Reduced Reconciliation Effort

Common data standards for less manual intervention

Inconsistency of data between systems can result in the need for resource-hungry manual reconciliation. The lack of any common data standards means that operational and IT resources are expended on understanding and correcting for the different definitions and terminology used. This in turn can introduce dependency on spreadsheets and other tactical data tools that further increase the complexity and risk of data operations.

The Datasynthesis Platform uses a common data model to represent all data entities and relationships in their simplest possible form. For each operational system only one transformation is needed from the common data model 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 straight-through processing and reduced reconciliation.

Real-Time Enterprise Data Quality

Pro-active prevention of the spread of data issues

In addition to data consistency, accuracy and completeness of data can contribute significantly to overall data quality and to the manual resources deployed in correcting any issues found. Typically, many data issues are not identified until they are self-evident in downstream operational systems, having a potentially severe impact on the timeliness of operational business processes. Given that the issues were not identified at source, then additionally such data issues will also have found their way into other downstream systems, entailing much effort in manually rewinding each one back to its original state before processing can continue.

The integrated design 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.

Data Quality by Data Experts

Decouple data from technology expertise for the benefit of all

For many data experts in operations, it is frustrating that many data management tools require programming skills to monitor and improve data quality. And for IT experts, it is frustrating to receive seemingly ad-hoc requests from operations staff to correct some data issue, particularly when these requests compete for time with the project deliverables they have been tasked with.

Adopting a self-service approach, the Datasynthesis platform offers a “data management in plain English” approach to 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, delivering a new level of transparency for enterprise data quality.

Data Collaboration for All

Self-service data access and analysis

Access to high quality data should be available wherever it is needed by operations. And in addition, contributing to data quality should be as integrated a part of everyone’s daily routine like answering emails. 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, 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 everyone in operations to contribute to enterprise data quality.

Pay-as-you-go infrastructure

Turn your upfront CapEx into controllable OpEx

On-premise solutions can involve a very high degree of upfront costs involving software licensing, hardware and implementation before any value is delivered. Add to this the risk of sizing the system before all the consequences and possibilities of its introduction can be properly assessed. The Datasynthesis Platform’s cloud-native design offers a pay-as-you-go approach. It offers low upfront costs, combined with rapid implementation and no sizing risks. Time to value is improved and ROI increased.

Near-zero systems maintenance

Use less resources on the mundane, focus on the strategic

Putting aside the direct licensing and support fees incurred in many legacy data architectures, so much resource is deployed simply to maintain systems, optimize databases and in the general administration of IT. The cloud-native design of the Datasynthesis Platform means that scale and capacity issues around processing, storage and user concurrency are no longer constraints. The platform’s modern, serverless design means that it automatically scales to meet the agreed performance service levels. Combined with a no-code approach to enterprise data rule management, systems maintenance is drastically reduced, enabling operations to concentrate resources on smoother, more efficient processing.

Provision with security and trust

Secure, governed access to your data

Data security is a key concern for all organizations. Regulations such as GDPR have driven many security initiatives, but in addition the sheer reputational damage of a data breach is something that is unquantifiable for many. In a data architecture of comprised of many legacy tools with complex cross-dependencies, it is unsurprising that weaknesses exist and are difficult to mitigate.

The Datasynthesis Platform provides a single set of secure services for accessing enterprise data. These services take care of authorizing users, authenticating credentials and granting access to only the data they have been permissioned to see, based on their role in the organization. Given the scalability of the platform, point-to-point and departmental tools can gradually be rationalized as you move towards a modern, simpler and more secure data architecture.

Next steps

Click here to find our more about how operations can benefit from processes based on high quality data.

Please contact us if you would like to discuss how to improve your operational data quality and reduce the resources, time and money spent fixing data issues.