Cutting Through the Data Fog With Industry Collaboration and Connectivity

  By James Oates, U.K. Client Delivery Lead

Data. Data everywhere.

I’m borrowing from Charles Dickens here, but the vast expanse of data available to us can feel overwhelming at times, and the challenge of interpreting the data can feel a bit like wading through the foggy landscape he once described.

As data analysts, we are continually challenged to make the most of the data we have in our organizations and to find a route through to the insights that lay within.

A recent MRS conference on analytics highlighted the vast range of analytic approaches being adopted across the industry. Regardless of the approach we may take, what struck me most is our collective reliance on the ability to connect different data to add deeper analytic value. Whether it be traditional modelling approaches for example, linking weather data to the adoption of a brand, or the application of social data as a determinant of consumer behavior, at the core of all of this is the principle of connecting data together to create better analytic findings.

While the practice of data connectivity is not new, there is an important opportunity for us as data analysts to think about the concept of this connectivity in a different way. Moving away from standard one-off solutions toward continuous connectivity to provide a more seamless and “always-on” analytic approach. But this requires us to be open; open to new approaches, open to new ideas, open to new partners and open to outside solutions and data sets so that we can be focused on the greater good, and the best possible solutions for our clients.

My own experiences of this thinking came when working in the digital industry, where openness and collaborative thinking across agencies created new measurement approaches that are now adopted across the media industry. This collaborative approach was refreshing, and a departure from that of the traditional market research world.

Data sets can operate in isolation, that we know. But where data can connect, that’s where we can build greater value and deliver greater insights to help shape our business and that of our clients. An increasing number of organizations are now focused on adopting this way of working and specifically on how continuous data integration can help them automate to move quicker and to help differentiate themselves from the rest of the pack.

APIs are now just the start of the conversation as businesses, like ours at Nielsen, are investing in the development of the necessary connecting pipes to create networks that connect disparate internal data sets to other third-party data so we can better meet the analytic demands of today. Our focus is firmly on opening our ecosystem and making our data available to partners so together we can better meet the needs of clients today and in the future and we’re doing this in three ways. We’re opening up and integrating our internal data sets to make our services even more useful and relevant to our clients. We’re also opening up our data to our clients so they can integrate and connect it into their own data networks. Finally, we’re also making our data available to other businesses, start-ups and developers so together we can connect and collaborate on new and innovative applications for clients. It’s not always easy to be open, but we firmly believe that where there is nervousness, we need to dust off the NDAs to provide the cover for this new way of thinking and at worst we walk away but with the knowledge we have tried.

Data is everywhere. As our individual behaviors leave an ever-expanding data footprint, we are faced with the challenge of making sense of all of this data and extrapolating meaningful insights to drive performance. Our challenge is to think bigger about how we can work together in the spirit of openness and collaboration to advance the abilities of our industry—to cut through the fog and push the traditional boundaries of analytics. Connected data approaches lead to faster, smarter and more coordinated decision making. Given the complexity of the data pool available today, connectivity needs to be at the heart of any data strategy and simplification should be the guiding principle to achieving it.