Assessing the high cost of bad data on the efficacy of banks
At a point in time when big data analysis is driving the growth of organizations, the impact of unstructured, inconsistent and inaccurate data on a bank's efficiency and efficacy can be immense. From the contextual viewpoint of banks, bad data doesn't only mean high costs of data management but also the failed opportunities to deepen customer relationships, that burden their processes in the form of increased customer churn rates.
Loss of customer trust and an impaired brand image usually follows inadequate data management, often evident in the wrongful alignment of a specific product offer with the target audience, as dictated by the misspelt, uncontextualized and invalid data. Undoubtedly, any data in a bank is a valuable resource as long as it is complete, accurate and useful; otherwise, it only leads to piled up opportunity costs.
Let's see in further detail how bad data is costing banks their efficiency, efficacy and ROI.
- The most apparent impact that financial institutions can feel in their workings is their productivity levels, with a pretty high probability of losing faith in leveraging their existing data in powering their decisions and other business initiatives.
- The ability of banks to offer a personalized experience to their highly valued customers becomes subsequently more complicated due to bad data. With incorrect data comes the enhanced possibility of failed attempts of creating optimal customer touch-points suitable for a specific segment.
- Incomplete, inaccurate and missing data also causes impaired decision-making wherein the presence of common yet costly errors would mean extra time and efforts to be put into making manual checks, despite which regulatory breaches happen.
- In terms of the bank's marketing efforts, bad data would mean low ROI due to failed consideration of the customer's evolving needs. The unoptimized customer targeting would mean student loan offers to be seen by recent graduates, the first-time home-owner program seen by retired people and more.
- Before leveraging predictive analytics in their operations, marketing, and other efforts, a bank shall ensure the quality of data to be analyzed to produce the relevant insights. Bad data doesn't allow this to happen, causing lost opportunities in utilizing such advantages.
- Bad data causes the operational cost of banks to go up, along with a slowed business cycle due to the inability to react to changing market scenarios.
In the big data era, banks should remain at the forefront of ensuring better experiences for their customers alongside maintaining their efforts at an optimal level through quality data, as it can empower better strategic planning through meaningful insights if broken down into an easy to digest manner.
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