“Data quality issues cost a lot,” said Jason Medd, director analyst at Gartner. “But the issues are not hard to fix and do not have to take a lot of time. If CDAOs don’t have impactful and supportive DQ programs in place, their organisation will face a multitude of complications and lost opportunities.”
Gartner lists 12 actions to help chief data and analytics officers (CDAOs) and other D&A leaders improve data quality (DQ) to avoid high costs and deliver sustainable value to their organisations.
Improving DQ is not a one-time effort. “One of the mistakes that CDAOs make is taking a technology-centric approach to DQ improvement, with little focus on organisational culture, people and processes to streamline remedial actions,” said Medd.
Gartner analysts estimate that through 2024, 50% of organisations will adopt modern DQ solutions to support their digital business initiatives better.
Gartner analysts condensed the 12 actions into four categories to enable CDAOs to prioritise their efforts based on the problem areas.
Focus on the right things to set strong foundations
First, CDAOs need to focus on the right things to set strong foundations. Medd points out that not all data is equally important. He suggests focusing on the data that most influence business outcomes, understanding the key performance indicators (KPIs) and key risk indicators (KRIs), and building a business case. CDAOS must then share common DQ language with stakeholders and establish DQ standards.
Apply data quality accountability
Once the foundations are established, CDAOs need to obtain sponsorship from D&A 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 CDAOs should form special interest groups who can benefit from DQ improvement, communicate the benefits and share best practices around other business units,” said Medd.
Establish “Fit for Purpose” data quality
To improve DQ it is important 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 DQ initiatives.
Integrate data quality into corporate culture
CDAOs can make DQ better by using technologies to reduce manual efforts and get faster results. They also do it by identifying frequent DQ issues and incorporating the solutions into the business workflow. CDAOs should also improve data literacy across the business by installing a DQ culture and facilitating knowledge sharing and collaboration among all the stakeholders of the program.
