Data governance refers to the people, processes, and policies that are in place to effectively create, use, store, and secure company data. Data governance is critical for any organization harnessing the power of big data.
Data governance is essential for business because simple, semi-organized data management is no longer enough in today’s marketplace. Instead, organizations must put strict processes into place to ensure complete control over their data, including how it’s used, stored, accessed, and maintained.
What are the Elements of Data Governance?
The elements of data governance comprise the people, process and policies that allow an organization to effectively manage all of its data flow.
- People: Data governance begins by defining which team members are responsible for managing and championing governance policies and processes. In addition to managing the governance software, these governance professionals ensure that policies are defined, procedures are sound, and data is protected. Data governance experts identify these data professionals as the most important element of the governance process.
- Processes: Data governance processes outline the proper actions necessary to create, store, secure, and access data throughout an organization. These processes include everything from data collection and analysis to the specific steps employees must take to access critical data remotely. For proper data governance, each process must be documented and followed by those involved with the data.
- Policies: While processes are the actionable steps organizations must take, policies are the standards developed for an organization’s data. Specific policies are required for all aspects of data management. For example, an organization should have policies that discuss the appropriate use of data as well as policies that detail overarching security standards.
For more information, also see: The Role of Data Governance in Effective Data Management
The Goals of Data Governance
Data governance goals may differ between organizations. However, some goals are universal. According to the Data Governance Institute, the most important objectives include:
- Build standard, repeatable data processes.
- Enable better decision-making.
- Train management and staff to adopt common approaches to data issues.
Other goals of data governance include improving compliance with data security standards, ensuring consistent data across the organization, and securing data to minimize risks.
For more information, also see: The Data Security Market: Key Strategies
What are the Benefits of Data Governance?
While the global data governance market was valued at $1.8 billion in 2020, it’s expected to grow at a CAGR of 22% to reach $7.2 billion by 2027.
This growth is not only driven by organizations’ need to prioritize data but also by the many benefits data governance provides, including helping to improve decision making, creating better strategic plans, and improving data security.
One of the primary reasons organizations are inspired to use data is for visibility into everything from their daily operations and finances to their marketing campaigns. This visibility helps them make the educated business decisions required to scale.
However, a lack of data processes and procedures often results in low-quality data that can’t be trusted or easily accessed in real time. Data governance eliminates these challenges, so stakeholders can make decisions that push the business forward.
Simplifies strategic planning
For data-driven organizations, data plays a key role in business strategic planning. For example, data enables stakeholders to see current business performance and forecast future performance.
Unfortunately, many organizations struggle with data silos or a repository of data that’s inaccessible to all teams and departments. And in many cases, there is no single source of truth.
This results in stakeholders making plans without seeing the whole picture. Data governance ensures data silos are nonexistent and that data is visible to all. The result is a clear image of an organization’s past and current performance that can be used for effective planning.
Improves data security
The average cost of a data breach within the US is $9.44 million. The global average is $4.35 million. To avoid these serious consequences, organizations must take data security seriously, and that means ensuring policies are in place for a fast response.
Effective data governance includes policies and procedures that dictate what should be done in the event of a breach or data loss. With these policies documented, organizations can move quickly to secure their data. Plus, proper data security policies can help prevent risks in the first place.
Regulations such as the US Federal Trade Commission Act and the EU’s GDPR require organizations to protect the personal data of their customers. However, when data is not governed, this can be difficult.
Data governance helps organizations remain compliant with these regulations by enabling data transparency and minimization, among other requirements.
For more information, also see: Why Organizations Need to be Data-Centric
The Challenges of Data Governance
Even though data governance simplifies planning and decision-making while ensuring data is secure and compliant, it isn’t without its challenges, some of which include volume of data, data silos, and establishing organizational buy-in.
Each day, quintillions of bytes of data are created across the globe. Organizations generate data from a wide range of sources, such as their CRM platforms, financial transactions, and customer communications. The sheer volume of data can feel overwhelming to manage, let alone properly govern.
As mentioned above, organizations just beginning their data journey often deal with data silos. For example, an HR department may have data that the finance department doesn’t have access to. These silos make governing data impossible by threatening data integrity and limiting visibility.
Just like any business initiative, data governance requires buy-in from all stakeholders and teams to be successful. But governance isn’t easy and may require culture change. It also requires all departments to collaborate and be willing to share information. It’s common for organizations to experience growing pains at the start.
5 Steps to Build a Data Governance Strategy
The above challenges can be avoided through the development and use of a solid data governance strategy. Although each organization’s strategy will differ depending on its needs and goals, there are five critical steps any business should take to get started.
1. Choose a Data Leader
Any business initiative requires a champion to lead the charge. A data leader ensures new policies and procedures are followed. This individual should be knowledgeable in data management and governance and passionate about ensuring all data is handled with care.
2. Take Inventory of Your Data
To properly manage and govern data, organizations must know what data they have and where it comes from. Organizations should begin by completing an inventory of all data and its sources. This process involves many steps, such as cataloging data assets and assigning metadata for easier organization.
3. Document Data Processes
Organizations should also document processes to guide the correct use of data. This includes everything from data analysis to access and beyond. For example, what processes should be in place to ensure data is only shared with those who need it? What processes are in place to safeguard data in the event of a breach?
4. Adopt a Data Governance Framework
Organizations can choose between various data governance frameworks to jumpstart their efforts. These frameworks act as guidelines to support organizations in developing effective data governance strategies.
Some examples of common frameworks include the Eckerson framework and the DGI Data Governance Framework. Organizations should choose a framework that fits their unique needs and goals.
5. Develop Policies & Continuously Revisit Them
Finally, organizations must develop data governance policies. These policies are standards created to communicate the key processes and team responsibilities required to govern data. Some examples of policies may include data usage or access.
Policies should be revisited often to ensure they reflect any changes within the organization, such as updates to data processes or job role changes.
Also see: Four Pillars of a Successful Data Strategy: Making Better Business Decisions
Examples of Data Governance Tools
Although data governance is complex, there are tools and platforms available that can support businesses in their efforts. Here are three of the top options for organizations seeking to establish effective governance in their operations:
SAP Master Data Governance
SAP is a leader in enterprise software. The SAP Master Data Governance software provides a unified view of an organization’s data. SAP’s platform features key capabilities such as data consolidation, central governance, and data quality analytics.
Alation offers robust data governance solutions that enable visibility into all data assets. Alation is unique in its ability to automate certain data governance tasks such as data stewardship, classification, and more.
Ataccama provides a suite of tools made to improve the process of governing organizational data. For example, through the data discovery solution, organizations can classify data into a single catalog for easy visibility. Other tools include data profiling and automated data quality monitoring.
Bottom Line: Implementing Data Governance for Better Operations
Launching a data governance strategy will almost certainly be challenging for many companies. Processes and data repositories that were once managed on an informal basis will require sustained effort – and a clearly documented plan – if they are to conform to an optimal data governance strategy.
Yet however challenging, a well-constructed and strategic data governance policy is crucial for competitive advantage because disorganized, unstructured data management will clearly lead to business losses. Therefore, each enterprise must create a data governance strategy to control all elements of company data, including how it’s managed, mined, analyzed, protected, gathered, and stored.