7 Data Quality Best Practices

After realising the cost of ‘Dirty Data’ to your business and recognising the benefits of achieving data quality nirvana, you will likely be interested in finding out how to get there. Here we share our Top 7 Data Quality Best Practices, to help you on your way.

Top 7 Data Quality Best Practices

Acknowledge the problem

Like with any problem, the first step to addressing it is acknowledging it exists. Getting others to acknowledge the problem is also useful, as it helps get everyone on board with the project.

Identify the scale of the problem

Once you recognise the problem it is import to establish the extent of the issue and where possible the root causes – this will help you to put preventative measures in place once the initial clean-up has taken place.

Appoint an owner

Although everyone in your business will have a responsibility to maintain accurate records, by appointing an owner (and perhaps some ambassadors) you will ensure data quality is given the focus it deserves.


Telling your people to keep accurate records is not enough. You need to ensure they understand the benefit to them in their role and the broader business benefits for them to change their behaviour.

Set goals

Setting goals and deadlines will help keep everyone on task. This will also act as a measure of achievement which you can share, acknowledging progress and providing positive reinforcement.

Introduce policies and processes

Once the data clean-up has taken place you will then need to introduce data policies and procedures to ensure dirty data is never a problem for you again. Consider using mandatory fields and picklists in your database to reduce human error.

Set up controls

Once the cleanup is complete and new processes are in place that is not the end, in fact it’s probably the beginning. From here you need to put in place measures and controls for regular review to ensure everyone adheres to the new ways of working.

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