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Business Analytics, Part I Introduction

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Title: Business Analytics, Part I Introduction


1
Business Analytics, Part I Introduction
  • Presented by Scott Koegler
  • Editor, ec-bp.org

2
Speaker
  • Scott Koegler
  • Editor of ec-bp.org
  • scott_at_ec-bp.org

3
Business Analytics
  • Business Analytics
  • What is it?
  • Where did it come from?
  • What is it supposed to do?

4
BI A Starting Point
  • Business Intelligence (BI)
  • Discovering what happened
  • Look at past events
  • Typical of ERP reports

5
BI BA
  • What differentiates BA from BI?
  • Looking forward
  • Trend moving to predictions
  • Predictive analysis

6
BA Data
  • Data is the key to BA
  • Lots of data
  • Real-time or near-time
  • Widest collection of data

7
Challenges
  • Data?
  • Access?
  • Reporting?
  • Outcomes?

8
Why Is BA a Hot Topic?
  • Optimization is the new growth
  • Expansion was the best way to grow
  • Now too expensive
  • Difficult to open new markets

9
Not About the Tools
  • Tools do exist
  • Know the desired outcomes

10
Outcomes
  • Outcomes define the project
  • Stakeholders must drive the quest
  • Business in / technology out

11
How Far to Reach
  • Not far-reaching
  • Best to start with smaller goals
  • Tactical goals first

12
Possibly Too Limited
  • Analytics are not in a box
  • Think of analytics as part of the holistic
    environment
  • Tactical goals are part of the overall plan

13
Leakage
  • Organizational process leakage
  • The key findings may be lost along the way

14
Focus on the Delta
  • Difference between
  • Current situation
  • What is possible

15
Close the Gap
  • The Gap is the difference between what is and
    what is possible
  • Dont worry about closing the gap completely
  • Incremental improvements do count

16
80/20 Rule Applies
  • Determine the most important changes
  • Monitor progress
  • Evaluate the results

17
Good Enough
  • Good enough is good enough

18
Its a Process
  • BA is not buy and push the button
  • Every implementation is different
  • Tools for custom outcomes

19
Processes
  • Create numerical results
  • Implement in meaningful ways
  • Integrate outcome to technology
  • Integrate  
  • Monitor and fine-tune

20
Refine Evaluate
  • Continuous loop
  • Measure the Gap
  • Fix what doesnt work
  • Measure the Gap

21
Categories of Analytics
  • Descriptive Analytics
  • Prepares and analyzes historical data
  • Identifies patterns from samples for reporting of
    trends

22
Categories of Analytics
  • Predictive Analytics
  • Predicts future probabilities and trends
  • Finds relationships in data not readily apparent
    with traditional analysis

23
Categories of Analytics
  • Prescriptive Analytics
  • Evaluates and determines new ways to operate
  • Targets business objectives and balances all
    constraints

24
Limits to Predictions
  • Long-term projections are difficult
  • 5- to10-year projections
  • Changes are difficult to predict

25
Barriers to Achievement
  • Massive amounts of data
  • Need for real-time access
  • Traditional data in transactional systems
  • Requires optimized computing platforms
  • Disk drives cant keep up

26
Combination of Changes
  • De-normalized databases
  • Removes multiple tables
  • Flat data file
  • Optimized data structures
  • Optimized computing

27
What About ROI?
  • ROI is not always immediately obvious
  • Results of analytics may be available only after
    years of following the prescription
  • Requires long-term efforts

28
Returns Defined
  • Viable Business Analytics
  • Results based on the business
  • Define the desired results
  • Agree on definition of success

29
Recommendations
  • BA initiatives are different
  • Commonality is in the approach
  • Treat BA as any project
  • Generally longer term
  • Iterative process
  • Constant updates

30
Recommendations
  • Monitor progress
  • Focus on outcomes
  • Review validity
  • Revise data collections

31
Analytics Everywhere
  • Increasingly used
  • Volume of data collected driving use
  • Optimization of business growth
  • Look for opportunities
  • Data collection
  • Future outcomes
  • Uncertainty
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