An Overview of Business Analytics

Min Li, Director of CSUS Center for Business Analytics

June 19, 2017

Outline

  1. What is Business Analytics?
  2. Descriptive Analytics
  3. Predictive Analytics
  4. Digital Analytics
  5. Prescriptive Analytics
  6. Big Data Analytics
  7. Emerging Trends
These slides were adapted from two business analytics textbooks (Sharda, Delen, and Turban) published by Pearson Education, Inc.

1. What is Business Analytics?

New Business Environments and Analytics

  • Growing hardware, software, and network capabilities
  • Powerful communication and collaboration tools
  • Better data management
  • Giant data warehouse and Big Data
  • More powerful analytical tools and support
  • Overcome human cognitive limits in processing and storing information
  • Anywhere, anytime support

Business Intelligence

  • An umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies
  • Descriptive analytics tools and techniques (i.e., reporting tools)
  • Origins and Drivers of BI (see picture)
  • Architecture of BI (see picture)

Origins and Drivers of BI

Origins and Drivers of BI

Architecture of BI

Architecture of BI

Evolution of Computerized Decision Support to Analytics/Data Science

Evolution of Computerized Decision Support to Analytics/Data Science

BI, DW, and OLAP

  • OLTP (online transaction processing): routine ongoing business
  • Data Warehousing (DW) - middle data tier, repository to support business reporting and decision making
  • DW is a distint system providing storage for data that will be used for analysis
  • 2000s, DW-driven DSSs called BI systems
  • DWs are intended to work with informational data used for OLAP (online analytical processing) systems
  • DWs contain data presenting a coherent picture of business conditions at one time
  • BI has evolved into analytics and data science

Three Types of Analytics - INFORMS

Three Types of Analytics

Eight Levels of Analytics - SAS

Three Types of Analytics

2. Descriptive Analytics

  • Data
  • Statistical Modeling
  • Visualization
  • Data Warehousing
  • Business Performance Management

From Data to Knowledge

From Data to Knowledge

Are Data Ready for Analytics?

  • Data source reliable?
  • Data content accurate?
  • Data accessible?
  • Data security and data privacy?
  • Data richness or comprehensiveness?
  • Data consistency?
  • Data currency/timeliness?
  • Data granularity?
  • Data relevancy?

A Simple Taxonomy of Data

From Data to Knowledge
[any material that should appear in print but not on the slide]