Data governance is all about keeping data safe, maintaining its high quality, and making it easily accessible for data discovery and business intelligence projects. It’s thanks to data governance that validated data travels through secure pipelines to trusted endpoints and users.
As new data sources, such as Internet of Things (IoT) technologies, generate more data, companies must reassess their data management procedures in order to grow their business intelligence (BI) operations.
This is where data governance frameworks come in. They help companies protect and manage huge amounts of data by increasing data quality, minimizing data silos, enforcing compliance and security regulations, and properly allocating data access.
What are data governance frameworks, how do you implement them and what challenges can you expect along the way? Keep reading to find out.
What is a data governance framework?
A data governance framework establishes a standardized set of rules and practices for data collection, storage, and utilization. It ensures that your policies, regulations, and definitions are applied to all data in your organization. Finally, a good framework lets you offer trusted data to people in a variety of roles, including business leaders, data stewards, and developers.
This type of framework also makes sure that data can be managed, transformed, and delivered across all application and analytics installations, both in the cloud and on-premises.
This opens the door to implementing self-service solutions available to non-technical teams, helping them identify and access the data they want for data governance and analytics.
Why do organizations need a data governance framework?
Without a data framework, vital data assets risk becoming fragmented, inaccurate, and non-compliant with applicable regulations. A lack of governance often results in confusion and duplication of work as multiple departments or individuals attempt to handle data using their own approaches.
In the absence of a framework, separate departments follow their own standards and processes, resulting in data silos that soon lead to inefficiencies. Instead of consulting a single source of truth within a company, employees only have access to the data collected in their respective tools. This generates knowledge gaps and, in certain cases, mismatched reporting amongst databases, leading to a general distrust of data.
Another risk of not having a proper data governance framework in place relates to compliance with a wide range of rules and regulations, including HIPAA and GDPR. Companies without a data governance system cannot guarantee data quality or compliance with privacy requirements.
How do data governance frameworks work?
Data governance frameworks will differ based on the business. However, here are a few of the overarching themes:
| Data Governance Area | Definition |
|---|---|
| Ownership | The first item of business is to determine who will be in charge of defining the rules and processes for your data governance system. Who controls the data decision-making process? Who is handling issues resulting from noncompliance with that framework? |
| Goal-Setting | Along with creating a data governance framework, you should determine the particular goals and metrics that will be used to assess the success of your program. |
| Approved Tech | A data governance framework requires stakeholders to approve the technology used to process, store, and use data, as well as ensure specific safeguards are in place to prevent data breaches. |
| Collaboration Standards | Data stakeholders include all personnel who create, use, and govern data throughout the firm. Leaders of the data governance initiatives must decide which stakeholders to include or consult with during the decision-making process and which should only be notified of the final choice. |
Pillars of data governance frameworks
Data governance frameworks are based on four main pillars that ensure optimal data management and utilization within a business. These pillars ensure that data is correct, can be successfully aggregated from many sources, is protected and used in line with laws and regulations, and is kept and managed in a manner that suits the purposes of the company.
1. Data Quality
Data quality is the foundation of every data governance framework. It ensures that the information utilized in decision-making processes is correct, consistent, and reliable. Furthermore, data quality management entails creating policies and procedures for data validation, cleansing, and profiling.
2. Data Integration
Data integration is the process of combining data from several sources using various methods to create a single view. This pillar guarantees that data from diverse departments, business divisions, and external partners is efficiently combined and used for analysis and decision-making.
3. Data Privacy and Security
This pillar entails putting in place policies and procedures to safeguard sensitive data and ensure compliance with data protection laws and regulations. It employs data encryption, access control, and anonymization mechanisms.
4. Data Architecture
The fourth pillar is data architecture, which encompasses the design and organization of data systems. It includes the planning and design of data systems to guarantee they fit the organization’s requirements – most of the time, it’s all about designing databases, data warehouses, and data lakes.
