Sponsor I n 2008, Chris Anderson, editor-in-chief of Wired, and the populariser of the long tail model of marketing that captured the imagination of a million start-ups, turned his attention to what he called the petabyte age. This would represent, he wrote, the end of theory. It calls for an entirely different approach, one that requires us to lose the tether of data as something that can be visualised in its totality. It forces us to view data mathematically first and establish a context for it later Googles founding philosophy is that we dont know why this page is better than that one: if the statistics of incoming links say it is, thats good enough. No semantic or causal analysis is required, he argued. Andersons hypothesis was that models of the world, and therefore the people who create those models, would be redundant in the decision-making process if only we had enough data. It was a powerful argument in the early days of big data politicians, policy-makers and marketers no longer needed to know why, because knowing was enough. Forget taxonomy, ontology and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves. Only, they didnt. In the decade since, market researchers discovered the limits of the petabyte age, factories, but to nuance decision-making. Paul Twite, sometimes by the costly misstep of throwing petabytes managing director, MENA, at Toluna, has seen at first of data at a problem. Rather than ending theory, most hand the evolution from no need for automation, to a attempts to remove the human from decision-making sudden rush to automate, to an accommodation of the have pointed out exactly why we are still, in many two. Toluna innovated in automated quant cases, essential. research: We launched QuickSurveys in This isnt the first time that inflated 2007, Twite recalls. And, to be honest, expectations of technology have been cut at that time no-one in the market cared down to size. In 1933, the Chicago Worlds The most effective because brand owners didnt need Fair had confidently promised that science uses of technology information back that quickly. Market finds, industry applies, man conforms, an are coming from research agencies could spend three apparently optimistic message about work innovators who months making a report and putting a at the time that, with hindsight, seems not understand that bow on it, because manufacturing cycles just misguided, but a bit creepy. In the minds and machines were nine months long. century since, we have consistently are complements When the length of those cycles shrank assumed that machines are replacements rather than dramatically, there was a sudden need for for the power of our minds. There is substitutes rapid responses, which Toluna could evidence in market research, however, that provide. Automation removed a lot of the most effective uses of technology are time from processes. You didnt need to coming from innovators who understand brief a corporate agency to brief an agency in the field that minds and machines are more effective when to collect data that was entered into a computer and regarded as complements, rather than substitutes. analysed, and we were ahead of the market. But Part of the reason for this complementary approach automation had also outpaced good decisions in some is that we are no longer using technology to retool 26