Develop a Data Governance Framework: understanding its 10 key elements

A cluster of processes and procedures to manage, use and control an organization’s data.  Data Governance has an important role in the business world due to the quantity of information generated every day from multiple sources. Also, Data Governance helps controlling risks, obtaining value of data and reducing costs. Data is definitely at the core of companies and Data Governance represents one of the best practices that can be performed to achieve digital transformation.

Additionally, Data Governance has different goals, such as guiding, coordinating and defining rules to collect, create and use data within a company. To know more about Data Governance, download our guide: 10 key points to understand what Data Governance is: Why it is essential for your business.

What is a Data Governance Framework?

One of the most important elements to make Data Governance effective is the framework. A Data Governance framework is equivalent to a captain’s compass guiding a ship in the sea. Achieving successful Data Governance requires a framework to solve errors and questions around its processes.

The Data Governance framework is a model of how information works, it is an organization’s backup that indicates how data should be managed according to company rules. In addition, a model like this makes it easier to reach objectives on time with the required quality standards. If a company already works with data and does Data Governance, establishing a framework is essential to successfully carry out these processes.

Data Governance Framework elements

The Data Governance framework will give you an overview of rules set on data processing, roles, and organizational processes in one place. Each company can have the framework which best suits its needs and methodology. Whichever one is chosen, it becomes a guarantee for data quality control, value, and decision-making process.

Now, according to the Data Governance Institute, there are 10 components that should be reflected in the framework construction. Although there are different frameworks, all of them guide companies to an ideal Data Governance implementation and you should choose the best for your organization. All frameworks seek to guarantee data quality for decision-makers.

Let’s describe each one of them in detail:

1. Mission and vision

Data Governance has three particular missions: to align rules, provide protection and services to data stakeholders and to solve issues arising from non-compliance of internal or external policies.

Organizations need efficient tools and strategies to get the most out of their data and achieve their missions.

When both, mission and vision, have been defined, the next stages will be easier to implement and monitor for stakeholders. The mission can be simple to establish but the vision must be an inspiration for the people who manage data to reach the company's objectives.

2. Goals, metrics and funding

In every company, goals must be smart, which means they should be specific, measurable, actionable, relevant and timely. For defining which objectives you should pursue, you need to consider programs, projects, professional disciplines and people. These components will give you the certainty to reach goals and support revenue and value, manage cost and face risks (compliance, security, privacy, etc.)

Metrics also should be smart because Data Governance needs to know how success is being measured and all professionals involved need to know if their strategies are functionals to determine if working in a new process is better or not..

When we talk about Data Governance, it is essential to have a funding strategy. This stage involves an initial investment to start the framework project, financial planning for staff, technology and possible issues which may arise during the implementation.

3. Rules and policies

Data Governance guarantees correct access for information and data value but it is not the only goal, it also helps to accomplish legal and company regulations.  This framework component refers to all policies, standards, compliance requirements, business rules and data definitions, which encompass the following:

1. Create new rules.
2. Gather existing rules.
3. Amend gaps.
4. Align conflicting rules.
5. Establish rules for determinate cases.

4. Decisions

Before any rule implementation, a follow up process must be done, where an assigned person should decide whether or not to apply said rules. This is a responsibility of Data Governance because one of its objectives is to facilitate decisions regarding metadata.

Making smart decisions should be applied by stakeholders, where negotiations can take place. As an example, Data Architecture may need input by other people in the organization to set a new system for the IT Department. Establishing the way of decisions is also a part of the final framework.

5. Responsibilities

A big problem in some organizations is when people don't know all compliance guidelines for different reasons like specialization, them being in opposite departments, subject ignorance, etc. The framework helps to determine who should do what, when and why.

A Data Governance program may be expected to establish responsibilities into an organization’s everyday processes. The Data Governance Institute says that any process is not finished until certain steps are pursuited: doing, controlling, documeningt and proving compliance. Governance’s efforts involve different teams where the coordinators need to understand phases for optimizing tasks, status and objectives.

6. Controls

When you give your information to any company for a product or service, you expect it to be safe and secure. In these cases, organizations should guarantee its security, but nowadays we know the possibility of data breaches happening and its consequences.

How to deal with risk? A good practice is to apply prevent risk management strategies and  multiple controls in tandem such as changing management, policies, training, credentials, controlled access, etc.

Data Governance can be involved in external and internal audities to show controls which help any organization’s development.

7. Data Stakeholders

A data stakeholder can be a person or a group of people in an organization which can affect or be affected by data.  Inside a company a Data Governance schema maps the members such as Data Architects, Data Scientists, Data Engineers or DBAs, who are responsible for data in different situations or departments. But what do stakeholders want?

They usually have expectations by implementing Data Governance some will want to be included in previous data decisions or maybe only be informed after decisions taken. 

