55% of businesses say they are being let down by data quality technology
By Joel Curry, Managing Director of Experian Data Quality
Most organisations today recognise the importance of data in fulfilling their business objectives. In the recent ‘Global Data Quality Research’ (2014 Experian survey), approximately 99% of companies had some kind of data quality strategy.
But despite this increased focus on data quality, many organisations are still dissatisfied with their approach. Even where specialist software is being used, 55% are not happy with the results.
The reality is that many companies are still using outdated methods of data quality management. These are often reactive in nature and can result in far longer lead times to actually resolve any issues found, which means that the effects of poor quality data are felt for much longer than they should be.
WANT TO BUILD A FINANCIAL EMPIRE?
Subscribe to the Global Banking & Finance Review Newsletter for FREE Get Access to Exclusive Reports to Save Time & Money
By using this form you agree with the storage and handling of your data by this website. We Will Not Spam, Rent, or Sell Your Information.
One of the biggest problems that many organisations overlook is the need to manage data quality holistically. At present, one in three large British organisations do not include all of their data in a data quality strategy – creating pockets of poor quality data. This, coupled with the fact that many organisations apply quick fixes as and when defects arise, means that it’s a case of ‘when’ not ‘if’ data quality will have an impact on performance overall.
Top tips to ensure your data quality strategy works for your business
- It’s all about big picture. Perhaps the most fundamental step is to make sure your data quality strategy is aligned to your business priorities. What are the business functions and processes that your data has to enable? Undertaking a comprehensive business fact finding mission – and talking to those in the know – will help to ensure that a more business orientated data quality strategy is defined.
- Look at the links. Once you have established how data quality should support the business, it’s important to look at how the data gathered across the organisation links, and works together as part of a corporate wide process. Sounds time consuming doesn’t it? It needn’t be. Modern data profiling functionality enables you to automatically discover relationships in the data without any prior knowledge of how that information is connected – which also helps to quickly track down the source of any defects.
- Lay down the rules. Setting clear rules that dictate what types of quality you want your data to be measured against, and also what thresholds and quality levels will be monitored in future is a critical step that will form the bedrock of your data quality strategy. Organisations often focus on a large quantity of basic data quality rules at the expense of understanding a smaller number of more complex rules. It is often the more complex rules that hold the key to data quality success. When these are well managed and continuously improved you will start to see immediate benefits to the business. Once your rules have been established they can be managed via your data quality tool and re-used across the organisation.
- Set standards. It’s also important to adopt standards for things such as naming and coding conventions, permitted formats, valid events and other company data standards.Some data quality tools allow you to link standards to enforceable rules, which can have a real impact on the quality of data entry.
- Know your weaknesses. Software tools and technology can monitor data quality levels across the organisation and detect defects in real-time – allowing swift identification of defects and resolution.
- No such thing as a quick fix. Quick fixes for data quality issues may seem easier in the short term, but often these mask much broader problems that stem from failures with policies, procedures, technology and training (to name but a few). Because you already have a clear view of the information links (and are undertaking regular monitoring) throughout the business you should be able to easily identify where people, process or technology changes are required to implement permanent improvements. Quite often it simply needs more education within the workplace so that frontline employees understand the impact of the mistakes they’re making. Schemes such as bonus-related rewards based on data quality levels can also have a significant benefit in getting corporate wide buy-in
- Ownership is key. Whilst the IT team can have a vital role in data quality management, it is ultimately the business that is the true data owner. All too often the responsibility for fixing and managing data falls with IT and this is a mistake. IT has a supporting role, but the onus is on the business to define data quality rules, internal processes, information chains, data standards and policies. Assigning one person to own the data quality issue will enable them to act as a roving problem-solver, working with the business, process, technology and customer facing units to resolve any defects found.
So there you have it. Taking a holistic view of data quality – and putting in place the people, processes and technology needed to facilitate it, will not only enable you to gain in-depth insight to business performance, but will also allow you to quickly get to the root cause of any data issues.
However, it’s not enough to simply purchase the biggest and best data quality management tools, or to have the most comprehensive data quality strategy around. Both are essential – and one cannot succeed without the other.