Data Quality: A springboard for more
How might data quality be a springboard for more?
Once an organisation has gone through their data quality fundamentals and is happy with the framework and data architecture that have been established to improve the quality of data and its flow across an organisation what next? Once DQ fundamentals are established, trust in the data improves and data can be relied upon to make better business decisions, the regulator is satisfied and the cycle increases ad infinitum.
While it is certainly the case that data quality foundations must be in place in order for businesses not to stutter in place, what can be done to move the business forward and unlock the real value-add benefits that good data quality can bring?
Why data quality means today
Congratulations. Your data quality framework is in place and you are beginning to see incremental drivers and efficiency gains as a result. This might have been enough for the business landscape of a few years ago but sadly the low-hanging fruit has all but been eaten.
In 2021, DQ needs to be a focus for all organisations, regardless of their size or their data proficiency. It is simply a prerequisite for today’s businesses. Data – and the journey towards the attainment of data quality – should be part of every organisation’s culture.
The financial services sector has been moving towards embedding active cultures of data for some time and highlighting the importance that good data quality practices can bring. Especially when thinking about regulation, though many of these are obviously enforced and had been viewed as box-ticking exercises.
But, increasingly, a robust approach to data quality is underpinning financial services organisations’ approach to growth, as well as being the tool that is increasingly used to meet customer needs and demands. The importance of DQ now stretches well beyond regulation. In fact, it is one of the cornerstones to achieving data-driven reinvention, acting as a springboard towards true data-powered growth.
A DQ ‘end state’
While there is no real ‘end state’ when considering data quality, there are certain data maturity levels to consider when assessing whether an organisation is a mature data organisation or not. Some common stumbling blocks to achieving higher maturity levels can be seen through a data quality lens.
First, it’s crucial that organisations get the data community on board as it’s near impossible to make DQ happen without organisation-wide buy-in. This is easier said than done, but it’s crucial to engage, enable and motivate the community, learning and building both internal data expertise while leveraging outside expertise when required while simultaneously driving building that sense of a data culture.
Second, some organisations struggle to offer a singular view of what DQ means to them. Many organisations reach their own idiosyncratic version of DQ, and this results in individuals having a siloed view. It is rare for an organisation-wide theme to bind DQ.
A siloed view of DQ can inhibit organisation-wide adoption, and this can lead to problems when seeking to improve reporting, monitoring and analytical insight.
The road towards effective DQ
DQ in financial services is evolving, with value and competition-led movements driving the adoption of DQ principles.
Organisations are now using data to drive customer and commercial benefits. For example, fintechs use data to create bespoke experiences for customers (usually via apps) that suit their specific needs. This approach to customer service, coupled with movements such as Open Banking, is meaning that DQ in the future will underpin customer growth in financial services.
Effective DQ can also be used as a catalyst for enhanced operational efficiencies and insight, and it can be made more impactful by deploying AI and ML. These technologies allow the sort of thinking that guides an organisation’s strategic direction, giving them competitive advantages.
Looking to the future
The future of DQ will build upon the solid foundations that most organisations have put in place in recent years. And whilst the current landscape is complex, there are many avenues available to organisations who wish to realise value through effective DQ.
Layering AI and ML on top of current practices will reveal improvements. This will lead to more intelligent and preventative DQ, whether that be from a source system or third party suppliers. It can also include the application of tooling or the leveraging of suppliers upfront to make sure that bad data never even enters an organisation’s pipeline.
Execution of near real-time issue detection will also be vital, with organisations able to firefight in real-time, rather than having to conduct retrospective corrections. ‘Self-healing DQ’, the type that leverages AI and ML, should enable companies to enrich their data cleansing, data enrichment, and automated remediation will be vital.
All of these upgrades will help an organisation to fix data issues in real-time, meaning that any damage done by poor data quality should be minimised. And this should lead to DQ becoming a reality for more companies, and a vast array of benefits will be derived as a result.