From traditional research agency to data-driven tech company
By Kyle Findlay, director of Kantar Innovation’s Global Data Science Team.
The business world talks about terms such as ‘big data’ and ‘artificial intelligence (AI)’ as if they referred to some magical panacea but it takes more than just words to truly orient an organisation around data and “AI”.
I’ve faced many challenges bootstrapping a data science team within a large research organisation. Most of the challenges are around mindset. Traditional corporates just don’t think like agile startups (because we’re not). But, given this mindset difference, how does one go about embedding a business unit that no one had even heard of five years ago? How does one begin changing a decades-old paradigm of how business is conducted? And, how does one do this without stepping on the toes of all the established, siloed business functions that instead need to be brought along for the ride?
Such questions rear their inconvenient little heads when a large organisation makes the decision to invest in ‘big data’ and ‘AI’ to become a more data-driven organisation.
I refer to terms like ‘big data’ and ‘AI’ in quotations because I believe that these terms have become generic catch-all terms in the business world – everyone has a “big data-powered AI solution” these days, even if said “AI” is nothing more than a smart Excel macro. As a result, I feel that these terms often obfuscate more than enlighten and so I use them somewhat cynically.
Part of the reason why I am cynical about the use of these terms is because they have passed through the Trough of Disillusionment in Gartner’s Hype Cycle and their adoption is now business as usual. In becoming business as usual, we find a broader variety of professionals and business functions interacting and leveraging these areas.
It was my frustration at the contradiction between the gay abandon with which these buzzwords are bandied about and the awed distance that colleagues actually kept from them that prompted me to write the conference paper that this article is based on. The end result was a few observations on the way in which I think traditional organisations’ mindsets need to change in order to effectively leverage emerging data sources and tools.
The article is aimed at organisations who deal with data (i.e. most organisations, even if they don’t realise it) and are faced with the challenges posed by the “new data dispensation” (a catch-all phrase for data science, machine learning and artificial intelligence). These are based on our own experiences as a data science team in a large organisation. It describes some of the paradigm shifts we’ve had to make, and continue to make, to become a more data-driven organisation.
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