Do you consider yourself data-driven? Do you use the data you have to make informed, strategic decisions? If so, you’re in good company. In fact, more than 80 percent of organizations believe that data is an integral part of forming their business strategy. With such an emphasis on making informed decisions, it’s really important to trust the data you’re working with. Right?
While having trustworthy data is a necessary prerequisite to making informed decisions, our most recent study, the 2017 global data management benchmark report, finds that less than half of organizations globally actually trust their data to make important business decisions. With diminished confidence in data, business leaders tend to rely on gut feelings or educated guesses to make decisions.
And this can increase a business’s risk exposure. Our study also finds that 52 percent of organizations say that a lack of confidence in data contributes to an increased threat of non-compliance and regulatory penalties. In addition, it also affects customer loyalty and the customer experience. After all, if you don’t have confidence in your data, how can you even hope to have a more personalized customer experience or really know your consumer to make decisions at all?
Businesses today can’t afford to make decisions based on assumptions. If your business lacks confidence in the quality of your data, you’ll need to work quickly to fix the problem. But for many organizations, it’s hard to know where to start.
How to know if your data is trustworthy
Generally speaking, good data that is trusted will deliver value back to your organization. While the definition of value can vary, trusted data will deliver on things like convenience, better products, better services, and so on. If your data is not delivering value to your organization, there is likely a lapse in one (or more) of the following areas: credibility, reliability, transparency, or origination.
Credibility
Your data’s reputation can have a big psychological effect on how much confidence you have in your data. How do people in your organization or on your closer team talk about the quality of your data? If individuals within your organizations spread rumors about unreliable data, like constantly having to double-check address or phone numbers before using them, you may begin to question the information the next time you go to use it. The delays caused by rumors like this can lead to diminished worker productivity and, ultimately, lost revenue to your business.
Reliability
How have your first-hand experiences with your data been? Have your interactions with your data been largely positive, or have you gotten burned by inaccurate data in the past? Do you have automated quality checks in place (like real-time validation tools), or do you rely on manual processes to catch errors? If you rely on manual processes, it’s likely that you’re the one catching the errors—or, worse, they’re slipping through and leading to a poor customer experience. Either way, it’s hard to forget an experience with bad data, so your personal experiences heavily influence your perceived trust of the data you have.
Transparency
The worst data is the bad data you don’t see. Do you have visibility into the quality of your data either by dashboards or regular reports? Even being able to see the uniqueness and completeness of values in your dataset can go a long way toward building confidence in the quality of your data. To do that, you’ll want a solution that can run full-volume analyses, not just samples of data. Further, being able to monitor your data over time will give you much more confidence in its quality—and even enable you to gauge the effectiveness of your governance around it.
Origination
You should always know where your data came from and the circumstances under which it was created. Does your organization track the lineage of your data as it is standardized and transformed over time? Making decisions on data with unknown origins can be risky business, so knowing the lineage is key for confident decision-making.
Conclusion
If you’re lacking confidence in your data, start by taking a good look at these four areas to identify where you could use some help. By establishing a solid foundation in each of these areas, you can begin to derive real value from your data assets. Remember, your data program is only as good as the quality of the data you have, so that should always be at the forefront of your strategy.
