HOW to define big data? At a meeting of the Organisation for Economic Co-operation and Development last week, about 150 delegates were asked to raise their hands if they had heard of the term—all had. How many felt comfortable giving a definition? Only about 10%. And these were government officials who will be called upon to devise policies on supporting or regulating big data.
The conference theme was ‘knowledge-based capital’. The good news is that the wise minds at the OECD can see there is something new and important taking place with the role of data in business and society, and they want to shape the intellectual agenda. The problem is that the civil servants and academics who flock to the meetings arent always as avant garde in their thinking. That is not to say the event wasnt useful—it was. But there is much more to play for.
Take the session on accounting for intangibles. Thomas Günther of the Technical University of Dresden spoke about the ridiculousness of accountancy rules for things like brands, in which ‘goodwill’ is treated as an expense but not an asset if it is developed internally; it only becomes an asset if it is acquired through a market transaction—even though it is the same thing, one day to the next. This begs the question of how to place a value on the data that firms hold. For instance, as much as one-third of Amazon’s sales are said to come from its recommendation engine: shouldn’t the vast pool of data that the company has on customers be considered an asset? But the idea went unaddressed.
Then there is intellectual property. It is a major impediment for big data. For example, data-mining techniques to put tens of thousands of research papers through a computer to spot patterns that may otherwise be missed (such as a drugs’ side-effects being eliminated in the presence of another drug) has shown promise. But copyright law means these sort of ‘meta mining’ studies require researchers to buy access to each article, just as if it were the 19th century and a pair of human eyes were to read it. Yet this and other issues weren’t raised. Instead, officials from national patent offices bellyached about things like the backlog and time it takes to examine a patent. Worthy matters, yes, but not cutting-edge ones.
Just as delegates thought they were talking about ‘knowledge-based capital’ as something with which they were familiar, the issues were more novel than they imagined. The uses of information are different with big data than in the past. They talk about one thing, but something else is happening. They are looking at the front door when it’s creeping through the side window.
The American economist Michael Mandel of the Progressive Policy Institute did an excellent job of putting a stake in the ground, defining big data as the idea that data is an economic input as well as an output (building on his paper on measuring the data-driven economy from last autumn). Jakob Haesler, the boss of Tinyclues, a Paris-based startup, raised a worry that big data may mean we lose a degree of transparency in why computers make the decisions they do. A recent article in Nature relied on a formula so complex that it couldnt be published in print. And as the data always change and algorithms adapt, it is not clear that the scientific standard of reproducibility may hold. Big data raises epistemological questions, he concluded.
Depressingly, a European delegate asked about possibly of taxing data as a way to fill national coffers. The idea is similar to the ‘bit tax’ that floated around Brussels in the 1990s. A big data tax smacks as retrograde—czarist!—that a nation might strangle a nascent area of economic growth by having the state enrich itself before its citizens can even reap the benefits.
The best comment of the day came from Andrew Wyckoff, the director of the division handling science and technology. The big data world relies on information, but we dont have any information about it with which to understand what is happening. How do we get the data?, he asked. As he explained: we have really good figures for R&D spending because companies break it out in their stock-market filings. And they do that because they get a tax credit. So what do we need to do to get the information from companies without having to pay them off?
‘To measure is to know,’ Lord Kelvin is said to have remarked. We need data about big data.
(Via Daily chart.)