Data Governance Frameworks: The Cornerstone Of Data-Driven Enterprises
With enterprise data growing exponentially and GDPR enforcement fines increasing by 40% last year, it’s no wonder investing in data governance is an urgent strategic initiative for data-driven businesses.
The data governance market is projected to be worth $5.28 billion by 2026, up from $1.81 billion in 2020.
If you’ve yet to establish a data governance framework, now’s the time.
Get started by understanding data governance frameworks, why data-driven businesses need them, challenges for adoption, and operational templates to consider.
Make sure you also overcome any confusion between data management and data governance.
We’ll cover all of that — plus, your next steps to achieving future-proof data governance that protects and drives revenue.
What is a Data Governance Framework?
To explain data governance frameworks, we must define data governance first.
- Data governance refers to how an organization leverages its people, processes, and technology to manage its internal data.
- A data governance framework or template is a specific set of principles and processes that defines how data is collected, stored, and used within an organization.
With the proper framework in place, organizations can transform their data into a valuable, powerful asset that can be leveraged to meet — or exceed — the business’ goals and objectives.
At a minimum, a data governance framework should establish the following policies for each data asset in the organization:
- Structure – Defines how data will be organized, retrieved, and stored
- Access – Designates which employees and third parties will have access to data
- Usage – Establishes parameters and restrictions on use to minimize legal risks, ensure consumer data privacy, and comply with regulations
- Classification – Categorizes data into levels of sensitivity such as internal vs. public or classified vs. restricted
- Integrity – Establishes standards to ensure accuracy, validity, and reliability so data can be trusted for decision making
The 10 Core Elements of a Data Governance Framework
People, processes, and technology must align for a data governance program to be successful. Creating the right framework to execute the program is also essential — and requires collaboration between dedicated IT professionals, business unit leaders, and data stakeholders.
Here’s what to include in your data governance framework.
Creating a data strategy is a critical first step for establishing a data governance framework. Data strategy refers to an organization’s high-level operating standards and requirements around data — which are often reflective of the culture within an organization. For example, will you be trusting of internal users, or will you be more restrictive? Will you collect as much data as possible, or will you be more targeted in your efforts?
Establishing critical processes for data management is essential for every data governance framework. Typical procedures address data sharing, data quality monitoring and testing, data issue tracking, etc.
A framework also establishes a data governance policy for enterprise data. Examples of data policies include internal and external data sharing, regulatory compliance, data storage requirements, data quality, etc.
Data standards define the overall approach for ensuring conformance to the data policy. Examples of data standards include data modeling standards, naming and abbreviation standards, metadata management, etc.
One of the most common use cases of data standards comes in the form of a marketing taxonomy — the common blueprint for creating marketing data (campaigns, assets, tags) accessible to every stakeholder.
Data security refers to safeguarding enterprise data throughout its lifecycle. The framework should detail the process for ensuring data is safe from cyber-attacks, unauthorized access, data breaches, and theft. It should also establish a clear plan of action to respond to all potential threats.
What’s wrong with low-quality data? Check out Claravine’s EJ Freni’s take on The Impact of Poor Data Quality.
Data quality determines whether or not a data set will serve its business purpose — so ensuring high data quality should be front and center as you design your data governance framework. Establishing policies prohibiting manual entry, requiring open standards, and restricting data access are good places to start.
This might be the most essential component of your data governance framework because poor data quality can kill campaign success.
Communication & Collaboration
Data governance is a strategic initiative that requires an enterprise-wide effort. So it is vital to create a plan for communicating the actions and accomplishments of the data governance team. Clearly define objectives, goals, and tools to foster enthusiastic support and widespread adoption of a data governance framework.
Much like data quality, your company’s internal culture around data sharing, communication, and collaboration can make or break data-driven initiatives (and your data strategy itself!). Which then highlights the importance of data literacy.
Sometimes a significant cultural shift is necessary for data governance to be successful. That can be accomplished through education or promoting data literacy. Prioritizing data democratization or ensuring all users can access, understand, and leverage data is part of that education process.
Educated users fuel better decisions that maximize the value of enterprise data.
