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"Evan Hanlon is the Director of Audience Strategy at Xaxis.\r\n\r\n\r\n\r\nThere's a certain type of person I've learned not to trust: someone who insists that they can make things simple. I recognize how problematic that is for me, a person whose job it is to in fact make things simple. But I suppose it's because I dive into what lurks behind the design studio slides and know just how deep the rabbit hole goes.\r\n\r\nIt\u2019s ironic that almost every eye-roll inducing invocation of the phrase \u201Cbig data\u201D is accompanied by a sales pitch for a tool that makes it easy. Data is wielded as a magic wand, a cure-all for the new information age, wedged firmly between dotcom bubble one and dotcom bubble two.\r\n\r\nIt's then boxed up like so many other solutions, placed on a well-manicured shelf, and then promptly flies off it and into the hands of brand marketers, their agencies, and dozens of third parties in between.\r\n\r\nThe main flaw in this system, however, is the reductio ad absurdum that something as chaotic, unwieldy, and often times unstructured as large, non-intuitive data can be turned into something easy. It can\u2019t. But that\u2019s not necessarily a bad thing.\r\n\r\nThere\u2019s no simple solution for winning at chess. However, by embracing the complexity that comes with today\u2019s intricate systems that knit together the programmatic audience buying landscape, we actually arm ourselves with more tools.\r\nWhere complex data matter\r\n\r\n\r\nTake predictive modeling. Instead of targeting just one or two obvious signals (think buying a pre-built segment of ice cream intenders when you\u2019re looking for people who want ice cream) a competent audience buyer looks at hundreds or thousands of non-intuitive, anonymous inputs to create models that are scored against a certain event (in this case, ice cream purchases).\r\n\r\nGym membership status, car ownership, the last time someone searched for flights, level of social media sharing, propensity to buy designer shoes, enthusiasm for team sports; these are all data points that at face value have no relation to ice cream. But with high enough volume, and when considered in the right combination, these things become statistically important.\r\n\r\nThe face value of purchase and intent signals is only the beginning; it\u2019s the complex qualifications on the rest of the world that provide the power.\r\nElements of complexity\r\nSo complexity, or more specifically, the ability to successfully harness complexity in the service of brand goals, is good. For marketers in the programmatic environment the question then becomes whether there are simple and effective ways to evaluate these complex systems.\r\n\r\nAs a starting point for evaluating that shiny black box, we\u2019ve assembled a short checklist of elements to consider.\r\n\r\nScale &amp;amp;amp; Speed\r\n\r\nProgrammatic is based around the law of large numbers. The more information and the more information sources immediately on hand allow for better precision when it comes to identifying the best audiences for any particular marketing message.\r\n\r\nProgrammatic is a highly probabilistic endeavor. Bumping up the number of datasets you\u2019re able to crunch in context with one another yields exponential improvements and allows ads to be delivered more quickly and with more confidence.\r\n\r\nScience &amp;amp;amp; Engineering\r\n\r\nSimply throwing a bunch of data into a bucket and stirring, doesn\u2019t automatically equal quality outcomes. Rather, the results will only be as good as the system\u2019s ability to make sense of all the data feeding into it.\r\n\r\nThe best systems have able teams of data scientists and engineers steering the ship and are constantly self-improving. The more decisions that are made, the more results we have, which, in turn, leads to clearer insight and ever improving decision making going forward.\r\n\r\nOpen Architecture &amp;amp;amp; Sequential Capabilities\r\n\r\nNothing exists in a vacuum, least of all digital media. As a result, you need to be able to ingest, integrate, and activate against data flows from every available source, and more.\r\n\r\nFor instance, you know how your mobile ads perform in context to display, online video, etc. That allows brands to create ad sequences to relevant audiences at scale that deliver better performance than single channel ads evaluated independently of the rest of the digital spend.\r\n\r\nIf you can\u2019t create sequential buys or measure various ad channels in context, you\u2019re getting a blinkered view of total performance and, consequently, are similarly limited in overall campaign effectiveness. And nothing is more important today than the biggest picture possible.\r\n\r\n\r\n\r\nThe last factor of a truly killer programmatic platform, however, is the human part of the equation. All the processing power in the world means little without a capable human hand steering the ship.\r\n\r\nEven though technology extends our capacity to understand complex data sets, we should never forget that people are not data points. The course of history has shown that irrationality is one of our specialties, which means there\u2019s no one better than a human to think like a human.\r\n\r\nSo the next time you run across someone bearing tidings of simplicity, should you run the other way? Absolutely not \u2013 after all it might be me. Instead, go in with a clear understanding that simple simply means the shiny logo capping what should in fact be a complex system.\r\n\r\nBig data is inherently intricate. While \u201Cmaking it easy\u201D is, of course, the goal of all the players in the space \u2013 when selling to non-players \u2013 the better your understanding of what exactly is being made easy, the more confidence you can have that your \u201Csimple\u201D solution is amply delivering on its big data promise."^^<http://www.w3.org/2001/XMLSchema#string>
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