Low Data Trust Limits the Value of Analytics and AI Investments, Says Info-Tech Research Group

Despite significant investments in analytics platforms and AI initiatives, organizations continue to face recurring data defects that weaken reporting accuracy, regulatory confidence, and model performance. Without clear ownership and effective validation controls, teams repeatedly correct defects instead of preventing them. Info-Tech Research Group’s Build Your Data Quality Program blueprint outlines a structured, step-by-step approach to eliminate root causes and institutionalize enterprise-wide data discipline.

Enterprises are expanding analytics and AI capabilities at a rapid pace, yet many leaders still lack confidence in the data informing critical decisions. New insights from Info-Tech Research Group indicate that fragmented ownership models, inconsistent validation mechanisms, and reactive cleanup processes are limiting the business value organizations expect from their data investments.

To help organizations address these structural gaps, the global IT research and advisory firm has published its Build Your Data Quality Program blueprint. The resource provides CIOs, CDOs, and data leaders with a phased methodology to shift from ongoing remediation cycles to a sustainable, governance-aligned data quality program tied directly to strategic priorities.

“Quality data drives quality decisions, but many organizations have not operationalized the accountability structures required to ensure consistency,” says 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.”

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Info-Tech Outlines Four Structured Steps to Operationalize Data Quality
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’s Build Your Data Quality Program outlines a clear sequence of actions that help IT leaders identify high-impact data issues, contain defects before they spread, eliminate root causes, and institutionalize sustainable improvement across the enterprise:

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.

“Organizations often view data quality as too complex to address within existing constraints,” explains Abdel-Kader. “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.”

The firm’s blueprint emphasizes that sustainable data quality is achieved through clear accountability, executive sponsorship, and continuous oversight, not one-time remediation efforts. When embedded into enterprise operations, a structured data quality program restores trust in reporting, strengthens compliance posture, and protects the long-term value of analytics and AI investments.

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