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

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Transcript

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).