Your Data Is Useless
You may have heard the phrase “A person is smart, but people overall are stupid.” This phrase is intended to demonstrate that, as individuals, people can provide brilliant insight, but as the number of opinions grows and insight ultimately becomes lost to rhetoric and groupthink, stupidity eventually ensues.
The same goes for your data.
Big data and data analytics are all the rage. Organizations of all shapes and sizes are continuing to capture increasing quantities and varieties of data all with an eye toward improving various aspects of that business. The idea is this: the more data that is captured, the more likely that it is that the company will stumble on some kind of gold mine hidden just beneath the surface.
Here’s the problem with that line of thinking: capturing data for the sake of capturing data is expensive and inefficient for many reasons.
Someone Needs to Capture That Data
Data doesn’t just magically appear in your databases. It originates from somewhere. Whether that origination point is one of your employees keying in the data or whether it’s a customer filling in a web form of some kind, there is effort required to capture each and every data element. It might not seem like much. After all, you’re just asking customers for that one additional piece of information, right? However, everything you ask people to do adds friction to the process. There comes a point at which that friction results in lost sales, dissatisfied customers, or reduce staff productivity.
As a customer, you’ve probably experienced this yourself. You go to fill out a web form and the company is asking you for too much information. It appears daunting, so you simply go somewhere else or are left with a less than positive experience.
You Need a Place to Store That Data
Even though storage has gotten less expensive, it still costs money. Every item you decide to store will consume capacity in some form. And, don’t think just primary storage here. Think about your secondary systems, such as backup and disaster recovery systems, too. Every element you save to primary storage will also be captured in your data protection and disaster recovery systems, too. This further increases the costs of storing that data.
That Data Can Get in the Way
Have you ever had a garage that was totally filled with stuff? And then, you needed to find something important that you know is stored in the garage? All of that other stuff just got in the way, but you kept it all around because “you might need it someday” when, in fact, you knew it would just get thrown on a moving truck someday and moved to your new garage without ever being touched in the interim. The same goes for your data. Are you just trucking it around with you with no real aim?
What You Need to Do
There are some things that you can do immediately to begin to turn useless piles of data into treasure troves of actionable information. The most important undertaking is to get rid of what you don’t need. If you’re not actively using a piece of data or you have no concrete plan for doing so, then you don’t need it. Get rid of it and simplify your business processes and reduce any potential friction in your customer’s interactions with your company. As you move forward and consider adding new data elements to capture, ask yourself, “How will I use this data and how does that use improve the business?” If you can’t think of a way that a data element would be useful, don’t capture it.
Next, look at everything that’s left and determine its use and how it fits into either your operational processes or in your analytics needs and build appropriate structures around those items.
This whole process can be an interesting and educational exercise. Beyond just cleaning things up and tidying your data warehouse, there is an opportunity to learn about what your data actually means. The ability to apply meaning to data is often one of the most elusive parts of the data lifecycle. Once you’ve been able to ascribe meaning to your data, you can begin to use it to help unlock new business opportunities and use it to make real data-based decisions using data that you know has purpose. Only then does your data move into the realm of being useful.