Data governance – definition, advantages, tips, tools

Definition: What is data governance?

Data governance deals with the effective use of information and data assets to achieve company-specific goals. It is about having control over data, its quality and security.

Effective data governance includes the following aspects:

• People: Responsibilities and roles, guidelines and standards
• Processes: Internal and external
• Tools: Infrastructure and technology

Good data governance answers the following questions: Who is allowed to use which data in which situations using which methods?

High Quality Data Matters

Governance also helps to ensure good data quality. It also prevents employees from wasting their working time due to unclear data access. Company data should be correct from the outset and handled properly throughout its lifecycle. It must reflect reality.

Background: The Flood of Data Is Swamping Companies

The flood of data that companies – and private individuals – are collecting is taking on enormous proportions. The amount of data produced worldwide per day is 2.5 trillion bytes, or 2,500,000,000,000,000,000,000.

Data production continues to gather pace: 90% of the world’s existing data was created in the last two years. Furthermore, the volume of data generated worldwide is growing by about 40% per year.

In other words, one of the key challenges of our time is to be able to use the available volume of data effectively.

This is precisely why data governance comes into play: It brings order and structure that eliminates data chaos. This makes it easier for companies to collect, store and protect data.

What’s more, companies are dependent on reliable data to make business decisions and support customers.

Governance is, however, a major challenge.  IT is increasingly becoming a decisive factor here, as it is typically its task to ensure that data is handled appropriately.

The Importance of a Data Governance Strategy

Organizations that work with big data must have a data governance strategy. In the information age, the conscious and targeted handling of data contributes more than ever to a company’s success.

Uniform and standardized processes, procedures and responsibilities are essential. Business drivers are used to show which data must be subject to detailed control and what advantages a well-developed data governance strategy can bring.

An important business driver, for example, would be the requirement to strictly protect personal data without compromising the work of employees who rely on it. This comes into play with banking secrecy or sensitive customer and patient data.

Advantages of Data Governance

Essentially, these three data governance cornerstones are important:

1. Thoughtful use to act effectively and purposefully
2. Good administration in order to have control over your own data
3. Satisfactory security and compliance to protect sensitive data

They enable companies to improve their data management, strengthen trust in data and ultimately achieve their goals more effectively.

Data Governance Benefits

In detail, the following factors also play a role.

• Improved data situation: It is possible to ensure that data is consistent, honest and accurate.
• Focus on goals: If data usage is managed, clear relations to key figures, KPIs and strategic corporate goals can be established.
• Better decisions: By making it easy to locate important data, all stakeholders can access it reliably and make better decisions based on it.
• Fewer risks: By identifying and thwarting risks, such as data misuse or compliance breaches, good governance can prevent any negative financial and legal consequences while also maintaining customer trust.
• Optimized collaboration: Clear distribution of roles, responsibilities and processes improves collaboration.
• Avoidance of fines: Not taking regulatory requirements seriously can often lead to fines for the company. Good data governance prevents this.

Goals of Data Governance

The benefits stem from the understanding the overall goals of data governance. In many cases, it is first of all crucial to ensure good data quality in order to work effectively.

Many companies use data governance, for example, to gain a clearer view of their customers and to be able to act in a uniform manner, especially externally. The principle is very simple: If you standardize target-oriented behaviors, success come more quickly.

Data governance is also an absolute necessity in highly regulated industries or when it comes to specific requirements, such as the GDPR.

Overall, the following dimensions highlight the objectives of data governance:

• Performance indicators in terms of corporate objectives
• Strategic aspects and knowledge gains
• Ensuring security and compliance
• Legal components

In many cases, a good governance strategy keeps companies healthy, secure and on the right track. The objectives are often expressed in terms of being able to work efficiently towards one’s own goals – without having to fear unpleasant surprises.

The Practical Implementation of Data Governance

Data governance describes a set of procedures and may therefore seem somewhat abstract. There are a number of ways to make data secure, valid, available and easy to use – while at the same time guaranteeing reliable protection.

Framework: How do I implement data governance?

A framework that provides useful processes and guidelines can help make data governance initiatives easier. As a holistic approach, such a framework supports a company’s overarching data and information management.

The following areas can be important for developing such a data governance framework:

• Goals and strategy: First of all, the goals and priorities for the use of data should be established. Key questions: How can data governance support the strategic corporate goals? What do we want to achieve through optimized data management? Where do we stand once we have implemented governance guidelines? Based on this, a targeted strategy can be designed to achieve these goals.
• Create structure and roles: Data governance thrives on structure and is all about people. It is, therefore, important to determine who takes on which role and is responsible for what. For example, it makes sense to appoint data owners (those responsible for data) and data stewards (those responsible for implementation and data maintenance) and other roles. The question here is: Who is responsible for managing which data areas and processes? In this sense, it can also be a good idea to appoint an entire committee for data governance.
• Introduce guidelines and standards: Guidelines and standards are important to ensure coherent, consistent and targeted data use. Examples include how to classify data, who exactly has access to which data and how to achieve sufficient quality and security.
• Communication and training: Employees should not only be sufficiently informed about a data governance strategy, but should also be able to understand and implement the relevant processes and guidelines. This includes open, clear communication and possibly training so that the most important points of a data governance program can be put into practice on a daily basis.
• Performance measurement and continuous improvement: Good governance processes do not set themselves up automatically, but benefit from dedicated process optimization. It is an important requirement to constantly scrutinize data and continuously improve procedures.