Data governance framework models
Before we dive into specific instances of data governance frameworks, let’s take a look at the five major data governance models. The models are based on how data governance decisions will be routed through your company:
| Governance Model | How It’s Routed |
|---|---|
| Top-down | Company leadership implements data governance policies, which are then distributed to individual business units and shared with the rest of the organization. |
| Bottom-up | Lower-level employees execute data governance measures, such as establishing naming conventions, which then spread to upper levels of the business. |
| Center-out | The team or individual in charge of data governance establishes data standards that the entire organization follows. |
| Silo-in | Various departments collaborate to align on data governance while taking into account the demands of each group. |
| Hybrid | Data governance decisions involve multiple layers of the company. For example, a corporation may utilize a center-out model to recommend a course of actions but then use a top-down approach to make the ultimate decision. |
Data governance framework examples
DGI
The DGI data governance strategy includes ten universal components that address the why, what, and how of data governance. Goals, measurements, and funding all focus on how the data governance program will enhance revenue, optimize expenses, and maintain company resilience in the face of risks or disruptions.
Controls are risk management tools that can be either preventative or corrective. They can be deployed at different levels of the framework to help the data governance program achieve its aims.
A DGO (Data Governance Office) manages the entire governance program, interacts and communicates with other stakeholders, aligns data-related rules and standards, and keeps extensive program records. To make things easier to understand, DGI splits each component into three key areas: rules, people, and processes.
McKinsey
McKinsey argues that redesigning the overall organizational design is the first step toward achieving success with data governance. Their data governance framework template consists of three essential components:
- A data management office (DMO) develops rules and standards, trains and guides data leaders, and ensures that data governance is integrated with all other aspects of the organization.
- Domain-based roles oversee the day-to-day operation of the data governance program.
- A data council oversees the overall strategic direction of the data governance initiative. It brings together DMO and domain leaders to review progress, authorize financing, and address concerns and impediments to effective governance.
PwC
The PwC enterprise data governance framework extends traditional models like DAMA, DMBOK, and DGI to account for next-generation data environments.
PwC’s data governance framework guidelines consist of five components. It begins with a data governance plan and progresses to a management layer that encompasses all parts of the data ecosystem.
The lifecycle management layer encompasses all of the controls necessary to maintain a smooth flow of data throughout its lifecycle. The stewardship layer is concerned with enforcing governance, whereas the governance enablers are in charge of the people, procedures, and technologies required to provide successful governance.
Deloitte
Deloitte defines future data governance practices as “maximizing the value of data for operational effectiveness, decision making, and regulatory requirements, while minimizing the risks associated with poor data management.”
Deloitte’s recommendations for a data governance framework include items such as:
- Policies and principles for data governance and management
- Establishing governance roles and responsibilities
- Processes that define how data is created, changed, and maintained
- Implementing tooling, modeling, and data architecture through technology
- Governance controls that define standards for measuring the effectiveness of governance
Deloitte also recommends constantly reviewing and enhancing the data governance system.
How do you benchmark a good data governance framework?
Data governance benchmarking is the systematic evaluation and comparison of an organization’s governance initiatives, processes, policies, and performance to industry standards, best practices, and peer organizations.
The purpose is to examine the governance program’s maturity, efficacy, and efficiency while also identifying opportunities for improvement.
Key features of data governance benchmarking are:
- Metrics and key performance indicators (KPI)
- External comparisons
- Internal assessments
- Data governance frameworks evaluation
- Gap analysis
Benefits of data governance frameworks
Data democratization
In recent years, companies have struggled to democratize data – 83% of organizations agree they can’t convert fragmented data points into comprehensive user information. One of the motivations for the increased usage of customer data platforms is to help in managing and organizing customer data so that everyone can profit from it.
A data governance framework opens the door to data democratization, allowing employees with varying technical skill sets to access and act on data. This autonomy and faith in data enables teams to accurately define goals, measure performance, strategize, and identify new opportunities.
Standardized and trustworthy data
Clear criteria for labeling and categorizing data are a key part of good data governance. Guidelines enable you to standardize data that the entire organization can trust. Efforts to standardize data may involve developing a shared data dictionary to ensure uniformity across teams in what is tracked and the naming conventions.
