EXAMINING THE HUGE COST OF KEEPING BAD DATA
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 on-demand, personalised and data-science-powered, 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 those errors. 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:
A Company Problem – Not Just 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.
Many companies, in an attempt to wrangle departments to make sense of it all, place the task of organising and managing all this information squarely on IT’s shoulders. But bad data affects more than just servers and databases – it affects everyone. In this day and age, it is very much a business problem.
And that’s not even factoring in the cost beyond customer data. A few inaccuracies in customer names or details are one thing. But oftentimes, depending on the company culture in relation to data upkeep, it can affect other areas of business as well – productivity, security and making cost effective decisions.
In short, this is not a problem we can continue to throw money at and hope it goes away or works itself out.
What Exactly Is “Data Quality”?
Before you begin to get a handle on the data itself, it’s important to understand what “it” is. According to another Gartner study, data quality is examined by several different points, including:
Existence (does the organisation have the data to begin with?)
Validity (are the values acceptable?)
Relevance (whether or not the data is appropriate to support the objective)
Consistency (when the same piece of data is stored in different locations, do they have the same values?)
Integrity (how accurate the relationships between data elements and data sets are)
Accuracy (whether the data accurately describes the properties of the object it is meant to model)
What a Difference Clean Data Can Make!
Of course, cost savings are one 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:
And in addition to revenues and savings, the benefits of clean data go much farther. 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. 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. Costs escalate. Efficiency plummets. 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.