Business Analytics, Part I
Introduction
Presented by Scott Koegler
Editor, ec-bp.org
Speaker
Scott Koegler
Editor of ec-bp.org
scott@ec-bp.org
Business Analytics
• Business Analytics
• What is it?
• Where did it come from?
• What is it supposed to do?
BI – A Starting Point
• Business Intelligence (BI)
• Discovering what happened
• Look at past events
• Typical of ERP reports
BI & BA
• What differentiates BA from BI?
• Looking forward
• Trend moving to predictions
• Predictive analysis
BA & Data
• Data is the key to BA
• Lots of data
• Real-time or near-time
• Widest collection
of data
Challenges
• Data?
• Access?
• Reporting?
• Outcomes?
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
Not About the Tools
• Tools do exist
• Know the desired
outcomes
Outcomes
• Outcomes define the project
• Stakeholders must drive the quest
• Business in / technology out
How Far to Reach
• Not far-reaching
• Best to start
with smaller goals
• Tactical goals first
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
Leakage
• Organizational process
leakage
• The key findings may be
lost along the way
Focus on the Delta
• Difference between:
• Current situation
• What is possible
Close the Gap
• The Gap is the
difference between what
is and what is possible
• Don’t worry about closing
the gap completely
• Incremental improvements
do count
80/20 Rule Applies
• Determine the most important
changes
• Monitor progress
• Evaluate the results
Good Enough
• Good enough is good enough
It’s a Process
• BA is not “buy and
push the button”
• Every implementation
is different
• Tools for custom
outcomes
Processes
• Create numerical results
• Implement in meaningful ways
• Integrate outcome to technology
• Integrate
• Monitor and fine-tune
Refine & Evaluate
• Continuous loop
• Measure the Gap
• Fix what doesn’t work
• Measure the Gap
•…
Categories of Analytics
• Descriptive Analytics
• Prepares and analyzes
historical data
• Identifies patterns from
samples for reporting of
trends
Categories of Analytics
• Predictive Analytics
• Predicts future
probabilities and
trends
• Finds relationships in
data not readily
apparent with
traditional analysis
Categories of Analytics
• Prescriptive Analytics
• Evaluates and
determines new ways
to operate
• Targets business
objectives and
balances all
constraints
Limits to Predictions
• Long-term projections
are difficult
• 5- to10-year projections
• Changes are difficult to
predict
Barriers to Achievement
• Massive amounts of data
• Need for real-time access
• Traditional data in
transactional systems
• Requires optimized
computing platforms
• Disk drives can’t keep up
Combination of Changes
• De-normalized databases
• Removes multiple tables
• Flat data file
• Optimized data structures
• Optimized computing
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
Returns Defined
• Viable Business Analytics
• Results based on the business
• Define the desired results
• Agree on definition of success
Recommendations
• BA initiatives are different
• Commonality is in the approach
• Treat BA as any project
• Generally longer term
• Iterative process
• Constant updates
Recommendations
• Monitor progress
• Focus on outcomes
• Review validity
• Revise data collections
Analytics Everywhere
• Increasingly used
• Volume of data collected driving use
• Optimization of business = growth
• Look for opportunities
• Data collection
• Future outcomes
• Uncertainty