There are three types of company in the big-data value chain: the companies who collect the data, data-analytics companies, and data-ideas companies. This new ‘organisational landscape’ will change the power-relations between businesses enormously, at least according to Viktor Mayer-Schonberger and Kenneth Cukier (2017) in ‘Big Data’: The Essential Guide to Life and Learning in the Age of Insight;.
‘Pure’ data companies are those which have the data, or at least access to it, but not necessarily have the right skills to extract the value from the data. A good example of such a company is Twitter, which has masses of data but licences it out to independent firms to use.
Data analytics companies are those with the statistical, programming, and communication skills necessary to mining insights from data – Teradata is a good exmaple of such a company.
Finally there are those companies with the ‘big-data mindset’ whose founders and employees have unique ideas about how to unlock and combine data to find new forms of value – for example, Pete Warden, the co-founder of Jetpac, which makes travel recommendations based on the photos users upload to the site.
Data analytics has recently been touted as being in the ‘prime position’ in the big-data value chain: there has been a lot of recent talk of the shortage of ‘data scientists’ in the age of ever increasing amount of data…. The McKinsey Global Institute has talked about this for example, and Google’s chief economist Hal Varian famously called statistician the ‘sexist job around’.
We have been given the impression that we are wallowing in data, but lack sufficient people with the skills to mine this data.
Cukier, however, thinks such claims are exaggerated because it is likely that this skills gap will close. Interestingly, in a recent talk on big data science, this view also seemed to be the consensus.
He predicts that what is more likely to happen is that firms controlling access to the data will start to charge more for it, and big data innovators will be be where the real money is…
Hyrbid Data Companies
Companies such as Google and Amazon stretch across all three links in the data value chain. Google collects data like search-query typos, uses it to create a spell-checker and employs people in-house to do the analytics. Such vertical integration is no doubt precisely why Google is today one of the world’s largest companies.
The New Data Intermediaries
Cukier also predicts that there are certain business sectors which will benefit from giving their data to third parties, because keeping it in-house will not be as beneficial to them as sharing their data and combining it with others – third parties are needed to facilitate trust – for example, travel firms will benefit from such an arrangement, not to mention the banking and finance sectors – where more data is better.
The Demise of the Expert
Cukier also predicts that big data analytics will see specialists in different fields being replaced with those with data-science skills able to manage whatever field based on data. He argues that ‘mathematics, statistics, perhaps with a sprinkling of programming and network science, will be as foundational to the modern workplace as numeracy was a century ago and literacy before that’.
Big Winners, Medium Sized Losers..
Large data companies such as Google and Amazon will continue to soar, but big data presents a challenge to the victors of small-world data such as Walmart, Nestle, Boeing…. How these will adapt remains to be seen.
There are, of course, opportunities for ‘smart and nimble start-ups’, but also individuals might start to sell their own data, possibly through new third party firms.