Data is what drives a company's business forward, and this applies to every industry. Without data, it would be impossible to get a clear picture of the state of the business or find out what the company should do to optimize key business outcomes. As a result, businesses today, in particular those that deal with large volumes of data or, more importantly, confidential data (such as healthcare providers or financial institutions), must have systems and processes in place so that data can be responsibly used. This is where data governance comes into play, and when good data governance practices are put in place, business value can be extracted from data.
As businesses become more complex, effective, responsible data governance policies ensure that the right individuals access the right information. Data governance is thus crucial for several reasons:
- Hours can be saved when teams know what type of data to collect and what to discard.
- Better decision-making can happen when there is stronger data consistency across information processing chains.
- Businesses can avoid embarrassment and lawsuits by ensuring that they have complied with stringent regulatory requirements (such as CCPA, GDPR, etc).
In this article, you'll learn what data governance means, how it works, who it's designed for, and the advantages of implementing it. We'll also share a few tips for setting up a practical data governance framework for your organization.
What is data governance?
Data governance refers to the processes, policies, standards, roles, and metrics around data capture and access. It is the control that ensures that data is consistent and trustworthy throughout the organization.
Most enterprises have a data governance framework for managing data. Data governance dictates who is allowed access to a particular piece of data, how that data is managed, and what can be done with the data. In practice, both people and processes are necessary to make it work.
For instance, when a company's goal is to comply with a region-specific regulation, such as the GDPR, their data governance needs to reflect the processes and personnel role allocations specified in that regulator's framework.
Because every enterprise has unique data governance needs, no single cookie-cutter data governance framework universally applies. As such, it's not rare for global companies to have data governance frameworks that are specific to particular regions. However, the specifics of these different frameworks depend on some vital common factors:
- The organizational business goals
- The business drivers of the organization, such as meeting compliance regulations like GDPR or maintaining HIPAA compliance
Benefits of implementing a data governance framework
Regulations and frameworks related to data governance can seem restrictive to companies, but they offer a handful of significant benefits.
Better data governance means better data quality
Everyone wants improved data quality, with is data that is more complete, consistent, and trustworthy. The rules for engaging with data are set through the data governance framework, and a quality metric can be formulated to assess how accurate your data is. A good data governance framework in conjunction with good data quality software will help you save valuable time and resources while improving the quality of your data.
Data governance provides a single source of truth across the enterprise
An enterprise-wide data governance framework makes it simpler to build a single source of truth. As a result, there are no competing datasets or datasets with outdated, duplicate, or incomplete data in the organization. Data can be easily integrated into downstream systems, and decision-making becomes simpler.
Data governance establishes a common understanding of the data
Data can be a tremendous asset for a business. With the proper processing, it can provide valuable insights for success. In addition, with a solid governance policy, users and stakeholders across the organization can all "speak the same language." This means that they can be more efficient cross-collaborating across the business and knowing exactly where data lives, so they can make data-driven decisions.
Data governance provides improved data management
Data governance doesn't mean data management, but it can improve it. It enhances data management by helping organizations understand what data they have, where it is located, and how it is used across the organization. Through data governance, roles and codes of conduct are clearly defined, which reduces the legal, security, and compliance risks of managing data.
Better compliance with regulations
Once there's a framework in place, data governance leads to better outcomes for compliance regulations. This is because data governance can provide transparent control processes over your data to align with pre-set business rules, ensuring that all principles needed by your organization are met.
Roles involved with data governance
Data governance is not something that one individual manages. Data governance requires a team effort from several different stakeholders, each with unique roles and responsibilities. In large organizations, this group of individuals typically consists of business executives, data management professionals, IT staffers, and end-users who are familiar with the data aspects of the organization. When it comes to data governance, here is a list of key participants and their roles and responsibilities:
Chief Data Officer (CDO)
The Chief Data Officer oversees the data governance program and is responsible for its success. They make executive decisions about how the enterprise should use and protect internal and external information. One example would be in a large organization where there are several data governance projects run simultaneously. In such a scenario, CDOs play a critical role in tying them together across the entire enterprise.
Data governance committee
The data governance committee (sometimes called the data governance council), comprises top decision-makers, including the CDO, other key business executives, and data owners. Their role is to approve standard business rules, policies, and procedures that must be implemented holistically across the enterprise. For example, it might be helpful to have a data governance committee when there is a need for consistent data practices, tools, resources, and communication channels to drive a successful data governance program.
Data governance manager and team
The data governance manager is a team leader with a clear focus on rolling out the goals, standards, practices, processes, and technologies specified in the data governing framework. In addition, they are skilled at managing efficiency in people, processes, and tech to enable an organization to use different kinds of data depending on the choices made in the data governance framework. A smaller organization may have just one data governance manager, while a larger organization may have multiple data governance managers.
