Dynamics Day 2015: Systems of Intelligence in Action
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The document discusses the importance of malleable, observable, and engaged systems of experimentation in achieving effective learning and innovation in technology. It emphasizes the need for experiments to be cost-effective, well-instrumented, and conducted at significant scale to yield meaningful results. Additionally, it highlights the growing accessibility of data tools and the relevance of big data in various applications, including customer acquisition and product improvement.
Dynamics Day 2015: Systems of Intelligence in Action
- 1. Dynamics Day 2015 Systems of Intelligence in Action
- 3. 1 MALLEABLE: It must be cost effective to change the way that the system or process behaves. Ideally for just a subset of usage or users. WHAT WE NEED 2 3 OBSERVABLE: The system or process must be sufficiently well instrumented such that we have the information available to test the hypothesis. ENGAGED: The system or process must be being used at sufficient scale and in a sufficiently ‘real’ environment for any results to be significant. An experiment is a procedure carried out to verify, refute, or establish the validity of a hypothesis… Experiments vary greatly in goal and scale, but always rely on repeatable procedure and logical analysis of the results. SYSTEMS OF EXPERIMENTATION MALLEABLE OBSERVABLEENGAGED Experiments
- 5. “arguments about whether or not a feature idea is worth doing or not generally get resolved by just spending a week implementing it and then testing it on a sample of users, e.g., 1% of Nevada users.”
- 7. There are over 500 different K-Cups
- 8. There Are Cartridge Loaded Software Systems
- 9. Microsoft Azure Demand Center Internal Systems & Product/Platforms Dynamics CRM Marketo Web Email Call Center Data Warehouse PowerBI Systems Of Record Systems Of Experimentation Systems Of Engagement
- 12. Storage is Cheap… …Data Is Not
- 13. Smart Appliances (Think Printers + Ink) Existing ‘Dumb’ Product SensorTag IoT Suite Smart Screen Mobile Apps(s) Raw Data Store ??? Personalization Platform PowerBI Systems Of Experimentation Systems Of Engagement
- 15. “…Our tool works best when you’ve got at least 1 million or so app installs to work with…”
- 17. Flipping The Model Inputs OutputsIT System Expertise InterpretationExperimentation
- 18. Flipping The Model Inputs OutputsIT System ExplanationExperimentation Automation Models Machine Learning
- 20. Much of the Tooling is Free
- 21. Is Becoming More Widely Available
- 22. Is Becoming Easier to Use
- 23. And Is Entering The Public Consciousness
- 24. And can be crowd-sourced
- 25. In Boots & All Use Cases We Are Seeing
- 26. Big Data Use Cases, BARC Research 2015
- 27. STORE OPTIMIZATION TRACKING, TARGETING SEGMENT OF 1 SMART PRODUCTS SMART ASSETS PRODUCT IMPROVEMENT CUSTOMER ACQUISITION & RETENTION USER ANALYTICS & OPTIMIZATiON SERVICE/B2B RETAIL SUPPLY CHAIN FRAUD & COMPLIANCE
- 28. • Embrace Experimentation • Malleable, Agile, Cartridges • Observable; Capture More Data • Engaged; Ship Early & Learn • Systems of Intelligence • Doing Data Science <- Easier • Knowing What We Can Do <- Hard • Analytics Inside Your Organization • Survey for Opportunities • Most CRM Systems • Customer Facing Apps/Sites • IoT