How will the Big Data solution you choose help you map progress and correct your course if you aren't making progress? In some cases, this may require visualization features. In others, this may require that the solution has APIs that will allow you to connect it to existing software suites, such as CRM.
While some business goals may be fairly straightforward, such as customer acquisition, others like customer "engagement" are more nebulous. Even so, you should keep those secondary, hard-to-measure benefits in mind because along the way you may find some proxy that can help you estimate things that are hard to quantify.
"One of my favorite examples of proxy variables is churn in the telecom business," Johnson said. EY was consulting with a mobile provider who found that they could predict with 80 percent accuracy which customers would leave within 15 days based on variables such as customer service calls. "The trouble is 15 days is not enough advanced notice to do anything about it."
Rather than obsessing about data that couldn't provide enough early warning, EY and the mobile provider began looking for a proxy that could help them expand their window of opportunity. They found that by assessing operational, rather than customer, data (dropped calls, 3G vs. 4G availability, data usage), they could get 60 days advanced notice.
"With a 60 day window, you have so many more options. You can do things like provide better QoS in the background for customers who may be thinking about leaving. With 15 days, the customer wouldn't even notice, and if they did, it would still be too late," he said.
Will the Big Data solution you're considering be flexible enough to shift to a very different set or type (i.e., structured versus unstructured) of data if the first data set can't deliver the results you seek?
Before buying, it's important to determine who will actually use the tool. If you have zero data scientists in house, you should probably limit yourself to Big Data as a service, or you should find analytics plug-ins for the software your team already uses and understands, such as from your marketing automation platform.
Many of the Big Data solutions hitting the market today are designed to abstract complexity, so pretty much any business unit leader can pose a questions and get actionable info from their data. Others are far more complex and will necessitate that you have Hadoop experts in-house or will only reveal their Big Data insights to trained data scientists.
Whatever the case, you should test drive your top two or three solutions to make sure that the usability actually matches your end users' skills.
Many early Big Data projects will be more exploratory in nature than anything. Think of it as data wildcatting. There's nothing wrong with exploration, per se, but while you're exploring your data for possible areas of value, keep track of how you decided which data sets to explore, how you extracted value, how the data drove actions, and how you measured what you did.
After a few projects stack up, you should be able to draw lessons from those projects and, perhaps, define processes. Better yet, with the right Big Data platform, you may be able to automate some of those processes along the way.
As you assess tools, ask how hard it will be to turn your progress into a process. Are there features that will help you identify processes you can automate?
And I'll offer one final piece of advice: be patient -- but not too patient. It can take time to gain the insights you seek, but even for big projects that could take years to complete, if you break them down and have goals for much shorter timeframes, your likelihood of following through will be much higher.
It's not wrong to have a big vision that will take time, but if you can reach goals or milestones along the way, you'll get more buy-in from other parts of your organization.
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