The amount of stored data is increasing rapidly these days. Data quality management is therefore indispensable for maintaining an overall view and ensuring the correctness of the data. This enables you to work in a data-driven manner and obtain 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 indispensable for maintaining an overall view and ensuring the correctness of the data. This enables you to work in a data-driven manner and obtain the greatest possible added value from your wealth of data.
To make the right decisions, you need reliable information. So it’s not just a matter of collecting data, but ensuring its quality in an active process. In a data warehouse, data from disparate sources is usually merged, which is a frequent source of error. But the sources themselves usually have to be checked and prepared as well.
These criteria are necessary to achieve high data quality
Flawlessness – the data matches the sources and represents the real world
Completeness – all data relevant for decision-making are present and available
Consistency – the data from different sources do not contradict each other and only one truth exists
Timeliness – the data is always up to date at the time a decision is made
Validity – the data conforms to the defined business rules and is in the valid range
Uniqueness – the data does not exist more than once and can be uniquely identified
With 30 years of experience in data warehousing, saracus masters the entire data quality process. In addition to data quality management, metadata and master data management as well as data catalogs are also part of a comprehensive strategy.
We have the expertise and experience to establish an efficient data quality management in your company. This includes the development of DQ rules, DQ criteria, DQ measurements, DQ scorecards and dashboards.
We master 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 bond data in retail to particularly sensitive data in the healthcare sector.
Over the years, we have gained in-depth knowledge of a variety of commercial tool vendors such as Informatica, IBM, Oracle, and Talend, as well as open source products and custom-built DQ solutions.
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