1. Big Data Analytics: Visualizing the Future in the Aftermarket
Justin O. Holman, Ph.D.
September 21, 2015
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2. Agenda What is Analytics? What is Big Data? What does the Future hold?
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3. What is Analytics? www.aftermarketanalytics.com
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4. Analytics is a Hot Topic Google Analytics = Web Traffic Reports
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5. Analytics is a Hot Topic IBM analytics for connected cars
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6. Analytics is a Hot Topic
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7. Analytics is a Hot Topic In Sports and in the Movies
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8. So, what is Analytics? Web traffic tracking system? Dashboard?
Reporting tool?
Buzzword?
Temporary Craze?
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9. Analytics Defined Analytics is a set of data analysis categories
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10. Data Analysis Categories
Descriptive
Exploratory
Inferential
Experimental
Predictive
Prescriptive
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11. Analytics Categories Descriptive > Exploratory Inferential
Experimental
Predictive
Prescriptive
Reporting
Summarizing
Measuring
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12. Analytics Categories Descriptive Exploratory > Inferential
Experimental
Predictive
Prescriptive
Looking for ...
Patterns
Opportunities
Vulnerabilities
Relationships
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13. Analytics Categories Descriptive Exploratory Inferential >
Experimental
Predictive
Prescriptive
Estimate
population parameters
based on
sample statistics
Surveys
Customer Loyalty Program
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14. Analytics Categories Descriptive Exploratory Inferential
Experimental >
Predictive
Prescriptive
Testing for ...
Significance
Confidence Level
Quality Control
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15. Analytics Categories Descriptive Exploratory Inferential Experimental
Predictive >
Prescriptive
Forecasting
Simulation
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16. Analytics Categories Descriptive Exploratory Inferential Experimental
Predictive
Prescriptive >
Optimization
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17. Aftermarket Applications
Descriptive - Category Management Metrics
Exploratory - Visualization of VIO
Inferential - Consumer Surveys
Experimental - Quality Control testing
Predictive - Repair/Demand Forecasting
Prescriptive - Inventory Optimization
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18. What is Big Data? www.aftermarketanalytics.com
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19. Big Data: In the news www.aftermarketanalytics.com
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20. Volume Velocity Variety Take #1: Lots of Data
Fills up your servers, crashes hard drives, difficult to manage
Volume
Sensor collection, streaming data, Twitter feed: coming at you fast
Velocity
Numerical,
Graphical,
Social Media, Text.
Etc
Variety
Sources: Gartner, IBM, Opex Analytics
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21. Take #2: Overwhelming
In his book, Bill Franks defines Big Data as an amount of data that overwhelms existing systems
This definition recognizes new technology will be developed, but data volume will continue to grow.
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22. Take #3: Buzzword www.aftermarketanalytics.com
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23. Big Data = Too Much for Excel
My Take:
Big Data = Too Much for Excel
Relational Databases. Not flat files!
Programming & SQL. Not V-Lookups!
Data Scientists. Not Excel Jockeys!
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24. Aftermarket Impact Opportunities for Competitive Advantage:
Change IT culture
From gatekeepers
To innovators
Investment in Personnel
From Server/PC Technicians
To Data Scientists
Migrate to the cloud
Outsource your data farm
Safer, more accessible in the cloud
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25. What does the Future hold?
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26. Big Data Analytics Outlook
Data Volume will continue to grow
Computing Power will continue to improve
Software will improve also, but lag behind
Cloud Migration will accelerate
Excel use will slowly decrease
Data Visualization will grow in importance
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27. Big Data Analytics Focus
Data Volume will continue to grow
Computing Power will continue to improve
Software will improve also, but lag behind
Cloud Migration will accelerate
Excel use will slowly decrease
Data Visualization will grow in importance
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28. Why Visualization? www.aftermarketanalytics.com
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29. Why Visualization? Patterns Participation
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30. John Snow: Broad Street cholera outbreak of 1854
Map of cholera cases helped identify the water source
Pattern was only detectable with a map
Would Dr. Snow have found the source with a spreadsheet?
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31. John Snow: Broad Street cholera outbreak of 1854
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32. Businesses try to find the source of problems with spreadsheets
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33. See any patterns here? www.aftermarketanalytics.com
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34. Would the US Military plan for war with spreadsheets?
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35. Could the US Military collaborate effectively with spreadsheets?
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36. The table map allows everyone to participate in the decision process
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37. The power of visualization: an example
Participants:
Branch Management
Finance Team
CEO
Problem:
Strong revenue but low profit margins
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38. The power of visualization: an example
Finance Presentation
Revenue and Profit by branch location
Lots of acronyms like EBITDA, IRR
Rows and Rows of data in Excel
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39. The projector looked like this: no patterns
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40. The power of visualization: participation
Limited Progress
CEO can see Branch Managers are frustrated
But they’re not participating
CEO wants input from boots on the ground
But they don’t speak finance
The meeting moves on to analysis of outbound delivery and transportation costs...
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41. Another Example www.aftermarketanalytics.com
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42. Another Example www.aftermarketanalytics.com
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43. The power of visualization: participation
Lively Discussion
Clearly branches delivering all over the market, in Atlanta traffic.
Branch Managers now on their feet.
Pointing at the screen
Participating the analysis
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44. The power of visualization: seeing is believing
Problem Identified
New compensation program rewarded revenue growth, not profit
Branch Managers had no incentive to share delivery responsibility.
CEO marching orders:
Branch Managers to coordinate delivery
Finance to revamp compensation program
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45. Wrap Up Analytics: Ask “what type” of data analysis?
Big Data: Too big for Excel
Future: More data, more analysis, more pressure to compete using analytics
Visualization: Why? Patterns and Participation
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46. Thank you! Email: justin.holman@aftermarketanalytics.com
Phone/Text: (719)
Blog: justinholman.com
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