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Innovation through data capitalisation


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Presentation for the Creative Industries

Presentation for the Creative Industries

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  • 1. Innovation through Data Capitalisation Joanne Jacobs Social Media Consultant Exploring Data Driven Innovation - Creative Consumer Workshop Wed 09 Mar 2011 Inspace - Edinburgh School of Informatics, Edinburgh Image source:
  • 2. Scope of the presentation
    • Primary argument
    • Value versus utility
    • The Value of Data
    • The Utility of Data
    • What data is collected?
    • What additional data can be collected?
    • How to capitalise on data
    • Disruptive versus continuous innovation
    • Innovation through data capitalisation
    • Case studies
    • Additional Resources
    • Questions
  • 3. Primary argument
    • There is an opportunity cost of failing to make use of data.
    • Data should trigger action, not just be support material in reports
    Image sources:
  • 4. Value versus utility
    • Value: Worth of a product/system in terms of use or in terms of market perception
    • Collectively determined
    • Utility: usefulness, capacity to generate positive outcomes and minimise negative outcomes
    • Individually determined
  • 5. The maths in brief (Metcalfe’s law, etc)
    • Value of a network determined by number of possible connections:
    • n 2 (Metcalfe’s law)
    • Can only have connections with other users, thus better equation is:
    • n(n - 1)=2 (Reed’s Law)
    • Odlyzko says total connections doesn’t represent true value of network, nor do networks grow exponentially, thus best equation is:
    • n log(n) (Odlyzko & Tilly’s Law)
    Image source: http://'s_law
  • 6. What does all this mean?
    • Value of networks not directly proportional to either number of people in a network, or the amount of data collected;
    • Value of data more directly associated with its usefulness and perceived benefits;
    • Value of data can be hidden as well as acknowledged.
  • 7. The Value of Data
    • Total tangible and intangible acknowledged benefits derived from data
    • DOES NOT include data collected but not capitalized
    • DOES NOT include unacknowledged benefits, whether tangible or intangible.
    Image source:
  • 8. The Utility of Data
    • Total possible interactions in a system
      • Supply chain databases (suppliers and customers)
        • Earnings per record
        • Risk/price of rebuilding
      • Customer interactions (online or in person)
        • Earnings per interaction
    • Total possible opportunities for collecting/recording data
    Image source:
  • 9. What data is collected?
    • Customer databases
    • Supplier databases
    • Sales
    • Media profile
    • Website hits/interactions
    • Social media followers/likes/retweets
    • Sentiment/brand awareness
    • -> Predominantly tangible data
    Image source:
  • 10. What additional data can be collected?
    • Tangible:
      • Content tags (for indexing content)
      • Resource cost per interaction (for staff costs)
      • Time per interaction (to understand speed of interactions)
      • Number of interactions (to understand increase/reduction in processing)
    • Intangible:
      • Employee happiness with/understanding of how to find information
      • Supply chain perceptions of efficiency
      • Network effects on productivity
    Image source:
  • 11. How to capitalise on data
    • Map what data is collected with distinct actions
    • Present data in different contexts (geographical maps, timelines, heatmaps, other data visualisation techniques)
    • Consider what opportunities for data collection have been overlooked
    • Use data crunching resources for easy visualisation and insight generation.
    Image source:
  • 12. Disruptive vs Continuous Innovation
    • Disruptive innovation: creates new markets that have never before existed
    • Continuous or transformational innovation: solve existing problems either in new or expected ways
    • Most innovation derived from data will be continuous/transformational
    Image source:
  • 13. Innovation through Data Capitalisation
    • Most innovation driven by tinkerers, not by R&D , thus needs-driven, not research-driven
    • Greatest needs are based on scarcity of resources
    • New knowledge emerges when existing data ‘mashed together’ with other content (ie: crime maps)
    Image source:
  • 14. Failing to use data
    • Much data collected never capitalized:
      • Insights from customer interactions in person and online
      • ‘ Uncleaned’ databases
      • Unindexed, non-contextualised content
    • All data collected, but not capitalized = COST
    • All data not collected where possible = COST
    Image source:
  • 15. Reports are not enough
    • Many organisations feel that by reporting data, they are capitalizing; this is not necessarily true
    • Reports of interactions for Board or stakeholders which do not result in action = COST
    • Reports should be presented with insights as well as a variety of scenarios for organisational behaviour change.
    Image source:
  • 16. Case studies
    • Police crime maps
    • Wordle on website comments/mentions
    • Network switching for mobile phone suppliers
    • YouTube: Map my summer
    • Fortune’s Best Companies to Work For
    • CO2 creation
    Image source:
  • 17. Police crime maps Insights: Relationships between crimes in various areas – data can be indexed by socio-economic factors, etc. From:
  • 18. Wordle for mentions Insights: Individuals, places, activities. From: Creative Industries Knowledge Transfer networks articles
  • 19. Mobile network switching Insights: Not just numbers, but patterns of change. From: Ken’s Tech tips
  • 20. Map my summer Insights: Awareness of the campaign, network spread. From: YouTube Map My Summer
  • 21. Fortune’s Best Companies to Work For Insights: Values of employees. From: CNN Money site
  • 22. CO2 Creation Insights: Comparison of activities or alternatives. From: General Electric data visualisation
  • 23. Additional Resources
    • MIT’s Exhibit: http://simile- /exhibit/
    • Open Heat Map: http:// /
    • Google insights search: http:// /insights/search/#
    • Forrester customer social technographics profiling: http://
    • Wordle:
    • Visualizing http:// /
    • Spicy nodes: http:// /
    • Slatebox mindmapping: http:// /Index
    • brianstorming: https:// /
    Image source:
  • 24. Social media monitoring
    • (client based)
    • (private beta)
    • (free trial)
    Image source:
  • 25. Questions?
    • Joanne Jacobs
    • Social Media Expert Consultant
    • Email:
    • Blog:
    • Twitter: joannejacobs
    • Skype: bgsbjj
    • Skype-in: (+44) 0208 144 9348
    • Mob: (+44) 07948 318 298