A new research from Ataccama shows that companies face important challenges in achieving sufficient data quality to support both artificial intelligence initiatives and regulatory compliance requirements.
Completed more than 150 times since March by the top data managers, business leaders and data management experts, the Ataccama Data Trust assessment provides a benchmark for data reliability within your organization. Results show an average of 42 out of 100 scores due to data trust maturity. The lowest scores are seen in remediation workflow, policy enforcement, and quality of reference data.
These findings suggest that the accelerated adoption of AI and the increased regulatory requirements make weaknesses in enterprise data more noticeable. If data cannot be relied upon, both AI and compliance efforts run the risk of failure.
This assessment was developed to guide organizations from running inconsistent, isolated data management projects towards achieving a comprehensive, mature-based programme. This approach addresses people, processes, and technologies, and outlines ranked priorities across four pillars: quality, governance, observability and improvement through assignment of baseline scores, providing peer benchmarks, and enhancing workflows. Participants are encouraged to repeat the assessment to monitor the progression and strategic scaling of the data maturation programme.
The struggle between AI and compliance
A broader Ataccama study finds that around a third of organizations report meaningful advances in AI, while most identify data quality as a major obstacle. Instead of deploying AI models, leaders spend a considerable amount of time searching, verifying, and tuning data. This report places the quality of data as the center of establishing trust – compliance to provide basic AI and compliance with positive outcomes.
There are differences in how these gaps manifest between sectors, but the underlying issues are consistent. The results are at risk without reliable data. In financial services, data lines are less observable and difficult to report audits and trace reports to the original sources, making the risk of escalating regulations difficult. For manufacturers, inconsistent product data has the impact of cascades, leading to delayed reporting, disruption to the supply chain, and increased costs of compliance. In all industries, reports conclude that high data quality is essential to produce reliable results.
Leadership perspective
“Quantitative intuition, a combination of data and judgment, drives better decisions. Untrusted data erodes every decision it touches,” said Jay Limburn, Chief Product Officer at Ataccama. “Too many organizations invest too much in their data programs without a clear view of how reliable a data landscape is. We have built the Ataccama Data Trust Assessment to guide the first thing to fix, trust has collapsed. Data trust creates value in all respects.
Ataccama Data Trust Assessment aims to provide organizations with actionable insights to prioritize and fix issues, and to support businesses towards robust and scalable data governance. Leaders can track progress over time and directly track investments in areas that have the most impact on the data ecosystem.
Research and evaluation tools reflect the focus of an industry that is growing in establishing strong data quality as a foundation, not only to improve AI performance, but also to address the escalation of regulatory and compliance obligations that require auditable and trackable data management practices.
