What is Data Governance?
Data Governance describes all processes that aim to ensure the traceability, quality and protection of data. With the effort to use more and more data from different sources for decision making and through the technical possibilities of integration in Data Warehouses or Data Lake, the need for documentation and traceability increases exponentially. Questionable data provides questionable information that is used by automated (AI) processes and provides questionable decisions. Data Governance describes and unifies data, documents data quality and processes, and ultimately protects it from unauthorized use.
Reasons why companies should use Data Governance:
Data quality, traceability and protection are important aspects for the use of data in analytical processes. With the methods and technical tools of data governance, the process of data management is monitored and documented, data assets and sources are documented technically and functionally, and access is controlled as needed. This creates the basis for any decision-making, whether manual or automated by AI / ML.
Data Governance includes the following aspects:
What is the worth of insights from information if the respective data is dubious? The process of Data Governance therefore comprises several aspects: the responsibility for information is clearly defined by the designation of a "data owner or steward" for each data asset. He or she also determines the rights of access as well as the necessary protection of the data, which is then enforced by the technology. Another aspect concerns data quality: Here, too, the data steward determines the predefined threshold values which the technology tool uses for its measurements.
In order for the information to be usable for everyone, each asset must also be documented, on the one hand technically (see data lineage or impact analysis - often provided by data integration), but especially functionally: what exactly does this asset say, for which analyses has it already been used?
Our services regarding Data Governance
We see ourselves as experts for the combination of design, Data Integration, Data Warehouse / Data Lake, reporting, analysis to Data Science. By looking at the complete architecture, we can develop better processes, meet analytical requirements even more accurately and ultimately create agile systems that deliver valuable information to the business.
The right vendor for every project:
Are you looking for technical support on Data Governance? We work with technologies and commercial solutions from the following vendors:
IBM began offering and integrating tools for Data Governance early on as part of its "Information Server" and "InfoSphere" products. QualityStage" was introduced for monitoring data quality, and the "Information Server Governance Catalog" (IGC) was offered for comprehensive cataloging. Together with the focus on the AI platform IBM Cloud Pak for Data, the solutions were integrated, technologically completely changed to new container platform and partly new functions were developed. The "Watson Knowledge Catalog" combines modern AI functions for the recognition of metadata, a data catalog including release and security processes, automatic data profiling and workflows including integration into the AI platform Cloud Pak for Data.Learn more
Besides the possibilities contained in SQL Server in the context of the development of Data Integration, Microsoft offers a solution in the Cloud with the "Azure Data Catalog". Multiple sources are supported and data can be added to a catalog and queried with simple functions. In this way, an overview of data can be created relatively easily and inexpensively, and the tight integration into the Azure Cloud Platform goes without saying.Learn more
Talend also benefits in the field of Data Governance from good connectivity to numerous source systems. The Talend Data Catalog is a comprehensive solution that is well integrated into the Talend Suite and offers comprehensive functionalities to ensure data quality and compliance. Based on intelligent mapping procedures and Machine Learning algorithms, a central data catalog can be implemented, access permissions can be centrally controlled and traceability of data processes can be ensured.Learn more
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