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Leveraged Analytics at Scale
The document discusses an industrial revolution in analytics, highlighting the challenges of model maintenance and the need for diverse methods as demand for analytics grows. It contrasts artisanal analytics with a scalable approach, emphasizing the importance of fundamental and connector models for effective forecasting and revenue optimization. The need for a new measure of analytics maturity and the introduction of a systems engineer role for analytics are also noted.
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Leveraged Analytics at Scale
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- 2. § Method Diversity §Limited Penetration § More model maintenance 2 Analytics Maturity Stages DOW RESTRICTED
- 3. Once group is successful,demand can be insatiable Still have to contend with model lifecycle 3 Analytics Demand Becomes Overwhelming DOW RESTRICTED Project-Based Analytics does not scale
- 4. § Artisanal manufacturing •Customerwants a chair •Artisan builds a chair § Division of labor •Customer wants a chair •Chair has components which are Assembled together and ready to go 4 Industrial Revolution DOW RESTRICTED
- 5. § Type ofmodel driven by ask •Churn model asked for ØData scientist build a model to predict churn • Prototype approach to Enterprise Analytics 5 Artisanal Analytics Models DOW RESTRICTED
- 6. § With experience,senior data scientists can determine common predictive elements between project silos § Model leveraging as core element of analytics strategy • Backwards—what past problem looks like this? • Forwards—model design so that they can be used over and over again 6 Industrial Revolution for Analytics DOW RESTRICTED
- 7. § Fundamental Models •Built outside of projects to be widely leveraged § Connector Models • Simple models built for need 7 Components for Analytics Assembly DOW RESTRICTED
- 8. Difference in Approachfor Forecasting DOW RESTRICTED 8 Transactional Data Modeled Transactions Modeled Results Aggregated Data Modeling Aggregation
- 9. § Fundamental models •Orderlikelihood •Transactional demand •Transactional unit raw material cost § Connector models •Customer demand à production needs •Customer demand à raw material needs 9 Dow’s Experience—Model-building DOW RESTRICTED
- 10. •Combinations of thesemodels drive –Near-term demand and earnings forecasting –Revenue optimization –Customer metrics for churn and sales intervention 10 Dow’s Experience—Predictive Results DOW RESTRICTED
- 11. § Components Approachto Analytics Model •Experience with prior models •Built to make predictions scale •Model Leverageability § Modeling business at a fundamental scale § New role: Systems Engineer for Analytics 11 Requirements for New Approach DOW RESTRICTED
- 12. 12 New Measure ofAnalytics Maturity DOW RESTRICTED Prototypes Models at Scale Analytics at Scale