In some cases Data Governance needs a committee who will oversee the program and its processes, which will also define policies and solve issues.  There could be changes where IT experts take control of oversight and results.

8. Data Governance office

It is advisable to create a Data Governance department or office within a company to support Data Governance activities, aligning departments and accomplishing data standards. Some of its responsibilities should be:

1. To run the platform.
2. Create links between some disciplines such as Data Quality, Compliance, Privacy, Security, Architecture and IT Governance.
3. Align policies and guidelines.
4. Organize information and analysis IT projects.
5. Coordinate data analysis.
6. Collect metrics.

Not all organizations create a Data Governance Office (DGO), but appoint individuals who work in specific tasks to gain data value and identify key points to improve an organization’s development. Whether there is a DGO or not, communication and links between departments will be key to accomplish governance goals.

9. Data Stewards

Data Stewards have a key role in organization: they align the company’s objectives and technique topics regarding data. Likewise, a Data Steward should have communication skills to share their knowledge with stakeholders and build the best strategy to make data-related decisions.

Data Stewards’ tasks are:

1. Create and manage metadata, including terminology.
2. Develop rules and standards.
3. Data quality management and resolution of data issues.
4. Operations activities.

Depending on the size of an organization, the maturity of its data, needs and strategies, there must be one or more Data Stewards to reduce risks and add value to companies through data or information.

10. Processes

This last component describes the tools to control data. All these processes must be standardized, documented and repeatable, they should be designed in a secure way to support regulatory requirements, Data Management, privacy and control access to information.

These processes can be created according to the strategy of your company. You should decide the structure and formality during the Data Governance execution, nonetheless, this is the framework that the Data Governance Institute suggests.

Data Governance Institute Framework and its components.

Why should you build a Data Governance Framework in your company?

Now that you know the components of a Data Governance Framework, maybe you are asking what is its purpose? Previously, we said that a framework is a guide on how data should be managed according to the company rules: this is when a framework appears for ensuring effective Data Governance.

 There are 7 stages that you can use to build an understanding of the importance of a Data Governance Framework within your company or organization.

1. Why do you need governance?

Develop a deep answer regarding the organization’s objectives, these could be related to business or for controlling data immediately.

2. How will you measure progress?

When your goals have been defined, the next stage is to figure out a correct way to measure deliverables with key metrics. Once you discover an accurate way to translate these metrics in business outcomes like revenue, costs or efficiency, your company may be able to  enter a competitive data world.

3. Scope and requirements

 In this phase you should standardize all organization rules, internal standards and all which  involves assets around data. Points like this help avoid errors such as duplicated fields with the same information, fields name, bad registers about sales or purchases and so on.

 For a common staff it can be difficult to remember or control all data in the organization but with a correct Data Governance implementation and responsible employees data will have big value.

4. Stakeholders acceptance

Organizational culture is a big topic to understand  because there are many departments to attend and to learn about them. Each of them have their own values, priorities, styles, methods, preferences, tools. To unify an entire organization behind data governance can be possible if all departments and people know the Data Governance plan and the stages of it.

5. Define the project leaders

 Data Governance success is only possible with the correct assignment of responsibles and it is necessary to create a committee in charge of the strategic decisions that will ensure the operation for the benefit of the company's development.

6. Outline processes

 In this stage, Data Governance committee should define the following data processes:

1. Data storage and modeling.
2. Data definitions.
3. Cataloging and mapping lineage.
4. Data security and access policies.
5. Data quality, integrity and operability.
6. Reporting.

To help Data Governance teams in each function, the outline processes should be clear and must include rules, best practices, recommendations, guidelines and use of succes historical cases to perform the current process.

7. Technology 

 Using technology improves and automates some complex processes of Data Governance, especially those that require great effort to set them.

When a company generates large volumes of data it also needs practical options to automate processes like data discovery, quality checks, profiling, cataloging, lineage, etc.

Basically, Data Governance needs technology because it gathers data from diverse sources, enriches data with metadata, makes data assets discoverable, generates behavioral patterns and insights, and provides details such as origin, modifications and historical changes. 

Conclusion 

• Data Governance is a necessary step to reach digital transformation and become Data-driven. Nowadays, business decisions and their main actors are a critical part of an organization's development.
• For harnessing data in your company, you should implement a Data Governance framework in order to help you implement a good data management strategy, ensuring your data’s security at all times.
• Having a strong framework is not optional and is the key to effective governance. Remember to allocate suitable resources and select the correct people who have governance as a principal job function.

 Starting a Data-driven world and getting valuable data is not an easy task. Would you like to know how to reach business intelligence and make decisions with accurate data? Let’s build your company’s future together with Arkon Data Solutions. We offer multiple ways to harness your data with multidisciplinary teams who work in an agile framework to increase organizations’ value.

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