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Gartner: 12 Actions to Improve Data Quality


Trusted, high-quality data is key to enabling a data-driven enterprise, yet many data and analytics (D&A) initiatives fail because of poor data quality. Chief data and analytics officers (CDAOs) can take 12 simple and pragmatic actions to improve data quality (DQ) and deliver sustainable value to their organization.


Data quality issues cost a lot, 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 organization will face a multitude of complications and lost opportunities. 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 organizational culture, people and processes to streamline remedial actions.


Gartner estimates that through 2024, 50% of organizations will adopt modern DQ solutions to better support their digital business initiatives.

Gartner condensed the 12 actions into four categories to enable CDAOs to prioritize their efforts based on the problem areas and to deliver improvement and assurance in their DQ.


Focus on the Right Things to Set Strong Foundations

First, CDAOs need to focus on the right things to set strong foundations. Not all data is equally important. CDAOs 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 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.


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 enterprisewide 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 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.


(The author is Jason Medd, Director Analyst at Gartner, and the views expressed in this article are his own)


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