Bad data may mean banks miss out on major moments in their customer’s lives — and big opportunities to cement and deepen customer relationships.

Providing personalized service and exclusive offers to customers is perhaps more important for financial institutions than any other industry. Consumers expect personalization, according to a study from Epsilon, and they become more comfortable with providing personal data when they believe there is a benefit or incentive to doing so. But most consumers don’t think their primary financial institution really knows the important components of their financial lives — according to research from Accenture, less than 3% of customers felt confident their bank knows them and their financial needs well.

As bank customers, we’ve all been on the receiving end of a new product offer based on bad data. From emails touting the first-time home-owner program sent to individuals preparing for retirement to student loan offers received by recent graduates, disconnects like this can plant the seed of perceived ineptitude for an otherwise successful company.

To prevent these common, and costly errors, banks need to prioritize maintaining their customer data. Not contextualizing your bank’s marketing is bad; what’s worse is when attempts at personalization fail. Your bank loses customers’ trust and undermines its own brand.

Banks can learn valuable lessons from the healthcare industry when it comes to maintaining customer data. Before patients ever talk to a doctor, they are prompted to verify basic pieces of information and to confirm that nothing has changed since their previous visit, alerting the healthcare provider to any recent life changes. This process typically takes less than 2 minutes and is a simple step banks can and should do to ensure customer data is accurate and updated.

Bad data is generally thought of as information that is inaccurate, incomplete, non-conforming, duplicative or the result of a poor input process. But this is not the complete picture. Data that is aggregated or siloed in a way that makes it inaccessible or unusable is also considered bad data, as is information that doesn’t garner any meaning or insight into business practices or isn’t available in a timely manner. Simply put, data that is not working for your organization is bad data.

The advancement of cloud storage has lowered the infrastructure cost of maintaining data over the last few years. At the same time, the exponential growth of collectable data points and the advancements of smart technologies have compounded the growth rate, leading to increased data management cost. If your bank is not scrubbing collected data to make sure it is complete, accurate and, most importantly, useful, your bank is wasting valuable company resources.

The cost of bad data to your institution is more than just dollars spent on data management.

  • It is the inability to take advantage of opportunities that utilize AI and predictive analytics.
  • It is the slowed business cycle that prevents bank executives from reacting to changes in their market.
  • It is the increased operational cost that forces managers to focus on data instead of on company initiatives.
  • It is a marketing campaign that results in unmeasurable revenue and no focused customer insights.
  • It is the misallocation of employee’s knowledge and potential disillusionment with the organization.
  • At its worst, it is the abandonment of your trusted customers.

Understanding the right information to collect and anticipating the future expectation to not only access, but also aggregate data in a meaningful way, is paramount to enduring success in this new “big data” era. Good data also translates into strong decision making. When an organization has access to critical consumer information or insights into market tendencies, it is equipped to make decisions that increase revenue, market share and operational efficiencies. When meaningful data is presented timely and in an easy-to-digest manner, executives can react quickly to changes affecting the organization, rather than waiting until the end of the quarter or the next strategic planning meeting.

Financial institutions that want to avoid marketing mishaps and the associated blows to their brand need to shift away from data silos and place a greater emphasis on their data quality. Providing departments across the bank with an accurate view of customers is essential to meeting their evolving needs. Institutions that ignore the growing importance of data quality risk losing customers and becoming irrelevant in today’s digital environment. Precise, up-to-date marketing and communication to your customers begins and ends with access to current and relevant data.

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