Effective data quality strategies
I believe that organisations worldwide are presented with a simple choice. They can commit themselves to improving data quality with a documented data quality strategy that is supported and enforced from the top. Alternatively, they can continue to ignore the issues and allow current problems to persist, leaving them further and further behind organisations who have proactively tried to overcome their data hurdles. Choosing to ignore contact data leads to unnecessary risks: wasting money, losing customers, breaking the law and damaging their brand reputation.
Make it a priority
Data quality needs to be a priority for organisations globally, not just because it saves needless aggravation but because wider business opportunities are more plausible when such strategies are in place.
Today’s business environment creates challenges for organisations when processing vast amounts of incoming data. Because customers can provide information at multiple entry points, such as the phone, web or point of sale terminals, the number of duplicate records in businesses’ databases has increased. Combined with the constantly changing nature of data, many companies struggle to accurately and quickly match information from each channel.
Data quality can seem like a daunting task, but it’s really all about having the right people, processes and technology in place. These simple steps will help you focus your efforts and build an action plan of how to approach this massive challenge.
How to improve data quality
Build a business case
Measure the current impact of data quality within your organisation. What type of data do you collect? What is it used for? Look at the financial implications. If data quality improved by just one percent, what impact would that have on your customer acquisition and retention, marketing campaigns and customer satisfaction?
Devise a data quality strategy
Look at the type of data that you want to collect and measure going forward. For example, if you operate in the B2B space, wouldn’t it make sense to append employee numbers/turnover to your data so you know the scale of the organisation you are working with? Tie in your objectives with the strategic objectives of your organisation so you’re all working to the same end gain. Set SMART targets around how complete, accurate and up-to-date your contact information is so that you can use them to monitor your effectiveness.
Secure buy-in
Many data quality projects fail because they don’t have support from all the necessary stakeholders. Typical stakeholders include the Board, senior management and IT. Education is vital to get everyone on board and explain what’s in it for them. You should discuss the options available to improve existing processes and manage control. Having a well communicated, formal data strategy will also help ingrain data quality into your organisational culture.
Make the technology work for you
Effective finance, CRM, HR and business intelligence systems rely on good data. If you put poor data in, you can expect poor data out which can have a serious impact on decision-making. Using software tools to control the data entering these systems, and manage data quality within, ensures that you get the most from your technology.
Don’t do it alone
Technology alone is not sufficient. Merging data from multiple sources, for example, can be a risky process. Pitfalls can appear along the way if the project is not managed correctly, so try not to tackle it alone. There are many organisations that can provide professional expertise to ensure that the project runs smoothly.
-Paul Vescovi is managing director Australia and New Zealand of Experian (www.experian.com.au).