Business Analytics, Part I Introduction

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Business Analytics, Part IIntroduction

• Presented by Scott Koegler
• Editor, ec-bp.org

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Speaker

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

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Business Analytics

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

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BI A Starting Point

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

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BI BA

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

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BA Data

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

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Challenges

• Data?
• Access?
• Reporting?
• Outcomes?

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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

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Not About the Tools

• Tools do exist
• Know the desired outcomes

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Outcomes

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

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How Far to Reach

• Not far-reaching
• Best to start with smaller goals
• Tactical goals first

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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

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Leakage

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

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Focus on the Delta

• Difference between
• Current situation
• What is possible

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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

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80/20 Rule Applies

• Determine the most important changes
• Monitor progress
• Evaluate the results

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Good Enough

• Good enough is good enough

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Its a Process

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

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Processes

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

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Refine Evaluate

• Continuous loop
• Measure the Gap
• Fix what doesnt work
• Measure the Gap

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Categories of Analytics

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

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Categories of Analytics

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

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Categories of Analytics

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

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Limits to Predictions

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

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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

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Combination of Changes

• De-normalized databases
• Removes multiple tables
• Flat data file
• Optimized data structures
• Optimized computing

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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

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Returns Defined

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

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Recommendations

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

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Recommendations

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

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Analytics Everywhere

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