In the fast-paced business landscape, data has become one of the most valuable assets for organizations. The key to unlocking that asset’s worth is solid data governance.
So, let’s explore what data governance is, its key components, advantages, and challenges.
By the end, you will clearly understand data governance and its vital role in today's data-centric world. Plus, you’ll get practical tips to help you implement efficient data governance in your company.
What is data governance?
Data governance encompasses processes, policies, and metrics that ensure the quality and security of data. In the era of data-driven decision-making, accurate and up-to-date data is crucial. It serves as the foundation upon which organizations build their strategies.
The definition given by 'The Data Governance Institute is this:
“Data Governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”
Why is data governance important?
Data governance helps senior managers make better decisions, empowers professionals with reliable data, and provides customers with trustworthy information. It enables compliance officers to demonstrate adherence to regulations, mitigate risks, and protect sensitive information. Lastly, organizations working with big data can ensure data quality, security, and preservation by implementing a well-designed governance strategy.
Key components of data governance
Data governance goes beyond rules and policies. It requires establishing infrastructure and technology, creating and maintaining processes and policies, and identifying responsible individuals for handling and safeguarding specific data types. Some key components of data governance include:
- Data strategy: Outlines the vision, objectives, and guiding principles for managing data effectively and is aligned with organizational goals and objectives.
- Data policies and standards: Involves defining how data should be managed, accessed, stored, and protected. These policies ensure consistency, quality, and compliance with regulatory requirements.
- Data stewardship: Involves assigning accountability and responsibility for data assets. Data stewards ensure data quality, accuracy, and adherence to policies and standards. They also facilitate data-related processes and provide guidance to data users.
- Data management processes: Ensures that data is effectively managed throughout its lifecycle, encompassing data acquisition, integration, quality management, classification, security, and privacy.
- Data architecture: Includes defining data models, structures, storage, integration, and data access mechanisms that support data management.
- Data governance framework: Includes the roles, responsibilities, and decision-making processes related to data governance and ensures initiatives are properly planned, executed, and monitored.
- Tools and technology: Covers the various tools and technologies that facilitate data management, quality, metadata management, data lineage tracking, and data access controls. These tools automate processes and provide visibility into data assets.
- Metrics and measurement: Involves defining metrics and measurement mechanisms to assess the program's effectiveness. Key performance indicators (KPIs) are established to track progress, identify areas for improvement, and demonstrate the value and impact of data governance.
- Training and communication: Requires training and communication programs to ensure that all stakeholders understand the importance of data governance, their roles and responsibilities, and the processes and policies they must follow. Training programs enhance data literacy and promote a data-driven culture within the organization.
- Continuous improvement: Includes regular reviews, audits, and feedback loops to help identify areas for improvement and address any issues or challenges that arise. Continuous improvement ensures that data governance practices remain effective and aligned with evolving business needs and regulatory requirements.
Data governance vs. data management vs. master data management
While data governance, data management, and master data management may seem similar, they serve different purposes within an organization's data strategy.
- Data governance focuses on establishing rules and policies, ensuring documentation and compliance.
- Data management implements these policies, putting them into action.
- Master data management specifically deals with critical data used for business transactions.
Establishing a data governance framework
Data governance comprises several elements, one of which is the data governance framework. This framework is crucial in outlining access and sharing protocols, maintaining data accuracy and control. It serves as a blueprint for an organization's data strategy and compliance. It details how data flows are managed and monitored to ensure that data assets are effectively utilized, and informed decisions are made.
Developing a framework involves setting data collection guidelines, defining key success metrics, and streamlining data distribution channels for improved communication. With a robust data governance framework, organizations can enhance decision-making, compliance, risk management, and customer trust.
How to start implementing data governance best practices
To develop a framework, an organization should first identify leadership, create a vision, define roles, responsibilities, objectives, and goals, and establish a data governance charter. Then, an assessment of the current landscape should be carried out to determine data quality and choose the appropriate data governance model that suits the organization's needs (more on that below).
The next step is to document assets, processes, and pipelines. This involves establishing business context, creating a data dictionary, developing data governance policies and procedures, and crafting a data governance roadmap. Finally, the right tools should be selected, and the strategy implemented. Data processes should also be monitored to ensure ongoing success.
Selecting the right data governance model
Choosing the appropriate model for your organization is crucial. There are three main types:
- Centralized Model: Consolidates data governance power within one group or individual. This model offers consistency and accountability, ensuring that decisions are made in a centralized and controlled manner. However, it may lack adaptability and could lead to longer decision-making processes.
- Decentralized model: Allows each department to have its own practices and approach to data governance. This flexibility enables faster decision-making and empowers departments to tailor their governance processes. However, it may result in inconsistencies across the organization and duplication of resources.
