Prioritize Data Quality: Strengthening Governance for AI and Business Success

86% of analytics and IT leaders agree: AI’s effectiveness depends on the quality of its data inputs. High data quality brings organizations closer to data maturity and AI success—and it all starts with strong data governance.

🔹 92% of analytics and IT leaders say there’s never been a greater need for trustworthy data.
📊 The State of Data and Analytics Report, 2023

Building a Strong Data Governance Strategy

Data governance is more than compliance—it’s a structured approach to managing data quality, security, and usability. With the right governance in place, teams gain confidence in their data, leading to smarter decision-making and a culture of trust.

Follow these six steps to lay the foundation for a successful governance strategy:

1. Align Governance Policies with Business Needs

Meet with stakeholders to understand how data is used across teams. Ensure governance policies cover every critical workflow and use case, helping teams get the data they need—accurately and securely.

2. Define What ‘Data Quality’ Means for Your Organization

Create a clear framework using these key data quality dimensions:

✅ Completeness: Are all necessary data fields populated?
✅ Timeliness: Is data up to date and aligned with business goals?
✅ Validity: Does data comply with governance rules and constraints?
✅ Usage: How frequently is the data used for reporting and decision-making?
✅ Accuracy: Does the data reflect reality, and is it sourced from trusted origins?
✅ Consistency: Are data formatting and structure standardized across sources?
✅ Reliability: Has data maintained quality and consistency over time?

3. Implement a Robust Quality Control Process

Standardized processes—data entry validation, deduplication, cleansing, and archiving—are essential for governance. Leverage AI-powered tools like Tableau CRM Analytics to automate these tasks with built-in data profiling and enrichment features.

4. Schedule Regular Governance Reviews

Your business evolves—your governance strategy should too. Establish a review cadence to assess policies, update processes, and address new data challenges.

5. Train Teams on Data Security and Compliance

Education is key. Assign role-based security permissions, ensure regulatory compliance, and provide a clear process for reporting data issues (e.g., a dedicated Slack channel or help desk).

6. Define Success with Data Governance KPIs

Tracking governance effectiveness is essential. Use these key metrics to measure impact:

Metric	Example of Smart KPI	How to Track
Data Quality	Improve overall data quality by 4% per quarter.	Assign values to frequency, error rates, and data usage.
Data Usage	Increase customer data-driven decision-making by 30% in 12 months.	Measure employee logins, reports accessed, and data utilization.
Time-to-Insight	Reduce time from customer action → dashboard update to 10 minutes by next quarter.	Track time-to-insight vs. benchmarks.

Moving Up the Data Maturity Curve

A well-governed data strategy leads to:

📈 Higher efficiency in decision-making
🚀 More successful AI and analytics initiatives
🏆 Competitive advantage through trustworthy data

🔍 “Ascending the data maturity curve unlocks new efficiencies and a competitive edge.”
— Funke Bishi, Associate Director, Data and Business Analysis, RBC Capital Markets

Take Action: Strengthen Your Data Governance

✅ Define what ‘quality data’ means for your business.
✅ Align governance policies with team needs.
✅ Use AI-powered tools like Tableau Data Prep for automated cleansing.
✅ Train leaders on data best practices and compliance.