Key Performance Indicators (KPIs)
Data governance programs require a concerted investment in people and dollars, it is critical to establish KPIs to measure their business impact. In addition, KPIs should be measurable and consistently tracked over time to provide stakeholders with an accurate return on investment (ROI).
Selecting data governance tools for your unique requirements is crucial to an organization’s data governance policies. Standalone solutions and integrated data platforms offer a wide variety of capabilities, so be sure to clarify what you need.
Most data governance software platforms offer essentials like creating a glossary of business terms, rules-based workflows, and automation. In addition, many offer a variety of data capabilities, including data discovery, management, cleansing, and integration.
Why Your Business Needs a Data Governance Framework
The ability to harness, store, and interpret data has transformed information into a valuable asset, one that enables organizations to be more agile and competitive in the global business environment.
That ability, however, comes with great responsibility — ensuring stored data is protected.
Though laws and regulations have improved overall data security and compliance, breaches that expose consumers’ personal information continue to make daily news. And it’s motivated companies to take additional strategic measures to safeguard their data.
By implementing data governance initiatives, businesses improve security and realize other valuable business benefits.
Here are the key benefits of effective data governance:
- Better decision support – Ensure data is consistent and high quality across the organization, instilling confidence and producing better insights to support decision making.
- Risk reduction – Promotes regulatory compliance, reducing risks that would otherwise threaten a business’s longevity and data security.
- Improved scalability – Enable seamless growth regardless of exponential increases in data volume.
- Improved efficiencies – Establish transparent processes and policies for data strategy and management, allowing a business and its IT department to be more agile and scalable.
- Cost reduction – Centralize the control of data, reducing costs associated with fragmented or siloed data management.
- Stronger collaboration – Promote a collaborative culture by providing consistent, accurate data across the organization.
Which Business Functions Benefit Most from Data Governance?
Of course, well-executed, comprehensive data governance policies benefit the enterprise as a whole. But they also provide specific benefits to functional areas of the business.
- Finance: High-quality, timely data provides accurate financial reporting for shareholders.
- Marketing operations: Customer data, engagement rates, lead metrics, and other key marketing data governance metrics support and inform sales and marketing strategies.
- Legal: Policies and procedures ensure regulatory compliance and consumer privacy, mitigating risks and protecting the business.
- Production: Governance tools and processes enable automation and increased operational efficiencies.
- Leadership: Consistent, accurate, high-quality data provides oversight, informs strategies, and uncovers potential market opportunities.
- Customer support: Accurate, complete, and centralized data enables customer service representatives to address questions and concerns quickly with fewer touchpoints.
See how Under Armour saw universal improvements that began at the marketing team.
3 Types of Data Governance Operational Models
As you design your data governance program, you need to consider which operational model is best for the size and structure of your business. Your model should detail operational guidelines, establish oversight, and provide infrastructure for data ownership and decision-making.
There are three basic models to evaluate for your organization: centralized, decentralized, and hybrid.
In a centralized data governance model, one person is usually designated as the data governance lead, responsible for making decisions and providing direction for the program. In some organizations, that person may also be accountable for managing master data and distributing it to users as needed or upon request. Other organizations may decentralize the distribution component, tasking team leaders with developing data sets and disseminating information to their users.
- Better focus: Teams can focus on essential tasks instead of collecting, managing, and distributing data.
- Quality control: Teams can make better decisions for the business when data is consistent throughout the organization
- Increased security: Data is easier to track and secure, reducing the risks associated with mishandling of data
- Cost control: Centralizing data requires fewer resources and streamlines processes.
- As a top-down approach, centralized models can fuel bureaucracy and fail to address the unique needs of every area of the business.
In a decentralized data governance model, a committee typically designs and manages the enterprise data governance strategy. However, functional areas of the business create and manage their own data sets and handle the distribution of information to their users.
- Improved representation: Considers all areas of the businesses and their unique challenges.
- Better data: Data is created by the people who will be using it to do their jobs.
- Increased efficiencies: Comparatively easy to set up and more accommodating of changing business needs and requirements.
- Shared maintenance: Multiple data owners share the responsibility of managing and executing the data governance strategy.
