What Is Data Governance And Where Observability Fits In ?

Data is the most valuable asset for most businesses today. Or at least it has the potential to be. But to realize the full value, organizations must manage their data correctly. This management covers everything from how it’s collected to how it’s maintained and analyzed. And a big component of that is data governance.

Data governance refers to the policies, processes, roles, and technology that businesses use to ensure data availability, usability, integrity, and security. This article will explore everything you need to know about data governance, including:

• What is it?
• What’s the difference between data governance vs. data management?
• Why is it important?
• What are the components of effective data governance?
• What are the key roles involved in it?
• What are its best practices?

Data governance is a core component of any big data management strategy that organizations introduce to drive insights. Effective data governance ensures quality and consistency in the data used to power critical business decisions.

At a high level, it can refer to data roles and responsibilities, data accessibility, data policies and processes, data creation procedures, data flows, and more. Digging deeper, it defines the architecture for decision-making and access rights around data, answering questions like:

• How do we define data?
• Where does data come from?
• How do we confirm the quality of data?
• How do we use data?
• Where do we store data?
• How do we protect data?
• How do we organize data?
• How do we connect data across systems?
• How do we maintain a current inventory of our data?
• How accurate does that data inventory need to be?

Software for data governance can either be purpose-built or baked into applications that make up the modern data stack.

Data governance and data management are often used interchangeably, however, the two terms refer to different practices.

It sets the strategy by introducing policies and procedures throughout the data lifecycle. Meanwhile, data management is the practice of enforcing those policies and procedures so that the data is ready for use.

In short, it is the cornerstone of all data management initiatives.

In today’s data-driven world, organizations need effective data governance to be able to trust in the quality and consistency of their data.

A strong approach to data governance benefits the entire organization by giving individuals a clear way to access data, shared terminology to discuss data, and a standard way to understand data and make it meaningful.

Some of the key benefits of data governance include:

• Introducing a clear data quality framework to bring together data and create a shared understanding for better insights and decisions
• Improving consistency of data across systems and processes, for efficient data integration
• Clearly defining policies and procedures around data-related activities to ensure standardization across the entire organization
• Outlining roles and responsibilities in terms of data management and data access for clarity among stakeholders
• Improving compliance by allowing for faster response and resolution to data incidents

On the flip side, poor data governance can hamper regulatory compliance initiatives, which can create problems for companies when it comes to satisfying new data privacy and protection laws.

In order for it to be effective, it must encompass several key components that support the follow-on data management activities. These components include:

Data Standards

It should set explicit data standards for consistency across the entire organization. These standards should assess and verify data quality and should be transparent to everyone in the company. As a result, they should help teams better comprehend and use data.

Data standards should also allow any third-party auditors to easily see how the organization handles sensitive data, how that data gets used, and why it gets used in that way. This transparency is essential for compliance, especially in the case of a data breach.

Data Integration

Data integration brings together data from diverse sources to make data more readily available and power deeper insights. Good data governance requires a complete understanding of how data gets integrated across systems and processes. Specifically, the data governance program should define the tools, policies, and procedures used to pass data across systems and combine information.

As a best practice, these data integration guidelines should be clear and easy to follow to ensure every new system adheres to them. Additionally, the team responsible for data governance should assist in reviewing these guidelines during any new technology implementations.

Data Security

Protecting the security of data is essential, as any unauthorized access to data or even loss of data can pose serious risks – from dangers to the subjects of data to financial loss to reputational damage. A data governance framework outlines a variety of elements related to data security, including where data is stored, how it’s accessed, and what level of availability it has.

Specifically, it should detail defenses like authentication tools and encryption algorithms that need to be implemented to protect the data network. Then, any teams working on data governance should partner closely with IT security to ensure adequate protection measures are in place based on those guidelines.

Data Lifecycle Management

Understanding the organization’s data lifecycle means knowing where data resides at any given time as it moves through systems until it eventually gets discarded. Good data governance allows you to quickly discover and isolate data at any point in the lifecycle.

This concept, also known as data lineage, allows analysts to trace data back to its source to confirm trustworthiness.

Data Observability

Data observability allows you to understand the health and state of data in your system to identify and resolve issues in near real-time. It includes a variety of activities that go beyond just describing the problem, providing context to also resolve the problem and work to prevent it from recurring.