Compliance with regulatory requirements
In an increasingly regulated world, companies must comply with a slew of data protection requirements, including the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), among others.
Data governance provides a framework for defining and enforcing the policies and procedures required for regulatory compliance. Data governance decreases the risk of costly penalties and reputational damage caused by noncompliance by ensuring that data is gathered, stored, processed, and disseminated in accordance with regulations.
Improved business performance
Data governance is an investment with demonstrable returns including greater data quality, better decision-making, increased operational efficiency, and lower risk, all of which can have a meaningful impact on the organization’s bottom line.
Better decision-making can lead to more effective strategies, increased efficiency can save money, and greater risk management can help you avoid costly regulatory penalties and reputational damage.
Increased data security
Data governance can significantly improve data security inside a company. Policies governing data access, use, and protection are important to any data governance endeavor. These policies specify who has access to what data, under what conditions, and how the data should be safeguarded throughout transmission and storage.
By establishing these principles, data governance helps to ensure that sensitive data is adequately protected, lowering the risk of data breaches. In the event of a data breach, a strong data governance framework will specify the procedures required to mitigate damage and avoid future breaches. This level of protection is critical for preserving customer trust and meeting regulatory standards. Furthermore, by improving data security, data governance protects the organization’s most precious assets: its data and reputation.
Challenges in implementing data governance frameworks
Organizational resistance
Siloed departments and stakeholders impede efficient data governance collaboration and alignment, especially when combined with employee resistance to implementing new data governance processes and technology.
Cross-functional collaboration is critical for developing an inclusive approach to data management. By encouraging teams to collaborate, organizations may break down silos and build a shared vision for data governance.
Complexity in data integration
Data quality is critical for effective governance. However, data inconsistencies can easily lead to inaccurate reporting and decision-making, potentially resulting in corporate losses and missed opportunities.
To address this issue, use data quality approaches such as profiling and cleansing to ensure precision and consistency in data management. Implement fundamental efforts and capabilities, such as using Master Data Management (MDM) in a particular department or region, to lay the groundwork for data quality improvement.
Data ownership conflicts
Conflicts over data ownership might develop from overlapping obligations or opposing viewpoints. Resolving these issues efficiently is critical to sustaining a harmonious and productive data governance environment.
To address these issues, put in place established conflict resolution procedures. This can include facilitating discussions among the persons involved, promoting active listening, and encouraging open dialogue. Mediation enables competing parties to communicate their concerns, discover common ground, and collaborate on mutually acceptable solutions.
When disagreements cannot be handled internally, seeking leadership involvement is important. Leaders should thoroughly weigh the benefits of each viewpoint and make an informed choice in the best interests of the organization. Their intervention should strive to achieve a balance between responsibility, collaboration, and the overarching goals of the data governance program.
How to create a data governance framework
Here are some common steps to establsihig a data governance framework:
- Outline your data governance architecture – Start with the ‘why’ – the purpose of data governance and end with the ‘how’ – how will that data be regulated, and what are the processes, people, and technologies involved.
- Determine your definition of data governance – Data governance is an ever-changing undertaking, so you should define your concept of data governance before developing a framework.
- Identify and specify data domains – Since the data management framework should encompass all data assets, the next step is to define and standardize data domains throughout your firm. Domains can be created for any function that generates data, such as finance, marketing, and sales.
- Identify the domain data owners and customers – Shared responsibility for data is a crucial principle of any data governance scheme. So, each domain that generates data is accountable for managing it and assuring its security, integrity, and privacy. That is why the next step is to assign data owners to each domain and understand its data consumption behavior, ensuring that the appropriate people have access to the data they require.
- Validate and document everything about the data – By this point, you should have a solid understanding of how data flows inside your business. The next stage is to standardize data domain definitions, data flow rules and workflows, access policies, and other elements by documenting them all.
- Perform data security and risk evaluations for each domain – To complete your data governance architecture, establish methods for conducting regular data security and risk assessments for each domain. That’s because enabling data governance is a journey, not a one-time project deployment.
