Data mining and data visualisation - Lance Nelson


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An overview of data mining and data visualisation from Mezzo Labs' "Getting Ahead in Web Analytics" event in February 2014.

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Data mining and data visualisation - Lance Nelson

  1. 1. Data Mining & Data Visualisation Lance Nelson Mezzo Labs February 2014
  2. 2. Data visualisation
  3. 3. Definition “The creation and study of the visual representation of data” (Wikipedia) “The main goal of data visualisation is to communicate information clearly and effectively through graphical means.” (Vitaly Friedman, ‘Data Visualisation and Infographics’ article, Column chart Streamgraph Treemap Scatter plot
  4. 4. “ “ What do you need it for? - Communicate business intelligence - Interpretation of the data in order to gain insight - Keep a closer eye on your business’ vital signs
  5. 5. Main vendors There are two types of data visualisation products: a) Presentation-only • View your data via a series of widgets b) Simple drill-down • Interact with the data • Measure campaign effectiveness • Add comments/insight • Manage users
  6. 6. a) Presentation-only Monitor your business’ vital signs
  7. 7. b) Simple drill-down ‘Democratises’ the software by encouraging collaboration
  8. 8. Functionality Graphic courtesy of Klipfolio
  9. 9. A word of caution… They say “a picture paints a thousand words”… (or in our case, numbers) BUT DOES IT?
  10. 10. “An ideal visualization should not only communicate clearly, but stimulate viewer engagement and attention” How To Make Data Look Sexy, Fernanda Viegas and Martin Wattenberg,, 2011
  11. 11. Data mining
  12. 12. “ “ Definitions • The analysis of historical business activities to reveal hidden patterns and trends. – Wikipedia • Data mining’s main function is to increase ROI…primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing. – Jason Frand, UCLA
  13. 13. “ “ What do you use it for? • Sell more products, increase ROI • Increase the effectiveness of campaigns • Attract new customers and increase customer loyalty Data mining helps to: • Determine sales trends • Segment customers based on activity and demographics • Develop marketing campaigns • Predict customer loyalty and future trends
  14. 14. Data sources: databases, flat files, feeds Data ware- house or mapping scheme Search for patterns: queries, rules, neural net, mathemati cal learning, statistics Revise and refine queries Analyst reviews output Report findings Interpret results Take action Pre-process data: collect, clean and store
  15. 15. Main Vendors There are two types of data mining products: a) Data-centric • Analyse offline and online data b) Web-centric • Analyse purely online data
  16. 16. Main Vendors SAS • Predictive analytics • Visual analytics • Forecasting and econometrics • Text analytics ijento • Started as a web analytics company • Marketing optimisation • Customer experience management • Visualisation
  17. 17. Output
  18. 18. Output +
  19. 19. Summary
  20. 20. Use data visualisation to: Monitor your business’ KPIs and/or enable data- driven decisions through collaboration and sharing Use data mining to: Learn from your customers’ past behaviour and use this to predict their future behaviour