Reference Data Management
Manage-control-collaborate in real time
Managing reference data is a significant challenge. Cinchy facilitates the real-time collaboration and validation of reference data by various stakeholders, ensuring data accuracy and consistency across the organization. By allowing controlled co-production of data, errors are minimized, and the accuracy of reference data is ensured, which is crucial for trade processing and risk management
Data controls ensure data can only be accessed and changed by authorized users, and collaboration logs provide lineage.
Ensure that reference data is consistent across the organization by allowing controlled co-production of data.
Facilitate the reconciliation of reference data from multiple sources, reducing discrepancies and ensuring data integrity
Support robust data governance mechanisms to maintain the quality and reliability of reference data.
By addressing the challenges of data silos, integration, governance, and real-time collaboration, Cinchy can significantly enhance your operational efficiency, compliance, and decision-making capabilities.
Data collaboration is the answer
Data and Analytics teams leverage Cinchy to standardize reference data on the way into a data lake to simplify the effort for data scientists/engineers/analysts. Rather than task them with deciphering 100’s of source system data and mapping it for projects, Cinchy provides a collaborative environment where source-application owners can provide the mappings for their systems. By enabling source application teams to own this work, it removes the burden on the data lake team to sift through and understand the data.
When changes happen at the source, the owners of that change are responsible for making sure it’s propagated for the data and analytics team. Data controls ensure data can only be accessed and changed by authorized users, and collaboration logs provide the lineage required to track changes.
See data collaboration in action!
Connected data without the effort, time, and cost of traditional data integration.