How much attention are you paying to data in your organisation? Chances are, if you don’t have an active data quality project, your data is losing integrity as you read this article. In addition, data may be recorded using inadequate software, rarely updated, and never scanned using data deduplication software. The result is clearly going to be undesirable.
However, businesses ignore poor quality data at their peril. Often, a business leader ignores data quality issues until the damage has been done, and that usually means a large-scale operation is necessary to reverse the damage. Wouldn’t it be better if the problem were dealt with at source?
Poor data quality is a very real problem, even if it’s not a problem that’s prioritized at the moment. It can take many forms. If your customer database is showing its age, your staff will know about it. They will already have developed workarounds to deal with data deficiencies. Some of those workarounds will have been incorporated into business processes. These processes, workarounds and fudges will be so ingrained that staff have given up on developing better ways of working with data.
It’s often difficult to focus staff on the considerable challenges relating to data quality. Some have their workarounds and see no reason to change them. Some see it as too much effort. Some simply don’t have time to learn a different way of working; some don’t understand the potential benefits.
Data quality becomes more of a problem when employees are not motivated to do anything about it. If they have their head in the sand, chances are that even a motivated data quality officer will have their work cut out to change company culture. But change it they must.
The cost of reversing poor data quality is bad enough. But while your data has been decaying, it has been silently, consistently racking up costs that you never even knew were there. It only takes one uncorrected mistake to cause problems hundreds or even thousands of times, and each problem can silently affect productivity and customer satisfaction.
As data quality has declined, so customers will have lost trust in your brand and defected to a competitor. Something really small – like two identical company catalogues arriving in the post – can suggest to your customer that their data is not given the attention it deserves. Something larger – like a billing error – could start ripples that spread for years.
Poor quality data also forces business leaders to make bad decisions and take the wrong direction. This is normally a silent corruptor of the decision making process, so it’s difficult to use it as a motivator, but it’s one of the best arguments for a data quality initiative – and for dragging various heads out of the sand in the boardroom.
Is your data accurate, reliable, credible, timely, complete and appropriate? Can you confidently say that your customers have complete confidence in the way data is handled, stored, cleansed and maintained? If not, you may be incubating a serious data quality problem.
Rather than putting your head in the sand, it’s better to proactively tend to your data so that things never get to such an extreme. Poor data quality is not inevitable, and with the right data quality software, you can prevent the serious impact it has on profit and productivity.