Apr 2018 | Data Quality

The importance of data quality

As part of our “Back to Basics” series, we’ve been championing the importance of data quality as a fundamental building block for everything from data management to innovation and regulation.


Our recent Global Data Management Research provides an interesting perspective on the current challenges and opportunities that data presents to organisations in the digital age. What it also highlights is why data quality is more important than ever in 2018 and some of the key challenges that organisations are facing when managing their data. To bring this to life, I’ve chosen 5 standout stats and suggested some useful resources for those looking to explore them further.

All the below stats (and many more useful insights) can be found in our 2018 Global Data Management Research.

1) Data inaccuracy is still a real issue

Our research told us that organisations still have work to do when it comes to having accurate customer and prospect data. Respondents estimated that an average of 30% of this data is inaccurate. So, what does that mean in real terms? Why should you care? In a nutshell, using inaccurate data can pose risks to most data related activities a business carries out. If 30% of your customer data is incorrect for example – you are instantly wiping out 30% of your potential earnings from a marketing campaign.

2) Fragmented ownership poses challenges to data quality

When it comes to ownership of data, the landscape is still fragmented, with IT by far still the biggest owner. Not having central ownership can have an impact on data quality initiatives. Without senior level sponsorship, or a strategy that considers data quality as part of a wider data management approach it can be hard to get buy-in or build a business case for data quality initiatives.

Ownership appears to be slowly becoming more centralised, but whilst silos remain, data quality initiatives may not get the attention they need. This is a common challenge and that’s why we published a guide to creating a data quality business case using a Lean Pilot approach. This gives good advice on how to get buy-in by starting small and proving the business value of data quality. You can download it here.

3) Data quality feeds data management success

The research gives great insight into what wider data management projects are planned in the next year. Whilst data quality initiatives are included in this, they’re not necessarily a focus for organisations, and nor should they necessarily be. What’s important however is that organisations understand that data quality is an enabler to all their data management projects as opposed to a project in its own right – especially with data management projects on the rise overall.

The top three data projects Рintegration, analytics, and migration are all great examples of where poor data quality can significantly impact successful outcomes.

4) Data quality matters for regulation

New regulations like the GDPR can pose challenges for organisations and the research told us that increasing volumes of data in particular, is making things difficult. How do business get over this hurdle? A good start is a well-defined data quality program which will guarantee the standard of data that an organisation holds. With this in place, it’s far easier to manage, analyse, and reconcile multiple records as larger volumes of data come in. This foundation will support the wider long-term requirements of the regulation.

5) Transparency is only possible with good data quality

Being transparent with customers about how organisations use their data is a hot topic as the GDPR approaches and consumers become more aware of their rights. Adopting a transparent, ethical stance to this is critical and the stats show us that organisations clearly have more to do in this area. Data quality must be a major part of any program in place to address this, indeed it’s the foundation of the GDPR which is essentially about protecting consumers’ interests. To deal with consumers’ data and be transparent about it requires that you get it right in the first place and keep it that way. You can read more about consumer attitudes to data in our whitepaper “Delivering value in the digital age”.

The above illustrates where organisations are right now and just how dependent they are on good data quality. There are more stats and commentary about how businesses are faring with data quality and wider management issues in our Global Research paper, which you can download here.