High Quality Data Benefits Everyone… Including You
Data quality sounds like an abstract term if you’re not used to dealing with facts and figures. After all, data is something that technical people deal with. Right?
It all seems rather difficult to quantify? What’s high quality data compared to low quality data? What are the tangible benefits of a data quality drive? Why should the manager, team leader or CEO care about the contents of a database anyway? Isn’t that a job for the IT department?
There are lots of misconceptions about data quality. Perhaps the most dangerous is thinking that it’s someone else’s problem. From small businesses to huge organisations, data quality is everyone’s concern. It affects us all, and we all need to make it our priority.
Deduplicate, Merge and Improve
In Data Quality: the Accuracy Dimension, Jack Olson says this: “Data has quality if it satisfies the requirements of its intended use.” It’s a brilliantly succinct way to describe the importance of good data. If our data is in good shape, we all benefit because we can:
- Support our customers more effectively
- Understand our customers’ needs
- Solve business problems
- Drive efficiency and productivity
- Improve ROI
- Make more money
- Make better, more informed decisions
- Waste less time at work
- Go home earlier… perhaps.
Good quality data supports all of the positive moves we can make in business. It helps the business achieve a joined up approach – the single customer view. It makes us faster, more efficient, more eco-friendly and better at what we do. And the end result is a more successful future.
How Do We Get There?
Data cleansing is necessary if data quality is to be improved. That means investing in data deduplication software, spending time merging records and putting systems in place so that we capture data more effectively in future.
The road to good quality data is a road we must all walk together. Everyone in the organisation needs to understand the benefits of data cleansing and commit to it as a long-term solution to a problem that affects us all.
One way of ensuring buy-in at all levels is to turn data quality into a goal that everyone can understand. Rather than using rather vague, technical language, be frank with everyone and describe the precise way they will benefit. For example:
- The sales team will have accurate leads to work from.
- The marketing team will achieve a better ROI.
- The support team will see a complete support history for each customer.
- The receptionist will have access to contact records they can trust.
- The customer will develop confidence and faith in the service we provide.
Once we understand how these objectives are achieved using data quality, we suddenly see how it’s in everyone’s interests to care about data cleansing.
We understand why the single customer view is so useful.
We develop an interest in the outcome of a data quality project, and we commit to the lifecycle of data cleansing, deduplication, merging and maintenance.
In fact, once we have the full support of the entire organisation, data quality suddenly becomes an essential – not a ‘nice to have’.