Innovation through Data Capitalisation

Joanne Jacobs: Innovation through Data Capitalisation, March 2011

Innovation through Data Capitalisation

Joanne JacobsSocial Media Consultant

Exploring Data Driven Innovation - Creative Consumer WorkshopWed 09 Mar 2011 Inspace - Edinburgh School of Informatics, Edinburgh

Image source: http://www.flickr.com/photos/inl/5097547405/


Primary argument

1. There is an opportunity cost of failing to make use of data.

2. Data should trigger action, not just be support material in reports

Image sources: http://www.flickr.com/photos/alishav/4253056121/ http://commons.wikimedia.org/wiki/File:Fullquiverofarrows.jpg http://commons.wikimedia.org/wiki/File:Reloj_despertador.jpg http://commons.wikimedia.org/wiki/File:Mascherano_liverpool.JPG


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


The maths in brief (Metcalfe’s law, etc)

• Value of a network determined by number of possible connections:

n2 (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://en.wikipedia.org/wiki/Metcalfe's_law


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.


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: http://www.flickr.com/photos/thomasvdb/379546998/


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: http://www.flickr.com/photos/vancouverfilmschool/5143625176/


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: http://www.flickr.com/photos/sixmilliondollardan/3852839454/


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: http://www.flickr.com/photos/swanksalot/2704017177/


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: http://www.flickr.com/photos/quinnanya/5066259987/


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: http://www.flickr.com/photos/opensourceway/4371000486/


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: http://www.flickr.com/photos/aalhajji/2604740451/


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: http://www.flickr.com/photos/fireflythegreat/2845637227/


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: http://www.flickr.com/photos/gadl/320300354/


Case studies

1. Police crime maps

2. Wordle on website comments/mentions

3. Network switching for mobile phone suppliers

4. YouTube: Map my summer

5. Fortune’s Best Companies to Work For

6. CO2 creation

Image source: http://www.flickr.com/photos/lamenta3/2603529812/


Police crime maps

Insights: Relationships between crimes in various areas – data can be indexed by socio-economic factors, etc.

From: Police.uk


Wordle for mentions

Insights: Individuals, places, activities.

From: Creative Industries Knowledge Transfer networks articles


Mobile network switching

Insights: Not just numbers, but patterns of change.

From: Ken’s Tech tips


Map my summer

Insights: Awareness of the campaign, network spread.

From: YouTube Map My Summer


Fortune’s Best Companies to Work For

Insights: Values of employees.

From: CNN Money site


CO2 Creation

Insights: Comparison of activities or alternatives.

From: General Electric data visualisation


Additional Resources

• MIT’s Exhibit: http://simile-widgets.org/exhibit/

• Open Heat Map: http://www.openheatmap.com/

• Google insights search: http://www.google.com/insights/search/#

• Forrester customer social technographics profiling: http://www.forrester.com/empowered/tool_consumer.html

• Wordle: http://www.wordle.net/• Visualizing http://

www.visualizing.org/ • Spicy nodes: http://

www.spicynodes.org/ • Slatebox mindmapping: http://

www.slatebox.com/Index • Bubbl.us brianstorming: https://

bubbl.us/

Image source: http://www.flickr.com/photos/opensourceway/5161093829/


Social media monitoring

• http://www.socialoomph.com/ • http://blogpulse.com/ • http://www.boardtracker.com/ • http://www.tinker.com/ • http://surchur.com/ • http://www.socialmention.com/ • http://www.icerocket.com/ • http://www.keotag.com/ • http://monitorthis.77elements.com/ • http://addictomatic.com/ • http://www.howsociable.com/ • http://www.monitter.com/ • http://www.postling.com/ • http://topsy.com/ • http://backtweets.com/ • http://www.backtype.com/ • http://www.twazzup.com/ • http://www.threadsy.com/ • https://www.myweboo.com/index.html?null • http://www.peerindex.net/ • http://twitalyzer.com/ • http://www.wikio.com/ • http://research.ly/ • http://www.kurrently.com/ • http://www.sensidea.com/socialseek/index.html (client based)• https://www.tribemonitor.com/ (private beta)• https://twendzpro.waggeneredstrom.com/default.aspx (free trial)• http://socialcollider.net/

Image source: http://commons.wikimedia.org/wiki/File:Fundraising_2010_social_media_monitors_logo.png


Questions?

Joanne Jacobs

Social Media Expert Consultant

Email: [email protected]

Blog: http://joannejacobs.net/

Twitter: joannejacobs

Skype: bgsbjj

Skype-in: (+44) 0208 144 9348

Mob: (+44) 07948 318 298

Innovation through Data Capitalisation

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