When Ad Age spoke with Chris Diener in January, he was in Singapore, thousands of miles away from his home office in Salt Lake City, meeting with one of the world's largest food producers, Mondelez International. The snack maker's analytics group from across the Asia Pacific region had gathered for an annual meeting, and "they invited us to be part of their meeting," said Mr. Diener, SVP analytics at marketing data consultancy AbsolutData.
"AbsolutData was really interesting to me because of its international nature," he explained. Mr. Diener joined AbsolutData only around four months ago, following two stints totaling more than eight years at marketing mix modeling and analytics outfit The Modellers, a firm he helped found in '98. The Modellers was acquired by Omnicom two years ago.A marketing science PhD, Mr. Diener also holds a law degree and MBA. His background enables him to straddle data analysis and business, a position many data-services firms find difficult to fill.
"I'm a relationship augmenter…. I work with all of the account-management folks to come into client relationships to help with confidence and solution building."Some who have worked in data analytics for as long as Mr. Diener take umbrage with the big-data concept, arguing big data has always been around. But he accepts the consensus that volume, velocity and variety of data available today have converged to create this new, rapidly growing level of information.
"Because of the volume we have the opportunity to find niches a lot more confidently," he said. "That takes a lot more volume processing to be able to see and react to it."
There may be a fourth "v" though, he suggested, noting that some are adding veracity to the list -- determining what information is meaningful and what isn't.
Ad Age: The data industry involves a lot of jargon. In your travels working with clients overseas, do you find anything especially difficult to express in languages other than English?
Mr. Diener: No matter what the country or language, jargon can get in the way of clear communication. But I have found that the jargon that matters, that has a purpose for getting things done, does not present a problem. This jargon carries across borders because of similar motivations and common information sources.
Ad Age: Gartner's Svetlana Sicular in January argued that "big data is at the peak of inflated expectations," marking a milestone in its maturation. What misconceptions around big data are contributing to what she called "the trough of disillusionment?"
Mr. Diener: As an attitude, just investing in the infrastructure will not ensure success nor produce the ROI justification that will be required once the dust settles. There is tremendous pressure on senior management to implement a big-data strategy and there are plenty of IT providers who bring with them many promises of success. But I see in this pattern something very similar to the CRM bubble of the 1990s. It burst on the back of many broken promises. Big-data investments must be planned with applications and their value clearly detailed. Then the required investment is "backed out" based on the specific applications planned.
This misperception probably has its root in the more dangerous one that says you need to make a big investment or move mountains in a radical way to "do" big data. It's dangerous in that it leads to unnecessary investment as detailed above, but alternatively it is dangerous because it keeps many from generating big-data value through measured investments and activities in the area. Big data can, and in many instances, should be approached in a measured, methodical, step-wise fashion.
Ad Age: A new report shows a significant increase in the amount of unique tracking tags and scripts on websites, many of them spawned by partners of site publishers, and sometimes partners of partners, and so on. Does the increase in the amount of data being collected and passed around have an impact on your work?
Mr. Diener: These tracking tags and scripts impact our work in two main ways. First, it does give us more data on more activities and touch points and it does increase the effectiveness of our analytics applications. However, it can only help if we can access the data and understand it well enough. So the second impact is to make our work more complex or require more effort or digging to make use of it. But that is a good problem to have because the more tagging and tracking we have the more effective we can be in CRM, attribution, and marketing mix. This means we can help our clients provide better services, more appealing experiences, and more appropriate communications. The other side of the cookie, that concerning privacy issues, is another issue and we advocate for full information and choice for consumers and a balanced consideration of business and privacy needs.
Ad Age: What's the biggest problem with data-science people as they navigate the world of marketing?
Mr. Diener: Data scientists come in many different varieties. The ones on the top of the game have mastered a solutions and benefits point of view and vocabulary with regard to their internal clients. They know how to persuade and help others to tell a persuasive story. The biggest obstacle a data scientist will face is to feel justified by numbers and science and to feel, however subtly, that they are playing in an "us vs. them" arena. It's easy to feel confident because of technical background or analytic acumen, but this confidence can lead to inadvertent or purposeful detrimental communications which can sour relationships.
Ad Age: Which educational fields of study and professional backgrounds do you think help develop the best data people?
Mr. Diener: This is a tricky topic, but there are some fundamental areas. You must have a solid grounding in statistics, data management, programming, machine learning, consumer behavior, social psychology, marketing, finance, operations, business and communications. Beyond these sorts of fundamentals it is very helpful to have a creative branch as well that shows curiosity, an ability to see whole systems, and the ability to make connections that are unexpected and elegant.
Ad Age: What's the coolest or strangest type of data set you've ever worked with and why?
Mr. Diener: Call me a mad scientist at heart, but I love to uncover subtle, unconscious tendencies that have far-reaching effects on how we perceive and react to the world around us. So I love a well-crafted psychological experiment. One of my favorite data sets is one that showed that mere familiarity actually drove us to feel truthfulness. Then there is the data set that I worked with on my daughter's science-fair project that showed if you very nicely wrap a cookie made with agave nectar instead of sugar people think it tastes just as good as if you had a regular sugar cookie, but it was poorly wrapped. Great stuff!
