Whether your business is laser-focused on driving a next-level customer experience, acing your compliance audit, or maximizing operational efficiency, one thing is clear: your success depends on your ability to understand and leverage reliable data. But becoming a data-driven organization doesn’t just happen at the flip of a switch.
In fact, we found despite a significant investment in data initiatives—data governance, machine learning, big data analytics, and more—69 percent of organizations still struggle to become data-driven. And often, bad data is undermining key business initiatives.
“Data is confusing. It is dirty, complicated, and spread out all over the place,” says Erin Haselkorn, head of market research at Experian. “We see a lot of data management initiatives happening to try and fix these issues and wrangle data into place.”
How reliable is your data? Would you bet your annual department budget on it? If you’re cringing at the thought, it’s likely you sit in the camp of 61 percent of businesses that have underinvested in data quality. Bad data means bad decisions and bad investments (think about the impact on your artificial intelligence or data governance projects), which leads to rework—costing you time, money, and not to mention trust within your organization.
Your solution: Investing in data quality—meaning accurate, well-formatted, complete data—is your fundamental first step to successfully and confidently leading any data project. And it’s the top data management initiative for 87 percent of organizations.
To start, you’ll need to understand the true state of your data quality today. Consider investing in a best-in-class data quality solution that can profile your data to tell you how good it is—and where there are errors or gaps. It can then help you transform your data in real-time so it’s standardized, enhanced, and error-free. Did we mention that the 11 percent of businesses that are mature in their data quality are also more likely to qualify as data-driven?
Much of today’s investment in data management happens within individual departments or pockets of the business. The result? A lack of enterprise-level insight and trusted data across the business.
“While individual department experimentation is important, we do need to approach data management with some degree of scale so we can have a common framework as we continue to improve,” says Haselkorn. If a senior executive asked you what the average customer balance was, would he or she get the same answer from finance and business analytics?
Your solution: Once you have your data quality initiative underway, the question is: how are you going to protect your data integrity across the business? You’ll need rules and processes around who should access your data, how it will be used, where it will live, and common enterprise-wide data definitions. Enter: data governance. Think of data governance as your company’s gatekeeper (read: bad data “shall not pass”—thank you the great Gandolf).
Consider creating a data governance committee that is chartered with setting up data rules, definitions, and scalable processes to prevent bad data from trickling in. Selecting a cross-functional team—think marketing, finance, business stakeholders, compliance, and data quality analysts—will help represent enterprise-level goals and success metrics as you operationalize data initiatives.
Companies that look at data management as a technical project or a one-time clean-up are falling short on expected outcomes. Projects have start and end dates. But should the quality of your data? If you have goals such as optimizing your customer experience or increasing revenue, leveraging quality data to drive business decisions never stops (and shouldn’t).
Your solution: We find mature data organizations approach data management as an ongoing process, and 83 percent report data enablement—empowering a larger group of individuals within a business to understand and harness the power of data and analytics—is a focus.