Expect from S/4HANA in 2022 The major stumbling blocks of the migration to SAP S/4HANA and how to avoid them. Insights into the status quo of 200 companies from Germany.
Relevant strategic and operative decisions are increasingly made based on big data analytics. As it is no longer a challenge to collect and process a wealth of data, the main challenge is to identify any issues in data quality.
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Data is the raw material of the 21st century
The Camelot Data Quality Health check is an industry and source system independent solution to analyze the quality of your data. It supports enterprises in facilitating collaboration across business units and departments, and can also be leveraged to prepare your data for your next data migration project. Business users get enabled to discover, prepare, and share data without help from IT, while data quality is continuously improved with proactive data stewardship.
Why Camelot Data Quality Health Check?
Robust data quality
Camelot Data Quality Health Check analyses the quality of your data: as starting point for data cleansing and data governance
Enhancements available
Expand our solution to your business requirements
No system prerequisites
Utilizable without the prerequisit of a specific system landscape and no unanticipated costs
Intuitive UI
A simple spreadsheet-like view makes it easy to work with for any business users
Why Camelot
Camelot ITLab is one of the leading consulting companies in Data Quality Management. With the Data Quality Health Check we provide an easy and fast approach to identify data quality issues – allowing companies to deliver a better data quality within a short time. Predefined business content based on Camelot’s deep Data Quality Management experience accelerates project starts.
Experience
Our constantly growing customer base and more than 20 years of experience make us the number one in Master Data Management.
Best practices
We have a track record of customers in many different industries and therefore can provide best practices tailored to your processes.
Rule framework
A set of best practice data validation rules built over many years provides a quick look at typical problems in the master data area.
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Trusted Advisor for Master Data Management, Data Quality Management and Data Transformation with a very profound knowledge of the current market and the business solutions behind it.