Low Data Trust Limits the Value of Analytics and AI

Companies are expanding analytics and artificial intelligence capabilities at a rapid pace, yet many leaders still lack confidence in the data informing critical decisions, according to new insights from Info-Tech Research Group, an IT research and advisory firm.

The firm's latest study found that fragmented ownership models, inconsistent validation mechanisms, and reactive cleanup processes are limiting the business value organizations expect from their data investments.

"Quality data drives quality decisions, but many organizations have not operationalized the accountability structures required to ensure consistency," said Ibrahim Abdel-Kader, senior research analyst at Info-Tech Research Group. "When ownership and validation are unclear, teams default to cleanup instead of prevention, and analytics investments underperform as a result."

To deliver reliable analytics and AI outcomes, organizations must move beyond reactive data cleanup and treat data quality as an operational and governance priority, Info-Tech said, noting that organizations should identify high-impact data issues, contain defects before they spread, eliminate root causes, and institutionalize sustainable improvement across the enterprise. Toward that end, it suggested the following four steps:

1. Discover the data problems that impact strategic initiatives. Business unit leaders and data stewards, in collaboration with IT and analytics teams, identify and prioritize the data quality issues most closely tied to revenue, compliance, and decision execution.
2. Prevent data quality issues from spreading using effective profiling. IT and data teams implement structured profiling, validation controls, and monitoring practices to detect defects early and limit downstream operational and reporting errors.
3. Address root causes through a well-designed improvement plan. Process owners and governance committees examine breakdowns in workflows, controls, and accountability to eliminate recurring data defects at their source.
4. Sustain continuous improvement by leveraging data management capabilities. Executive sponsors, supported by CIO and CDO leadership, embed performance metrics and stewardship accountability into governance forums to maintain data standards and protect long-term business value.

By formalizing ownership, strengthening governance, and addressing root causes rather than symptoms, organizations can transform data quality from an operational burden into a strategic capability, Info-Tech added.

"Organizations often view data quality as too complex to address within existing constraints," Abdel-Kader said. "A structured program enables leaders to prioritize high-impact issues, eliminate structural weaknesses, and build a scalable foundation that strengthens analytics performance and AI outcomes."