Improving business outcomes through rapid data visualisation

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Visualising data provides clarity, increases engagement and delivers unexpected insights. A rapid and adaptive approach to building visualisations can help you realise value with a minimal investment.

David and Ray shared thoughts and client stories from work in Perth and Melbourne at an evening briefing in Perth, Western Australia, on 29 October 2013. David is a lead management consultant with a mathematical visualisation bent (find him on LinkedIn or see his blog). Ray is a lead developer consultant who enjoys thinking up and building products (twitter @grassdog).

Published in: Technology

Improving business outcomes through rapid data visualisation

  1. 1. Improving business outcomes through rapid data visualisation David Colls & Ray Grasso 29th October 2013
  2. 2. Spatial visualisation by John Snow, 1854 Mapping cholera deaths London,1854
  3. 3. Image from GE Communications at GE
  4. 4. Image from HBR courtesy of P&G Decision cockpits at P&G
  5. 5. More organisations using visualisation
  6. 6. HealthHack Oct 2013
  7. 7. Why visualise data? Easy to understand Shared view Holistic view New insight
  8. 8. Independent Market Operator (IMO)
  9. 9. IMO Goals Facilitate competition between power generators and retailers Encourage private sector investment in power generation and retailing. Support the development of sustainable energy sources
  10. 10. The Problem
  11. 11. o be ant t ew rent W nspa tra more … people to understand what we do and why it’s valuable
  12. 12. The Approach
  13. 13. Decide on an initial direction
  14. 14. Get our hands on the data
  15. 15. Follow the data
  16. 16. Rapidly build and refine Shift from the data problem to the communication problem
  17. 17. Get it to the audience
  18. 18. Example Evolution of a visualisation
  19. 19. Let’s look at it live…
  20. 20. Outcomes
  21. 21. … I am a developer and would be interested in accessing your data... ...can I just say how awesome the IMOWA data visualisation page is! … take that NEM …
  22. 22. Takeaways
  23. 23. Follow the data Minimise speculation Use real data Be open to unexpected opportunities
  24. 24. Rapidly refine the story It’s okay to start out vague Test with users to see if your story is being communicated effectively
  25. 25. IMO Summary 6 Weeks Open source technologies Explore where the data leads and release early and often.
  26. 26. Improving a Call Centre
  27. 27. What does a big Call Centre look like? 200,000 calls each day 10,000 agents 500 products 24 hours 7 days
  28. 28. The Problem It’s so big, we don’t have a clear picture what it looks like now, let alone how to improve it
  29. 29. We also saw… Surprises in demand and supply Data issues answering queued calls Same-queue transfers lls, ancies, very long ca Plus: timing discrep ,… gent skills, etc, etc mischaracterised a
  30. 30. The Approach
  31. 31. Get the data
  32. 32. Evolve a fuzzy visualisation
  33. 33. Ask: Why? Deliberate fuzziness leaves room for ambiguity and interpretation
  34. 34. Pursue quantitative investigation
  35. 35. Takeaways A fuzzy visualisation helps you discover questions Visualisation gives insight on operations and on data quality You can rapidly evolve a very complex visualisation
  36. 36. Call Centre Summary Undertaken as part of a major programme 2 weeks to build Used “Processing” software Accelerated learning reduced programme duration & operational improvements were realised sooner
  37. 37. NOPSEMA National Offshore Petroleum Safety and Environmental Management Authority http://www.flickr.com/photos/19779889@N00/ (Arby Reed)
  38. 38. NOPSEMA Summary 2 Weeks Simple visuals on existing systems can provide benefits.
  39. 39. Benefits Provide a holistic view of complex systems Glean unexpected business insights Craft engaging communications
  40. 40. Where to from here? Start small and stay lightweight Use real data throughout Refine and adapt
  41. 41. Questions?
  42. 42. Thank you

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