Data quality is of paramount importance to the effective use of data analytics, but it remains a tall order to improve data quality which can become a huge barrier to cost savings and sustainable value delivery.
Improving data quality is not a one-time effort, said Jason Medd, Director Analyst at Gartner.
“One of the mistakes that organisations make is taking a technology-centric approach to data quality improvement, with little focus on organisational culture, people and processes to streamline remedial actions,” he noted.
12 actions in four categories for data quality improvement
The advisory firm condensed 12 actions into the following four categories for organisations to prioritise their efforts based on the problem areas.
Focus on the right things to set strong foundations
CDAOs need to focus on the right things to set strong foundations.
Not all data is equally important and organisations must focus on the data that has the most influence on business outcomes, understand the key performance indicators (KPIs) and key risk indicators (KRIs), and build a business case.
Then, they need to share common data quality language with stakeholders and establish data quality standards.
Once the foundations are established, organisations need to obtain sponsorship from data and analytics governance committee and dedicate data stewards from business units and the central D&A team who will proactively shift gears based on priority, look at new avenues to aid improvements, and potentially look at building real-time data validations where needed to help bridge the gaps.
“Data is a team sport, so chief data and analytics officers (CDAOs) should form special interest groups who can benefit from data quality improvement, communicate the benefits and share best practices around other business units,” Medd said.
Establish fit-for-purpose data quality
When organisations improve data quality, it’s important for them to perform data profiling and data monitoring to understand and validate current data gaps and challenges, monitor and build improvement plans.
Then, CDAOs need to transition to a governance model based on trust to drive enterprise-wide adoption of data quality initiatives.
Integrate data quality into corporate culture
CDAOs can improve data quality by using technologies to reduce manual efforts and get faster results.
They also do it by identifying frequent issues and incorporating the solutions into business workflow.
CDAOs should also improve data literacy across the business by installing a data quality culture and facilitating knowledge sharing and collaboration among all the stakeholders of the program.