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Gartner: 12 Actions to Improve Data Quality


May 22, 2023

Gartner identified 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 organization.

“Data quality issues cost a lot,” said Jason Medd, Director Analyst at Gartner. “But the issues are not hard to fix and does 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,” said Medd.

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

Matt Hooper, the Chief Marketing Officer with Quantexa added, “At Quantexa we say – the Right Data – the Right Decisions. In a world with heightened uncertainty, organizations are turning to Quantexa to ensure they can trust their data and make quicker and more informed decisions to protect, optimize, and grow.

Quantexa’s Decision Intelligence Platform gives customers the ability to understand their data by connecting siloed systems and visualizing complex relationships. The result is a single view of data that becomes their most trusted and reusable resource across the organization. Quantexa helps customers establish a culture of confident decision making at strategic, operational, and tactical levels to mitigate risk and seize opportunities on their path to building efficient and resilient organizations.”

Gartner analysts shared 12 actions for CDAOs and D&A leaders to take to deliver improvement and assurance in their DQ at the Gartner Data & Analytics Summit, taking place in London through Wednesday (see Figure 1 - 12 Action to Improve an Orgs Data Quality)

Gartner analysts condensed the 12 actions into four categories to enable CDAOs to prioritize 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. “Not all data is equally important,” said Medd. “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,” 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 enterprisewide adoption of DQ initiatives.

Once that model is in place, fit-for-purpose data quality improvement tools or platforms like IRI Voracity with data profiling, validation, scrubbing, standardization, and enrichment capabilities can be deployed. According to IRI SVP David Friedland, "data cleansing, like data masking, is a basic data governance requirement for CDAOs charged with protecting the value, reliability, and safety of source (and test) data and fostering stakeholder trust in analytic results."

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.

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