The majority of companies consider data governance vital for the success of their BI implementation projects, as confirmed by various research reports. For instance, 75.9% of respondents to Wiiisdom’s State of Analytics Governance 2025 report state they have implemented governance standards and policies specifically for their BI and analytics initiatives, which comes as no surprise.
After all, efficient data governance helps companies ensure their data is managed to maintain accuracy, completeness, and security, which is critical for reliable, trustworthy analytics insights. If you plan to implement a BI architecture that enables effective data exploration and analysis, you should have a well-defined data governance framework.
In this article, experts from Itransition, a company providing business intelligence services for 15+ years, share three practices to help you build a solid data governance framework.
Creating a well-structured data catalog
A comprehensive data governance framework ensures control over data throughout its entire lifecycle, acting as the bridge between raw data and valuable analytical insights. Effective data control, however, becomes impossible, if you do not have visibility into where your data originates from, where it resides, and how it is used. In this context, implementing a well-structured data catalog can be vital to the success of your BI initiative.
In short, a data catalog is a systemized inventory of your data sources, like datasets, files, and reports. It does not store the data itself, but contains metadata (also called “data about data”). Metadata describes the characteristics of data resources, including their format, creation and modification dates, location, ownership details, and usage rights. By containing this and other critical information, a data catalog improves data discoverability and traceability. 83% of organizations that participated in Secoda’s State of Data Governance in 2025 report stated that using data catalogs for centralizing metadata helps them improve data discoverability and transparency.
You can build and implement a data catalog using one of the data cataloging tools, such as Azure Data Catalog, AWS Glue Data Catalog, or Qlik Catalog. When selecting the optimal technology, prioritize solutions with robust automation capabilities, such as AI-powered metadata harvesting, AI-driven classification and tagging, and automated catalog updating, as they can help you improve the accuracy and speed of your data cataloging processes.
Implementing automated data quality monitoring
When creating a data governance framework for your BI project, you will need to establish standards and processes for maintaining the high quality of your data, so that it is reliable and trustworthy from the analytics perspective. In particular, you need to define quantifiable metrics for data accuracy, consistency, and completeness to be used as baseline data quality benchmarks. You will also have to embed data quality monitoring and checks into a governance framework to be able to continuously assess the health of your data and its alignment with predefined benchmarks.
It is highly recommended to automate data validation and checking processes with dedicated tools, which can help you proactively catch data issues, while eliminating bottlenecks and inefficiencies commonly associated with manual data quality monitoring.
Data quality tools like Talend Data Quality and Oracle Enterprise Data Quality can examine data at rest, scan it for issues like duplicates or missing values, and automatically remediate problematic data by combining rule-based logic and AI. Data observability tools like Monte Carlo and Datadog can be used to monitor your entire data pipelines in real-time and analyze data in transit, looking for anomalies like broken data relationships or schema changes and facilitating their root cause analysis.
Ensuring continuous training and support for data stewards
The majority of senior data leaders who participated in the 2025 State of Enterprise Data Governance survey from Board.org consider data stewardship the top critical data governance aspect. In BI implementation projects, data stewards are accountable for determining data quality criteria and regularly reviewing data to ensure it adheres to them, contributing to the reliability of generated insights. Data security also largely hinges on data stewards, who are responsible for establishing policies governing secure data access, use, and disposal. On top of that, data stewards act as educators for business users on how to use data correctly during the analytics process.
Properly defining the data steward role, assigning it to the right individuals, and embedding data governance policies and tools into daily operations are fundamental steps that lay the foundation for effective data stewardship. Additionally, data stewards should be positioned as data advocates rather than data governance enforcers. In this role, they help build trust in data among business users, identify new opportunities for data usage and analytics use cases across departments, and encourage more people in a company to utilize BI. This way, they contribute to a more data-driven corporate culture, maximize BI adoption among different teams, and, eventually, improve BI implementation ROI.
Transforming data stewards into enthusiastic advocates requires continuous training and executive support. After building your data stewardship foundation, consider taking additional steps. Invest in developing a comprehensive training program that enables stewards to continuously improve their communication and collaboration skills, which are vital to their advocacy function. Training initiatives should help data stewards develop strong business acumen, enabling them to align data governance efforts with specific business objectives.
In addition, define measurable performance metrics aligned with your data governance goals, for instance, Policy Adherence Rates or Meantime to Detect and Respond. Tracking the defined KPIs over time will help you assess the training effectiveness, the performance of individual data stewards, and provide them with targeted training opportunities to address performance gaps.
Ideally, you should review stewards’ performance regularly, so consider implementing an Objectives and Key Results (OKR) goal-setting and tracking methodology, and holding OKR meetings monthly or quarterly. As stated in the aforementioned Secoda’s report, currently just 28% of companies link data governance to team OKRs, and only 6% prioritize training of data stewards, while these are the opportunities to strengthen their alignment and accountability.
Final thoughts
To implement and use BI effectively, you need a robust data governance framework. The practices listed in this article can help you build a stronger framework that enables you to maintain data transparency, quality, and literacy across your company. If you lack in-house competencies to create it, consider turning to business intelligence experts, who can analyze your business goals, audit your data landscape, and create a tailored framework. If needed, experts can also help you address the practical implications of BI implementation to ensure you end up with governed, trusted insights.