HOW MUCH IS BAD DATA COSTING YOUR COMPANY?
TL;DR - Courtesy of DiscoverOrg
In the world of big data, bad data is becoming more and more commonplace. Part of the issue is fuelled by the technology we use to help manage and organise that data. In our rush to be more responsive, personalised and powered data science, we’ve embraced cloud computing, mobility, social collaboration and enhanced analytics. Every scrap of every shred of customer data is valuable. But in doing so, we’ve also let our data quality control lapse.
And when departments are clamouring for numbers despite the inaccuracies, it leads to a ripple effect of poor decisions based on erroneous data. But just how much is it really costing us? And what can we do to stop poor data hygiene before it spreads? Let’s take a closer look.
It Is More than an IT Problem
Even just a few years ago, in 2013, the looming spectre of bad data was apparent. Gartner surveyed a wide range of companies in its study and learned that data quality costs them over $14 million dollars a year. Now, imagine how much more connected we are today, and you can see how the problem could compound exponentially.
In an attempt to wrangle departments to make sense of it all, company Executives tend to place the task of organising and managing data squarely on shoulders of the IT department. But bad data affects more than just servers and databases – it affects everyone and, in this day and age, it is very much a business problem.
And that’s not even factoring in the cost beyond customer data. Inaccuracies in customer names or details is one thing, but oftentimes, depending on the company culture in relation to data upkeep, it can end up affecting other areas of business like productivity, security and cost-effective decisions.
In short, this is not a problem we can continue to throw money at in hope that it will go away or work itself out.
The Impact of Having Clean Data
Cost savings is a good thing, but oftentimes management and other Executives don’t just want savings – they want to see a direct correlation in terms of revenue as well. The real question is, how much can clean data make for us? Here’s a hypothetical, albeit very realistic, example from the same Ringlead chart:
In addition to revenues and savings, the benefits of clean data go much further. With greater data reliability comes greater credibility and a stronger decision-making foundation backed by data. Reports become more accurate; customers respond to more accurate personalization, and all departments enjoy greater productivity and efficiency. It’s a cycle of wins.
So, as you can see, a few inaccurate records or non-standardised entries don’t seem like a big problem, but as your business scales, more and more information becomes fragmented and fraught with issues like escalating costs escalate and lower efficiency.
But by the same token, by spending a little now, you reap far greater benefits over time. And any campaign started or improved based on solid, reliable information is one you can look to time and time again for greater insights and metrics that count.