This article was written in German, automatically translated into other languages and editorially reviewed. We welcome feedback at the end of the article.
What is data governance?
Data governance represents a comprehensive solution for the professional handling of data. It refers to the process of developing, implementing, and maintaining rules, policies, procedures, and mechanisms to effectively manage data in an organization. It is a comprehensive framework that ensures that data is stored and processed in a high-quality, consistent, available, secure, and accountable manner.
The main objectives of data governance include promoting high data standards and integrity, ensuring all data protection measures, and optimizing internal processes and decision-making based on reliable data. In addition, the minimization of risks and the creation of transparency are intended to sustainably strengthen trust in the organization.
The framework includes, among other things, which employees in an organization can access which types of data and information and how these must be implemented to optimize processes and compliance and data security. If this is successful, internal and external standards for the collection, storage, processing and destruction of data can be defined and reliably adhered to.
Advantages of data governance are:
- Better and faster decisions and processes
- Better risk management and cost control
- Improve compliance with internal and external data policies
- Better and more effective collaboration within the company
- Strengthened trust among customers and suppliers
Why is data governance so important for companies?
In the modern business world, companies are confronted with an ever-growing volume of information. This has become a valuable resource in the form of data, which forms the basis for competitiveness in the digital era. In this context, professional data management is essential for companies to sustainably optimize processes and make informed decisions.
A comprehensive and high-quality data governance solution can therefore generate an enormous competitive advantage. Transparency and accountability in the use of data also have positive effects on the perception of the company and the trust placed in it by customers and partners. In addition to internal and external processes, this can also sustainably strengthen image and customer loyalty.
In summary, data governance in companies ensures:
- Data quality and reliability
- Process optimization
- Data-driven decision making
- Compliance and legal certainty
- Risk Management
Data quality and reliability:
One of the most important tasks of data governance is to ensure the quality of data at the highest possible level. Through a professional management, the definition of standards and guidelines, and the implementation of control techniques, companies can ensure that their stored data is consistent, up-to-date, and reliable. This creates the basis for informed and quick decisions and increases trust in the data.
Process optimization:
This high Data quality enables a smooth data flow and optimized use of data and information. By classifying data and defining clear responsibilities and processes, access to it is facilitated and redundant data storage is avoided. This leads to greater efficiency and productivity in the organization. Not only can processes be automated, employees can also access the information they need more quickly and complete their tasks more effectively.
Data-driven decision making:
Automated decision-making is a central component for the comprehensive optimization of complex processes. Therefore, the evaluation of data and information is also becoming increasingly important for organizations. Effective data management ensures that the data used is relevant, up-to-date, accurate and quickly retrievable. By implementing data governance measures, the accuracy and consistency of data is ensured, leading to better analyses and evaluations and, in this way, enabling informed decisions.
Compliance and legal certainty:
Companies are obliged to comply with a large number of legal and regulatory provisions when handling data. In addition, the demands of customers and partners for sensitive handling of data are also increasing - they expect responsible handling and a high level of security. Data governance ensures that companies meet these requirements and guarantee data security at all times. Legal requirements include, for example, the General Data Protection Regulation (GDPR) in the European Union or industry-specific regulations such as the Money Laundering Act.
Risk management:
The handling of sensitive corporate data is of crucial importance for the evaluation, perception and credibility of an organization. Data governance helps to identify and minimize risks associated with data and thus guarantee the security of internal and external data. This includes protection against unauthorized access, compliance with data protection regulations, and reduction of data leakage or loss. Effective data governance thus minimizes the risk of reputational damage and legal consequences.
How can data governance succeed in the enterprise?
Successful implementation and execution of data governance requires a holistic and well thought-out strategy and comprehensive management within the organization. It is important to acknowledge the challenges and to allocate appropriate resources and Expertise for implementation. A clear vision, a structured approach, and the involvement of all relevant stakeholders are critical to the success of data governance.
This chapter presents the central challenges that companies must overcome when introducing data governance and discusses initial approaches to solving them. By taking a holistic approach and overcoming the challenges presented, data governance can be implemented effectively in organizations, the company's data strategy can be strengthened and thus a wide range of competitive advantages can be generated.
Challenges in implementing data governance include:
- Cultural change
- Clear responsibilities
- Data identification and classification
- Data quality
- Measurement and monitoring
- Technological infrastructure
Cultural Change:
One of the biggest hurdles in implementing data governance is the cultural change within the organization. Implementation requires changes in workflows, processes and systems. It is therefore important to establish an effective Change Management to implement in order to overcome resistance in the organization and promote acceptance of the changes. It is critical to raise awareness of the importance of data governance and management and to gain the support of employees. Data must be recognized as a strategic asset and integrated into decision-making processes. For this, professional communication, training and continuous engagement are essential.
Clear responsibilities:
Data governance requires clear responsibilities and accountabilities for the administration as well as the management of data. It is important to establish clear guidelines and structures to make the responsibilities as well as the management of data transparent. Identifying the right people and assigning and defining appropriate tasks and decision-making authority is therefore central to the success of data governance. Lack of clarity can lead to inefficient processes and delays.
Data identification and classification:
Companies often have a variety of data in different systems and formats. One challenge is to identify the relevant data and classify and merge it according to its importance and sensitivity. A clear definition and classification increases the quality of the data and at the same time facilitates the implementation of appropriate security measures and compliance with all relevant data protection guidelines. Here, digital tools for the automated selection and processing of data and information can make an important contribution.
Data quality:
Ensuring data quality and integrity is critical. Data can be erroneous, incomplete, or inconsistent, which can lead to incorrect analysis and decisions. It is important to put mechanisms in place to monitor, assess, and improve the dataset. Regular data audits, data cleansing, and setting data standards help ensure data integrity. High-quality data culling tools also help here, as they minimize the error rate.
Measurement and monitoring:
In order to to permanently ensure the high data standards as well as the best possible process automation, the monitoring and measurement of the data governance measures is of great importance. Companies should define appropriate metrics and implement monitoring mechanisms to assess the progress and success of data governance initiatives. Regular assessments and adjustments help to ensure that the data governance strategy is continuously improved.
Technology Infrastructure:
An appropriate technological infrastructure is fundamental to the successful implementation of data governance. Companies must have appropriate tools and technologies to capture, store, process, analyze, and protect data. The selection and integration of Data Governance Tools into the existing IT infrastructure can be challenging and may require adjustments or investments. When implementing data governance measures, it is advisable to work with a professional and experienced partner who understands the challenges of the process and can provide reliable, technical solutions.
Data Governance with Konfuzio
Professional data governance provides a wide range of competitive advantages for companies. However, a number of challenges must be overcome during implementation. Digital tools for automated reading, classification and further processing of data are a central component of a modern solution and thus a prerequisite for successful implementation.
One solution that can contribute a key building block to a comprehensive and high-quality data governance solution is Konfuzio. The intelligent OCR solution from Germany provides a digital and automated readout of all relevant information and ensures a reliable classification and location of the collected data. With its high-quality readout, Konfuzio meets all the challenges of a digital data governance tool.
In addition, it can be easily implemented in the company software via flexible interfaces and increase the quality of the data in the short term. Thus, Konfuzio lays the foundation for optimized processes and reliable, automated decision making. In addition, data security and compliance with all compliance and data protection standards can be comprehensively enabled, which in turn optimizes collaboration within the organization as well as sustainably improves the perception of the company among customers and partners.
