High expectations research shows that 77% of organizations have data quality issues

Amazing experienceectations, a leading open source data quality platform, announced the results of a study that highlighted the key pain points and consequences of poor data quality within organizations. Insights from 500 data practitioners (engineers, analysts and scientists) showed that 77% have data quality issues and 91% said it affects the performance of their business.

“Poor data quality and pipeline debt create friction between stakeholders in the organization, resulting in loss of trust,” said Abe Gong, CEO and co-founder of Superconductive, the company that makes Great Expectations. “This study made it clear that data quality issues are common and hurt business results.”

Data quality issues can make it difficult or impossible to see a “single picture” of an end user or service, lower productivity, obscure reliable performance metrics, and overwhelm development teams and budgets with data migration tasks. Data practitioners blame poor data quality on lack of documentation (31%), lack of tooling (27%) and teams not understanding each other (22%). They said data scientists spent too much time preparing data for analysts, end users complained about gaps in their data (such as lost transactions), and production teams suffered delays.

Trust in data is critical for organizations to make informed business decisions. Less than half of respondents expressed high confidence in their organization’s data, and 13% had low confidence in data quality due to broken apps or dashboards, decisions based on unreliable or bad data, teams lacking shared understanding of metrics and silos or conflicting departments. Additional issues that impacted data trust included alert fatigue, misalignment with certain metrics, and friction between teams.

“Data quality is critical to facilitating confident decision making across the organization, enabling a single understanding of what that data means and what it is used for. That’s why support for data quality efforts must be found at every level of an organization, from data scientists and engineers to the C-suite and executives who have confidence in the results for decision-making,” said Gong.

89% of respondents said their leadership supported data quality efforts, and 52% believed that leadership views data quality with great confidence. When asked about their company’s current approach to data quality, 75% answered that they had validated data. Only 11% think they have no data quality problem. Data quality efforts include creating and budgeting a data quality plan (22%), using a dedicated data quality tool (19%), manually checking data (14%) and building their own systems ( 15%).

This survey was conducted in May 2022 by Pollfish, an independent research platform, leveraging responses from 500 information services and data professionals in the United States (57% males and 43% females, ages 18-54). 60% of respondents worked for companies with 250 or more employees.

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