How to establish a data governance framework with lakeFS
How can you create data lake governance using the open-source data version control solution lakeFS? Here are several data governance areas that stand to benefit from data versioning:
1. Role-Based Access Control
Data lakes often collect data from multiple systems and make it available to other systems and users. As a result, controlling access, particularly to portions of the data, becomes a complex problem.
lakeFS supports Role-Based Access Control (RBAC) at the branch level. Because lakeFS restructures the data on the object storage, all access will be made through lakeFS. Every activity in the system, whether it is an API request, UI interaction, S3 Gateway call, or CLI command, necessitates a set of activities to be permitted for one or more resources.
A simple IAM-like authorization framework enables granular control over which groups or individual users have access to particular branches on specific repositories, as well as the types of operations (read/write of data/metadata, etc.) they may perform.
This is extremely strong when used with lakeFS hooks, which force the removal of Personal Identifiable Information (PII) on any branch off of production that will be utilized for isolated development or ETL testing.
2. Immediate Backup And Restore For The Entire Data Lake
A critical component of data governance is resuming service in the event of an outage. In the context of data lakes, this frequently entails restoring the service if the production data becomes corrupted.
One method is to make regular backups of the storage. The drawbacks of this strategy are that it’s expensive, time-consuming, and only allows you to restore to the point of backup.
Another approach would be to use an open table format, such as Delta table, Hudi, or Iceberg. Open table formats provide a historical audit of all table modifications and allow you to conduct operations or query/restore a table at a specified point in time. The drawbacks of this strategy are that it frequently requires restoring many tables simultaneously throughout multi-table transactions, and it’s limited to structured data.
Backup & Restore With lakeFS
Most files in a data lake are static, with just a tiny subset of items being added/removed on a regular basis. lakeFS uses a copy-on-write method to prevent data duplication. For example, creating a new branch is a metadata-only action in which no objects are copied. Only when an object changes does lakeFS create a new version of the data in storage.
Aside from the storage savings and performance benefits of this architecture, this approach enables quick recovery of old commits without taking repeated snapshots of the lake because lakeFS deduplicates the objects in the data lake over time.
3. Branch-Aware Managed Garbage Collection
When managing data, you must balance the ability to restore and retrieve past data while also deleting old data. Data deletion may be required solely for cost reduction. Furthermore, legislation such as GDPR may mandate you to delete users’ information from your whole data stack (even prior versions).
lakeFS has GC (Garbage Collection) capabilities that are branch and repository aware. This provides a straightforward way to control how far back data should be replicable for various sorts of data.
4. GDPR Support
Traditional ways to lower storage costs, such as backup and archiving, have issues in fulfilling users’ rights to be forgotten under the General Data Protection Regulation (GDPR).
Using a system like lakeFS, users can support the right to be forgotten while sacrificing repeatability. Alternatively, maintain repeatability up to the point at which support is forgotten.
5. Data Lineage
Data lineage is an essential component of the data lake governance plan. As these complexities continue to rise, businesses face manageability issues in collecting lineage cost-effectively and consistently.
In data lakes, data lineage frequently refers to both the transformation of data (what data was used to create the data) and the associated code.
lakeFS provides powerful lineage by assigning responsibility to every object in the data lake, connecting it to a commit, and providing metadata for that and any other commit, including correlations between those commits.
6. Auditing
When managing data, it’s vital to keep a detailed audit record of activity and determine what data is in the lake, who is using it, and how much has been consumed. This basic criterion is required to prevent/detect data leaks and guarantee that privacy standards are followed.
lakeFS offers an audit log that can be searched and exported for any data actions. What resource was accessed when, and with what API against which portion of the data? This is not only necessary for governance, but it is also a simple approach to interact with monitoring systems in order to immediately detect problems, outages, or security breaches.
Learn more about how lakeFS enables efficient data lake governance.
Conclusion
The advantages of data governance frameworks go beyond simply managing data. They lead to better business operations, more trust, better compliance, and improved financial success. In the digital age, where data has become a critical strategic asset, creating a strong data governance structure is no longer an option but a requirement for enterprises that want to prosper.
