How big data can result in bad data; ‘A fool with a tool is still a fool.”

How big data can result in bad data

July 23, 2013 – 8:13AM

Drew Turney

Stacks of information is just yada yada yada until it’s analysed properly. A couple of years ago, ratings agency Standard & Poor’s downgraded US debt. Not because of the state of the economy, but because of an error in its original calculations – a mere $US2.1 trillion. Nate Silver, the poster child for analytic predictions, told a recent conference that the financial crisis was as much about bad modelling as greed. The ratings agencies, he said, based assumptions on past mortgages, not the number of people who would default. Welcome to the world of bad data, something that’s caught on in Australia, too. GS1, the agency responsible for barcoding and product identification systems, recently released a report that found bad data will cost Australian grocery retailers $675 million in lost sales over the next five years, and that 65 per cent of ”data misalignment” problems led to lost sales.Bad data mostly falls into two categories. First is the ”rubbish in, rubbish out” phenomenon of starting with the wrong data set because of false records or simply looking at the wrong information. For example, your customers’ incomes aren’t really related to their ages – and may even corrupt the analysis.

”It’s crucial big data efforts only consider the right set of data points and that they’re clean,” says David Bernstein, vice-president of eQuest’s big data division.

In one example, NBC used test audiences but paid for it when many in the audience ranked successful shows such as Seinfeld poorly, and cheap copycats better. Eventually, the marketers discovered people were only responding to familiarity, not quality.

In fields such as health or the military, bad input can be tragic. In 2011, a US drone spotted a group of Afghan villagers and attacked, killing all 23. Concerned with protecting US troops nearby, the operators were so flooded with intelligence that someone overlooked the fact that because the gathering contained women and children it was almost certainly civilians.

We even have bad data in fiction. In Arthur C. Clarke’s 2001: A Space Odyssey, the ship’s artificial intelligence, HAL 9000, is programmed with human-like responses. When secret orders it can’t reveal send it into a feedback loop of paranoia, it solves the logic problem by killing the crew.

The second and more common type of bad data is poor interpretation, often because of a lack of context. LatentView Analytics chief executive Venkat Viswanathan uses the analogy of noise-cancelling headphones: ”Maybe you can hear glass breaking outside while the music’s playing. Maybe it’s important like when there are small children under your supervision, but not if you’re in a restaurant where breaking glass isn’t a big deal.”

BloomReach’s head of marketing Joelle Kaufman says data interpretation is all about intuition and control. ”Machine-generated data can be useful, but without the human element of intuition and control it can be downright offensive. We’ve all been the victim of poorly targeted data-driven marketing – I recently purchased a dryer and have been stalked by dryers on the web ever since. I only need one.”

Omer Trajman, vice-president of field operations at big data applications company WibiData, agrees the human element is what’s most important. When there’s a bad reading, he says it’s us, not the data, to blame. ”There’s no such thing as bad data,” he says, ”just bad models. Data analysts just need to extract the right data at the right time, then act on the insight in a timely manner.”

Some industries seem to be taking pause, and might even have been burnt. Kaufman cites a Duke University Business School study that says marketing projects using analytics to drive decisions decreased from 37 per cent in February 2012 to 30 per cent in February 2013.

But eQuest’s Bernstein believes the smartest executives and ICT managers are learning that the resources to interpret are as important as the mass of data. ”Big data isn’t the be-all and end-all you just switch on and wait for amazing answers. Data itself – no matter how big – has little value unless it can drive quicker, more effective decisions,” he says. ”As famed software architect Grady Booch said, ‘A fool with a tool is still a fool.”’