Data quality issues affect a high percentage of datasets across tens of thousands of businesses. But many businesses fail to spot the danger signs right away. The longer the problems go undetected, and the longer people fail to act, the more expensive it will be to fix the data quality issue.
And it’s not just a question of cost, as we will see.
Data quality goes unnoticed or ignored in all businesses, even the most diligent and careful. Time and again, businesses think they cannot possibly deal with data quality challenges, or assume they do not have any issues to deal with anyway:
But data quality is critical to every business in the list, since all are vulnerable to performance and efficiency problems.
The small business could offend a customer or client by getting the details wrong, and the medium-sized business may fail to capitalise on growth once its database starts to age. Large businesses may be sitting on a data warehouse full of inaccurate, useless data; by the time anyone does anything about it, it may have generated thousands of hours of extra work.
So how do you spot a data quality problem? What are the warning signs?
Different data stakeholders interact with data in different ways. The feedback you get from one department will be different from another, but everyone has their own way of detecting data quality problems.
Here are some of the red flags to look for, across all departments:
If you hear your team mention any of these problems, you have a data quality problem. And this is by no means a definitive list.
We think of data quality as being a convenience and cost problem, but it can spark all kinds of unwanted chain reactions.
In our list, the last point – number 23 – is the one that will worry your CIO.
If employees are not using the tools you have provided, you are paying for systems that don’t work, and you are paying each person to come up with alternatives. So business data may be saved randomly, with no security, on multiple devices and potentially outside of the business’ control.
They might be recording customers’ details in draft emails, in a notes application, on an unencrypted mobile device, on a memory stick, in an excel spreadsheet on their desktop – the possibilities are endless, and the consequences disastrous.
Any business can have a data quality problem, and the biggest indicators are employees. They are the ones using the data; they are the ones who stand to gain the most when data quality is maintained. As quality drops, their jobs get harder, and morale crashes.
While maintaining data is a joint effort, the business must take ownership itself. It is the CEO, the managers and the board who are responsible for owning the data and managing it effectively. These same people must make it fit for purpose.
The first step is to implement a data quality solution that meshes with existing systems. For example, a tool that integrates with the CRM will clean data without the need for importing and exporting. Cleansing, deduplicating, matching and enhancing records will help to get the problem under control. Control over data entry – such as checking form values in a field – will help to maintain a better standard of data going forward.
Once the data is clean and nurtured, there is no reason for any employee to take that data into their own hands. The business benefits from a leaner, more accurate dataset, and improved efficiency that directly benefits its bottom line.
This blog was written by Martin Doyle, CEO and Founder of data quality software company DQ Global. From time to time guest contributors write on the Workbooks Blog – have something to say? Email the Workbooks Marketing Team on firstname.lastname@example.org