How to create and implement Data Governance Frameworks?

Payal Wadhwa

Payal Wadhwa

Jun 28, 2024

Data Governance framework

As data becomes a vital component in fuelling business strategy and outcomes more than ever before, forward-looking organizations are striving to continuously enhance the trustworthiness of data. This is because data-driven decisions enable better insights and meaningful changes for the organization.

However, building a data-informed culture calls for better data ownership, sharing, collaboration, and ongoing monitoring. And that’s where data governance frameworks come into play–they add structure to disorganized data management and ensure sustainable enhancements.

This blog explores data governance frameworks, including their key components, creation, and implementation.

TL,DR:
Data governance frameworks offer structured guidance on implementing a robust data governance program to maximize business value
A data governance framework has 10 constituent components—mission and value, beneficiaries, data products, controls, accountability, decision rights, policy and rules, data governance processes, work programs, and participants
Creating and implementing a data governance framework is a five step process that involves identifying objectives, picking a reference framework, drafting policies, establishing roles, and monitoring performance

What is a data governance framework?

A data governance framework is a set of guidelines and procedures for collecting, managing, securing, processing and governing an organization’s data and data assets. It offers structured guidance in the form of policies and processes to enhance data quality, improve data security, and facilitate better decision making.

Some popular data governance framework examples include the DGI data governance framework, the McKinsey data governance model, and the PwC enterprise data governance framework.

Components of data governance framework

The components of a data governance framework help establish standardized data management practices and ensure integrity, security and availability of data for better business outcomes.

According to the DGI, there are 10 universal components of a data governance framework. These include:

1. Mission and value

Data governance programs should aim to add value to products, services, and assets and minimize costs, ambiguity, and risks. To bridge the gap, the value optimization strategy must be created by understanding existing policies and procedures as well as program expectations.

2. Beneficiaries of data governance

Depending on the mission and value of the program, the beneficiaries of data governance will vary. The program must benefit a specific set of stakeholders while considering overarching data governance objectives.

3. Data products

Data programs must create or add value to data products that in turn become reusable assets. These data products will act as a single source of truth and must be accessed via inventories, dashboards, catalogs, or portals.

4. Controls

Data governance programs must specify controls to minimize data-related risks and breaches and enrich the value of data. The implemented controls are a combination of general controls, process controls, and technical controls that are executed by humans or through automated tools.

5. Accountability

Data governance programs must clearly define roles and responsibilities to establish accountability and facilitate cross-functional collaboration. All data-related activities such as compliance, software development lifecycle, data development lifecycle, etc. should have owners with clearly defined roles.

6. Decision rights

Every data governance program must ensure clarity on decision-making rights including the people who are responsible and the criteria for data-related decisions. The decision-makers must be appointed based on the products, services or assets affected by the decisions and their representatives.

7. Policy and rules

Data governance programs must establish policies and procedures that follow a top-down approach. At the same time, they must enable teams to understand and interpret the data rules, thereby encouraging analysis and teamwork.

8. Data governance processes, tools, and communication

Data governance processes must be standardized, well-documented, and replicable. You can make use of governance tools to enable these processes such as to collect and display relevant metrics. Additionally, these data processes must be made known across the organization to facilitate a better understanding of framework implementation.

9. Data governance work program

Data governance has many moving parts and involves several stakeholders. So, it makes sense to divide the program into several small projects or workstreams with distinct goals and focus. All these projects come together to form a work program that aligns with the organization’s governance mission and value.

10. Participants

Every data governance program must have a DGO (Data Governance Officer), whether a single person or a team. The DGO must support data governance and stewardship activities, i.e., the implementation of data policies, to achieve the overarching objectives.

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The 5 approaches to implementing data governance frameworks

The approaches to implementing data governance are categorized based on where it begins and how the adoption expands.

There are 5 approaches:

  • Top-down: Data governance begins at the top with senior leadership implementing policies that are then adopted across the organization.
  • Bottom-up: Data governance implementation kickstarts at the bottom levels by employees and gradually reaches the top.
  • Center-out: Data governance rules and standards are established by a centralized team or individual (such as a Data Governance Officer) and followed by the entire organization
  • Silo-in: Data governance is implemented at the function level but aligns itself with the overall objectives.
  • Hybrid: Data governance is implemented by leveraging the strengths of various approaches such as centralized execution and top-level decision-making.

