Improving business outcomes through rapid data visualisation
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The document discusses the importance of rapid data visualization in improving business outcomes, highlighting its ability to provide easy-to-understand, holistic insights. It emphasizes a data-driven approach to evolve visualizations, address communication issues, and leverage real data for operational improvements. Case studies, including call center analysis and initiatives by the Independent Market Operator, demonstrate the effectiveness of simple visuals in unveiling complex system dynamics and driving investment.
Improving business outcomes through rapid data visualisation
- 1. Improving business outcomes through rapid data visualisation David Colls & Ray Grasso 29th October 2013
- 2. Spatial visualisation by John Snow, 1854 Mapping cholera deaths London,1854
- 4. Image from HBR courtesy of P&G Decision cockpits at P&G
- 7. Why visualise data? Easy to understand Shared view Holistic view New insight
- 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. The Problem
- 11. o be ant t ew rent W nspa tra more … people to understand what we do and why it’s valuable
- 12. The Approach
- 13. Decide on an initial direction
- 14. Get our hands on the data
- 15. Follow the data
- 16. Rapidly build and refine Shift from the data problem to the communication problem
- 17. Get it to the audience
- 18. Example Evolution of a visualisation
- 23. Let’s look at it live…
- 24. Outcomes
- 25. … 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 …
- 26. Takeaways
- 27. Follow the data Minimise speculation Use real data Be open to unexpected opportunities
- 28. Rapidly refine the story It’s okay to start out vague Test with users to see if your story is being communicated effectively
- 29. IMO Summary 6 Weeks Open source technologies Explore where the data leads and release early and often.
- 31. What does a big Call Centre look like? 200,000 calls each day 10,000 agents 500 products 24 hours 7 days
- 32. The Problem It’s so big, we don’t have a clear picture what it looks like now, let alone how to improve it
- 34. 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
- 35. The Approach
- 36. Get the data
- 37. Evolve a fuzzy visualisation
- 38. Ask: Why? Deliberate fuzziness leaves room for ambiguity and interpretation
- 40. 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
- 41. 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
- 42. NOPSEMA National Offshore Petroleum Safety and Environmental Management Authority http://www.flickr.com/photos/19779889@N00/ (Arby Reed)
- 46. NOPSEMA Summary 2 Weeks Simple visuals on existing systems can provide benefits.
- 47. Benefits Provide a holistic view of complex systems Glean unexpected business insights Craft engaging communications
- 48. Where to from here? Start small and stay lightweight Use real data throughout Refine and adapt
- 49. Questions?
- 50. Thank you