This document discusses how analytics has progressed from an artisanal, project-based approach to an industrial approach similar to the industrial revolution. It outlines how traditional analytics has been reactive and focused on individual models for specific asks. However, more mature analytics involves identifying fundamental and connector models that can be combined and leveraged across different use cases at scale. The document provides examples of how Dow has developed foundational models focused on customer demand, production needs, costs, and orders that are combined to power various predictive and optimization applications across the company. It argues this component approach allows analytics to scale and better model the business.

1. An Industrial
Revolution for
Analytics
ADVANCED ANALYTICS

2.  Method Diversity
 Limited Penetration
 More model maintenance
2
Analytics Maturity Stages
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3. Once group is
successful, demand
can be insatiable
Still have to contend
with model lifecycle
3
Analytics Demand Becomes Overwhelming
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Project-Based Analytics does not scale

4.  Artisanal manufacturing
•Customer wants 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
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Typical Analytics Process is Reactive

6.  Type of model driven by ask
•Churn model asked for
Data scientist build a model to predict churn
• POC approach to Enterprise Analytics
6
Artisanal Analytics Models
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7.  With experience, senior
data scientists can
determine common
elements between project
silos
•Determine basic elements
that can be leveraged across
models
7
Industrial Revolution for Analytics
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8.  Fundamental Models
 Connector Models
8
Components for Analytics Assembly
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9.  Fundamental models
•Order likelihood
•Transactional demand
•Transactional unit raw material cost
•Orders at Risk (cancellations, delays)
 Connector models
•Customer demand  production needs
•Customer demand  raw material needs
9
Dow’s Experience—Model-building
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10. •Combinations of these models drive
–Near-term demand and earnings forecasting
–Revenue optimization
–Customer metrics for churn and current order
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Dow’s Experience—Model Combination
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11.  Components Approach to Analytics Model
•Built to make predictions scale
•Model Leverageability
 Modeling business at a fundamental scale
 Systems Engineering for Analytics
11
Requirements for New Approach
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12. 12
New Measure of Analytics Maturity
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Prototypes
Models at Scale
Analytics at Scale