When it comes to operating with data, it's the data stewards' job to ensure that many people use that data and that it is used properly. For example, a large organization might have several datasets. It's the data stewards' responsibility to oversee these datasets and ensure that they stay in order and that the consumers of these datasets follow the data governance policies.
Best practices for crafting your data governance framework
Data governance frameworks serve as a guideline and are not a mandated set of rules. This means that the data governance frameworks for different businesses will look different. However, there are a few common themes and best practices for crafting a data governance framework for your organization. In most cases, these apply either when you start building a new data governance framework or in the case of updating an existing framework. Now, let's take a brief look at some of these best practices.
Best Practice # 1. Set specific and measurable goals
Data governance goals must be clear, specific, measurable, and directly tied to the business outcomes or the critical process and initiatives helping the business succeed. This has the potential to help you hyperfocus your efforts by involving the individuals responsible for driving these results. Additionally, make sure that you have a good mix of both long- and short-term goals.
Best Practice # 2. Start small and work slowly towards larger goals
To build a solid data governing framework, it's essential to start with small elements and add new ones as time goes on rather than dumping templates and rules into spaces where your domains don't belong.
Best Practice # 3. Focus on integrating your data governance framework into your existing practice, so it will be easy for everyone to follow
To ensure business continuity and productivity, it's essential to focus on integrating your data governance framework into your current business processes, tweaking it along as the business evolves. This will make the data governance framework sticky and more accessible for everyone to follow through without drastically changing how they work.
Best Practice # 4. Clearly define ownership
Providing team members with roles and responsibilities in creating and implementing an information governance framework makes accepting the new practices and rules easier. The individuals in their charge should have clear roles and responsibilities, and the duties should motivate them.
Best Practice # 5. Communicate thoroughly and often
A successful launch of any large-scale organization effort is dependent on effective communication, and data governance is no exception. To achieve successful outcomes, the business executives, data management and IT professionals, as well as end-users familiar with data aspects of the organization, need to communicate thoroughly and often.
Best Practice # 6. Educate your stakeholders
As an organization, you've set rules, policies, and processes from a data governance perspective. Next, you need to educate people about what they need to do to comply in their respective roles. In the same way that communication needs to be ongoing, education also does. Rules can evolve as organizations grow, new data types are created, data definitions are updated, and new data threats emerge. Through education, you can effectively translate data governance's complex and technical parts into business terms and socialize that with your teams.
Best Practice # 7. Map your infrastructure, architecture, and tools
A blueprint map of your organization's infrastructure, architecture, and tools can help you understand how data flows across the organization's complex range of data entities. Moreover, these maps can help pinpoint areas of risk and where changes need to be made to achieve data governance objectives.
Best Practice # 8. Identify your data domains
Working with data effectively starts with identifying and understanding its semantic meaning based on the data source using data domains. After you identify data domains, you can apply rules and policies to the data. For example, it may be necessary for you to determine the PII (personal identifiable data) data domains, such as credit card numbers, full names, and social security numbers, so that data encryption and masking rules can be applied. In addition, data domains assist you in exploring and finding other data domains that share the same ruleset.
Best Practice # 9. Understand that implementing a data governance process is an ongoing and evolving process
As in all effective continuous improvement processes, the relationship with data is also iterative. In part, this is due to regulations constantly changing, which means that your framework should also. Regularly revisiting your data governance framework with a fresh set of eyes can ensure that it remains relevant and meets regulatory compliance regulations. This continuous improvement must not be a one-time thing but should be sufficiently operationalized in the data governance process from the get-go.
Best Practice # 10. Know that you’re not alone, and you can procure technology that supports your data governance systems and processes
Data governance cannot be entrusted to one person or entity. It must be a shared responsibility between the people with business knowledge and the technical experts who know how the systems and data work together. As an organization, you aren't alone when it comes to improving data governance. Along with knowledge sharing, you can also procure technology that supports your data governance systems and processes. For example, consider a compliance regulation requiring that user data not be sent outside of a geographic area. Implementing the technical controls by yourself would be one way to ensure compliance. If, however, this is not your business's specialty, you can procure technology to assist you.
When it comes to picking a database for your data governance strategy, Fauna is worth looking into.
Fauna is a flexible, developer-friendly, transactional database
delivered as a secure and scalable cloud API with native GraphQL. Fauna now offers Region Groups
in its infrastructure, which allows users to specify where their data resides: each database, its storage, and its compute services exist in a specific geographic region. This resolves data locality concerns with regulations like the GDPR and can be an important part of your data governance framework.
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