Book Notes: Enterprise Analytics - Davenport

  2017-11-23


Introduction: The New World of Enterprise Analytics

why now?
  powerful computers
  increase in data
  trained people
  need to differentiate products
jobs
  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
  goal
    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
    definition 
      use of 
        data
        statistical and quantitative analysis
        explanatory and predictive models
        fact-based management
    variations
      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
  analytics
    contemporary synonym
    with more quantitative slant
Three Types of Analytics
  descriptive analytics
    ex
      standard reports
        what happened
      ad hoc reports
        how many, how often?
      queries/drill down
        what exactly is problem
      scorecards
      alerts
        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
    optimization
      what's the best that can happen
    tell you what to do
Where Does Data Mining Fit In?
  features
    discovery of trends and patterns
    automated
  intersection of
    AI, ML, statistics, database
Business Analytics Versus Other Types
  health
    health care analytics
    informatics
    clinical decision support
Web Analytics
  new
  parts
    reporting
    A/B testing
Big-Data Analytics
  newest
  features
    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
  ex
    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
  process
    • 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.
    includes
      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