Big data…it’s all the rage.
Why wouldn’t it be? It’s big. It offers promises of lots of data. Who wouldn’t want to know more about their customers, their employees and their operations?
With big data, we’ll be able to know everything about our business, develop new innovative ideas and serve our customers in ways that we never imagined. Mainstream emergence of social media has certainly created an environment where consumers are willing to share every aspect of their daily activities, so we should be able to filter out the noise and get to the juicy nuggets. Right?
While the promises of big data are almost too good to be true, there is a dark underside that needs to be understood. The fact that big data is, well…BIG and it offers us…ermmm…DATA.
If you’re scratching your head, thinking I’m a loon, or ready to click the close button on the browser hang with me for just one more second. I promise I’ll explain.
You see, a lot of folks out there confuse data with information. Let’s be clear, they are NOT the same thing. Data is raw. It’s unorganized. And, honestly…it’s not worth much when it comes to improving your business. Information, on the other hand, is what data becomes when you organize it, analyze it and give it some sense of structure.
And from information, we gain access to some of the most valuable opportunities available to us as business professionals. Insights.
Insights are what help us understand what our customers want. They help us learn whether our services are working or whether our products are succeeding. Insights help us identify new business channels and find ways to serve our customers in a more meaningful (and often more efficient) way.
Insights help us identify new business channels and find ways to serve our customers in a more meaningful (and often more efficient) way.
So, when developing a big data strategy, the questions that you should be thinking about include:
- How can we gather data from the appropriate sources?
- How do we organize that data and convert it into information?
- How do we glean insights from that information that we can act on to improve our business or serve our customers?
The answers to 1 and 2 are usually the traps that suck businesses into the never-ending spiral of analysis-paralysis. We sometimes become data fiends, trying to locate every source that might have information that relates to our customers, our competitors, our industry and our business. We gobble up customer data like Pac-Man, under the misconception that if we have more data, we’ll be able to succeed. We spend time and money on sourcing data, buy servers with terabytes of drive space where we can organize it into information…and then what? We live as data hoarders and watch as the information becomes stale and useless.
Why? Because we miss out on the fact that #3 is the most critical element of the process. Without insights, all of the time and money spent on data and information was wasted.
Why is it, then, that business have such a hard time with step 3? Because it’s a step that requires significant human intervention and in these times of business automation and budget cuts, budgets sometimes simply do not afford another human on the project.
That’s right. I said it. If we remove the human analyst from our big data project, we might as well turn off the lights and close down the big data project.
Humans understand speech patterns. Software can, too…but not very well. Trust me on this, I work with very advanced social media listening tools that still think that the message “I love your product…NOT!” reflects a positive sentiment because the word “love” was close to the word “product”. Software also can’t truly understand your customers. It doesn’t know when they are sick or celebrating a life event. It doesn’t know their kids’ names and it certainly can’t see through the sarcastic undertones of a message to read between the lines and understand what a customer wants
I’ll say it again.
If we remove the human analyst from our big data project, we might as well turn off the lights and close down the big data project.
So, when thinking about your organizational big data project, do you have the right resources? Software, data partnerships, servers, terabyte drives, data cubes…all good things. But don’t forget about the analyst who is going to disect that data and help you understand it. Care for them, feed them, give them M&Ms and lots of coffee.
With all the data you’re about to throw at them, they’re going to need it.