Data Governance Frameworks

- Benefits of Data Governance

Managing the flow of data to the stakeholders who need it - data governance - now plays a vital role in every company's data strategy. 

Data leads to better, more informed decisions, so as your organization's data starts to grow, business processes of all kinds will benefit from being data-driven. Of course, the growth of data also brings new challenges. The more data assets your organization collects, the more data problems you face. 

Organizations often lose control of the data lifecycle as more and more data elements pour in from dozens or hundreds of sources. If you can fully meet the needs of the end users of your data, it is very difficult to scale to meet their needs. 

Without good data governance, there is no way to know when inaccurate data enters your system, where it came from or who is using it. This results in poor data quality and reduces stakeholder trust in the data. 

Data concerns also increase the risk of non-compliance with government and industry regulatory requirements, such as the Global Data Protection Regulation (GDPR). More data also leads to higher costs, because managing increasingly complex data environments isn't cheap. 

This is why high-quality data governance practices that ensure data privacy and compliance are so important to business success.

- Data Governance Frameworks

Data governance refers to how an organization leverages its people, processes, and technology to manage its internal data. Data governance is the practice of identifying, organizing, and managing data across an organization. It provides standards and procedures to keep data readily available and usable with data protection to ensure data privacy and data integrity. It is the decision-making function that overlays data management. In other words, a data governance framework is a "blueprint for how governance will be enforced."

• Command and Control - The framework designates some employees as data stewards and holds them accountable for data governance.

• Traditional - The framework designates a large number of employees as data stewards on a voluntary basis, with a few serving as "key data stewards" with additional responsibilities.

• Non-intrusive - The framework identifies people as data stewards based on their existing work and relationship with the data; everyone who creates and modifies data becomes a data steward for that data.

• Funding and Management Support – A data governance framework is meaningless unless it is supported by management as an official company policy.

• User Engagement - Ensure that data governance rules are understood and adhered to by those using the data.

• Data Governance Council - A formal body responsible for defining a data governance framework and helping to implement it in an organization.

A data governance framework is a collection of rules, processes, and role delegations that ensure privacy and compliance in an organization's enterprise data management. Every organization is guided by certain business drivers – key factors or processes that are critical to the continued success of the business.

A well-planned data governance framework covers strategic, tactical, and operational roles and responsibilities. It ensures that data in your organization is trusted, documented, easy to find, and also remains secure, compliant and confidential.

Data governance frameworks can be divided into three types:

The essential elements of a data governance framework include: While many companies independently create data governance frameworks, there are several standards that help develop data governance frameworks, including COBIT, ISO/IEC 38500, and ISO/TC 215.

- The Three Pillars of Any Data Governance Framework

If you look at the pillars of any data governance framework, you'll find that responses include standardized policies and procedures, data security and access, compliance and risk mitigation, and more. However, these can be seen as components of a data governance framework template.

These pillars must reflect the essence of modern data stack governance—making data flows traceable and data-related processes transparent so you can understand your operations, improve performance, and achieve your goals.

That's why the following three pillars form the crux of any data governance framework for the modern data stack:

• Governance across all data assets
• Practitioner-led bottom-up approach
• Governance practices embedded in daily work processes 

- Benchmarking a Good Data Governance Framework

According to this paper on Minnesota State University's proposed SMB Data Governance Framework, a solid data governance framework should:

• Enable better decision making
• Reduce operating friction
• Protect the needs of data stakeholders
• Train management and staff on a common approach to solving data problems
• Establish standard, repeatable processes
• Reduce costs and increase efficiency through coordinated efforts
• Ensure Process Transparency 

[More to come ...]