Data is an essential part of any organization today. We rely on it to tell us about our customers, what products we should invest in, how we should adjust our sales and marketing strategies, what new markets to invest in and much more. We have become obsessed with data and the insight it can provide.
However, the information we are often relying on for critical business decisions is flawed. On average, organizations in the U.S. believe 32 percent of their data is inaccurate. That is a 28 percent increase over last year’s figure of 25 percent. When one considers how much data is used across the business, it is easy to see how this high level of inaccuracy can create wide-spread business problems. Many organizations experience consequences such as a poor customer experience, impacts to revenue or efficiency, and much more.
The trouble is that many organizations are investing in data quality. Eighty-eight percent of global companies have a data quality solution in place today and if we look ahead to the next 12 months, we see that 84 percent of companies plan to make some sort of data quality solution a priority for their business to implement for the first time or to improve upon.
So with all that investment, why are we still struggling to obtain accurate and reliable data?
Well first, it isn’t easy. The massive influx of data we have seen over the past few years is creating new challenges of a scale that few were thinking about just a few years ago. We are also dealing with new types of data, like unstructured customer social messages and location data that are large and create challenges around management.
But the volume isn't the biggest problem. The problem is in our strategies and the investment being made in people. In the past, we looked at pointed solutions to fix certain types of data. For example, email addresses collected in an eCommerce platform were inaccurate, so an email validation service was purchased to clean up data for that particular channel. That made sense when the data was used only for operational purposes and was siloed into individual channels.
Now, information is used for intelligence almost the instant that it comes in. In fact, 95 percent of companies are looking to turn data into insight to understand customer needs, increase the value of each customer, finding new customers, and much more.
That circular structure of data constantly flowing in and out of the organization can’t be maintained in today’s current structure. Organizations need to invest in a centralized and sophisticated approach that is consistent across the organization, not just departmentalized and siloed.
Today we see that just one in four organizations has a sophisticated approach to data management. That lack of sophistication is driving up levels of inaccuracy and hurting the bottom line.
However, those organizations that are more sophisticated are seeing benefit from that central investment. The companies who have enjoyed significant increase in profits in the last 12 months manage their data strategy centrally, with ownership resting with a single director.
While the technology necessary to deal with the volume and variance of data that we see today is still critical, there is an under investment in strategy and people around data management. As organizations look to gain more insight from their data, the quality level needs to be improved. But to make real progress, organizations need to look at data quality as a strategic initiative and put more than just dollars and technology behind it.
For more information, please download The data quality benchmark report from Experian Data Quality.
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