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Andrey Novikov. Data Science in the Greenfield: How to Initiate Analytical Projects

Andrey Novikov. Data Science in the Greenfield: How to Initiate Analytical Projects

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July 13, 2021
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  1. Data Science in Greenfield. How to initiate analytical projects. Andrey

    Novikov, Syndata.io
  2. What if companies of all industries could make full benefit

    of data available to them? Syndata.io evolutionary goal
  3. How do companies benefit from analytical projects?

  4. How do companies benefit from analytical projects? • To manage

    it, you need to measure it.
  5. How do companies benefit from analytical projects? • To manage

    it, you need to measure it. • Who is “YOU”?
  6. Team for analytical projects • “Subject matter expert (SME)” –

    understands client’s realities and priorities. Can interpret findings. • Analyst – able to generate insights, build visualizations.
  7. Team for analytical projects • “Subject matter expert (SME)” –

    understands client’s realities and priorities. Can interpret findings. • Analyst – able to generate insights, build visualizations. A project is the communication between these two.
  8. How do companies benefit from analytical projects? • To manage

    it, you need to measure it.
  9. How do companies benefit from analytical projects? • To manage

    it, you need to measure it. • What does it mean “To measure”?
  10. How do companies benefit from analytical projects? • To manage

    it, you need to measure it. • What does it mean “To measure”? • To measure = To reduce the level of uncertainty
  11. Level 1: Descriptive analytics

  12. Level 1: Descriptive analytics Show only relevant info! On/off Number

  13. What do we need from a dashboard? • Shows all

    relevant (and only relevant) data • Data updates automatically • Can be easily modified to support changing requirements
  14. A simple Excel dashboard

  15. … with drill down

  16. A more advanced L1 description with Tableau

  17. Even more advanced, still Level 1.

  18. Who and what do you need? People • Customer /

    Subject Matter Expert • Analyst Tools • Excel • PowerBI • Tableau • Python Prototype gets embedded to corporate portal, ERP, CRM…
  19. Level 2. Predictive analytics: “what will happen”, “what if”.

  20. Level 2. Predictive analytics: “what will happen”, “what if”. Indicators

    come from Level 1 (temperature, humidity, rain)
  21. Level 2 task examples • Churn prediction • Market campaign

    prediction • Demand prediction
  22. Level 2 task examples • Churn prediction • Market campaign

    prediction • Demand prediction • Task duration prediction… When general public talks about ML, they mean L2 tasks.
  23. Level 2. Who and how? Approaches • Regressions • Machine

    learning People • Analyst • Subj matter expert • Data Scientist
  24. Level 3. Prescriptive analytics: how to optimize?

  25. Level 3. Prescriptive analytics: how to optimize? Critical KPI for

    optimization from Level 1 (ATM downtime, project duration…) Model from Level 2, predicting that KPI
  26. Level 3 tasks examples • How to optimize project flow

    • When to change replacement parts of ATM to minimize downtime • How much food to produce • How long a maintenance process will take • …
  27. Level 3. Who and how? Approaches • Machine learning •

    Optimization (any type) People • Analyst • Subj matter expert • Data Scientist
  28. Customer: “Should I buy it?”

  29. Project economics: Return on Analytics Q: Analytics is expensive. A:

    True. Resolutions: 1) Start small with what is really important 2) Use simple tools/techniques for PoC 3) Run, Stop and think, Repeat.
  30. Wrap up: how to initiate a meaningful project • Define

    the business-relevant focus. • Level 1: Slice it through numeric and visual representations • Level 2: Analyze for interdependencies, build prediction models • Level 3: Find room for optimization
  31. Wrap up: how to initiate a meaningful project + Be

    sure to involve Subject Matter Expert to any analytics project you run. + Be reasonable at economy and use simple tools (they work).
  32. Bonus: Several words on Tools. (Do not underestimate simple ones!)

  33. PowerBI Good for lots of simple charts. Build fast, auto-update,

    connect to anything.
  34. Excel Great to handover prototypes to developers. If you need

    a prototype and you can do it in Excel, then do it in Excel.
  35. Python models/visualizations Use it on more mature stages, especially levels

    2/3.
  36. Yed. Visualize with graphs if you want people think of

    connections.
  37. Yed. Visualize with graphs if you want people think of

    connections.
  38. Yed. Yed can build graphs from adjacency matrices in Excel

    (so you can play with sizes, colors, forms…) Photos can be embedded (with some XML-level tricks).
  39. Yed. Yed can build graphs from adjacency matrices in Excel

    (so you can play with sizes, colors, forms…) Photos can be embedded (with some XML-level tricks).
  40. None