Berlin, September 9, 2020 | The introduction and development of optimum master data management is divided into different project phases and requires close cooperation between the individual business units and the IT department. From our experience, we have identified four project steps to ensure the best data quality and a high level of MDM maturity in your company.
Screening The Data Landscape
Situation:
Would you like to improve data quality in your business? Have you had trouble finding a concrete problem/application or measuring the added value of high-quality data?
Procedure:
We work with you to analyze your data landscape, identify quality problems and measure the additional value. This can be achieved with the help of comprehensive master data management.
Result:
You will receive a plan of action in the form of a condensed report, with guidance on how to shape your master data management project.
Database Testing
Situation:
You know what data quality problems are present in your business, but you are not sure how good the data quality is?
Procedure:
We measure the quality criteria of a data source with DataRocket (syntax test [font] + semantic test) in a two- to three-day starter project.
Result:
A data profile report on your data landscape (e.g., duplicates, Golden Records, error and plausibility problems, content, minimum and maximum values, descriptive statistics).
Cleansing Projects
Situation:
You know what the problem is and want to bring your data quality to the next level?
Procedure:
We work with you to create a set of quality criteria and key figures (for measuring additional value) that should be calculated, tailored to your needs. We put this together in the form of a data pipeline in DataRocket, which can be used to carry out workflow-optimizing cleansing.
Result:
You will receive top-quality data, continuous reporting and a structured and detailed data quality policy.
Real-Time Quality Control
Situation:
You want to automatize quality control and make sure early on that nothing can negatively impact the quality of your data – e.g., errors in data entry/dataset changes or during data migration – and to maintain a constant and continuous level of data quality in your master data so that it can be used by different departments and provide a solid foundation for reporting.
Procedure:
DataRocket acts as a central hub and other relevant systems can be connected to it. The predetermined quality policy conducts checks automatically, e.g., even during data entry, it checks the values for accuracy and duplicates.
Result:
A comprehensive master data management system. You will have a central data source with verified data (single point of truth) and a 360° view of your data. Most importantly, you will be able to take control of your master data – in short: your business will benefit big time.
