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5 common data quality myths – setting the record straight

Goldfish have a six second memory. The Great Wall of China can be seen from space. We only use 10% of our brain. Myths have an annoying habit of becoming accepted as truth if enough people hear them and enough time passes – and the Data Quality industry isn’t immune to this.

So, I have taken it upon myself to ‘debunk’ some Data Quality myths and provide some great resources to help you succeed in your data quality initiatives.

Myth 1: Data Quality is the Chief Data Officer’s (CDO) responsibility.

Truth: Data Quality is everybody’s responsibility in the business. It may be that you have a CDO in place or that data initiatives are run by IT but one thing’s for sure, whoever is leading your data strategy will need everyone on board.

No matter what role you play in managing data, be it the owner or simply the end-user, you need to make sure the data your company has stays accurate. It may not appear to affect you directly but good data quality impacts an organisation’s bottom line and that makes it everyone’s business. If you feel like you don’t have the knowledge or tools required to achieve this – that DOES fall into a CDO’s list of responsibilities.  Our research, ‘Rise of the Data Force’ considers how organisations are introducing senior data roles to ensure data remains a valuable asset to the whole business.

Myth 2: ROI from Data Quality initiatives is difficult to prove.

Truth: Effective Data Quality initiatives typically result in great ROI, the issue is that many companies aren’t sure how to, or don’t bother to measure it or link it to strategic targets that will resonate with senior stakeholders.

Research in the area strongly proves the ROI benefits of Data Quality initiatives. Our 2017 Global Research found 69% of businesses have cited that where they have made investments in data quality solutions they have seen a positive return on investment, and 50% of companies are using data to increase revenue.

If data quality initiatives have a bad reputation in your organisation, it’s critical to prove the value when you build your business case.  This can be tricky - how can you prove the case for investment until you have investment? And how do you tie it into wider corporate objectives that tick the boxes of business stakeholders? But it is possible.  Read our Dylan Jones blog post series for some great actionable advice.

Myth 3: Migrating to a new CRM system will fix all your Data Quality issues.

Truth: Technology is a key part of Data Quality management, and if you have taken measures to protect the integrity of your data once it’s in then that’s a great step in the right direction.

However, first thing’s first, you need to ensure that the data you put into your new system is clean and accurate before you do anything. A Data Migration isn’t something that should be underestimated and therefore under prepared for. A poorly executed migration can lead to worse data quality issues than you had in the first place – if you put rubbish in, expect to get rubbish out.

When migrating to a new system, planning is key.  Make sure you follow a clear process such as our Seven Steps to a Successful Data Migration which includes a Data Migration Project Checklist that breaks the whole process down into manageable stages. As part of this you’ll also need to consider your technology options.  A tool such as Experian Pandora can offer an end to end solution through a single platform and provide considerable advantages to business users involved in executing the migration.

Myth 4: Data quality is a ‘nice to have’.

Truth: Not only is good quality data critical to informing accurate decision making and vital for growing customer relationships – with the new General Data Protection Regulations (GDPR) being introduced in May 2018, it will become an absolute priority.

Our 2017 research with DataIQ on preparation for GDPR shows that there seems to be a gap in perception of how prepared businesses think they are for the changes versus their perceived level of maturity in terms of adoption of data and analytics. A comprehensive data quality strategy is the best way of helping your business get ready for the deadline and avoiding large financial risks (failure to comply with the new regulations can result in fines of up to 4% of a business’ global turnover or €20,000,000).

Myth 5: My data is already excellent.

Truth: Okay not quite a myth but a common misconception people like to believe about their own business. The fact is, without stringent and continuous data quality processes in place – your once clean data will become vastly outdated and inaccurate quicker than you’d imagine. The Postcode Address File (PAF), the UK’s most prominent address dataset for example, will average 100,000 record changes every month[1]. This really exemplifies the speed in which data changes.

A proactive Data Governance framework is the best way of futureproofing your data quality by ensuring that the right people, processes and technology are in place to constantly monitor and maintain the quality of your data. Our Data Governance Business Case blog explains the best way to get buy-in from your business and have total faith in your data.

The good news is that as well as those I’ve highlighted above there’s a wealth of fantastic resources out there to help you keep on track with the latest advice and techniques.  You can visit our resources page for some of our own papers and make sure to keep checking our blog for a regular feed of new perspectives on the data quality market.

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