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Business Analytics
The extensive use of data and quantitative analysis to support fact-based decision making within organizations
Business Intelligence
Includes a wide range of applications, practices, and technologies for the interpretation and presentation of data to support improved decision making
Internal sources and external sources
Where data that is used in business intelligence is pulled from
Detect Fraud
Improve Forecasting
Increase sales
Optimize operations
Reduce Costs
Benefits achieved from business intelligence and analytics:
Strong business acumen
Deep understanding of analytics
Healthy appreciation of the limitations of their data, tools, and techniques to deliver real improvements
Data scientists are individuals who combine:
Mastery of statistics, math, and computer programming
Require advanced degree
Educational requirement for being a data scientist
Existence of a solid data management program
Creative data scientists
Management team
Components required for effective business intelligence and analytics
Data governance
Defines the roles, responsibilities, and processes for ensuring that data can be trusted and used by the entire organization
Management team
Must have a strong commitment to data-driven decision making
Data visualization tools
Data Mining
Dashboards
Drill-down analysis
Spreadsheets
Reporting and querying tools
Linear regression
Online analytical processing (OLAP)
Business Intelligence and analytics tools:
Spreadsheets
Can be used to perform operations on the data based on formulas created by the end user
Spreadsheets
Used to create reports and graphs based on that data
Excel Scenario Manager
Used to perform “what-if” analysis to evaluate various alternatives
Reporting and Querying Tools
Can present data in an easy-to-understand fashion via formatted data, graphs, and charts
Data Visualization tools
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
Conversion Funnel
A graphical representation that summarizes the steps a consumer takes in making the decision to buy a product and become a customer
Online Analytical Processing
A method to analyze multidimensional data from many different perspectives
Online Analytical Processing
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
Data Cubes
Can be built to summarize unit sales of a specific item on a specific day for a specific purpose
Drill-down analysis
Involves the interactive examination of high-level summary data in increasing the detail to gain insight into certain elements
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
Linear Regression
Consists of finding the best-fitting straight line through a set of observations of the dependent and independent variables
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
Association Analysis
A specialized set of and forms statistical rules about relationships among the items
Neutral computing
Historical data is examined for then used to make predictions
Case-based reasoning
Historical if-then-else cases recognize patterns
Cross-Industry Processing for Data Mining
A six-phase structured approach for the planning and executing of a data mining project
Measures
Are the metrics that track progress in executing chosen strategies to attain organizational objects and goals
Key Performance Indicators
Consists of a direction, measure, target, and time frame
Dashboard
Presents a set of KPIs about the state of a process at a specific point in time
Operational Dashboards
Can be designed to draw data in real time from various sources
Self-service analytics
Includes training, techniques, and processes that empower end users to work independently
Self-service analytics
Encourages nontechnical users to make decisions based on facts and analyses rather than intuition
