3 Lessons CMOs Take Away From IT's Flawed Approach to Big Data
There's no question that data analytics are playing an increasingly important role for marketing departments, but many marketers aren't getting what they need from the CIO. If IT can't deliver, CIOs should expect CMOs to go around them.
Tue, September 03, 2013
CIO — It's commonly accepted among marketers that data-driven marketing powered by big data analytics is the wave of the future. That has led Gartner to predict that by 2017, CMOs will spend more on IT than CIOs. Others have suggested that the CMO will become the CIO's biggest customer.
Just how that relationship will shake out depends on how CIOs approach the problem of big data and business intelligence (BI). Jennifer Zeszut, former CEO and co-founder of innovative social media monitoring specialist Scout Labs (acquired by Lithium Technologies in 2010), and current CEO and co-founder of Beckon, a software-as-a-service (SaaS) offering for gaining insight from marketing data, says most IT departments have a flawed approach to big data and BI that forces smart CMOs to seek alternatives.
"You keep giving me reports that in no way resemble what I think about my business, what I care about in my business," Zeszut says, speaking of IT from marketing's perspective. "Yes, you give me a million things, but I never look at them."
Zeszut says that she's been in countless meetings in which marketing has been enthusiastic about a new approach or offering until it becomes clear that IT will have to get involved.
"If something has to go through that bottleneck, then honestly, it's not worth doing," she says. "That's why there's a shadow IT organization growing up under marketing ops."
IT's 3 Flawed Approaches to Big Data and BI
Zeszut sums up IT's three flawed approaches to big data and BI as follows:
- IT tackles problems from the bottom up. She says IT's plan seems to be: Gather up a big pile of whatever data we can most easily get our hands on, wait for someone to ask a question, then query the database. Business intelligence is essentially an after-the-fact exploration of data.
- IT typically pulls in data that's easy to pull in, rather than pulling in what matters. "There's an old joke about a guy taking a nighttime walk who sees another person searching under a lamppost," Zeszut says. "The first guy asks, 'What are you looking for?' 'I lost my keys,' the searcher replies. 'Oh, you lost them right around here?' 'No, I lost them over there in the dark bushes, but the light is so much better here.'
That, Zeszut says, is what IT is doing when it pulls in only the easiest data. It's true that some of the data that matters most is easy to get (Salesforce data via an API, for instance), but most of it is hard to get. For instance, reams of agency data comes in PDF format. Then there's PR data and all the data and planning docs in PowerPoint and Excel that hold the keys to marketing performance calibration. That data isn't easy to access, so it's probably not coming in to IT's data warehouse any time soon.
- IT continues to recommend big, lengthy and expensive data infrastructure projects. When IT presents its plans, they usually conclude with something like, "And then in 2017 we roll out reporting &." Translation: We need to do years of infrastructure work, and after that you might be able to see something.