Glossary term

Data Governance

Proprietary data is one of the most important assets your organization has—it includes your plans and goals and allows you to shape and enhance business outcomes. Proprietary data belongs to your company and yours alone, and it allows you to choose what data you collect and how you collect it.

But the real question is, what is your organization doing to protect and secure that data?

Enter: data governance—the policies that manage how data is collected, stored, and shared within an organization. How well an organization governs its data will define, to a large extent, the organization’s overall success, as companies with a poor strategy will be left grappling with inconsistent data availability, usability, and security. Efficiency is a must for data analysts and top-level management alike to maximize data’s value throughout its entire lifecycle.

Read on to understand why it’s important to have a solid strategy in place and common roadblocks organizations face when refining their approach to data governance.

What Is Data Governance?

Data governance refers to the set of processes, policies, roles, standards, and best practices that ensure the effective acquisition, management, and utilization of data. It ensures that available data provides as much value as possible within an organization.

Data governance guarantees that data is safe, secure, consistent, trustworthy, and doesn’t get misused. It also defines who has access to what data, who is responsible for managing and owning it, as well as how it should be used and distributed to employees.

It’s increasingly important to have a sound strategy in place as both privacy regulations and reliance on data analytics to optimize operations and make informed business decisions continue to grow.

Why Is Data Governance Important?

In a 2020 survey, McKinsey cited that approximately 30% of enterprise time was spent on non-value-added tasks due to poor data quality and availability.

To avoid wasting valuable time, and to improve data analysis, business operations, and decision making, organizations need effective data governance. It’s also essential for maximizing your customer data while minimizing risk. For instance, by giving your employees more reliable, secure access to real-time customer data, you can ensure you take the right actions with these customers. There’s also the added benefit of taking advantage of timely cross-sell and upsell opportunities.

By reviewing data quickly with the help of real-time analytics, you also prevent data inconsistencies or errors in different systems across the organization.

Key Components of Data Governance

A well-executed strategy breaks down silos in an organization and provides the right people access to the right data at the right time. There are several key components that support data governance, including:

Data architecture

Data architecture acts as a blueprint for managing data assets. It outlines the structure of a business’s logical and physical data assets as well as data management resources. It also explains where data exists and how it moves across the organization and its systems. It highlights critical changes and transformations made as data travels from one system to another.

Data quality

Data integrity, completeness, timeliness, and consistency across systems are critical components of a successful governance initiative. Data cleansing is another common data quality element that quickly identify errors, fixes inconsistencies, and remove duplicate instances, ensuring the continuity of business operations.


Metadata is data that describes other data (hence: meta), including how to search for and locate essential company data. It helps your team understand why certain information is being collected and its contribution to the short-term and long-term business goals.


According to a report by Dataversity, 48 percent of organizations indicated that regulatory compliance is their primary driver for data governance. Data governance sets rules and procedures around data ownership and accessibility so that organizations can conform to data standards, ensuring data policy compliance within fixed regulations.

Data management

While data governance refers to the documentation and policies in place around data, data management is the execution piece. It comprises the collection, storage, and use of data in action, rather than the guidelines around it. According to Forrester Research, efficient and effective data governance grows out of data management maturity.

The Challenges of Data Governance

Data governance is not simple. Gartner predicts that by 2025, 80 percent of businesses looking to scale digitally will fail because they lack a modern approach to data and analytics governance.

So, what are some of the challenges organizations face while trying to implement policies, and how can you combat them? Let’s find out!

No data leadership

Data governance travels across multiple channels and departments within an organization and requires clear leadership and education from the top down.

A successful strategy also requires cross-functional collaboration. The first step toward achieving this is to set up a structured data governance team. This includes a knowledgeable Chief Data Officer (CDO) to articulate the need for data governance in the organization and keep stakeholders informed. The CDO needs to have a supporting team including dedicated project managers responsible for training and reporting the progress of data governance to key decision-makers in the organization.

Siloed data

In many organizations, data is owned by different teams and stored in different formats. This happens due to the fast pace of data collection, introduction of new data sources, existence of communication barriers, and constant growth of new technologies.

Siloed data systems often hinder the free flow of data across the digital ecosystem. As a result, it becomes hard to organize, share, and update this information within the organization, which makes it impossible to draw meaningful conclusions from the data.

The ultimate solution is to move the data from silos to a centralized data governance framework. This should be an ongoing activity to streamline data search and management and encourage better decision-making within the organization.

Poor data context & quality

A critical part of data governance is ensuring the organization only gathers the data it needs to foster growth and meet its bottom-line goals. Data stewards should identify inaccurate, corrupt, and old data and realize when it’s been analyzed out of context. They should also be able to set rules, regulations, and processes to govern the company data and ensure it can be trusted and prioritized correctly.

Lack of control over data

Lack of control over a company’s data can result in non-compliance, especially when people process data unlawfully. If employees access data they’re not supposed to, it can lead to legal injunctions. This might open up your system to security breaches and legal concerns under the HIPAA, CCPA, GDPR, and other recent legislation.

All these regulations require the business to have a solid system that shows a data roadmap from the source to the destination. This way, companies can safeguard critical information from getting to the wrong people and establish control over their data.

Choosing The Right Data Governance Solution

Data governance isn’t a one-size-fits-all approach, it’s an ongoing process tailored to meet each organization’s needs. It has an impact on a business’s strategic, operational, and tactical levels. As AI and ML technologies become more integrated in enterprises, data governance is becoming increasingly critical. Organizations will need to implement dynamic, holistic data governance as high-quality, secure data is more important than ever before.

Ready to see how RapidMiner can support your data governance initiatives and allow you to make the most of your company data? Download RapidMiner for free today to get started!

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