What means Data Governance?

Introduction

In today’s business world, everything revolves around data. It is the backbone of modern businesses and plays a central role in decision making and the success of a company. But simply collecting data is not enough. A clear strategy is needed to ensure that data is used correctly, reliably and securely: data governance.

Data governance encompasses all processes, guidelines and roles that ensure that data is managed correctly within an organization. It helps to minimize risks, guarantee data quality and ensure that the right data is available at the right time. In this article, you will find out exactly what data governance is, what benefits it offers and how companies can implement it effectively.

Definition: what is Data Governance?

Data governance describes the definition of internal rules and guidelines relating to the handling of data – from collection, storage and processing through to final deletion. It defines who may access certain types of data and which types of data are covered by the governance requirements.

Goals of Data Governance

• Ensuring the quality of data
• Adherence to legal regulations (compliance)
• Protection of sensitive data
• Improving data consistency across all systems
• Optimizing data usage for business decisions

By achieving these goals, a company can not only minimize risks, but also increase confidence in its own data. This leads to better, more informed decisions and a more efficient use of resources.

Data governance includes the following data and information:

• Master data (e.g. customer data, product data)
• Transaction data (e.g. orders, payments)
• Metadata (information about other data)
• Operational data (e.g. log files, system data)
• Sensitive data (e.g. personal data, financial data)
• Unstructured data (e.g. emails, documents, social media data)
• External data sources (e.g. market data, supplier data)
• Historical data (e.g. archived reports, past transactions)
• Analytical data (e.g. business intelligence data)
• Big data (large, complex data sets from various sources)

Data Governance Framework

A data governance framework serves as the basis for managing and controlling all data within a company. It defines who is responsible for which data, which processes must be followed and which tools can help to comply with the guidelines.

Components of a framework

A typical framework consists of the following elements:

• Guidelines and standards: Defined rules that describe how data must be used and protected.
• Roles and responsibilities: Definition of responsibilities, e.g. of data stewards, who ensure that the guidelines are adhered to.
• Processes: Structures that define how data is managed, from creation to deletion.
• Technology: Tools used to manage data quality, catalog metadata and meet compliance requirements.

Important roles and responsibilities

In every company, there are certain people who are responsible for managing the data. The so-called data stewards ensure that data quality remains consistently high and that compliance with guidelines is guaranteed throughout the entire data lifecycle.

Building a successful data governance framework

A successful data governance framework begins with the definition of clear guidelines. These must be accessible and understandable for all employees.

The definition of responsibilities is also crucial so that every person in the company knows who is responsible for which data.

Finally, suitable processes and tools must be implemented to ensure that the data is managed and protected correctly.

Choosing the right data governance tools

Implementing data governance is hardly possible without the right tools. These tools help to automate data processes and ensure compliance with regulations. They enable the cataloging of data, the monitoring of data flows and the management of access rights.

Important functions of tools

• Data cataloguing: recording and organizing databases.
• Compliance monitoring: Ensuring compliance with regulations such as the GDPR or HIPAA.
• Data quality verification: Monitoring and improving the accuracy, consistency and completeness of data.
• Metadata management: Managing information about the data itself to enable better use.

How do you choose the right tool?

Choosing the right tool depends on the specific requirements of the company. It should be able to be integrated into existing systems and offer functions that are necessary for cataloging, monitoring and managing data. It is also important that the tool meets compliance and data security requirements.

Data Governance vs. Data management

Many people confuse data governance with simple data management. However, while data management focuses on the operational management of data, data governance is about defining and adhering to guidelines and processes that ensure that data is managed correctly and securely.

What are the differences to data management?

Data management encompasses the administration, storage and processing of data. It is more operational and technology-driven. Data governance, on the other hand, defines how data should be strategically managed to ensure that it meets quality and security requirements.

How are the two connected?

Data governance is the strategic basis for effective data management. Without clear guidelines, no data management system can function properly, as there are no fixed guidelines on how the data should be used.

Master Data Management

Another important aspect is master data management. Master data is the core data of a company, such as customer data or product data. Correct management of this data is crucial for the success of a company.

Master data management as part of data governance

Master data management is closely linked to data governance, as it deals with the quality, consistency and availability of the most important company data. It ensures that master data is used correctly and uniformly across all departments.

Challenges and solutions in master data management

One of the biggest challenges in master data management is ensuring data quality across different systems. Companies must ensure that all departments have access to the same, up-to-date master data. A clear process for data management and the use of suitable tools are crucial here.

Best Practices

• Assign clear responsibilities: Determine who is responsible for which data, e.g. by introducing data stewards who monitor compliance with guidelines.
• Create data catalogs: Catalog all data sets and metadata to create a central overview and facilitate data access.
• Perform regular data quality checks: Implement processes to continuously check and improve data quality.
• Ensure compliance: Ensure that all data policies comply with legal requirements (e.g. GDPR, HIPAA) and are updated regularly.
• Employee training: Ensure that all relevant employees are trained and know and follow the data governance guidelines
• Use automated tools: Use technologies that automate and optimize processes such as data monitoring, cataloging and backup.
• Break down data silos: Ensure that data is shared across departmental boundaries to ensure consistency and availability.
• Implement lifecycle management: Develop processes to manage the complete data lifecycle, from creation to storage to deletion.
• Manage access rights: Implement strict regulations and technologies to restrict data access to authorized individuals.

Common mistakes and how to avoid them

A common mistake in data governance is the lack of clear responsibilities. If no one is responsible, data quality suffers and errors occur more quickly. Another problem is insufficient data integration between different departments, which leads to data silos. These should be avoided by using centralized systems for data management.

Machine learning in the context of data management

Machine learning (ML) and artificial intelligence (AI) are changing the way companies work with data. However, these new technologies also bring with them new challenges for data management and maintaining data sovereignty.

Challenges posed by machine learning

As the use of machine learning increases, so does the volume and complexity of the data that needs to be managed. Data governance must ensure that the data used for ML is of high quality and correct.

How machine learning influences data management

Machine learning can help to monitor data quality and compliance. By using algorithms, companies can automatically detect and correct errors in the data, which improves the effectiveness of data governance.

Conclusion

Data governance is essential for any company to ensure that data quality remains high and all legal requirements are met. With the right framework, the right tools and clearly defined processes, data governance can become a key strength of a company. The use of new technologies such as machine learning also plays an important role in managing data efficiently and securely.