Forrester on Data Governance: Approach, Challenges, Best Practices, and Tooling Recommendations

Updated November 24th, 2023
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Leverage Forrester’s research to understand modern data governance and pick the right governance tool for your team.

Forrester is a research and consulting firm that specializes in offering insights on technology, marketing, customer experience (CX), product, and sales functions. Forrester runs annual surveys of 675,000+ consumers, business leaders, and technology leaders worldwide to get these insights, along with its Forrester Wave™ evaluations.

In this article, we’ll review how Forrester perceives data governance and its role in data-driven decision-making. We’ll also take a look at Forrester’s recommendations on data governance tools.

Want to make data governance a business priority? We can help you craft a plan that’s too good to ignore! 👉 Talk to us

Table of contents #

  1. Forrester on data governance
  2. Forrester’s view of the essential data governance activities
  3. Forrester on the challenges with traditional data governance
  4. What does the future of data governance look like?
  5. How to improve the effectiveness of your data governance efforts
  6. Atlan’s approach to data governance
  7. Forrester Data Governance: Related reads

Forrester on data governance #

According to Forrester, governance, risk management, and compliance are “coordinated functions to set and enforce the boundaries within which an organization seeks to maximize performance.”

Forrester recommends taking an agile approach to data governance that emphasizes implementing “just enough controls for managing risk.”

Also, read → The 3 principles of data governance that can support a diverse data culture

This is because business needs are constantly evolving and expanding. Data governance must adapt accordingly to ensure you can extract the necessary insights from your data.

What should such an approach to data governance account for? Let’s take a look.

Forrester’s view of the essential data governance activities #

According to Jayesh Chaurasia, Forrester’s Analyst focusing on Master Data Management, Data Governance, And Data Quality, data governance includes 3 pivotal activities:

  • Data security and privacy compliance
  • Data ownership, sharing, and collaboration
  • Data accessibility and self-service

Let’s explore each activity further.

Data security and privacy compliance #

A key tenet of data governance has always been regulatory compliance and protection from data breaches. Data governance frameworks, policies, and procedures play a central role in safeguarding sensitive data, ensuring role-based access control, and preventing unauthorized access.

Data ownership, sharing, and collaboration #

Managing data flow and access becomes increasingly challenging as organizations scale and expand. The influx of vast volumes of data, used by various stakeholders and segmented across different systems or departments, often leads to data chaos.

That’s why another essential tenet of data governance is “defining roles and responsibilities to ensure data ownership that supports data definitions, quality standards, sharing parameters, and access controls.”

This helps everyone within your organization find, understand, and use the data assets they need for their work. It also eliminates data siloes, increasing transparency and trust in data.

Also, read → Data governance roles and responsibilities

Data accessibility and self-service #

When data governance becomes all about control, there’s little room left for democratizing access and utilization of your data assets. However, driving decision-making at scale requires empowering all data consumers to find and use the data they need, precisely when they need it.

Well-defined data governance policies that provide guidelines for data access, usage, and interpretation can help data practitioners (especially business users) discover and analyze data on their own.

Through robust governance, organizations strike a balance between data democratization and data control, enabling users to make informed decisions while maintaining data quality and security.”

Also, read → How data governance and compliance is an act of checks and balances

It’s essential to note that data governance extends beyond mere compliance and control. Forrester advocates moving away from this traditional approach in favor of a modern, business-centric, collaborative approach. Let’s delve deeper.

Forrester on the challenges with traditional data governance #

The primary challenges that hinder the success of your data governance efforts are related to:

  • Enforcing data privacy and security
  • Ensuring data integrity
  • Integrating data analytics, and IoT in an ever-expanding data estate
  • Contextualizing data to make it valuable
  • Sharing data and insights derived from it

These challenges point to a deeper issue. Making data governance all about control, compliance, and an IT-centric function isn’t working — 53% of data and analytics leaders considered data governance to be IT’s responsibility.

Forrester’s research, titled Break Through Data Governance Fatigue: Framework For Effectiveness And Sustainability, succinctly summarizes the problem with the aforementioned approach:

Focusing on command and control cultures, bureaucracy, complexity, and technology has hobbled data governance success. Data governance strategies tend to focus on new capabilities and rolling out policy compliance, then moving to the next thing.”

So, what’s the way forward for data governance?

Forrester underscores the importance of aligning data governance with your business outcomes. Data is only valuable if your people can trust and use it for making decisions. This is why data governance is less about data and more about your business.

According to Raluca Alexandru, Analyst at Forrester, the responsibility for data governance shouldn’t lie only with IT. It should be driven by business.

💡 “Asking questions, such as “How can I create value from data?”, “What does data mean to me?”, or “How can I create processes that help me generate value?” are crucial for effective data governance.”

At Atlan, we define data governance as a personalized, community-centered approach toward data enablement. Data governance should be the responsibility of everyone and be a part of their daily workflows.

This approach is further endorsed by how Michele Goetz, VP, Principal Analyst at Forrester, describes data governance.

Goetz states that data governance objectives should tie into the purpose, culture, and actions that live within your business practices.

For example, identity management and preference management need to align with privacy regulations, but they also improve customer understanding and yield better results from loyalty programs and targeted sales initiatives.

Also, watch → A masterclass on trends driving modern metadata management from Michele Goetz

Lastly, Raluca Alexandru also states that organizations will be adopting more flexible hybrid, federated models for data governance to suit their organization’s context.

