3 Steps for Putting Big Data to Work for Your Business

Does your business data work for you?

Or do you have to do all sorts of work (and workarounds) to make heads or tails of your data?

Unfortunately, many businesses find themselves in the second category. They need to use their data to get insights on their customers, or to find areas of inefficiency, or to identify new market opportunities. But their data doesn’t answer those questions—at least not without a lot of exporting and sorting and Excel magic. And even when those workarounds work, they only work once—because the next time you need an answer, you have to go through the entire rigmarole again.

Any C-level executive worth her salt knows this is an untenable situation over the long term. And that would be the case if your data was a bunch of static inputs. But it’s not, of course. In this new big data world we’re all living in, every business is collecting more data, and more types of data, than ever before.

Big data is going to make it even harder for businesses to keep using their current workarounds. That means now is the time for your business to start the process of getting ready to big data success.

But how do you start?

We’re glad you asked. Here are the first three steps you need to take to move your business toward successful use of data. There may be more steps down the road, but this is a way that every business can start.

Step One: Gather the data in one place

To make the best use of your data, you must get it together. That means you must integrate the data from all the different sources.

That’s not as easy as it seems, because data is exploding. Where you once had customer interaction data, inventory data, and fulfillment data, you now may add data from sensors located anywhere and everywhere. To give you a sense of the scale of this, think about driverless cars. Intel’s Kathy Winter recently discussed how these sensors will produce both massive amounts of data and a variety of types of data at once.

Your business may not produce data at that level of complexity, but your data is undoubtedly getting more complex. You need to make sure that data is accessible in one place—because if it’s separated in silos, your business won’t be able to make heads or tails of it.

Now, this doesn’t necessarily mean that all the data has to live in a single database. You could set up a service bus that pulls data from multiple places and lets you analyze and report on it. Either way, you need a single, reliable point of access for your data, before you can truly start to leverage data for your business’ needs.

Step Two: Structure data for searches

It’s not enough to have reliable access to your data. You also need to set up your data in a manner that is easy to search.

One of the keys to doing this is moving data out of generic text fields and into more specific fields. If you can’t do this, you will have to parse words out of the text fields, and it will be significantly harder to get accurate data.

A data scientist should probably have the last word on your data structure. At the business operations level, it’s enough to know that you need to ask about what the data structure is, and whether you need changes to make the most of your data.

Step Three: Learn from your data

Once your data is aggregated and structured correctly, it’s time to start learning from it.

This is where things get fun, because there are a world of possibilities emerging in this field.

One big one is in the idea of O data and X data. O data is operational data, which is the kind of data businesses are used to looking at. These are things like sales data, finance data, HR data, etc. Most successful businesses are good at using and analyzing data in this way.

But there is a different kind of data emerging—experience data, or X data for short. This kind of data is designed to understand what people are doing, and why. This can include experience of your customers or employees, the experience of with your products, and the experience your brand creates.

Ryan Smith of Qualtrics had a good summary of the difference: “O data is about the past. X data is about the future. O data tells you what happened. X data tells you why it’s happening.” Smith also asserts that most organizations are X data poor. Is yours?

Perhaps an even bigger horizon in the world of data is machine learning. When your data is in one place and structured correctly, you can start learning from it without touching it. We’re still in the initial stages of this field—which means Watson is the start but not the epitome—but already we are working with clients who are unlocking better, quicker, more insightful analysis using machine learning technology.

Conclusion

Software innovation isn’t easy, and that’s especially true of data initiatives that deal with legacy data, myriad data sources, and new types of data collected by sensors of all shapes and sizes. But getting your data structure, access, and reporting right is vital to your business success.

So start with these three steps, and you’ll start to see the power that your data can bring your business. That may unlock even more powerful data initiatives in the future.

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