There have been murmurings that we are now in the “trough of disillusionment” of big data, the hype around it having surpassed the reality of what it can deliver. Gartner suggested that the “gravitational pull of big data is now so strong that even people who haven’t a clue as to what it’s all about report that they’re running big data projects.” Indeed, their research with business decision makers suggests that organisations are struggling to get value from big data.

Data scientists were meant to be the answer to this issue. Indeed, Hal Varian, Chief Economist at Google famously joked that “The sexy job in the next 10 years will be statisticians.” He was clearly right as we are now used to hearing that data scientists are the key to unlocking the value of big data. This has created a huge market for people with these skills. US recruitment agency, Glassdoor, report that the average salary for a data scientist is $118,709 versus $64,537 for a skilled programmer. And a McKinsey study predicts that by 2018, the United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise and a 1.5 million shortage of managers with the skills to understand and make decisions based on analysis of big data. It’s no wonder that companies are keen to employ data scientists when, for example, a retailer using big data can reportedly increase their margin by more than 60%. However, is it really this simple? Can data scientists actually justify earning their salaries when brands seem to be struggling to realize the promise of big data?

Perhaps we are expecting too much of data scientists. May be we are investing too much in a relatively small number of individuals rather than thinking about how we can design organisations to help us get the most from data assets. The focus on the data scientist often implies a centralized approach to analytics and decision making; we implicitly assume that a small team of highly skilled individuals can meet the needs of the organisation as a whole.

This theme of centralized vs. decentralized decision-making is one that has long been debated in the management literature. For many organisations a centralized structure helps maintain control over a vast international operation, plus ensures consistency of customer experience. Others, meanwhile, may give managers at a local level decision-making power particularly when it comes to tactical needs. But the issue urgently needs revisiting in the context of big data as the way in which organisations manage themselves around data may well be a key factor for brands in realizing the value of their data assets.

Economist and philosopher Friedrich Hayek took the view that organisations should consider the purpose of the information itself. Centralized decision-making can be more cost-effective and co-ordinated, he believed, but decentralization can add speed and local information that proves more valuable, even if the bigger picture is less clear. He argued that organisations thought too highly of centralized knowledge, while ignoring ‘knowledge of the particular circumstances of time and place’. But it is only relatively recently that economists are starting to accumulate data that allows them to gauge how successful organisations organize themselves.

One such exercise reported by Tim Harford was carried out by Harvard Professor Julie Wulf and the former chief economist of the International Monetary Fund, Raghuram Rajan. They reviewed the workings of large US organisations over fifteen years from the mid-80s. What they found was successful companies were often associated with a move towards decentralisation, often driven by globalisation and the need to react promptly to a diverse and swiftly-moving range of markets, particularly at a local level. Their research indicated that decentralisation pays.

And technological advancement often goes hand-in-hand with decentralization. Data analytics is starting to filter down to the department layer, where executives are increasingly eager to trawl through the mass of information on offer. Cloud computing, meanwhile, means that line managers no longer rely on IT teams to deploy computer resources. They can do it themselves, in just minutes. The decentralization trend is now impacting on technology spending. According to Gartner, chief marketing officers have been given the same purchasing power in this area as IT managers and, as their spending rises, so that of data centre managers is falling.

Tim Harford makes a strong case for the way in which this decentralization is important given that the environment in which we operate is so unpredictable. Innovation typically comes, he argues from a “swirling mix of ideas not from isolated minds.” And he cites Jane Jacobs, writer on urban planning– who suggested we find innovation in cities rather than on the Pacific islands. But this approach is not necessarily always adopted. For example, research by academics Donald Marchand and Joe Peppard discovered that there was still a tendency for brands to approach big data projects the same way they would existing IT projects: i.e. using centralized IT specialists with a focus on building and deploying technology on time, to plan, and within budget.

The problem with a centralized ‘IT-style’ approach is that it ignores the human side of the process of considering how people create and use information i.e. how do people actually deliver value from data assets. Marchand and Peppard suggest (among other recommendations) that those who need to be able to create meaning from data should be at the heart of any initiative.

As ever then, the real value from data comes from asking the right questions of the data. And the right questions to ask only emerge if you are close enough to the business to see them. Are data scientists earning their salary? In my view they are a necessary but not sufficient part of the solution; brands need to be making greater investment in working with a greater range of users to help them ask questions of the data. Which probably means that data scientists’ salaries will need to take a hit in the process.

Colin Strong is a consumer researcher focusing on the way in which the data economy is reshaping our personal, social and cultural dynamics. He is editor of the book Humanizing Big Data, released by Kogan Page.