What happens to businesses when data quality is low? Can you ‘muddle through’ a data quality disaster and come out the other side still firing on all cylinders?
Bad Data = Big Costs
A recent report from Artemis Ventures indicated that poor data quality costs the United States economy roughly $3.1 trillion per year. To provide some perspective on this unimaginably large figure, that’s twice the size of the US Federal deficit. An estimate from the US Insurance Data Management Association puts the cost of poor quality data at 15% to 20% of corporations’ operating revenue.
Meanwhile in Australia, David Howard-Jones of management consultancy Oliver Wyman confirms a similar trend. Speaking at an Institute of Actuaries of Australia conference, he said, “Overall estimates of the costs of poor data quality are 15 to 25 percent of operating profits for insurers and potentially even more for large banking groups.”
Low Quality = Low Efficiency
Even more jawdropping than the cost of poor data quality is the typical lack of action to improve it. The flawed data may be easily corrected with a cloud data quality tool like Match2Lists, but flawed data quality systems are often allowed to continue producing data that could have been better. And that’s when the wider effects of low data quality come into play: where there are data quality issues, the data is less trusted. Decisions become overly conservative and opportunities are missed due to lack of timely, reliable, usable data.
Failure to attend to the sources of data quality underperformance leads to unmanaged workflow friction that can slow a business’ operations and reduce their effectiveness. In turn, revenue and growth will be limited, and the economy as a whole will be carrying one more data dead-weight.
Hollis Tibbetts, managing director and principal analyst at Artemis Ventures, commented, “About half of IT executives consistently agree that data quality and data consistency is one of the biggest roadblocks to them getting full value from their data, yet consistently organizations fail to address this issue.”
So, what now?
1. Get Clean
Use Match2Lists to compare and match data, remove duplicates from your data, and merge lists of data without introducing new duplicates. It’s swift, smart and simple.
2. Stay Clean
Keep working. Data quality isn’t a one-off task or even a static goal to work towards; it’s a continuously evolving set of strategies that require ongoing contemplation and implementation. Evaluate your data and processes regularly, and fix any quality issues that you find.
3. Fix The Plumbing
Look upstream. Look downstream. Where is your data coming from, and how does it reach its users? What happens to it along the way? Now look again. Did you answer those questions by observing, or did you make assumptions? What really happens to your company’s data may not be what you intended or what you anticipated. Identify the problem points and involve the relevant teams in developing solutions.
Don’t leave poor data quality unmanaged. Take a free Match2Lists trial to check your data for duplicates and inconsistencies now.
Happy matching!
