No matter the size of your organisation, data quality is of vital importance.
Unreliable information, incomplete data, duplicates, ambiguous definitions, and outdated records all cause errors that cost time and money. Gartner has found that organisations believe that poor data quality is responsible for an average loss of $15 million per year.
Yet it need not be this way. Here are our top tips on how to address this with a data quality improvement strategy.
Prioritise Data Quality
It might seem simple, but the first – indeed fundamental – step is to make data quality a top priority. After all, data quality is all too easily sidelined when more focus is paid to seemingly more important goals. Senior management need to be leading the way not just supporting from the sidelines.
Nevertheless, understanding the impacts of data quality across your organisation is crucial. Once you and your team realise that high quality data enhances competitiveness, helps retain customers and minimises the costs associated cleaning data, you will never look back.
Introduce Training
A critical success factor related to prioritising data quality is training. Teams will only attack the root-cause of poor data quality if they see the value in doing so.
Indeed, taking strides on a data quality improvement strategy is a long-term process. As the data of your organisation grows, it is essential that your team knows the latest data management techniques.
This, of course, takes training.
Data management courses that highlight the underlying concepts of data quality, the challenges of improving it, the cost of poor quality data and how data works in real-life situations are ideal.
Accessibility
In the past, data was owned by specific business units that collected and used it for their own ends. This approach has increasingly been discredited, however, as it generates siloed outcomes that do not bring businesses holistic benefits.
Instead, data should be made accessible to all authorised data users across the organisation. The true value of your data can only be realised when the right data is available to the right users at the speed of business.
Clear Guidelines
Finally, adhering to regulation is critical. Violations of data protection law can result in fines, penalties and harsh repercussions. As such, it is better to conduct company-wide discussions to create clear data governance guidelines and then decide how these can be implemented. The guidelines should cover every aspect of how data is handled, from creation to destruction.
Become future-ready. Poor quality data yields poor decision-making and poor business outcomes.
Ensuring the health of your data will lay the groundwork for optimal data-driven outcomes, and improve the calibre of your insights.
If you’re ready to begin a data quality improvement strategy but need guidance or training, contact Robinson Ryan to find out how we can help you make a start.
