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Data Governance Check
Data Management and Corporate Data Governance
Data obtained from complex process, plant, or company structures are often extremely heterogeneous. Processing and maintenance is time-consuming and cost-intensive. However, it is essential for the successful application of AI methods that data is prepared in a consistent manner. We describe the quality of data management in companies and organizations with the term corporate data governance.
The AI Marketplace's free Data Governance Check.
Complete the following free Data Governance Check for your company to assess your current corporate data governance with the help of the AI marketplace. We will then send you valuable tips and information on how you can improve your improve your corporate data governance in such a way that the use of AI applications in your company.
What is the purpose of the
Data Governance Check?
What are data preparation
and consistency?
How quality assurance works
in Data management?
What is the purpose of the Data Governance Check?
For years, the structural change brought about by digitization towards an information society has led to explosive growth in companies' internal IT structures. These historically grown and heterogeneous IT structures often lack standardization, integration depth and transparency. At the same time, the quality of data and information is increasingly a decisive factor for the success of a company. Therefore, optimize your data structures, data governance and data consistency with the support of the AI Marketplace team and use the Data Governance Check. Over the course of the project, this offering will be built up and continuously expanded so that we can analyze and evaluate your existing data structures. After all, only on the basis of this assessment can the necessary resource decisions be brought about and goals set. Subsequently, your data structures will be optimized to ensure data consistency.
What are data preparation and consistency?
In particular, data obtained from complex process, plant or company structures are extremely heterogeneous, both in their information content and in the form in which they are available. For the successful application of AI methods, it is essential that data and their information are prepared in an end-to-end manner, from the sensor in the process to the database. Data continuity concepts are created based on Industrie 4.0 technologies. This ensures that data is available without manual intervention along the utilization chain.
How does quality assurance work in data management?
The assessment of data quality goes beyond statistics on the completeness and accuracy of objects. Other important characteristics that determine the quality of information are, for example, clarity or relevance. An effective method for increasing data quality is based on the techniques of Total Data Quality Management. The main goal is to meet authoritative quality, security and processing standards for data. By treating data and information in the same way as products in the manufacturing industry, it is possible to apply the quality assurance and quality management methods developed there to the handling of data and information.
