Data governance ensures that the right people are involved at every step of the data management process – making decisions, understanding impact, supplying context, prioritising deliveries, and staying informed. Our data governance framework provides a best-practice guide to establishing an effective data governance organisation, tailored to your business.
While CIOs and CDOs realise that simple data management allows organisations to generate insights from their data, they cannot be sure whether this data is accurate, relevant, or where it originates from without data governance. It is therefore questionable whether conclusions drawn from this data are of any real value to the business.
Table of Contents
- What is a Data Governance Framework
- Lean Data Governance is a way of thinking, not a checklist approach.
- How to implement Data Governance using our Framework
- Still difficult to find an appropriate framework?
- Good business practice
What is a Data Governance Framework
A data governance framework is a plan or structure that outlines how an organisation will manage, protect, and use its data assets. The framework provides a clear and consistent approach to managing data, which in turn can lead to such goals as ensuring that personal data is protected, improving the integrity of data; delivering trusted AI models and reports; and all-around productivity improvements for any knowledge worker that has to find or understand data in order to do their job.
Using a data governance framework helps organisations avoid common pitfalls and mistakes that can arise when implementing a data governance program. It provides a roadmap for identifying and addressing critical data-related issues, such as data quality, security, and privacy. By following a framework, organisations can minimize risk and ensure that their data governance initiatives are aligned with their overall business strategy and goals.
In summary, using a data governance framework is important because it provides a structured approach for implementing data governance, helps ensure consistency and alignment with organisational goals, and minimizes risk by identifying and addressing critical data-related issues.
Why does a one-size-fits-all data governance framework not work
Our framework provides a standardised approach and set of themes that must be considered when establishing your data governance capability. It must, however, be adapted to your unique set of circumstances because:
- Data governance is not a one-size-fits-all solution: Different organizations have different priorities, sizes, cultures, maturity, structures, resources, and data complexity, which means they require different approaches to data governance. Therefore, a standardized framework cannot accommodate the unique needs of every organization.
- Data governance requires flexibility: The traditional command-and-control-based IT governance model is becoming obsolete because it does not meet the needs of digital businesses. Adaptive data governance, on the other hand, allows organizations to apply different governance approaches to specific business scenarios. This enables flexible and nimble decision-making processes that help an organization respond quickly to opportunities while continuously addressing investments, risk, and value.
- Data ownership is inadequate: Data ownership is unlikely and inadequate as an answer to data governance problems because data is often shared, and individuals may not have complete control over their data. Additionally, data ownership does not address the power imbalances between data subjects and data collectors.
An appropriate data governance framework – that can be tailored based on your organisation’s maturity, business needs, and priorities – ensures a structured approach to meeting your data management goals, now, and in the future.
A lean approach to Data Governance, as described in our Precisely whitepaper, is useful for organisations that are struggling with overly complex data governance approaches
Lean Data Governance is a way of thinking, not a checklist approach.
This is not a “scrap and start again” approach, and there is no need for your workforce to be formally trained in Lean.
This approach is designed to help you adopt what you need and adapt as you see fit. We encourage you to start small, address immediate concerns, and then extend and adapt based on lessons learnt. Always remember that data governance is about embedding data-related decision-making into your normal business-as-usual processes.
How to implement Data Governance using our Framework
Defining the Scope of Your Data Governance Framework
The first step in developing a data governance framework is to define its scope. This should start with an understanding of the business goals that data governance will enable, which can come from your data strategy. This includes identifying the target audience for the policy, the regulatory or executive requirements that the policy should adhere to, and the business objectives that the policy should support.
Many organizations approach data governance in a holistic manner, looking at all data assets at once. Ultimately, this should be the goal, but such a large scope means slow relative progress in any given area and a risk that efforts aren’t linked directly to business needs. If you are just starting, the scope should be limited to allow you to start your program small, learn lessons, and adapt as you scale.
Our framework gives you a structure that allows you to start small, and scale, without missing critical components.
