5 Steps to Dealing with Data Overload
It’s no secret that modern businesses are awash in data. IBM’s recent CMO study shows that over 70 percent of marketers say that the data explosion is what they feel the most unprepared for, and it’s no wonder: every day we create an additional 2.5 quintillion bytes of data—so much, that over 90 percent of the data in the world today has been created in just the last two years.
Because there’s so much data, we have a big data problem, primarily technical in nature: the volume of data is just so vast, that many of the existing tools for capturing, storing, managing, searching, sharing, analyzing, and reporting on this data are inadequate. But we also have a business problem in addition to this technical problem: big data will generate a vast number of insights to sift through. These insights need to be examined, evaluated for impact, prioritized, and implemented to achieve real business results. This is the key issue: how do we deal with this on a practical, day-to-day basis in a way that extracts the maximum business impact from all these data and insights?
We need a practical approach to generating and using insights in day-to-day business. I suggest the following five tips to help us get there:
1. Embrace it (learn to stop worrying and love the data)
Since it’s obvious big data is here to stay, we’ve got to embrace it. It will impact each and every business decision you make, so start thinking of ways to incorporate data-based learning in those decisions. Ask yourself: what data do I need to support the decision I’m making right now? Where does that exist in my organization? Who are the gatekeepers? Are there tools I can use myself to get access to this data and generate my own insights?
Although we don’t all have to be experts in data warehousing and multiple regression, we’ve all got to learn to become data manipulators and insight generators to a certain extent, and have to become quite comfortable with using data on a day-to-day basis in our work. So learn to stop worrying, and love the data!
2. Start with what you know (trust your feelings, Luke!)
This is probably going to make a lot of analysts out there cringe, but I’m going to say it anyway: you have to start with the base of knowledge and experience you already have in order to generate new insights from data. Many die-hard business analysts will say you need to trust the data and let the data tell you what’s important, but that ignores the important role of prior knowledge and experience in formulating insights. We need to put those priors front and center and test them out using the available data; they’ll either be proven or disproven, and either way, you’re on the path to generating new insights based on what you already know.
3. Take one step at a time (make incremental progress)
Rome wasn’t built in a day. A journey of a thousand miles begins with a single step. You get the idea: you and your organization are not going to snap your metaphorical fingers and find yourself in the ideal world of your analytic dreams, with all data throughout your organization mastered and at your fingertips for instant insight generation, no matter what business problem you may encounter. It takes time, and the only way to begin is, well, to begin. Start small: use the data you have in your organization to solve the problems you can solve with that data. Add data with your internal teams, driving it with business need. Identify gaps and plug them. And, above all, remember to keep moving and making incremental progress, every day.
4. Act now (do something)
With all this data and insights, we often forget that the whole point of insight generation is to have a real business impact. This means that you actually have to do something with the insights you generate—you can’t just pat yourself on the back after producing a masterful and insightful piece of analysis, you have to implement it and obtain a real-world business result (hopefully a positive one that will drop extra dollars to the bottom line). It’s good practice in general to have what the experts call a bias to action, a willingness to act and to do something rather than nothing. Make it a habit to perform at least one action as a result of any insights you generate, and soon you and your organization will be an insight-to-action machine.
5. Measure what you do and repeat (it’s a continuous process, not a one-off)
Finally, it’s important to realize that all of this insight-driven activity has to be measured, tracked, and reported on. How are you going to know whether your insights had validity, whether the actions based on those insights had the desired effect, and whether you should do more of the same or stop doing that and do something else instead? It’s crucial to realize that this is a continuous process you’re embarking on, one of constant data-gathering, insight-generation, directed and measured actions, and tracking to determine success or failure. It must be repeated, over and over again, as a core business function as important as generating invoices or keeping track of accounts receivable.
If you treat this as a one-off and not a continuous process, your efforts will be doomed to failure before you even start, because you will cut yourself off from the opportunity to use the learning generated from your actions to do better next time.
Tony Coretto is the co-founder and co-CEO of PNT Marketing Services, Inc., a database marketing consultancy. PNT specializes in helping companies grow their profitability through the strategic and tactical implementation of customer intelligence solutions. For more information on PNT, visit pntmarketingservices.com. He can be reached at firstname.lastname@example.org.