Data Collaboration for All
Self-service data access and analysis
Trading staff, analysts, data experts and IT staff all want easy access to high quality data. The Datasynthesis Platform offers everyone the data they need, where they need it and when they it. A single set of services to find data, to improve data quality in tools such as Slack and Microsoft Teams, to access data in all major programming environments and to analyse data in BI tools like Microsoft Power BI and Tableau. The Datasynthesis Platform is the technology foundation for encouraging data collaboration, a foundation that can help reduce complexity, reduce costs and encourage innovation with data.
Data Quality by Data Experts
Decouple IT and data to the benefit all
For many business users, getting data issues fixed can be frustrating. Data stewards might help you to find the data you need, but sometimes they seem to have to wait on IT staff to fix the issue you have reported to them. Many organizations use data quality tools that need programming skills improve data quality, and as a result the interface between the data expert and the IT expert becomes a bottleneck slowing progress.
Adopting a self-service approach, the Datasynthesis platform offers no-code data governance and data quality management. This frees the data expert from the dependency on IT time and resources needed to address data issues and increases the productivity of the IT expert through leaving data issues to the people who know the data best. Combining this approach with the scalability of the platform enables data experts to ask questions such as “Tell me about any data issues right now” and receive enterprise-scope responses in real-time, enabling them to validate and fix your issue faster than ever before.
Real-Time Enterprise Data Quality
Pro-active prevention of the spread of data issues
Many errors are only identified downstream in the destination systems that need the data. Without the propagation of the correction back to the data source, then at best this results in multiple data teams identifying and duplicating corrective action. At worst, data errors are corrected locally in just one system, leaving the other users and systems unaware of the issue. This siloed approach to data quality, often with multiple data quality tools used across multiple departments, leads to inefficiency, expense and inconsistent data quality at an enterprise level.
The cloud-native scalability of the Datasynthesis Platform enables a much more pro-active approach to be taken to enterprise data quality. Rather that manage data quality downstream on a local value-by-value exception basis, data quality can be managed upstream as an enterprise process. Hence no waiting needed for batch processes to complete, data quality for the entire enterprise can be monitored in real-time on data quality dashboards and issues mitigated before data errors have spread to downstream systems and users.
Achieving Data Standards
Improved understanding of data
Whilst good data quality local to any system is to be encouraged, the inconsistency of data between systems can result in enterprise data quality being compromised. This inconsistency and lack of shared understanding of data is at best confusing but is often the root cause of resources being expended on costly integration and reconciliation efforts.
The Datasynthesis Platform uses a Common Data Model (alternatively known as a Canonical Data Model) to represent all data entities and relationships in their simplest possible form. For each operational system only one transformation is needed from Canonical form to that of the system’s local data model, allowing enterprise consistency to be maintained through this common understanding used by all systems. Combine this enterprise level understanding with industry standards such as FIBO and you have a platform for increased data collaboration and reduced reconciliation.
Click here to find our more about how to reduce data issues and drive data collaboration.
Please contact us if you would like to discuss how you and your colleagues can benefit from a new approach to data quality management.