Establishing the Data Governance Organisation
To ensure that your data governance framework is effective, it's important to assign structures, roles, and responsibilities. This includes identifying data stewards, data custodians, and other stakeholders who are responsible for managing and using data. You should also define the roles and responsibilities for data quality management, security, privacy, regulatory compliance and other priorities.
Again, focus your attention on the agreed priority areas, reusing existing structures where possible, rather than trying to build enterprise structures. These will come.
Implementing Data Governance Processes and Procedures
Once you've defined the scope and established roles and responsibilities, it's time to implement data governance processes and procedures. This includes developing procedures for data collection, storage, analysis, and reporting. You should also establish procedures for data access and security, as well as procedures for data quality management and data privacy.
Documenting Data Governance Policies
Data governance policies outline the rules, procedures, and guidelines for managing data in your organization. They guide how data will be governed, how it will be used, and who will be responsible for it. Your policy will ultimately cover all aspects of data governance, including data quality, metadata management, security, and privacy, but, in the early stages a focused approach, on a key project or business outcome, is best.
Monitoring and Maintaining Your Data Governance Framework
To ensure that your data governance framework remains effective, it's essential to monitor and maintain it regularly. This includes conducting regular data quality assessments, reviewing policies and procedures, and providing training and support to data stewards and other stakeholders. Your process should agree on metrics and KPIs that measure both the success of the data governance operations, and their impact on business.
Guiding Principles for Implementation
The term “data governance” has been around for decades, but only in the last few years have organizations begun to understand what it means in the age of big data. Data governance is the orchestration of people, processes, and technology to enable any organisation to leverage data as an enterprise asset. Yet, according to recent research, 80% of organizations that have just started their data governance journey have come to quickly realize the complexity of the challenge.
The most effective data governance implementations focus on clearing up complexity and solving challenges.
This Precisely whitepaper, Developing a Data Governance Framework for Your Business, identifies three guiding principles to a successful data governance rollout
- Enterprise scope
- Business focussed
- Guided by F.A.C.T.S
We apply these principles as we roll out our data governance framework, with the goal of ongoing improvement.
Still difficult to find an appropriate framework?
Finding an appropriate data governance framework can be difficult. Many of the available frameworks are too specific, created for distinct vertical and specified company requirements that may not match those of your organisation; while others are too broad, generic, or high-level, making, it tough to relate back to the business or specific use case,
Our industry-agnostic and adaptable data governance framework provides a structure to implement a world-class data governance capability within any organisation that can be tailored according to an organisation’s specific business requirements.
The implementation is a change management process
This approach to data governance comes at a time when organisations are increasingly recognising the need for data management and governance. The need is largely driven by legislation such as the Protection of Personal Information Act (PoPIA), as well as the realisation that data has value and can yield increased earnings when used correctly in the digital age.
With a customised governance framework in place, organisations can increase their levels of efficiency and improve their data management practices. Improved practices allow companies to know what data they have and where it is stored, thus also enabling them to identify duplicates, consolidate information, and streamline processes.
Working with an experienced partner, like Master Data Management, allows organisations to tweak the framework according to their immediate need, phasing in additional complexity later.
Case Study: Implementing data governance using our framework
In this presentation, delivered at Airside'22, we shared our experience implementing data governance using our framework at an institution that is focused on delivering self-service business intelligence may begin by putting controls in place to enable data scientists and other knowledge workers to find and access the data sets they need to do their jobs, whilst an implementation focused on the Protection of Personal Information has the opposite goal – that of ensuring access to sensitive data is restricted. The framework must provide the road map to deliver either goal, but eventually cater for both.
Good business practice
Improved data management practices not only equate to good business practice but also facilitate regulatory compliance, by allowing companies to control access to and modification of their data.
Most organisations find it difficult to successfully implement a data governance framework, which prompted us to take a collaborative approach, working hand-in-hand with the client to identify priorities and plan implementation accordingly.
We jointly identify a particular starting point, based on a broader picture that includes the client’s data strategy, culture and current levels of maturity. Once this is established, we collaboratively implement the governance framework, adapting it to your specific requirements.
Companies that practise active data governance – embedding data stewardship and curatorship activities into business-as-usual data processes – report significant advantages in their ability to use data effectively