MNIT – Public Data & Crowdsourcing

dataI had an interesting meeting with the MN Director of Innovation to continue our conversation around public data.   In the meeting he shared several interesting stories about the power of crowd sourcing solutions with public data.   A large, east coast, metropolitan had made a number of its government data sources public through a reporting platform.   The goal was to see what value could be created through partnerships and crowd sourcing solutions.   The initiative created a wide variety of new and valuable insights by connecting data that was seemingly unrelated. One example was combining public tree trimming data with emergency response data.   Turned out to be direct corrections to intersections and the number of accidents.   This informed the planning process around the importance and criticality of tree trimming around high volume intersections.  Two separate government agencies,  that didn’t interact prior, are now collaborating in new ways simply by leveraging the data they both had in more collaborative ways.     There was little cost in this discovery, but leveraging the crowd’s creative energies.
This example reminded me a a wide number of similar projects I have done in corporate America.   I’ve worked with companies to identify categories of data that are better served by being made available on public APIs.   Enlightened organizations have seen huge benefits by providing APIs and cultivating data marts across multiple internal data sources.   Some of the oportunities they have been able to create include:
– Crowdsourced solutions that give new insights into their business
– New applications, both PC and mobile based that serve their market or customers
– Attract new strategic partnerships around data sharing.  This opened a variety of new business opportunities to expand the data analytics capabilities of both companies by have data beyond each partners current operational data.  It also put those companies into a new level of partnership when insights where found and they could both respond to market opportunities in a joint venture.
– By making some data public,  it provides a new data source of who and how that data is being consumed.   The insights and analytics of the data consumption and how it was being used has also been of great strategic advantage to the providing companies to proactively engage the market that is consuming it by spotting the opportunities or disruptors early in the process.
– Consumption data itself can be a new revenue business for the data leaders and aggregators.   Its an interesting model as your selling consumption data and in turn track who is consuming the consumption data to add to your data pool.  ( Recursive in nature )
– Companies that lead in their industries in data sharing also tend to gain an advantage in setting up strategic relationships and ultimately become more of the broker of data in their industries.  As the broker of data, you become the aggregator and ultimately have a larger industry view of how and who is consuming not only your data,  but all the other data sources you are aggregating.
Public data leadership is similar to a land grab.   Pro-activity is the key.   Its never too early to lead,  but for those that are followers, it will always be too late.

FEDA / Harvard Cluster Studies Follow Up


I had a follow up to the Cluster Analysis kick off hosted at the UMN last September 2014.  Joining me on the call was the MN Director of innovation and we where exploring partnership opportunities around the collection of economic data in both the public data and innovation space.   My particular interest was in capturing addition data around innovation centers the full lifecycle of start-up maturation on a regional level.   Though partnerships with centers and through integration with public data sources  we could get a level deeper in the economic activity happening in the corporate and entrepreneurial areas.  By building on the standards already set forth in the cluster analysis we hope to define the next level of data in this area so that all regions could capture data consistently for analysis.   We are looking to develop analytics for the health and activity within clusters around the time, cost, and progress made by start-ups and corporate innovation initiatives.  The innovation centers provide a great base where much of this activity is happening and can be one of key sources of data for the overall model.   I’ve been working with economist and other data analytics specialists to develop economic data models to this end and identifying the public, private, and NGO data partnerships that could provide valuable in a integrated data capture strategy.