Tip: The IT department is not responsible for the amount of internal company data, but should manage and control its flow. It is typically their task to set up a suitable framework – in particular guidelines and standards – for this purpose. Practicing and maintaining data governance on a day-to-day basis should be a company-wide task.

Requirements for Data Management

Without data management, the collection, processing and provision of data would be reduced to absurdity. This means that a number of requirements for the data itself must also be met.

The following requirements can be taken as examples:

• a sufficient scope, including master data, operational data, analytical data, etc.
• a good data architecture, including the definition of standards, models, metadata and integration processes
• high data quality, ensured by cleansing, validating and monitoring metrics
• metadata management, making data easier to find, understand and use

Remember – it is important to clearly differentiate between data management and governance: Governance strategies focus on quality, control and the type of usage; management is more concerned with the generation of data itself.

Further tips

Implementing a successful data governance strategy can be quite challenging. Especially when it comes to change, many employees are initially skeptical and unclear about what exactly awaits them.

With this in mind, the entire company should be able to participate in the planning and execution:

1. All relevant stakeholders should be involved and be able to express their needs and requirements.
2. Employees should not feel left out and should ideally be able to participate in the development of the corresponding governance strategy.
3. Above all, the project needs approval at the top level. In other words, if at least one person from management supports the project, the chances of success are much higher. (This is also an immensely important factor for resource planning.)
4. By integrating data governance directly into business processes, it becomes part of ongoing operations. This should not be considered a separate project
5. Silos must be eliminated so that data can be used across departments and used for strategic decisions.
6. Internal data usage should be democratized so that employees receive all the resources and information they need to perform their tasks and create value.

It is important that companies see data governance as an important issue, put it into practice and continue their efforts.

Data Governance Tools

Data governance also requires the right tool support. An adequate IT strategy is required to gain a comprehensive overview of internal company data and to make it usable in a targeted manner. Different software solutions can be used depending on the specific aspects that are in focus.

To ensure good governance, it is advisable to use centralized solutions that structure and document information and data – and through which granular access can be controlled. The use of automation or artificial intelligence may also make sense for this.

Here are some categories of tools that make sense in the context of a governance strategy:

• Master data management tools
• Risk management software
• Information security management systems
• Metadata management tools
• Data quality tools
• Governance dashboard and reporting tools
• Classification tools
• Data catalog tools

Risk management software, for example, follows the GRC (Governance, Risk & Compliance) principle and makes it possible to manage company processes on the basis of current data. This allows companies to clearly identify risks and opportunities and avoid problems, such as data silos.

Of course, the use of specific tools depends on individual needs, requirements and objectives. The importance of scalability, integration and user-friendliness should also be considered.

Related Topics

At this point, it’s important to differentiate data governance from other terms.

Data Governance vs. Data Management

Both areas have a fairly high degree of overlap, but there are also some differences. Data management refers to the administration of data throughout its entire lifecycle in a company. On the other hand, data governance deals with processes, standards and guidelines as well as technology in order to be able to use information and data as effectively as possible. It describes a holistic approach.

Data Governance vs. Data Stewardship

In contrast to data governance, data stewardship is more concerned with data maintenance itself. The task of a data steward is to prepare it so that it is error-free and accessible to all stakeholders. Governance is more about strategic aspects and the overarching view.

Data Governance vs. Information Management

Information management deals with information as a resource and focuses on making it available in a targeted manner. One key difference is that information has a different structure than data: While information may have various meanings, data is information about facts. Governance also places greater emphasis on processes, standards and technologies.

Conclusion: Data governance is a Core Business Requirement

Whether data really is the gold of our time is debatable. However, it cannot be denied that it is of great importance to companies – and that a strategy for dealing with it appropriately is needed. More important than the actual collection of data is often the way in which companies use it.

Data governance offers a wide range of support and is also a necessity in many cases – and not just in terms of security and compliance. Governance is crucial for controlling the prevailing flood of data, gaining structure and acting strategically and efficiently.

A framework, including strategy and objectives as well as suitable software solutions, helps with implementation. It is crucial not to see data governance as an end in itself, but instead to use it to solve specific problems and drive decisions that are more thoughtful, secure and profitable.

Find out how OTRS can support your data governance.

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