A federated model is a hybrid of centralized and decentralized approaches to data governance. In her masterclass on The Future of Data Governance, Alexandru mentions how to go about it:

Decentralize data governance from a central authority. At the same time, build a network of federated models that create a unified structure around data management. This still allows independence of different domains, teams, stewardship roles, etc., thereby letting them deal with their data in their own way.”

💡 Creating a federated model that leads to some unification in the data management approach and still allows independence of domains, teams, or stewardship roles, while adhering to some enterprise-wide regulations or policies is the recipe for a hybrid model.

What does the future of data governance look like? #

Raluca Alexandru notes that data governance maturity has been steadily rising. Let’s take a look at some data points alluding to this growth:

  • According to Forrester’s Data and Analytics Survey 2022, 21% of data and analytics leaders have clearly defined their data governance program with documentation
  • According to Forrester’s Data and Analytics Survey 2023, 56% have a centralized data governance team for the entire organization
  • The same 2023 survey also states that 73% plan to increase spending on data governance solutions

As the maturity grows, Alexandru predicts that the share of organizations with a formal data governance team will increase by 30%.

She also noted heightened interest (along with investments) in using AI for:

  • Automated data classification and tagging
  • AI-infused search functionalities
  • Data quality and anomaly detection
  • Predictive analytics
  • Natural language processing for data governance documentation
  • Automated data privacy and compliance management

Additionally, Raluca also talks about AI governance gaining more importance. Forrester predicts that one in four tech executives will report to their board on leveraging AI in data governance.

📽️ Also watch → A masterclass on the future of data governance, featuring Raluca Alexandru, Analyst at Forrester

Taking these predictions into account, what’s the way forward for your data governance efforts. Let’s explore.

How to improve the effectiveness of your data governance efforts: Forrester’s take #

The best way forward to be more successful with data governance is to:

  • Define data governance success factors
  • Establish accountability
  • Start small
  • Link data and business processes
  • Explore opportunities to automate and scale

Let’s look into the specifics.

Define data governance success factors #

Forrester recommends defining your data governance success factors through close collaboration with the business. This involves considering factors such as data ownership, adoption, usage, quality scores, and more.

Establish metrics, baselines, and dashboards that show business impact upfront.” How to Build A Data Governance Practice

Establish accountability #

After defining the program’s objectives, priorities, and success factors, the next step is to establish accountability. As mentioned earlier, data governance is a collective responsibility. Data practitioners involved in creating data assets should bear the responsibility for how data is consumed and managed within your organization.

This entails looking at data governance as a collaborative approach where the processes, policies, and roles that makeup data governance are crowd-sourced.

Such an approach also increases transparency, builds trust in data, and raises the likelihood of having your governance structure adopted.

Start small #

Forrester also recommends starting with small pilot projects to demonstrate the benefits of data governance and to evaluate whether your approach is working.

Shape a data governance pilot for one criterion related to a specific outcome, and execute to this goal quickly. Have a steward oversee the effort, documenting the roles, process, communication, and techniques. Break Through Data Governance Fatigue

Another reason to start small? Deliver quick wins as calculating the short-term ROI of data governance efforts can be tricky. Quick wins, on the other hand, can “quiet the cynics and detractors looking to derail the effort before it begins.”

To achieve success with data governance, Forrester suggests linking data and processes.

A promotional products distributor needed to shorten its time-to-market for new products. Rather than seeing data quality as the issue, the company instead treated it as a symptom and looked at business process inefficiency. Fixing it helped the company bring products to market in minutes and cut down the catalog production time by 75%.” How to Build A Data Governance Practice

Automate and scale #

Lastly, Forrester stresses the importance of automating and scaling your data governance efforts, which in turn warrants the use of data governance tools.

Let’s see what these should offer.

Also, read → Automated data governance for granular access controls, policy propagation, lineage, and more

What to look for in data governance tools #

Forrester defines a data governance solution as a suite of software and services, designed to help create, manage, and assess the corporate policies, protocols, and measurements for data acquisition, access, and leverage.

Their capabilities must include the ability to handle:

  • Data definitions
  • Policies
  • Data quality
  • Data stewardship
  • Data literacy
  • Regulatory requirements
  • Ethical considerations
  • Risk management
  • Privacy and security
  • End-to-end lifecycle management

Data governance solutions should provide the foundation to drive the usage of data across organizations and cater to a broad user audience, including IT, data stewards, data scientists, and compliance managers.

Forrester highlights how data governance has emerged as an umbrella theme covering multiple use cases. These include data discovery, data privacy, regulatory compliance, data integrity, and data quality, among others.

As the demand for data governance grew, several data technology vendors pivoted to data governance, catering to policy management, data stewardship, collaboration, etc.

Choosing the right data governance tool means looking for a solution that caters to the capabilities mentioned above, while being easy to use and adopt for all data practitioners.

Though various options exist, few provide a scalable approach to securing your data while ensuring data democratization, similar to what Atlan offers. Let’s explore how.

Atlan’s approach to data governance: Bottom-up, community-centered, with privacy at its core #

Atlan is an active data governance platform that ensures data enablement with a community-centered approach and privacy at its core.

With Atlan, you can:

  • Auto-document data definitions, descriptions, and other elements that enrich your data assets
  • Define data governance policies according to data team personas, projects, or domains
  • Automate sensitive data classification, policy propagation, compliance reporting, and more
  • Auto-generate actionable, cross-system, column-level lineage mapping
  • Set up custom masking and hashing policies to secure your data
  • Capture tribal knowledge by letting anyone with view access to data offer suggestions
  • Integrate with popular modern data stack tools such as Databricks, Snowflake, S3, and more

A demo of Atlan’s data governance capabilities - Source: Atlan.

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