The primary objectives of data governance are to:
- Improve the agility of data-driven business decisions
- Seamlessly share knowledge across the organization
- Eliminate uncertainty and instill trust in data
- Drive value through collaboration in current workflows
- Make security and privacy compliance effortless
To understand these data governance objectives, we first must acknowledge the role data governance plays in an organization’s overarching data management strategy.
As the Data Management Association’s Data Management Body of Knowledge (DAMA-DMBOK2) states, “Data governance is defined as the exercise of authority, control, and shared decision making (planning, monitoring, and enforcement) over the management of data assets.”
Framing the specific goals of data governance, Wikipedia notes its key focus areas include data availability, usability, consistency, integrity, compliance, and security. Governance also encompasses the creation and maintenance of processes (and process owners) to ensure high-quality data can be used by all.
All businesses make decisions about data — data governance helps them make smarter decisions faster and more efficiently while keeping data protected. Let’s take a closer look at the core objectives of data governance.
Improve the agility of data-driven business decisions
It’s time for data governance to shake its reputation for being a bureaucratic discipline that pits the agility desired by business users against restrictive rules set forth by IT. Using data governance to bring agility to DataOps allows teams to significantly increase their output with the same amount of resources.
Human-centric data governance is the best way to achieve this goal, as it gives users the ability to discover and use data on their own terms. This requires a centralized inventory of all data assets that can be easily curated and accessed in one searchable view.
Seamlessly share knowledge across the organization
Data governance aims to empower data users to seamlessly share knowledge and remain in agreement about how to interpret disparate sets of information.
Promoting knowledge-sharing across an organization starts with the establishment of a single repository of all data and knowledge. A democratized single source of truth provides crucial context using metadata (e.g., tags, previews, column descriptions, READMEs). This enables teams to stay on the same page about business information.
Eliminate uncertainty and instill trust in data
Another objective that sits at the intersection of data discovery and data governance is trust. Users need to be confident that the data they are using is accurate, high-quality, and up to date. As Tristan Handy, Founder and CEO of dbt Labs, once said, “Without good governance, more data == more chaos == less trust.”
A business glossary is vital to promoting trust through governance as this tool ensures alignment across departments on data terms and their definitions. Additionally, cataloging data lineage enhances user trust by documenting:
- Who owns the data
- When data was produced
- How data has changed over time
- What logic has governed data changes
- How additional changes will impact downstream processes
Drive value through collaboration in current workflows
The adage “two heads are better than one” is hundreds of years old, yet it rings true across many aspects of life today — including data governance.
Data governance might as well be called data enablement because at its heart it is about enabling teams to work together to unlock the full value of data. Teams such as sales operations, product marketing, software engineering, and customer success often examine overlapping data sets and have different (but equally valuable) takeaways.
Fostering collaboration between diverse data users requires a focus on maximizing visibility and context, not control. Preferably, this context is embedded into the tools teams are using to connect disjointed workflows and enable data discussions in the places where work is already happening.
Make security and privacy compliance effortless
Last but not least, maintaining data security and compliance with applicable regulations will always remain a fundamental goal of data governance.
Adopting an established data governance framework or building your own is key to defining the scope of your security and compliance requirements in order to standardize rules and definitions that align with these priorities. However, relying 100% on employee input to maintain the implementation of your data governance framework can be quite burdensome.
To take these processes into the 21st century and reduce manual work, programmable-intelligence bots can easily be customized to emulate human decision-making processes for different contexts and use cases.
For example, these bots might be used to identify and tag sensitive information based on the regulations that apply to your company. Or they could automatically organize, classify, and tag data across your ecosystem based on preset rules.
Data governance powers data democratization and data security
The core data governance objectives mentioned above all map back to two concepts: data democratization and data security.
Using data governance to enable data democratization
The new vision of data governance is driven by a shared goal of data democratization, meaning everyone in the organization can access, understand, and use data to drive decisions.
As Big Data in Practice author Bernard Marr said, “Data democratization means that everybody has access to data and there are no gatekeepers that create a bottleneck at the gateway to the data. The goal is to have anybody use data at any time to make decisions with no barriers to access or understanding.”
Marr points to Walmart as a prime example of enabling data democratization through their Data Café, an analytics hub that enables any data user to gain insights about the company’s operations. He notes that “[the] Data Cafe system has led to a reduction in the time it takes from a problem being spotted in the numbers to a solution being proposed from an average of two to three weeks down to around 20 minutes.”
In response to analysts’ concerns that the data volume was growing at a rate that could eventually hinder their ability to analyze it, the company set forth a policy to intelligently manage data collection by automatically refining and categorizing data before it is stored.
Walmart provides a valuable lesson about the importance of using data governance to advance agility and accelerate time to insights. However, it’s entirely possible (and advisable) to evolve this “data as a service” approach so data users don’t have to rely on analysts for insights.
Instead, treating data as a product allows you to evaluate data assets as valuable standalone products that are documented and easily discoverable by anyone in the business. By removing the dependency on analysts, employees can use data assets at their own pace and return to them whenever they’d like.
Using data governance to enable painless security and compliance
The stakes associated with protecting high-value data have never been higher. IBM’s Cost of a Data Breach Report 2021 found that the average cost of a security breach has grown to $4.24 million, the highest amount in the 17-year history of this report.
Data governance and cybersecurity are closely related because, in order to determine how to best protect your data, you must know its value, where it is located, and who has access to it. As Y’vonne Sisco of 5p Consulting put it, “Data Governance and cybersecurity share a common goal of protecting valuable data assets and ensuring high-quality data is made available to the right people at the right time.”
Making data available to “the right people at the right time” is the key to effective governance and cybersecurity compliance. To streamline these processes and ensure employees are acting in accordance with policies and regulations, best-in-class data governance programs utilize auto-classification and masking of sensitive information that can only be viewed by users with authorized access.
For example, Scripps Health used Atlan’s third-generation data catalog to create a company-wide single source of truth to unite users from finance, supply chain, and hospitals on one platform. Enterprise-grade compliance and security features allowed the healthcare company to collaborate on data projects without having to worry about HIPAA sensitivity.
Mapping data governance objectives to business objectives
The key to successful data governance is to make sure your governance objectives are closely aligned with business objectives. McKinsey notes, “The problem is that most governance programs today are ineffective. The issue frequently starts at the top, with a C-suite that doesn’t recognize the value-creation potential in data governance.”
How can you create a transformational data governance program rather than one strictly managed by IT, resulting in a set of distant policies that are loosely followed and even resented?
To embrace the new vision of data governance as a value driver, you must commit to making your governance program more agile and collaborative. This investment starts with establishing clear objectives across people, processes, and technology to create a collaborative data culture that is comfortable making changes and “rolling out new rituals” bottom-up rather than top-down.
Making this simple shift will allow your organization to innovate and ship products faster with data governance and enablement features baked into existing workflows. Embracing this type of data enablement is how we increased our agility output by six times.
Ready to make data governance effortless?
Try Atlan — Deploy best-in-class, catalog, metadata management, and data governance without compromising on data democratization.
Related reads on data governance objectives
- What Is data governance & why does it matter?
- The importance of a data governance framework
- 8 data governance best practices to help you get started in 2022
- What is the difference between data governance and data management?
- What is data stewardship: Meaning, benefits, and its importance in data governance