- Decentralized models can result in inconsistent and duplicate master data, impacting consensus, reporting, and decision-making.
In a hybrid or federated model, a centralized enterprise data governance structure provides the framework, technology, and best practices to follow, but application owners operate autonomously. In other words, each area of the business owns its data and metadata and is free to develop standards, policies, and procedures that best fit their business needs.
- More autonomy: Teams can manage and scale their own data without impacting other teams
- Faster issue resolution: Issues get resolved where and when they occur
- Improved agility: Teams can experiment and make adjustments to data as soon as the business need arises
- You must have deep expertise within each area of business to function autonomously. And you’ll have to take extra measures to ensure data consistency and coordination between teams throughout the organization.
How to Create a Data Governance Framework: 6 Best Practices
Although every organization is different, you shouldn’t have to recreate the wheel to establish your framework. Instead, here are some data governance best practices to guide you.
Think big, but start small
Implementing sound data governance doesn’t happen overnight — it takes time to find an approach to meet business needs and encourage adoption among data consumers. Starting with a quick win, like improving data quality in a few key business processes, will demonstrate the value of implementing corporate governance.
Educating key stakeholders about the business benefits of data governance is essential for obtaining buy-in. Translate technical aspects into meaningful business terms to uncover advocates within key functional areas of the business to set the program up for success.
Determine roles and responsibilities
Assigning deliverables and ownership levels across the business provide foundational support for what is often a significant cultural and procedural shift for an organization. Here are the standard roles to establish early in the process.
- Executive sponsor: C-level sponsor(s) who understands the data governance vision and ensures the program has enough resources
- Data governance council: The team responsible for guiding the strategy of the program, prioritizing projects and initiatives, and approving data definitions, policies, and standards
- Data governance board: The team responsible for developing tactical practices and policies to leverage data as a business asset
- Data owners: Individuals who are responsible for the data for specific functions within the business
- Data stewards: Individuals who enact data governance processes that ensure the quality of data elements and bring order and value to unstructured data
- Data consumers: Individuals who enter and use data to perform their assigned work duties
Build a business case
Full dedication and the investment of significant time and resources are essential for reaping the enterprise-wide benefits of a data governance program. It can be difficult to convince stakeholders to embrace the challenge unless you have a compelling business case.
Start by identifying essential data elements and the critical business processes they support. Then detail the costs associated with managing, integrating, and validating those elements through current manual processes. Highlight the potential business impact of manual process failures to prove the value of adopting a data governance strategy.
Measure progress with metrics
Insights gained from consistent tracking and collection of metrics help maintain buy-in and demonstrate the success of a data governance program. Metrics also reveal the effectiveness of processes and policies and identify areas for improvement.
Reinforce participation with rewards
Sharing organizational wins through regular updates will help cement the necessary cultural and process changes for long-term success — of course, rewards always help. Consider introducing an incentive-based reward system to encourage the adoption so you can drive the maximum benefit from a data governance program.
Data Governance vs. Data Management
Although the terms data governance and data management are often used interchangeably, there is a difference.
Data governance relates to a business’ overall enterprise data strategy, establishing policies for appropriate use, handling, and information storage. Executives, stakeholders, and data trustees are responsible for creating the organization’s data governance vision and framework.
Data management involves the tactics used to execute the data governance strategy, ensuring data is handled per policy throughout its entire lifecycle. Data management also encompasses data quality and security, master data management, and database operations. Data custodians and stewards are responsible for implementing and maintaining technology and enforcing procedures and policies outlined in the data governance framework.
Both disciplines work in tandem to ensure enterprise data remains a valuable business asset. This is especially important as businesses migrate their master data to the cloud, where information governance and skilled data management are crucial for a successful transition.
Next Steps: Establish Your Data Governance Framework
Creating a data governance framework is a massive undertaking, involving nearly every person in the organization. But with careful planning and execution, you’ll reach data governance maturity and widespread adoption before you know it.
Ready to bring industry-leading data governance to your organization? Take the next step by auditing your marketing data taxonomy and discovering The Data Standards Cloud, as well as additional data integrity resources.