What are the Key Drivers for a Data Quality Strategy?

99% of organisations have a data quality strategy in place¹ and collectively 78% of large British companies will invest in data quality techniques during 2014, with 51% investing in areas that they currently don’t invest in already.² Clearly this demonstrates that data quality strategies are high up on the agenda of the majority of organisations.

But what are the drivers for these organisations to invest in their data quality?

1. Brand reputation and regulatory risk

Our recent research revealed that 40% of organisations have a data quality strategy to be compliant with industry and government legislation. This increases to 48% when we profile Financial Services and 49% when we segment Utilities and Telcos. 41% of organisations said their driver for maintaining a data quality strategy is to protect the organisation’s reputation and brand.¹

Brand reputation and regulatory compliance are intrinsically linked as through being compliant organisations are protecting their brand reputation. If we look specifically at regulatory risk, it is hardly surprising that this is a key driver for organisations. As the regulation of information security tightens worldwide, and the associated penalties for non-compliance grow more punishing, the requirement for effective data governance grows ever more urgent. Securing compliance with regulations such as Solvency and Basel, means effectively governing the data that is subject to these regulations by putting in place appropriate business processes and controls, supported by appropriate data quality technologies.

For organisations aiming to drive compliance and reduce regulatory risk, they must look at tools and technologies which are out there to simplify data governance and enable progressive compliance.

2. Customer engagement

54% of respondents maintain a data quality strategy to enhance customer and citizen satisfaction and 43% want to capitalise on market opportunities through customer profiling.¹

It isn’t unsurprising that customer satisfaction features so highly given the huge impact that poor data quality can have on our customers. Let’s take for example poor data quality within your email database.

Email deliverability problems caused by low sender reputation will affect the likelihood of your email communications reaching an inbox, and instead being diverted into the junk folder. Collecting invalid emails at the point of capture, or not maintaining your database over time will also affect the likelihood of your communications reaching the intended recipients. If the communication you are sending them is informing the recipient of a delay to their order, or a query with their payment process – the customer will not receive crucial information, and undoubtedly will impact your brand reputation and customer engagement. A point of capture email validation tool, or a bulk cleanse of your database will instantly see your deliverability improve and your customer engagement and reputation increase as a direct result.

Data quality can enable linkage between customer records which will also mean that you can communicate more effectively with your customers; sending them more targeted communications through knowing their full customer journey. However knowing that you have a clean and accurate database is only stage one. Utilising additional data and appending information will help you develop an actionable strategy to your customer database.

3. Driving bottom-line performance

Cost savings accounted for 44% of respondents’ motivation for maintaining a data quality strategy and increased efficiency accounted for 62%.¹

Using the right tools and technologies in your data related projects can reduce costs through improved business process accuracy and accelerate all data related projects to avert the business impact of programme delays.

Technology assisting in root cause analysis can help to identify where the challenges and issues exist with data quality processes. Implementing automated tools and processes within your dirtiest channels can speed up processes and remove the efforts and costs needed to clean it. For example, our research showed that 52% of respondents think their Call Centre is the most problematic channel for collecting data.¹ If this is the case, then implementing rules and automation in this channel will speed up collection times and reduce the time and effort spending on reactive cleaning. 

Get much more granular insight into the make-up of your data sets, profile and use this insight for clearer strategic decision making.

4. Strategic decision making based on data insights

47% of respondents said that enabling more informed decisions was a key driver for maintaining a data quality strategy.¹ The right tools and the right technology can demonstrate the true cost that poor data quality is having on your business. For example, a 97% accuracy rate for your email addresses might sound like a good statistic. But, if the sales value of the 3% of inaccurate email address was worth 20% of your annual return – then it might not be all that great. Data quality tools can manipulate your data to easily identify such anomalies.

¹ Global Data Quality Research 2014,’ an independent market research report commissioned by Experian Data Quality and produced by Dynamic Markets.

² Gartner, The State of Data Quality: Current Practices and Evolving Trends, Ted Friedman | Saul Judah, 11 December 2013