According to research from Statista, the yearly volume of data collected by businesses increased from 97 zettabytes to 120 zettabytes in 2022 alone. This growth shows no sign of stopping, with data collection expected to grow even faster in the coming years.
Given this vast amount of information, today’s companies need frameworks to capture, store, and analyze internal and external data to securely optimize its use. Data governance refers to business processes and policies for managing data, as well as the responsibilities different team members have to ensure data privacy and security.
When implemented properly, data governance initiatives can help your company reduce risk and costs and remain competitive within your industry. Additionally, data governance makes business intelligence (BI) more effective, which can empower you to make better-informed decisions that ultimately improve your performance. Learn more about the concept of data governance, including implementation best practices and all of the advantages it brings to your business.
What Is Data Governance Framework?
There are many approaches to handling enterprise data, and data governance often gets conflated with data management. However, data governance is distinct. Data management is the comprehensive term for all data-related efforts, from data storage to the removal of sensitive corporate information. Data governance is one component of a larger data management approach — a very integral component.
It’s helpful to think about data governance as a framework or roadmap that supports your organization’s data management goals. Beyond augmenting decision-making, governance helps you assign roles to different employees, protect sensitive data, and maintain better data quality.
There are several models to consider for your data governance framework, and each approach will work best in different situations and corporate cultures. The traditional approach to data governance involves making many employees data stewards, and then assigning only a select few people as owners of more critical tasks like big data management. With command and control, only a handful of data stewards are responsible for data governance. The non-invasive data governance method has employees only act as data stewards when their work directly involves managing data.
The Three Key Components of a Data Governance Framework
To understand how a data governance framework serves your company, consider its three key elements — people, processes, and technology. These core aspects enable the meaningful use of data.
Contribution from employees is necessary for data governance. The steering committee is in charge of the data governance process, which includes the Chief Data Officer or Chief Information Officer, as well as various department heads. This committee establishes policies and standards for data usage. It also determines the overall goals of data governance, sets the mission statement, and decides with which metrics they will measure success.
The data governance team takes what the steering committee develops and puts into action the necessary systems and tools. Data architects and similar subject matter experts make up this team. Lastly, data stewards oversee datasets, taking care of the daily tasks of data governance and ensuring information is handled according to established guidelines.
Having well-defined data governance processes makes it easier to enforce policies and standards. Data privacy, data security, data stewardship, and data integrity are just some common aspects that governance processes will incorporate.
Technology is the tools and techniques designed to help with processes and uphold data standards. Increased innovation has made many aspects of data governance easier through automation and machine learning.
What Are the Goals of a Data Governance Framework?
As mentioned, a primary responsibility of the steering committee is to create the goals of a data governance framework. The objectives of effective data governance will consider the various parties impacted by your handling of data, such as employees, customers, and stakeholders. The following are the general goals of a data governance framework:
- Identifying the types, value, and sensitivity of data
- Enhancing privacy with customer data and similar information
- Evaluating and maintaining a high level of data quality
- Instructing how to share information and providing multiple levels of data access based on employees’ responsibilities
- Establishing when to retain useful information and when to eliminate obsolete data
- Providing strategies for protecting against data breaches and similar occurrences
- Streamlining data lifecycle management
- Ensuring data usage conforms to regulatory guidelines like General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Health Insurance Portability and Accountability Act (HIPAA)
What Are the Four Pillars of Data Governance?
There is no one approach to data governance — your framework will depend on your specific needs. However, some strategies can help you maximize the effectiveness of a data governance program, called pillars. Consider the following four pillars:
Establishing Use Cases
Data governance will only be successful with buy-in from stakeholders. One way to get people on board is to show how it will impact your business. Working with stakeholders, identify reasons why you might use a data governance framework, whether to increase revenue, reduce cost, limit risk, or any other application. Including stakeholders in this process is likely to boost engagement.
Without concrete metrics, evaluating the success of a data governance framework is challenging. Measures like key performance indicators (KPIs) help you understand what works and what requires improvement to determine the overall value of the framework.
Boosting Data Capabilities
If metrics indicate areas for improvement, you need capabilities that allow you to sharpen your data governance framework and enhance the value of data. Augmenting data capabilities will involve thinking about the processes and technology necessary to make these improvements.
Follow a Scalable Delivery Model
Once you apply the data governance framework to the primary use case, it should become easier to integrate it into subsequent use cases. This progression means a higher return on investment with each new use case until you reach organization-wide integration.
