The amount of stored data is increasing rapidly these days. Data quality management is therefore essential to maintain an overview and ensure the accuracy of the data. This allows you to work in a data-driven manner and achieve the greatest possible added value from your wealth of data.
The amount of stored data is increasing rapidly these days. Data quality management is therefore essential to maintain an overview and ensure the accuracy of the data. This allows you to work in a data-driven manner and achieve the greatest possible added value from your wealth of data.
The amount of stored data is increasing rapidly these days. Data quality management is therefore essential to maintain an overview and ensure the accuracy of the data. This allows you to work in a data-driven manner and achieve the greatest possible added value from your wealth of data.
In order to make the correct decisions, you need reliable information. It is therefore not just a matter of collecting data, but of ensuring its quality through an active process. In a data warehouse, data from disparate sources is usually merged, which is a frequent source of error. However, the sources themselves also usually need to be checked and prepared.
These criteria are necessary to achieve high Data Quality.
Accuracy – the data matches the sources and reflects the real world.
Completeness – all decision-relevant data is present and available.
Consistency – data from different sources does not contradict itself, ensuring there is only one truth.
Actuality – the data is always up-to-date at the time of a decision.
Validity – the data complies with the defined business rules and is within the valid range.
Uniqueness – the data does not exist more than once and can be clearly identified.
With 30 years of experience in data warehousing, synvert has mastered the entire data quality process. In addition to data quality management, our comprehensive strategy also includes metadata and master data management as well as data catalogs.
We have the knowledge and experience to establish efficient data quality management in your company. This includes the development of DQ rules, DQ criteria, DQ measurements, DQ scorecards and dashboards.
We have mastered the various DQ use cases, from address cleansing of customer data to ensuring DQ for IoT data. We know the DQ requirements of different industries, from receipt data in retail to particularly sensitive data in the healthcare sector.
Over the years, we have gained in-depth experience with a large number of commercial tool manufacturers such as Informatica, IBM, Oracle and Talend, as well as open-source products and customized DQ solutions.
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