Creating and implementing a data governance framework

Let’s discuss this in two phases—creation and Implementation. The first phase is all about preliminary work and planning, while the second phase is about the execution.

Here are the 8 steps to build and implement data governance framework:

Phase 1: Creating a data governance framework

1. Determine the data governance objectives

Establishing data governance objectives helps you tie business context to governance practices you seek to implement. 

For this, you must have a solid understanding of the organization’s current state. This includes knowledge about data flows, existing data policies, the current security and privacy practices followed, and so on. 

Next, based on the business outcomes that you aim to achieve and the gaps that exist, start identifying measurable and achievable objectives such as:

  • Seamless inter-departmental data transfers
  • Ensuring high-quality data that is reliable
  • Maximizing business value with data-driven decisions
  • Achieve compliance with data privacy regulations and so on

2. Pick a framework reference and establish a strategy

There are a number of data governance frameworks to choose from such as the one from DGI or McKinsey. It is crucial to make the right selection because the framework chosen can have a long-standing impact on how you approach governance. Secure support from top leaders and involve key stakeholders when establishing the strategy.

The data governance strategy must have a vision statement and objectives and should define the overall governance structure including rules, roles, operating models, timeline meetings, and reviews.

3. Start drafting and documenting policies

Create documented guidelines on how critical assets and data will be secured, shared and governed. If you are subject to regulatory compliance, the governance policies must align accordingly. Next, work on the org-wide distribution of the policies. Make sure that the employees understand the overall business goals as well as the policy contents for better adherence.

4. Establish governance roles and responsibilities

Define strategic, tactical, and supportive roles and responsibilities for specific governance processes. The head of the data governance program will be at the top of the hierarchy, overseeing the implementation process. Then, there will be data owners, data stewards (for implementing data policies), and IT experts. Additionally, you must specify the reporting channels for the governance team to ensure accountability.

Phase 2: Implementing a data governance framework

1. Conduct workforce training

The implementation phase begins with workforce training on policies and procedures. Start by communicating the value of data governance to the team and ensuring that they understand the importance of governance. If you are subject to privacy laws such as GDPR, it is important to track the completion of mandatory security training.

2. Implementing security and privacy controls

The next step is to start building a pipeline of security and privacy controls such as access controls, data encryption, centralization, authentication controls and more. However this is not a one and done task and requires you to continuously monitor controls to leave no blindspots or anomalies. 

3. Establish KPIs 

Establish metrics and key performance indicators to track the implementation’s results. For example, you can establish an acceptable percentage of errors to measure data accuracy, the number of data breach incidents, compliance audit readiness, and so on. Conduct regular review meetings to go over performance and discuss steps for improvement.

4. Continuously monitor and improve

Data governance is an iterative process that requires you to identify gaps, adapt to changing requirements, and actively improve your system. Use the ongoing monitoring mechanism to keep track of your governance controls in real time.

Benefits and challenges of data governance framework

The benefits of data governance frameworks come from realizing the strategic importance of data and treating it as an asset. The benefits include:

Enhanced data quality

An effective data governance framework ensures standardized processes, well-defined data policies, assignment of data owners and stewards, and tracking of metrics for improvements. These measures ensure the reliability and accuracy of data thereby enhancing its quality.

Better decision making

Well-informed decisions are directly related to the enhanced data governace. It ensures that the data becomes trustworthy and is capable of better insights and analytics. Additionally, there is better integration of data that gives a comprehensive view of the current state of business affairs and enables proactive responses.

Better data access

One of the key outcomes of a data governance framework is the centralization of data for better, more secure access. This ensures that data is available to the right set of people at the right time and for the right reasons. Even non-technical employees can access data as there is a seamless data flow across the organizations without unnecessary restrictions.

