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A Different Data Science Approach - StampedeCon AI Summit 2017


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This session will focus on how to execute Data Science caliber efforts by creating teams with the attributes of Data Science to deliver meaningful results. As Data Scientists are harder to find and keep, this session should appeal to anyone who is either seeking an alternative approach to executing Data Science delivery or augmenting their current Data Science model with additional options.

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A Different Data Science Approach - StampedeCon AI Summit 2017

  1. 1. Post Holdings Decision Science A Different Data Science Approach October 17th 2017
  2. 2. We are committed to running a smarter business. To do this we need to empower people throughout the organization to ask and answer their own questions. Our mission includes enabling the organization to answer the pressing questions of tomorrow now. We will do this by providing data driven solutions to develop and execute on new insights with fact based information. Enhanced data used intelligently to make better decisions! VISION
  3. 3. Who is Post Holdings? 3 Post Holdings is a $5B+ St. Louis based company that focuses in the Consumer Packaged Goods (CPG) space. Below is a brief history of our evolution: • Post Foods – Feb ’12 (Ready to Eat (RTE) Cereal Products) • Attune – Dec ’12 (Organic Cereal) • Attune Golden Temple – June ’13 (Organic Cereal and Granola) Post Holdings System Separation – Jul ‘13 Go Live from Ralcorp • Premier Nutrition – Sep ’13 (Protein/Nutrition Bars and Beverages) • Dakota Growers – Jan ‘14 (Private Label Pasta) • Golden Boy – Jan ‘14 (Private Label Nut Butter and Snack Nuts) • Dakota Growers – Jan ‘14 (Private Label Pasta) • Dymatize – Jan ‘14 (Protein Powder and Beverages) • Michael Foods – June ‘14 (Eggs, Potatoes and Cheeses) • Quaker Oh!s – Aug ‘14 (Ready to Eat (RTE) Cereal Brand) • Nestle PowerBar – Oct ‘14 (Nutrition Bar) • American Blanching Co. – Nov ‘14 (Private Label Peanut Butter) • Malt O Meal – May ‘15 (Ready to Eat (RTE) Cereal Products) • Mushasi (Divesture) – June ‘15 (PowerBar Australia) • Willamette Egg Farms – Oct ‘15 (Eggs and Egg Products) • National Pasteurized Eggs– Oct ‘16 (Pasteurized Shell Eggs) • Weetabix– July ‘17 (Ready to Eat (RTE) Cereal Brand UK)
  4. 4. Why does this Matter Now? 4 • Not making the best decisions possible. We need to leverage all available data to drive strategic fact based decision making. • Risk of being “Ubered on Analytics” by competition (they could win a price war today by better knowing us than we do them)! • Technology rapidly advancing; availability of new data sources • Need change culture to leverage tools & data and to compete
  5. 5. Evolving to a Data Driven Culture 5 Post Holdings has taken a proactive steps toward empowering a Data Driven Culture leveraging our “Big Data” in a few ways: • Data Lake - Building a consolidated model to house and normalize all PHI required data from each Operating Platform (Finance, Procurement, Quality, etc.…). • Tableau - Implementing a Data Visualization tool to begin growing self- empowered usage of this information. • Operational BI Evolution - Evolved our Enterprise Intelligence ecosystem to be “leveraged” by Operating Platforms for daily use to run their business (Dual Use Platform). • Operational Consulting – Engaging with Operating Divisions directly to solve business problems at lower cost and faster speed. • Data Science - Implementing Data Science “Pods” to deliver Data Science quality work by leveraging existing team skills.
  6. 6. How are/could we Leverage “Data” for Information in the Business – Day to Day The following are the Four Segments around using Data to empower Action 6 Data Visualization Answers the Question: “What’s Happened & What’s Happening Now” • Enterprise Intelligence Dashboards • Procurement • Securitization • Consumer Affairs • Operational Reporting PHI is leveraging Tableau to Address Smart Data Discovery Answers the Question: “Why did it happen” Opportunity Areas • Brand Insights - Market Sentiment • Competitor Launches - Products • R&D - New trends (ex: Proteins) • M&A - Public Domain Insights • Legal - Patent information Predictive/Prescriptive Analytics Answers the Questions: “What will most Likely Happen & How can we Make it Happen” Cognitive Analytics Discovers, Questions, and Provides Options: “I want to learn, interact, and reason” Opportunity Areas Education Exploration Leveraged & Opportunity Areas • Cash Flow Model • Use Case Discovery from Data Lake • DC Optimization • Plant Line Optimization • Predictive AR Delinquency • Procurement – Teach a virtual procurement lead to provide options with current insights & more • Help Desks – Replace with learned response models • M&A – Expand M&A vetting faster
  7. 7. How Did We Build Data Science Capability with existing Teams 7
  8. 8. 8 What are Data Science Key Components? Business Acumen Statistics & Math Foundation Architecture & Development The above assumes you have in place a managed data infrastructure foundation and are already doing basic analytical reporting (as those are prerequisites) The Three Legs of Data Science
  9. 9. 9 How did we fill the functions? • Business Analysts (BA) • Business Partners • Operational Teams • Technical BAs • Finance Team • Stat Grads • App Developers • ETL Developers • Data Architects The Three Legs of Data Science No One Person knows all areas, but teams of three from the functions above know their area as an expert
  10. 10. How did Evolve Our Model? 10 We took a Proof of Concept approach: • Used External Firms- Built several proof of concepts with firms that had Sr. Data Scientists so we could learn the process and understand the skills required. • Trained a few leads - Invested in our team to ensure we had a few leads that could understand the external firms models and replicated a “simplified version” for our use. • Failed Fast- From those external engagements we executed, all but one failed for the stated result but we evolved at 10X the speed had we tried on our own. • Offered On-line Training– Coursera for most to compare R, Python, and general Data Science Toolkit knowledge. • Dove into the Business– Dual approach of seeking business partners with issues they were open to help on as well as reviewed data to find opportunity. • Kept a Data Science Mentor– Leverage a few high end firm Data Science senior consultants a few hours a month on retainer for idea vetting. • Team Project to Cross Train- Implemented wider team projects called Team Mouse and Team Keyboard to evolve folks into 2 of 3 Data Science areas.
  12. 12. 12 The Goal of Team Projects? • Business Analysts (BA) • Business Partners • Operational Teams • Technical BAs • Finance Team • Stat Grads • App Developers • ETL Developers • Data Architects Evolve One Skill Set into Two Over Time
  13. 13. A Few Results 13
  14. 14. 14 Data Science Delivery Predicting Production Frequency & Safety Stock
  15. 15. 15 Data Science Delivery Master Data Advisor Master data issues causes reports to be wrong, reconciliation difficult and finding the issues is painfully time consuming.
  16. 16. 16 Data Science Delivery QUALITY & FOOD SAFETY How do you derive insight from 67,000 annual consumer emails and phone calls?
  17. 17. Questions? 17