Business Analytics
What is Analytics?
● It is concerned with turning raw data into insight for making better decisions
●Analytics is a field that combines data, information technology, statistical analysis, quantitative methods, and computer-based models into one.
●Analytics is the discovery, interpretation, and communication of meaningful patterns in data
Need for Analytics
●Cost Reduction
●Better marketing and product analysis
●Organizational Analytics
●Better and faster decision making
Analysis vs Analytics
Analysis looks backward over time, providing marketers with a historical view of what has happened.
Typically, analytics look forward to modeling the future or predicting a result.
Business Analytics
Business analytics (BA) refers to the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning.
The study of data through statistical and operations analysis, the formation of predictive models, application of optimization techniques, and the communication of these results to customers, business partners, and college executives.
Domains for Analytics
Behavioral Analytics
Cohort Analysis
Collections Analytics
Cyber Analytics
Enterprise Optimization
Financial Services Analytics
Fraud Analytics and Healthcare Analytics
Marketing Analytics
Pricing Analytics
Retail Sales Analytics
Risk & Credit Analytics
Supply Chain Analytics
Talent Analytics and Telecommunications Transportation Analytics
Business Analytics vs Business Intelligence
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Tools of Analytics:
Levels of Analytics
Gains insight from historical data with reporting, scorecards, clustering etc
2. Diagnostic Analysis
Drill down to the cause, Modeling the behavior using regression, logistic, classification, association rule analysis
3. Predictive Analytics
Employs predictive modelling using statistical and machine learning techniques to forecast
4. Prescriptive Analytics
Recommends decisions using
optimization, simulation, etc.
CRISP-DM Framework: How to do Analytics
CRISP-DM stands for Cross-Industry Process for Data Mining. This methodology provides a structured approach to planning a data mining project.
Steps Includes:
●Business Understanding
●Data understanding
●Data preparation
●Modeling
●Evaluation
●Deployment / Data Presentation