At the beginning of November I shared with you a few simple tips to help you get started with your data quality programme. This blog was titled ‘Top Tips for Data Quality.’
I didn’t want to overload you with all of my tips at once, so here are my next 5. Hopefully these will help you ensure you have a strategy and the tools in place to consistently deliver good data quality.
Implementing a data quality programme is a long term investment and like most business cases the cost needs to be justified. Start by demonstrating the benefits of what improved data quality will mean to your business. Identify your businesses key objectives, then start to show what effect data quality will have on these. If some of the key drivers are revenue, profit and compliance or reputational benefits, how will better data help achieve these all important board level objectives?
Put effective processes in place
Your processes and your people are instrumental when building a data quality strategy. Firstly you need to ensure the quality of your data is upheld by processes such as capturing and cleaning methods. Employees need to be trained on how the processes work and have a full understanding of how their actions can benefit the overall business performance. Implementing a data strategy requires both the right processes and right people, and both of these must work together.
Capturing your data correctly straight from the offset and cleaning your legacy data doesn’t have to be a difficult process. Sophisticated solutions are readily available to meet your specific needs. These tools will help you avoid potential data pollution, manage data decay over time and segment and profile contacts.
Have you improved?
It might be true that the improvements in your data quality are clear for everyone to see. However, to ensure you are receiving the most ROI from your investment, it’s crucial you report and measure your data quality programme on an on-going basis. Firstly, a key metric to look at is customer satisfaction. Have your customer satisfaction scores improved? Next is time. How much time is saved by capturing and cleaning data? What’s the difference between last year and now? Then look at accuracy. By how much has your returned mail reduced? If you aimed to better target customers in marketing campaigns, what is the improvement in your response-to-conversion ratio?
When your data quality programme is running successfully it’s easy just to sit back and reap the benefits. But don’t. Revisit your initial objectives to see how the programme is performing and keep your stakeholders regularly updated. Once implemented it doesn’t stop there, data constantly changes over time, so keeping it up to date is an on-going process.
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