Data governance helps set the framework for data observability, setting guidelines for what to monitor and when and what thresholds should set off alerts when something isn’t right. A good data observability platform can handle these activities, making it important to choose a platform that can meet the requirements for identifying, troubleshooting, and resolving problems outlined in your strategy.

Metadata Management

Another critical component of data governance is metadata management, which focuses on maintaining consistent definitions of data across systems. This consistency is important to ensure data flows smoothly across integrated solutions and that everyone has a shared understanding of the data.

The framework should include details on data definition, data security, data usage, and data lineage. In doing so, it should make it possible to clearly identify and classify all types of data in a standardized way across the organization.

Data Stewardship

Data stewardship is the practice that guarantees your organization’s data is accessible, usable, secure, and trustworthy. While the data governance strategy determines your organization’s goals, risk tolerance, security standards, and strategic data needs to set high-level policies, data stewardship focuses on making sure those policies get implemented.

To achieve this follow-through, data stewardship assigns clear roles and responsibilities for various initiatives outlined in the strategy.

Data governance programs can only succeed if they have clearly defined roles and responsibilities. As a result, it’s important to identify the right people within your organization to take on this ownership and establish their roles in the program.

Specifically, every data governance program requires people in three critical roles, each of which must be filled with qualified individuals who understand their specific responsibilities and how they contribute to the bigger picture. These roles include:

Chief Data Officer

The Chief Data Officer is the data governance leader. This person is responsible for overseeing the entire program, including enforcing and implementing all policies and procedures and leading the data committee and data stewards.

Data Committee

The data committee is a group of individuals that sets data governance policies and procedures, including rules for how data gets used and who can access it. They also resolve any disputes that arise regarding data usage or its role within the organization. The committee’s purpose is to promote data quality and ensure that data owners and data stewards have what they need at every point in the data lifecycle to do their jobs effectively.

Data Stewards

The data stewards are responsible for carrying out the data governance policies set by the data committee. They oversee data, making sure everything adheres to policies throughout the entire data lifecycle from creation to archival. The data stewards also train new staff on policies.

In some cases, data stewards might also be the data owners. In other cases, those might be two separate groups. Either way, the data owners are the people who manage the systems that create and house data.

When it comes to getting data governance off the ground (or improving what your organization already has in place) there are several best practices to consider:

Get Buy-In from the Top

As with any initiative, buy-in for data governance needs to start at the top. This top-down buy-in is important to make sure that everyone in the organization adheres to data governance policies and that those who are in a position to influence that acceptance understand the importance of your work.

To achieve this buy-in, share with executives how your data governance plan can help them realize their strategic objectives. The more you can highlight the advantages of the program and how it relates to their work, the easier it will be.

Communicate Often

Communication beyond top-level executives is essential to effective data governance. To ensure everyone is aware of what your team is doing around data governance and why it matters, make a list of everyone in the organization who has a stake in or would be affected by that work.

Then establish regular communications to share updates about program changes, roadblocks, and successes, that way everyone knows where to go for updates and can stay informed on a regular basis.

Combine Long-Term Goals with Short-Term Gains

When it comes to data governance, you won’t be able to tackle everything at once. Instead, it should be a continuous effort to support data-driven decision-making and open up new opportunities for people throughout the organization.

As a result, your long-term plan needs to include smaller, short-term initiatives that you can weave into the day-to-day operations of your company for immediate wins. This approach ensures that you see progress quickly and can help uncover any potential roadblocks faster. It also opens the door to new ideas that can even improve your long-term plan.

Assign Clear Responsibility – and Train People Accordingly

You can’t simply assign someone the role of data steward and hope for the best. You need to make sure that anyone playing a role in your data governance program takes their part seriously, and that means you need to take their responsibilities just as seriously.

This means you need to be clear about the responsibilities that data stewards and data committee members take on and offer training to support those people in their data governance roles. This training should cover everything from why it is so important to what’s expected from people in different roles.

Audit Process Adoption

A big part of data governance involves developing processes for how the company will handle data, especially when it comes to sensitive information. Auditing how these processes are actually living in your organization and how well people are adopting them can be extremely informative as you continue to make program improvements.

That’s because even the best processes won’t do your organization any good if no one adheres to them.

Finally, remember that data governance is not a one-and-done effort. It’s a program that must continuously evolve based on factors like adoption and changing business needs.

As a result, it’s important to regularly check in on how policies are faring and the impact on data quality. The more you can measure that progress, the better you can manage the situation and identify what’s working well and what needs to be improved.