Regulatory compliance

Data governance frameworks help establish procedures that minimize the risk of security and compliance deviations. It helps establish roles and responsibilities, implement the right controls, ensures ongoing monitoring of data assets, and makes it easier to ensure compliance with industry regulations.

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Challenges

Implementing data governance frameworks can be tricky especially if you are starting from scratch. We’ve discussed the challenges and ways to solve below:

Communicating the business value of data

At the onset of implementing a data governance framework, organizations must communicate the business value of data to ensure stakeholder buy-in. This is challenging for organizations at lower levels of data maturity because there is nothing concrete to demonstrate how data quality maximizes business value.

How to solve for this:

  • Start with a pilot program in one department and show tangible results to the organizations
  • Appoint a chief data officer if the budgets allow and entrust them with the responsibility of ensuring buy-in
  • Try quantifying the wins, such as approximate cost savings, to facilitate a better understanding

Data silos

The traditional way of thinking states that business functions own data relevant to their activities and there isn’t a need for collaboration with other departments. This creates data siloes that can make it difficult to facilitate inter-departmental coordination and centralize data.

How to solve for this:

  • Implement a data centralization tool as well as tools that can facilitate data integration using APIs
  • Encourage a culture of collaboration across departments and promote data-driven decisions

Creating policies from scratch

Every data governance program requires well-structured policies, which require a lot of bandwidth. Small and mid-market businesses will find it hard to formalize data governance, especially when it also has to align with complex regulations and understand the scope of implementation.

How to solve for this:

Leverage in-built policy templates from tools like Sprinto that expedites the process and can be customize as per your organization’s needs.

Continuous monitoring

Implementing a framework is not a one-time activity and requires organizations to continuously adhere to policies and procedures. Without a monitoring mechanism in place, IT teams may feel stretched. Moreover, implementing new security tools can also add to an already constrained budget.

How to solve for this:

Use Sprinto’s in-built continuous monitoring dashboard to gain a real-time view of controls and health status. 

How does a data governance framework support compliance and regulatory requirements?

A data governance framework enables data management in a way that supports compliance and regulatory requirements. It does this by 

  • Clearly defining roles and responsibilities that cover governance tasks, compliance management and other workflows.
  • Aligning data policies with regulatory requirements if the organization is subject to data privacy or security regulations
  • Properly tracking, monitoring and reporting data to support evidence collection for audits
  • Arranging training and awareness programs to establish a culture of data governance that also includes security training for compliance frameworks
  • Minimizing risks related to data such as breaches and reducing the chances of penalties.

How Sprinto can be an enabler in your data governance journey

Data governance and compliance are complementary initiatives with one supporting the other. Achieving compliance with data privacy and protection laws can help you establish stronger data governance. 

Tools like Sprinto can be an enabler in the journey by

  • Helping you identify gaps initially when you are looking to implement a data privacy framework
  • Enabling you to quantify risks, prioritize them and mitigate them proactively using the integrated risk management module.
  • Helping you build a solid pipeline of controls and activating automated checks to minimize any deviations
  • Providing in-built policy templates, training modules, role-based access controls, automated evidence collection and hundreds of other features to enable audit readiness in weeks
  • Ensuring ongoing monitoring to help you stay ever-compliant and establish data governance with ease

Kickstart your data governance journey with Sprinto. Talk to our experts today.

FAQs

What is the purpose of a data governance framework?

A data governance framework ensures that well-structured data policies are implemented throughout the organization to deliver reliable and accurate data to achieve business objectives.

How does a data governance framework impact data quality and security?

Data governance framework impacts data quality and security by ensuring standardized data, assigning data owners, tracking key metrics and implementing controls such as access controls and encryption. All these measures and structured policies enhance reliability and accuracy of data while ensuring protection.

What are the 5 elements of a data governance strategy?

The 5 elements of a data governance strategy are data quality, metrics, change management, data cataloging, and data lineage.

Payal Wadhwa

Payal Wadhwa

Payal is your friendly neighborhood compliance whiz! She turns perplexing compliance lingo into actionable advice about keeping your digital business safe and savvy. When she isn’t saving virtual worlds, she’s penning down poetic musings or lighting up local open mics. Cyber savvy by day, poet by night!

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