Book Notes: Enterprise Analytics - Davenport


Introduction: The New World of Enterprise Analytics

why now?
  powerful computers
  increase in data
  trained people
  need to differentiate products
  tenfold increase from 90 to 2010 (linkedin)
  google trends
    10x from 2005 to 2012 "analytics"
enterprise analytics
the rise of big data
  what is?
    too voluminous
    too unstructured
IIA and the Research for this Book
  International Institute for Analytics: IIA
  reserach outputs
    research briefs
      3-5 page topics
    leading-practice briefs
      case studies
    write-ups of meetings
    short documents
  initial focus
    general enterprise topics

Part I: Overview of Analytics and Their Value

1. What Do We Talk About When We Talk About Analytics?

terms for decision-making from data
  70s: decision support systems
  80s: executive information systems
  80s: online analytical processing (olap)
  90s: business intelligence
  new: analytics
      use of 
        statistical and quantitative analysis
        explanatory and predictive models
        fact-based management
      predictive analytics
      data mining
      business analytics
      web analytics
      big-data analytics
Why We Needed a New Term: Issues with Traditional Business Intelligence
  bi used 
    standard reports
    answering queries
  wikipedia definition
    too much verbiage
    contemporary synonym
    with more quantitative slant
Three Types of Analytics
  descriptive analytics
      standard reports
        what happened
      ad hoc reports
        how many, how often?
      queries/drill down
        what exactly is problem
        what actions needed
    what happened in past
  predictive analytics
    statistical modeling
      why is it happening
    predictive modeling/forecasting
      what will happen
    use models to predict future from past
  prescriptive analytics
    randomized testing
      what if we try this
      what's the best that can happen
    tell you what to do
Where Does Data Mining Fit In?
    discovery of trends and patterns
  intersection of
    AI, ML, statistics, database
Business Analytics Versus Other Types
    health care analytics
    clinical decision support
Web Analytics
    A/B testing
Big-Data Analytics
    too big
    too unstructured
    too many different sources

2. The Return on Investments in Analytics

Traditional ROI Analysis
  roi = ( total value / benefits - total investment ) / total investment
    selecting high-potential customers for direct-mail campaign
      mine CRM data
      send mail to customers who meet a criteria
    building model
      investment cost: 50 K
    expected benefit: 75 K
    roi = ( 75 - 50 ) / 50 = 50%
  cash flow and roi
    assumption: costs and benefits occur at the same time
    rarely occurs
  credible roi
    based on credible business case
The Teradata Method for Evaluating Analytics Investments
    • Phase 1: Validate business goals and document best-practice usage.
    • Phase 2: Envision new capabilities.
    • Phase 3: Determine ROI and present findings.
    • Phase 4: Communicate.
  Phase 1: Validate business goals and document best-practice usage.
      strategic business initiatives
      progress measures
      documenting best practices
        reviewing reports, plans, ...
        interviewing executives

Part II: Application of Analytics

3 Leveraging Proprietary Data for Analytical Advantage

4. Analytics on Web Data: The Original Big Data

5. The Analytics of Online Engagement