Sep 26 2014   5:50PM GMT

Marketing wants a data analytics education. Will CIOs oblige?

Nicole Laskowski Nicole Laskowski Profile: Nicole Laskowski


It isn’t news that marketers are interested in analytics and big data. (Who hasn’t pointed to the Gartner prediction that by 2017, the CMO will spend more on IT than the CIO?) What’s surprising is that a data conversation at a marketing conference is starting to sound an awful lot like a data conversation at an IT conference — at least on the surface.

At last week’s FutureM, an event organized by MITX about the future of marketing, an entire track was devoted to data. Raja Rajamannar, CMO at MasterCard, walked attendees through marketing’s evolution — from reason to emotion to today’s current phase that he’s dubbed marketing 4.0, an iteration that depends not only on data and analytics, but also on how data connects consumers together.

And that wasn’t the only mention of IT infrastructure. The senior director of analytics at Wayfair, an online retailer based in Boston, stressed the importance of building a flexible infrastructure to keep an enterprise’s data options open. “Even if you change directions on infrastructure, you can’t analyze data you don’t have,” David Drollette said.

And Amit Phansalkar, chief data officer at MassMutual Financial Group in Springfield, Massachusetts, said one of his challenges is building a data-centric organization — and by organization, he was referring to the lines of business, not just the IT function data. Marketing, product development, sales all have to become data-centric.

Is it time to launch an in-company IT University?

One of the most surprisingly well attended data track sessions was called Turning Data into Insights. The crash course was given by Christopher Penn of SHIFT Communications, a public relations agency based in Brighton, Massachusetts.

Penn laid out an analytics framework he called marketing DAIS, which stands for data, analysis, insight and strategy. Data and analysis answers the questions of what happened; insight “goes beyond analysis” to answer why something happened; finally, strategy poses the question of what to do next. So far, so familiar, right? (Full disclosure: The entire process of leveraging data is composed of seven steps. Penn said to think of the four that make up DAIS like preparing a menu; the three additional steps — tactics, execution, measurement — are more like preparing the actual meal.)

A good chunk of Penn’s presentation focused on data analysis tools. He assumes, of course, the data is “good,” which means that it’s been carefully selected and is relevant to the question you’re trying to answer, it’s clean and it’s in the proper format. (How do you get good data? Penn breezed through that part, boiling it down to making good choices and buying tools.) Here are the three tools Penn recommended for data analysis:

  1. Visualization tools. Simple tools organize the data into rows and columns. As data becomes more complex, parsing rows and columns can be difficult. That’s where data visualizations come in. “Visualization is nothing but taking data and painting pictures with it,” he said. He recommended starting simple, with a spreadsheet, before jumping into expensive tools like Tableau.
  2. Derivatives. Visualizations are great but don’t always tell the full story. With derivatives “We want to see percent change,” he said. He provided marketers with this equation: new minus old divided by old. “When you do that, it takes really, really big numbers that are almost mind blowing and turns them into a much easier number to crunch,” he said. He also talked about second order derivatives to determine how fast change is happening.
  3. Moving averages. To dig a little deeper, Penn suggested calculating the average from a certain time period. “If you want to kick it up a notch,” he suggested a little trick he stole from the stock market: Compare a seven-day average to a 30 day average to find out what’s trending well and what isn’t.

The audience was riveted.

Message to CIOs

If marketers are willing to sit through a session on turning data into insights at a conference, it might be time to think about offering training sessions by your own IT staffs.

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  • cspenn
    I intentionally breezed past data quality for a couple of reasons. First, things like referential integrity and statistical validity would have consumed most of the time (heck, you can spend days on either one), and second, the folks in the room aren't at that level yet. Hopefully it will help broker new Friday beers between Marketing and IT. Thanks for the great writeup!
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