Three Steps to Enabling Better Use of Business Data

Effective data mining derives from linking all the right data to create a complete and timely picture of the business issues you are trying to engage.

There is a glaring need in the business world for a simpler way to unlock the value of data.

The speed, velocity and unrelenting proliferation of all types of data from an ever-increasing number of sources is increasing. Enterprises struggle with harnessing data to realize value and achieve the ultimate goal of monetizing their business data through bold, data-driven actions.

The key to enabling this kind of ambitious action, however, does not come from the sheer abundance of data. Instead, it comes from the linkage of all the right data to create a clear, complete and timely picture of the circumstances, customers or business issues you are trying to engage.

This linkage is what truly unlocks the power and potential of the data to be directly converted to business value and outcomes. This linkage is not without its challenges, however. It requires some key steps.

A Precisely report on data integrity trends illustrates the scale of the problem: A typical enterprise has, on average, 27 data sources currently integrated.

Of hundreds of C-level data executives polled, 82% said that data quality concerns are “very” or “quite” challenging, while 74% lack integration technology or services. Many data pundits define Big Data as “huge, overwhelming, and uncontrollable amounts of information.”

And numerous business surveys by top analyst and consulting groups conclude that only about a quarter to a third of businesses across all industries feel like they have mastered the data necessary to be able to monetize the intrinsic value of their data. Some see this as a way for business to differentiate themselves and they are not wrong.

I see this as a situation where the state of the technology is insufficient to the magnitude of the problem. More accurately, it’s the combination of technology and skills, otherwise thought of as capabilities, that are not up to the task. The technologies for solving this problem exist but there are too few, sufficiently skilled people that know how to work these tools to solve the market problem. Just let the sheer size and reach of the problem sink in and you will quickly conclude that this is a situation that is ripe for disruption.

Until disruption happens, however, what is to be done? How can any businessperson initiate the right activities to get their data into a usable form?  Like many problems, there are three key points:

The sheer number of data sources, each with its own formatting rules and lexicons is one key barrier to linking data to make it intelligible and valuable throughout the enterprise.

When bringing data together from multiple sources, there are many details to sort through to make sure each source is speaking the same language, in a figurative sense. Zip codes, for example, are a text field with numeric characters, and a leading ‘0’ – as in Massachusetts, 02482 – tends to create chaos in things like spreadsheets and other data stores that auto detect the field type.

Reaching consensus on business definitions provides another example. In the insurance industry, for instance, different regions, waiting periods, policy start dates and other variables often mean different sources have a different understanding for something as basic as what constitutes a sale. All these details need to fly under the same flag for business data to have the same meaning between sources, departments, channels and processes.

Data standardization is the second data quality issue that often confounds organizations dealing with a tsunami of incoming data. Names and addresses with seemingly infinite nuances are the classic example. But simply matching a Jon Smith with a Jonathan Smith when the names may – or may not – represent different people may present several negative implications downstream.