- Hybrid model: Combines aspects of both the centralized and decentralized models, offering a balanced approach. It provides consistency while allowing flexibility and participation at all levels. The hybrid model addresses the potential conflicts between centralized and decentralized entities, but managing it can be complex.
Consider your organization's size, data requirements, and business objectives when selecting a mode. Each one has advantages and disadvantages; the choice should align with your organization's unique needs.
Benefits of implementing data governance
There are many potential benefits to data governance, but the three most important ones are improved decision-making, enhanced compliance and risk management, and cost savings and efficiency.
- Improved decision-making: By ensuring data accuracy, consistency, and up-to-date information, it enables organizations to make fact-based decisions that in turn contribute to better business outcomes.
- Enhanced compliance and risk management: By establishing policies and procedures for data security, it minimizes the risks associated with data breaches and unauthorized access. It also helps organizations comply with data privacy regulations, reducing the risk of fines and penalties and safeguards sensitive customer information.
- Cost savings and efficiency: By utilizing accurate and reliable data, organizations can optimize operations and streamline processes, improving data quality, efficiency, and reducing manual data entry and cleaning. This allows organizations to allocate resources more effectively, leading to cost savings.
Overcoming data governance challenges and implementing some best practices
Implementing data governance can be a complex process, but following best practices can significantly contribute to the success of your program. Here are strategies to overcome common obstacles:
- Foster a culture of data literacy, ensuring employees understand the value of data and their role in maintaining its quality. Integrate data governance into every department to promote collaboration and data-driven decision-making.
- Establish ownership of data assets throughout the organization, promoting a culture of accountability and responsibility. Employees must know their responsibilities and take ownership of their data to maintain data quality and security.
- Build a solid business case, clearly defining goals, benefits and establish a realistic timeline with milestones and measurements for monitoring progress and success.
- Form a cross-functional team, bringing together representatives from various departments, including IT, legal, finance, and operations. This diverse team ensures all stakeholders have a voice in decision-making and promotes collaboration across the organization. Additionally, consider including individuals with strong communication skills, such as the Chief Data Officer and Data Governance Manager, to effectively communicate the importance of data governance throughout the organization.
- Define key roles to ensure clear ownership, accountability, and responsibility within the data governance framework. For example, some of the roles could include a chief data officer: responsible for establishing the overall strategy and overseeing its implementation.
It may also include a steering committee that provides guidance and support and oversees execution of the strategy and alignment with organizational objectives. You may need a “data owner” who holds responsibility for ensuring the accuracy, security, and compliance of data assets. They oversee data quality and make informed decisions regarding data management and usage.
You may also consider a data steward who Oversees data usage and ensures adherence to organizational policies and procedures. They provide guidance , enforce data standards, and facilitate data-related processes.
Data governance tools
As larger companies grapple with vast amounts of data, the complexity of managing this asset grows exponentially. In such environments, data governance tools are essential. They help maintain data accuracy, ensure compliance, and improve decision-making.
Here are three examples of tools that stand out in the data governance landscape:
Atlan: This data governance platform is designed to automate governance tasks, making it easier for teams to comply with regulations and protect sensitive information. Atlan's features include OpenAPIs, Playbooks for automated workflows, PII tagging, and masking, as well as column-level lineage that helps in establishing trust in data assets.
OneTrust Data Access Governance: Specializing in controlling access to sensitive data, OneTrust helps organizations find and remediate instances of excessive permissions. It automates the discovery of sensitive data and triggers workflows to restrict access, ensuring that only authorized personnel have access to critical data.
Alation Data Governance: Alation's approach to data governance simplifies the process, addressing the common challenges of fear and resource constraints within the ever-changing data landscape. It provides tools for success by facilitating a better understanding of data governance and its implementation.
Incorporating such tools into their data governance strategies allows companies to safeguard their data and to unlock its full potential for driving business growth.
An example of how Funnel approaches data governance
At Funnel, each team is responsible for providing data to our business intelligence (BI) team. These teams are considered data owners and are accountable for maintaining data quality. The BI team has checks in place to detect any anomalies or issues with the data, and they work with the respective data owners to address any problems.
To ensure high-quality standards for all data used in official reporting and analyses, the BI team manages and owns the tech stack. Other teams, such as Product Analytics, have their own projects and are not allowed to directly access the BI team's models and code base. This approach is part of our current data governance strategy, which we are continuously improving.
Also read: an expert's view of modern data governance.
The key takeaway to data governance
An effective data governance program is important for organizations to maximize the value of their data assets. By implementing best practices such as building a business case, creating a cross-functional data governance team, and defining key roles, organizations can ensure the success of their data governance program.
Remember, data governance improves decision-making, compliance, risk management, and results in better business outcomes. Embracing these strategies and leveraging these tips will empower your organization to thrive in the data-driven era.