Benefits of Robust Data Governance
Once you establish a formal data governance framework, you will realize just how much the program puts your business at an advantage. The following are some key benefits of a strong data governance program:
- Promotes company-wide consistency with data usage
- Offers a means for detecting problems with data management and ensuring data accuracy
- Increases confidence that data is reliable and of a high quality
- Enables data stewards and governance teams to ensure data usage aligns with regulatory requirements
- Optimizes data to allow more comprehensive decision-making
Data Governance Best Practices for Effective Implementation
No data governance framework will be perfect right away, and you will likely need to make adjustments as you go. However, these best practices can increase your success with data governance:
Customize Your Framework
The template for your data governance framework should match the volume and types of data you handle. It should also be feasible for organization-wide integration.
Instead of applying your data governance framework to the entire organization at once, start with a single department or data challenge, preferably one that poses a more urgent risk. This strategy helps you understand the strengths and weaknesses of your framework before utilizing it further.
Invest in Data Mapping
Creating a map that details where data will live can help you determine which systems it belongs to and how it moves throughout your company. It can also guide you in categorizing datasets according to key considerations, such as privacy, so you know which data governance policies apply to them.
Communicate with Stakeholders Frequently
Establishing a data governance framework is a significant change. You need to ensure your employees and other stakeholders understand how this shift will affect their daily activities. Involving these individuals in the development of the framework and communicating about any changes can make the transition to data governance smoother.
Since the terminology surrounding data governance can be complex, creating a data glossary can be helpful. These data definitions ensure everyone involved in the strategy understands important concepts.
Create a Data Catalog
A data catalog offers essential information about different datasets, such as associated tools, search functions, and data lineage (a history of all changes made to a specific set of data). Well-organized data catalogs allow different users to quickly access data without increasing risk.
Choosing the Right Data Governance Tools
When the time comes to select tools for achieving your data governance strategy, opt for scalable technology and applications that you can assimilate into your existing data management approach. The following are some abilities the right tools will help you accomplish:
- Monitor and assess data for better control
- Enhance data quality with capabilities like data cleansing
- Augment the expertise of your data specialists and boost stewardship tasks
- Document data to increase its value
Account for Challenges
As beneficial as data governance is for companies, it poses some challenges. Being aware of these difficulties can help your business develop strategies to prevent them or minimize their risk. Common challenges include:
- Inadequate resources: Data governance can lead to more effective decision-making, which can boost revenue, but the framework itself seldom generates profit. Ensure your budget has enough space to allocate toward data governance to avoid further costs.
- No leadership team: Deploying a data governance framework without executive leadership is difficult and likely to be unsuccessful. Make sure you have the personnel to accommodate this task before strategizing.
- Presence of data silos: Data silos are a common occurrence in business. They happen when data owners isolate information from other departments. It tends to become a bigger problem as your organization encounters new data sources. Data governance must constantly combat data silos, which can be a tricky task without the proper strategy.
Measuring the Success of Data Governance
As with any other new business strategy, data governance requires ongoing monitoring to ensure the framework’s effectiveness and continually spot areas for improvement. KPIs are advantageous because they provide a more quantifiable means of evaluating your success. These metrics consider specific aspects of data, such as:
- Accountability and ownership: How many data domains have defined owners?
- Customer experience: How satisfied are customers with the quality and accessibility of data?
- Data quality: How many issues have occurred with data within a specific period?
- Training: How many employees have received data governance training and know their roles and responsibilities?
- Metadata management: How many data elements have a clear taxonomy or complete metadata?
If you want to measure how much data governance has improved decision-making and performance, look at the impact it has had on marketing, sales, customer service, and similar aspects. For example, has your engagement rate with your target audience gotten better? Is there a relationship between the consistency data governance delivers and this improvement? Each of these categories has its own KPIs, so comparing these metrics can reveal to what extent data governance has led to stronger business decisions and performance.
Data Governance in the Real World
Data governance can feel abstract, so seeing how it operates in real time for a real company can be helpful. These frameworks are beneficial no matter the industry, including government organizations like the United States Army.
Before partnering with Spider Strategies, the U.S. Army faced a challenge with data that influenced performance. With its many separate organizations, the U.S. Army often encountered challenges with linking and aggregating data across varied databases.
Working with Spider Strategies, the U.S. Army enhanced the Army Strategic Management System (SMS) to deliver a data governance framework for evaluating strategic capability and promoting top-down collaboration on initiatives. A key benefit of this data governance model is that now, the U.S. Army can consolidate data from different systems into dashboards for comparing actual performance against KPIs.
Unlock the Full Potential of Your Data with Spider Impact
With a data governance framework in place, your business can realize more accurate and consistent data, plus a more effective way for leveraging this information. When data is more useful for your company, better decision-making and a boost in performance are sure to follow.
For a single-source solution that delivers guidance with the many aspects of strategy and performance management, including data governance, choose Spider Impact. This software offers provisions for data governance and security that can empower your organization to make more informed decisions, advance performance, and ultimately achieve business excellence. To learn more about our solutions for data governance, sign up for a free test drive or demo today.