of Y are distributed in a manner that approaches the normal distribution curve
Errors in the prediction of the value of Y are all independent of one another
Linear Regression
The following key assumptions must be satisfied when using linear regression on a set of data:
A linear relationship between the independent (X) and dependent (Y)variables must exist
Errors in the prediction of the value of Y are distributed in a manner that approaches the normal distribution curve
Errors in the prediction of the value of Y are all independent of one another
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Data Mining
Data mining
A BI analytics tool used to explore large amounts of data for hidden patterns to predict future trends and behaviors for use in decision making
Most commonly used data mining techniques
Association analysis: a specialized set of algorithms sorts through data and forms statistical rules about relationships among the items
Neutral computing: historical data is examined for patterns that are then used to make predictions
Case-based reasoning: historical if-then-else cases are used to recognize patterns
Data Mining
Data mining
A BI analytics tool used to explore large amounts of data for hidden patterns to predict future trends and behaviors for use in decision making
Most commonly used data mining techniques
Association analysis: a specialized set of algorithms sorts through data and forms statistical rules about relationships among the items
Neutral computing: historical data is examined for patterns that are then used to make predictions
Case-based reasoning: historical if-then-else cases are used to recognize patterns
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Data Mining
Cross-Industry Process for Data Mining (CRISP-DM)
A size-phase structured approach for the planning and execution of a data mining project
Data Mining
Cross-Industry Process for Data Mining (CRISP-DM)
A size-phase structured approach for the planning and execution of a data mining project
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Data Mining
Data Mining
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Data Mining
Examples showing how data mining can be used:
Based on past responses to promotional mailings, identify those consumers most likely to take advantage of future mailings
Examine retail sales data to identify seemingly unrelated products that are frequently purchased together
Monitor credit card transactions to identify likely fraudulent requests for authorization
Use hotel booking data to adjust room rates so as to maximize revenue
Analyze demographic data and behavior data about potential customers to identify those who would be the most profitable customers to recruit
Study demographic data and the characteristics of an organization’s most valuable employees to help focus future recruiting efforts
Recognize how changes in an individual’s DNA sequence affect the risk of developing common diseases such as Alzheimer’s or cancer
Data Mining
Examples showing how data mining can be used:
Based on past responses to promotional mailings, identify those consumers most likely to take advantage of future mailings
Examine retail sales data to identify seemingly unrelated products that are frequently purchased together
Monitor credit card transactions to identify likely fraudulent requests for authorization
Use hotel booking data to adjust room rates so as to maximize revenue
Analyze demographic data and behavior data about potential customers to identify those who would be the most profitable customers to recruit
Study demographic data and the characteristics of an organization’s most valuable employees to help focus future recruiting efforts
Recognize how changes in an individual’s DNA sequence affect the risk of developing common diseases such as Alzheimer’s or cancer
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Dashboards
Measures are metrics that track progress in executing chosen strategies to attain organizational objectives and goals
These metrics are also called key performance indicators (KPIs) and consist of a direction, measure, target, and time frame
Examples of well-defined KPIs:
For a university. Increase (direction) the five-year graduation rate for incoming freshman (measure) to at least 80 percent (target) starting with the graduating class of 2022 (time frame)
For a customer service department. Increase (direction) the number of customer phone calls answered within the first four rings (measure) to at least 90 percent (target) within the next three months (time frame)
For an HR organization. Reduce (direction) the number of voluntary resignations and terminations for performance (measure) to 6 percent or less (target) for the 2018 fiscal year and subsequent years (time frame)
Dashboards
Measures are metrics that track progress in executing chosen strategies to attain organizational objectives and goals
These metrics are also called key performance indicators (KPIs) and consist of a direction, measure, target, and time frame
Examples of well-defined KPIs:
For a university. Increase (direction) the five-year graduation rate for incoming freshman (measure) to at least 80 percent (target) starting with the graduating class of 2022 (time frame)
For a customer service department. Increase (direction) the number of customer phone calls answered within the first four rings (measure) to at least 90 percent (target) within the next three months (time frame)
For an HR organization. Reduce (direction) the number of voluntary resignations and terminations for performance (measure) to 6 percent or less (target) for the 2018 fiscal year and subsequent years (time frame)
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Dashboards
Dashboard
Presents a set of KPIs about the state of a process at a specific point in time
Provide rapid access to information in an easy-to-interpret and concise manner
Provide users at every level of the organization the information they need to make improved decisions
Operational dashboards can be designed to draw data in real time from various sources
Including corporate databases and spreadsheets
Widely used BI software comes from many different vendors, including:
Hewlett Packard, IBM, Information Builders, Microsoft, Oracle, and SAP
Dashboards
Dashboard
Presents a set of KPIs about the state of a process at a specific point in time
Provide rapid access to information in an easy-to-interpret and concise manner
Provide users at every level of the organization the information they need to make improved decisions
Operational dashboards can be designed to draw data in real time from various sources
Including corporate databases and spreadsheets
Widely used BI software comes from many different vendors, including:
Hewlett Packard, IBM, Information Builders, Microsoft, Oracle, and SAP
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Dashboards
Dashboards
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Dashboards
Dashboards
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Dashboards
Dashboards
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Self-Service Analytics
Self-service analytics
Includes training, techniques, and processes that empower end users to work independently to access data from approved sources to perform their own analyses using an endorsed set of tools
Encourages nontechnical users to make decisions based on facts and analyses rather than intuition
Using self-service analytics, users can:
Gather insights
Analyze trends
Uncover opportunities and issues
Accelerate decision making by rapidly creating reports, charts, dashboards, and documents
Self-Service Analytics
Self-service analytics
Includes training, techniques, and processes that empower end users to work independently to access data from approved sources to perform their own analyses using an endorsed set of tools
Encourages nontechnical users to make decisions based on facts and analyses rather than intuition
Using self-service analytics, users can:
Gather insights
Analyze trends
Uncover opportunities and issues
Accelerate decision making by rapidly creating reports, charts, dashboards, and documents
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Self-Service Analytics
A well-managed self-service analytics program allows technology professionals to retain ultimate data control and governance while limiting information systems staff involvement in routine tasks
Self-service analytics tools must be intuitive and easy to use
Will only be embraced by end users if it allows them to easily access their own customized information, without extensive training
Self-Service Analytics
A well-managed self-service analytics program allows technology professionals to retain ultimate data control and governance while limiting information systems staff involvement in routine tasks
Self-service analytics tools must be intuitive and easy to use
Will only be embraced by end users if it allows them to easily access their own customized information, without extensive training
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Self-Service Analytics
Self-Service Analytics
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Self-Service Analytics
Self-Service Analytics
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Summary
The goal of business intelligence (BI) and analytics is to support improved decision making
There are many BI and analytics techniques and tools that can be used in a wide range of problem-solving situations
Summary
The goal of business intelligence (BI) and analytics is to support improved decision making
There are many BI and analytics techniques and tools that can be used in a wide range of problem-solving situations
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