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Have you ever wondered what the cost of poor data quality in banking is and how it affects the industry?
While the term ‘clean data’ is relatively common, what about the reverse? ‘Dirty data’, or low quality data, is a growing problem across the banking industry. Infact, the financial cost of poor data quality is $15 million per banking firm, according to a report by Gartner in 2017.
But what does this mean for global banking at large? Regardless of revenue, it is clear that this figure is likely to impact even larger financial bodies. Let’s consider the cause of poor data and how corporate banking may start to overcome it.
What is Dirty Data?
Dirty data may include duplicate records or inaccurate figures. In banking, duplicate financial data could amount to inaccuracies in personal accounts. It could also lead to security problems.
Essentially, any data said to be ‘unclean’ is often disorganized or difficult to correlate, this dirty data often causes problems to emerge from a lack of structure and clarity.
An example of unclean data could be that there is simply too much. Some analysts refer to poor data management as ‘data laking’. As an increasing amount of information piles up, it can lead to a surplus ‘lake’ of data. That is difficult to navigate and even harder to disseminate.
Data under this umbrella is unlikely to be easy to work with. It may be out of date or inconsistent. It may even fail to show the bigger picture. In a sensitive industry such as finance, it’s easy to understand why the need for clean data is so critical.
Related Reading: Data Quality Issues In Banking
Cost of Dirty Data
As mentioned above, the cost of poor data quality for banking firms, on average, is around $15 million per year. However, let’s consider the broader impact on global finance.
Harvard Business Review reveals that, shockingly, the US alone wastes $3 trillion per year on ‘bad’ data. While these statistics point to the situation as it was in 2016, the national debt is rising. The US owes more than $27 trillion at the time of writing.
Therefore, firm by firm, the banking industry’s big data problem is likely exacerbating national economic woes.
But how does the individual banking firm feel?
Further statistics show that just over a quarter of businesses, cross-industries, feel their data is poor in quality. That is a worrying trend. It doesn’t just show that 26% of companies think their data pooling isn’t good enough. It shows that almost 75% of business owners may not know they have a problem – whether they do or not.
Therefore, solutions against the impact of dirty data are naturally in high demand. But how does low quality data affect life beyond economic strain?
Related: Data Challenges In The Banking Industry
What is the Impact of Poor Data Quality?
There are not only financial implications for poor data in the banking industry. There are implications for productivity, too. Data laking and inaccurate record-keeping can slow down processes. It could lead to extended manual hours spent fixing problems and chasing data trails that lead nowhere. In a crucial industry such as banking, it’s safe to assume maximum productivity is a must.
Anyone making money for a living will likely have a bank account. Therefore, they are part of the big data problem. With more than 7.6 billion people alive, even if only half the population depends on banking, it is still an astronomical number.
Lack of productivity, efficiency, and accuracy resulting from data problems could also lead to reputational issues. Public trust in banking was already worryingly low in 2013. A YouGov study found at least 72% of people surveyed felt bankers were not doing enough to help the economy. In the years since big data has only grown, and new financial problems are emerging globally.
Therefore, it stands to reason that clean data will help to reinforce trust in banks. However, the chief concern is how banking bodies are going to remove themselves from their data lakes.
To do that, there needs to be a more comprehensive understanding of how dirty data in banking emerges in the first place.
Related: Top 5 Best Practices For Data Hygiene
Where Does Poor Data Come From?
As mentioned, there are various causes of poor data quality likely to impact banking bodies. Let’s consider the following poor data quality examples to understand the problem a little clearer.
Duplicate Information
Duplicate data is likely to be a bigger problem than many imagine. Without a data cleaning system, repeat records and information compounds easily. Double the data means double the work. This means that there is needless manual expense pumping into erasing and collating information into single figures.
A smart data cleaning solution, therefore, can sift duplicates from the ‘lake’. That removes the need for banking professionals to pore over double credit records and card applications, for example.
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Data Inaccuracies
Even big banking companies may struggle with data inaccuracy. That may be as simple as customer information being out of date. Another good example, too, is financial data that is irrelevant to a given customer. This may arise from duplicates, as explained above.
A proactive data cleaning operation will ensure customer information is up to date and relevant. This assures customers receive the service they expect and that bankers spend less time chasing data rabbit holes.
Incomplete Data
Dirty data is even incomplete or unsatisfactory in many cases. Even if a banking firm uses impressive software to collate and process data, some fields may go empty. Data precision relies on human input to an extent. However, software needs to be easy to understand and to deliver clear parameters to its users.
Data software such as WinPure presenting simple, yet comprehensive information capture can help to remedy this problem. Bankers may benefit from simpler forms and fields, and therefore not contribute to inaccuracy problems further down the line.
Conclusion
The cost of poor data quality in banking is hard to understate. Finance is perhaps the most depended-on industry in modern society. If its data is dirty in the main, how can we rely on our banks to offer us confidence day-to-day? WinPure, therefore, is helping to lead the fight against poor data pooling in banking – no matter how long it takes.
