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From Science to Product (Company)

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Presenter: Cameron Turner, The Data Guild

We live at a time when data science, as sexy as it is, is too often still considered exotic and disconnected from the main line goals of organizations. How can we turn that model around to data science being a core engine of value and force for good in the world? I'll discuss our experiences at The Data Guild working with some of the largest and most innovative companies and nonprofits in the world and our approach to building a purposeful community around data product development.

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From Science to Product (Company)

  1. 1. Data Science To Data Products Cameron Turner The Data Guild October 22, 2015
  2. 2. A tale of three models…Model #1 The Bearded Research Team
  3. 3. A tale of three models…Model #2 The Exploited Data Science Team
  4. 4. A tale of three models…Model #3: The Integrated Data Team
  5. 5. Model 1: Beards Dev/Eng Mtkg Mgmt Sales Data Science
  6. 6. Model 2: Exploited Dev/Eng Mtkg Mgmt SalesData Science
  7. 7. Model 3: Integrated Dev/Eng Mtkg Mgmt Sales Data Science
  8. 8. Vote Type I. Type II. Type III.
  9. 9. Who are we?
  10. 10. A little about The Data Guild… • What – We are a Data Product Studio in Palo Alto – We create data-driven solutions that serve our customers’ highest strategic priorities • Who – Data as science, practice, and mission – Experience required – Full-stack product teams – The importance of being T-shaped…
  11. 11. Scale: Data Product for Everyone?
  12. 12. Overcoming the Trough (Pit)
  13. 13. Finding a Balance Finding Scale: One Size Fits All Generating Value: Last Mile Integration “Firm” Value: Scalability “Client” Value: Solves Problem
  14. 14. Design Tenet: Economies of Scope Energy Healthcare Finance/Fraud Education Agriculture IoT/Sensors Prediction/Recommendation/Machine-Learning Data Design/Applied Data Strategy Data Cleansing/Aggregation/Architecture
  15. 15. Example: Energy Optimization
  16. 16. 25
  17. 17. April Plant efficiency by Equipment Combo Active 20
  18. 18. Modeled Set Points Value (Yellow):  Modeled Pre Intervention Value (Orange):  Actual Value (Red):  Machine Learning on/off (Black):  Observed Savings*:  Plant kW/ton, 2014-09-28 to 2014-10-04
  19. 19. Closing Thought…the last mile...
  20. 20. Thank you! Cameron Turner cameron@thedataguild.com @cturner50

Notizen

  • In this study, we hoped to build a picture of the opportunity for machine learning and artificial intelligence in the business of HVAC system optimization.
    To do so, we took first one month, then one year of historical operating data from a large Optimum Energy Deployment (IBM 901 in Austin, TX). We analyzed the performance of the system over time. This helped us to gain intuition about the systems performance, and formulate hypotheses for formal statistical testing. TODO: ZOOM IN TO SHOW WHAT YOU WANT TO SHOW.
  • One area of opportunity that was noted was the spread of efficiency between the different chiller combinations due to covariance. This was counter to the intuition that this site (chosen for its simplicity: 5 homogeneous chillers) should have consistent performance between combinations.
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