Data Driven Innovation

The OECD published a report, Exploring Data-Driven Innovation as a New Source of Growth in 18 June 2013. The report highlights key areas where big data and data analytics can enhance competitive advantage and inform policymaking. Data driven innovation can be understood as the use of data as a resource:

for enabling the development of new industries, processes and products. (p.7)

New technologies and pervasive computing are principal drivers in the generation and use of data. The scale is immense - 1,700 Facebook updates every second, 5,700 tweets every second and consumers, according to Mckinsey are sharing more information across multiple venues and platforms.What struck me about the OECD report was the observation regarding the task that lies ahead for industry, which is to:

analyse a variety of mostly unstructured data sets from sources as diverse as web logs, social media, mobile communications, sensors and financial transactions. This requires the capability to link data sets; this can be essential as information is highly context-dependent and may not be of value out of the right context. It also requires the capability to extract information from unstructured data, i.e. data that lack a predefined (explicit or implicit) model (p.12).

From an innovation perspective, policymakers may have to balance innovation opportunities with the rules on privacy and intellectual property.Jess Hemerly, wrote an article Public Policy Considerations for Data-Driven Innovation in which she urges policymakers to:

to establish a flexible, forward-looking policy and reshape existing laws for the rapid pace of technological change.(p.27) She has a point. However, the market model that defines economic, cultural and legal interactions will need to be revised.

As Townsend in Smart Cities: Big Data, Civic Hackers, And the Quest for a New Utopia reminds us, we should not have not have to subsidise the corporate vision for leveraging big data (p.12). There is a separate point that needs to augment discussions on data driven innovation and that is innovation risk. Innovation brings with it not only opportunities but risks. Big data introduces a complexity that needs to be better understood. Innovation risk, as Robert Merton, suggests in his article, Innovation Risk: How to Make Smarter Decisions that innovations:

are always likely to have unintended consequences, and models are by their very nature incomplete representations of complex reality.(p.56)

Can we leave machines and algorithms to make these decisions? How do we bridge the Human-Machine interface, is a question that I will be exploring in subsequent posts under the heading Robots, Innovation Risk and Tort Liability.