The Obama administration has apparently fallen in love with Big Data (especially yours) with its tracking of mountains of phone calls and Web data. But for business customers, the Big Data buzzword might actually slow sales.
Twice in one week I’ve heard customers or providers complain that Big Data is not what it’s cracked up to be. Meanwhile, market researcher Gartner reported that confusion over the terms “analytics,” “BI” (business intelligence) and “Big Data” chopped growth in BI software sales from 17 percent in 2011 to seven percent in 2012.
Complaint #1 came from an attendee at the 2013 MIT CIO Symposium. Analyzing millions of patient records to assess someone’s chances of coming down with a disease was fine, he said. But patients don’t have the time or skill to understand a complex chart with a pile of medical jargon, he said. They just want to know their chances of getting sick.
Complaint #2 came from a post on the B2B Marketing blog. In it, Pamela Bath, who herself does “Big Data” analysis for marketers. She described how she recently took two cars to different dealers owned by the same chain, and got 13 voice mails asking if she was satisfied with their work. If Big Data and customer relationship management are so good, she said, why didn’t the dealership understand she was one customer and just call her once?
(For the record, “Big Data” means analyzing a large volume and variety of data coming in at high velocity. “Business intelligence” refers to the software and processes to analyze the data and present reports to users. “Analysis” means…all of the above, and more. See why everyone’s confused?)
Lesson One: Focus on the End User
I do a lot of writing for clients in the “Big Data” and it gets very techie very quickly. That’s because it’s a new area coping with huge technical challenges. Still, the business conversation around “Big Data” can quickly degenerate into which Hadoop distribution to use, horizontal vs. vertical sharding and extraction layers vs. integration layers.
To keep business buyers interested in your “Big Data” product or service, explain who might use the analysis and how the results will look to them. Remember the “dashboards” (right) from the executive information system days with red, yellow and green lights showing what was going right and what was going wrong? The less technical the end user, the closer we should come to that simplicity.
And describe whether that end user is, for example, a sales manager facing an end-of-month quota, a pharmaceutical researcher looking for patterns in gene expression or a mid-80s diabetes patient with bad eyesight hoping to avoid yet another hospitalization. Each requires a different level of complexity, different flexibility in drilling into the data, and even different colors and type sizes.
You could argue this reporting format question is not specific to Big Data and you’d be right. But Big Data gives you more ways to waste time with a flood of meaningless analysis, and gives vendors more temptation to get caught up in speeds and feeds rather than business cases. It’s our business as marketers to keep the conversation on track.
Lesson Two: Define the Business Process
You’re also not selling Big Data right if you’re not explaining where and how it fits into all the processes that make money for the business.
In Pamela Bath’s car dealership run-in, she complained marketers weren’t making the common-sense connections among their marketing channels that would tell them she’s one person, not two. The dealership’s CRM system should have identified her as a single customer, she argued, while their call log system “should have kicked in to ensure dealerships don’t end up overzealously quizzing their clients.
“As marketers,” she said, “We need to focus more on the ‘little data’ that we already hold to get a clear transactional and demographic understanding of our customers, the permissions they have given us and their channel choice.”
Therefore, questions to address in your marketing material include:
- “Which business groups are asking for this Big Data analysis, and why?
- How will their analysis fit with that done by other units within the business?
- Who decides what questions we’re asking and which results would be most relevant to the business?”
- “Does the information we gather come from all the systems that are most relevant to the business?”
- And (with an eye to government spying headlines) have we really thought through the privacy concerns?
Again, you could argue these questions could apply to generic data analysis or to any business function. But again, Big Data is so new and so…well, big, it gives you more room to hang yourself.
Yes, highlight technical features like your stateful failover, self-healing clustering and automated compression. But the decision makers who write the checks want to know who will use the data analysis and how Big Data helps the business. Answer those questions well, and they’ll think of you first when they have a technical need.
Is Big Data confusion turning off your prospects?
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