An Introduction to Data Quality
The benefits being derived by organisations from data are almost endless. It’s informing strategy, underpinning products and driving revenues and, ultimately, being used to give organisations a competitive advantage.
In theory, this all sounds great, but to achieve any of these benefits, an organisation must ensure that its data is fit for purpose. It must be of sufficient quality. At Mudano, we find data quality comes up as one of the top reasons organisations give as a barrier to adoption of data solutions, and why they are failing to get the most out of their data.
To help organisations overcome these barriers, we’re going to break down some of the myths around data quality, setting out what it is and how it can help you and your organisation.
Data quality demystified
Data quality means different things to different people depending upon their specific user requirements. Broadly speaking, it means making an assessment of how well data is fit for its intended use, whether that be for analytics, decision making, processing and operations, and whether it fulfils business requirements and acceptance criteria.
Once an assessment has been made, action can be taken to improve the quality of data and remediate any issues that have been identified. As a result, the quality of the data improves to better suit its intended purpose.
Why data quality really matters
Quality data is no longer the purview of large, sophisticated organisations. All organisations, regardless of size or data proficiency, should look to harness quality data wherever possible and make it an integral part of their day-to-day business. It should be part of their culture.
This is especially true in financial services, where data is changing the landscape beyond recognition. One area being impacted is regulation, where changes are transforming requirements for how management uses data. Regulators are also designing new standards for data that protect customers, promote risk management, and support competition (e.g. BCBS 239, GDPR).
As a consequence, data must underpin all financial services organisations’ approach to growth, as well as being the tool they use to meet customer needs and demands. By considering data an asset, organisations can use it to drive customer and commercial benefits. It can also deliver competitive dynamics, creating alternative business models and transformational capabilities.
All of these outcomes rely on data quality. Without it, more problems are created than solutions.
The path to quality
Data quality is dynamic and must be measured, maintained and improved in a way that reacts to current circumstances. We recommend our clients maintain and improve the quality of their data by taking 4 simple steps:
- Measure – “We know the quality of our data”
Data profiling provides ‘facts’ about the data that creates insights into data quality levels. This involves Data Quality Rules (reflecting the requirement of a business rule) and Data Quality Tolerances (which define the error/fail rate of a Rule). Assessments of data quality gives data stakeholders all they need to promote data quality.
- Remediate – “We correct inaccurate data”
Data quality issues are defined as those that prevent the effective use of data. Logging these issues means that remedial action can be coordinated and prioritised. Root cause analysis will identify the reason and corrective action can then be taken to eliminate or minimise the risk of reoccurrence of the issue.
- Improve – “We takes steps to continually improve our data”
Data Quality Controls are actions, and they reduce risks associated with the quality of the data. The process of improving existing Rules, Controls and Tolerances makes assessments more precise. A Data Quality Action Plan formalises how to make the improvements but it’s no good just having a plan – implementation is required, too.
- Govern – “Data Quality is considered in all design phases”
Data quality improvement can be achieved through day to day business operations, in many cases sourcing investment for resources (e.g. technology). This allows action plans to be realised. But all data quality activity requires stakeholders to be incentivised to push forward with strong governance. Data quality is a subject that impacts all colleagues, so regular and productive engagement with stakeholders is necessary.
Quality leads to a competitive edge
Data is driving organisations towards becoming the best version of themselves, delivering positive, transformational change organisation-wide. But data is only fit for purpose if the quality can be trusted. The fact data quality is becoming a core component of regulation means that it should be a vital consideration for an organisation’s data strategy and data management policy.
Achieving data quality isn’t straightforward – but it is possible, and that’s why it should be looked upon as an opportunity by all organisations, large and small. By harnessing data quality, organisations can give themselves a scalable, competitive edge, driving growth and engagement for the long term.