Data is not the new oil...
  • William Beresford

Data is not the new oil...


In his latest article from the World Economic Forum, Adam Schlosser writes eloquently on why data is not the new oil. He raises valid points for Government that apply equally to business and help explain why many are getting their investment in data so wrong.

(“Here's why data is not the new oil.” Adam Schlosser, World Economic Forum Jan. 23, 2018, 10:59 PM http://read.bi/2rGnAc2)

He reminds us that whilst both generate value the parallels stop there and gives a number of examples…

· Whilst there is a fixed amount of oil on the planet, the growth in data is exponential and only set to increase

· The value of oil comes through its scarcity and the difficulty in extracting it, however its increasingly easy to produce massive amounts of data

· Data can be re-used, though certainly up until now oil is largely a single use commodity

· The value of oil accumulates with the more you have, whilst the value of the data accumulates via the insights that you can create through it Adam tells us that unfortunately as a result of everyone treating data as the new oil in a quite literal sense, we are seeing Governments hoarding data in silos behind locked borders. He argues for greater movement and freedom of data across borders and the current approach doesn’t just result in economic detriment but also the broader social good. He uses the power of data in pharmaceuticals to find cures for diseases such as cancer as a worthy example.

I agree with him.

We see the same parallels in the business world, where the herd mentality seems to be just as strong. Because of business treating data like oil, they too have wrapped themselves up in knots and are not just missing out on huge opportunities, but wasting unfathomable amounts of valuable investment resource in systems, tools and people frantically trying to get value from their data. So much so, I think we are may now be years away from many businesses being able to compete effectively in a digital age. They now need to go through the painful process of unwrapping themselves from the immediate past without losing face.

So, what happened? I think it went broadly along the following lines starting back in about 1998/9, about the time I started working in the industry.

Well intentioned, but unfortunately ill-informed management had heard about some big data success stories (Harrahs, Tescos etc), jumped on the band wagon and mandated the business to invest in storing and processing this new oil with shiny new data warehouses, expensive new tool kits and large insight teams.

They didn’t want to admit that they didn’t quite understand it all, weren’t entirely clear what they were going to do with it and found it mildly threatening. As such they delegated the strategy to a bunch of consultants who were green themselves and focused on what they thought was the job in hand – turning the data into insight right? The equivalent of processing in the oil value chain.

This led to the rapid rise in numbers and salaries in the data processing side of the value chain in the form of Business Intelligence teams and very expensive analytical resources called Insight teams. These people where all new to the sector (data was after all a new phenomenon, so there was an element of making it up as we went along) all needed to buy lots of shiny new equipment to store, investigate, analyse their data and keep looking busy with their earnest endeavours to transform the business. And so it went on, as a whole new industry and profession was created, quite literally from nothing.

Meanwhile more data was being created, budgets got bigger and bigger and more and more people wanted to jump in. Who doesn’t remember the Hal Varian quote “the sexy job in the next ten years will be statisticians”?

Silos appeared, data hoarding became commonplace and so much was being spent on new systems and teams that now just had to prove their value or admit defeat. They stopped focusing on the customer, both external and internal, and became introspective and focused on the data itself.

Innovation and new ideas springing up from around the business got drowned out and pushed into dark corners so they didn’t show anyone up creating a new breed of cottage data industry within companies. Analysis was now coming from every direction all adding more and more expense and complexity to the value chain of this valuable new commodity. All business got was analysis paralysis and a complete breakdown in trust where data was concerned because no one could tell you what the correct number was.

Why? A bit of software here, a few people there, it was a comparatively easy space to get into. A low cost of entry. But over time that apparent ease of entry has meant that businesses have just carried on adding and adding to it, trying to fix it and find that elixir.

Unlike oil, the set-up costs to gain entry into this data market didn’t seem to be all that high, so everybody jumped onboard. The high fixed costs of processing oil, converting it into new products and its distribution limited the amount of players who could get into the market and ultimately held their focus on the returns need for it to pay off. Perhaps a more suitable analogy would be the gold rush of the 1850’s?

Adams parallels are spot on and they are as relevant to Business as Government. We need to see a step change in attitudes and approaches to data if it is to bear fruit. Oil succeeded because it met the needs of its customers. The same goes for data in business – it has to focus on the pointy end which means directly enhancing the customers experience or product, or be actively supporting and enhancing the effectiveness of the frontline. It’s time for business to move away from a focus on building its own processing plants in the shape of BI and Insight teams, and focus on distribution.

How? Data Automation and Artificial Intelligence. You don’t need people behind closed doors processing and agonising over your data anymore, let technology do the lifting for you. Business needs people out front putting the data to work with their customers and their frontline.

#puttingdatatowork

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