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What is Data Governance's Role in Business Intelligence

Without effective data governance framework from which to build on, data inconsistencies across disparate systems throughout an organization might not get resolved. For example, clients naming conventions may be listed differently in sales, logistics and customer service systems. This may complicate data integration efforts and create data integrity issues that affect the accuracy of business intelligence, enterprise reporting and analytics applications.

Data governance framework is the backbone of most data-driven organizations today, considering the huge amount of data companies send, receive, process, analyze, and store, data governance is required to ensure such data is accurate, consistent, reliable, secured and accessible. This is the reason strategic and effective data governance is the foundation of a solid architectural strategy.

So, what exactly is data governance and why is it so crucial for business intelligence?

What are Data Governance Best Practices?

Data governance is a strategic management system that defines policies, enforces decisions, and creates the processes whereby data is collected, regulated, appropriately stored, secured, analyzed, integrated, shared, accessed and used without breaching data privacy and protection laws.

An effective data governance framework in place guarantees integrity, security, transparency, availability, decreases data risk and indicates how data is generated, managed and consumed. In addition, data governance defines various layers of responsibilities in an organization. Data governance ultimately determines data architecture, data warehousing, and management alignment.

Flexibility is one of the key features of an effective data governance framework. This means it can be tailored to meet the data needs and situations of an organization and changed accordingly if required. This is because data is used for business intelligence which is dynamic and constantly evolving.

An effective data governance framework encapsulates data management, project management, and program coordination. Here are the scope and goals of each of these aspects:

  1. Data Management
    An administrative process that involves how data is acquired, validated, stored, protected and processed to guarantee how such data is accessed, its level of reliance and how timely it is for users.
  2. Project Management
    A practice that provides a structure for using data and making decisions based on the requirements of precise projects within a bigger project.
  3. Program Coordination
    This is a structure and framework for tactical planning, setting goals, decision making for programs as well as for assigning responsibilities and roles. Program coordination also recognizes the major stakeholders concerned and who is permitted to grant program events and priorities.

So why is Data Governance important for business intelligence?

Importance of a Solid Data Governance Framework for business Intelligence

A solid data governance program simplifies and enhances business intelligence process. Below are the key reasons why data governance is so crucial for business intelligence:

Easier Access to Specific Data

Data governance tools are designed to clearly specify where a particular data is sourced from and who has access to what data. This makes it easier for business intelligence to know exactly who to turn to for a precise data. For example, if business intelligence requires data about accounting, data governance will clearly indicate where such data is sourced from, stored, and who has access to such data. This makes it easier for business intelligence to obtain data easily, structure it accordingly, and analyze it to ensure they can make meaningful business decisions.

Access to Accurate and Consistent Data

Effective data governance ensures that data inconsistencies and inaccuracies in different silos or systems in an organization are resolved seamlessly. For instance, a particular customers name may be erroneously listed differently in customer service system, logistics, and in sales. Errors such as these cause problems with data integration efforts and related data integrity issues that can affect the precision of business intelligence, enterprise reporting as well as the application of analytics. Data governance ensures accuracy and consistency across various silos, data warehouses, and systems.

Making smart strategic business intelligence decisions is impossible without accurate and consistent data. With the help of data governance, business intelligence results are more effective because the data will be accurate, consistent and easier to analyze. Data governance makes it easier for business intelligence to have access to information that is not only crucial but also required to succeed in multiple areas including finance, marketing, sales or any other department.

Effective Data Enterprise Management

One of the most glaring risks of providing advanced analytics tools to organizations is that their data technology knowledge and access to limited data could cause conflicting data interpretations, data definitions, and metrics. Having a strategic data governance program will ensure that an organization’s in-house business intelligence platforms are monitored appropriately and audited as required without delay to avoid any trace of bad data quality. It is also done to eradicate data inconsistencies by defining and implementing strict policies and protocols across the whole business intelligence architecture.

Boost Confidence in Your Analytics with Data Governance Best Practices

Data governance is also a key aspect of quality control. Business decisions made based on the wrong set of data will yield poor results. Data governance best practices are built on the foundation of accuracy and consistency to ensure that business intelligence only has access to accurate data. This translates to trusted analytics for making effective and strategic business decisions.

A lot of business intelligence projects fail because they focus solely on technology. For instance, Microsoft’s Power BI tool is an effective business intelligence tool. But if you’re using the wrong set of data for Power BI, you will end up with the wrong insights and analytics and make wrong business decisions accordingly. Ultimately, the organization will end up losing trust in the people conducting the analytics and making the business decisions.

However, data governance can boost confidence in your analytics because it ensures that business intelligence decisions are made based on trusted data. Data governance gives you the confidence that your metrics are accurate as well as your KPIs. It also gives insight into what your most significant metrics should be based on trusted analytics.

Break Down Silos in an Organization

Strategic data governance programs are often designed to break down silos in organizations. Silos often build up when distinct business units use separate transaction processing systems that lacks a centralized control or data enterprise architecture. One of the key essences of data governance is to standardize the data in such separate systems via a common process that involves key decision makers from the various business units contributing. Once data is centralized, it is easier for business intelligence tools to transform them into meaningful insight and analytics that can be used for making sound business decisions.


Having a strategic data governance framework in place gives business intelligence the foundation it needs for creating reports that lead to making smart and effective decisions based on accurate and consistent data that yield meaningful outcomes.

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