Data discovery

Use advanced, agile, data flow analysis, profiling and data quality assessment to rapidly deliver data models that represent true semantics and business rules.

Kinaesis Wave
The Challenges

How do you approach providing clear data lineage and traceability as well as an enterprise data architecture from a set of complex legacy systems and data flows? Modelling based on the fields in legacy data models will provide some of the answers, however, the content of the data itself will mean these unravel rapidly on implementation.

The Approach

Kinaesis use best in class tools and techniques to profile data for the purpose of defining, tracing, cleansing and measuring data quality. This leads to agile, evolving enterprise data models, and integration. We use short delivery cycles, user feedback and testing to get to the right answer rapidly and efficiently.

Case Study
We faced numerous data and processing challenges that were causing our major strategic reporting and control programme to fail. We faced critical delays, production outages and performance problems.
Their Problem

Our client had developed a flexible processing engine for the accounting and control procedures. The operational side of the process processed data at a very fine grain rapidly and enabled them to achieve a single ledger across their businesses globally. Despite this they had significant problems using the data in reporting systems. Their reporting function had swelled to huge numbers of resources and many IT projects to provide strategic reporting automation were failing to meet expectations and timelines.

How We Helped

Kinaesis used its best practice techniques to implement a meta data driven data integration layer to enable the projects to discover the meaning of the ledger data through the project process. Changes to the ETL could be made within minutes and fields remodelled to meet new understanding and knowledge enabled the projects to move forwards rapidly and efficiently. Kinaesis used its experience in data governance to manage the centralised data model to conform information across the enterprise to a semantic schema.

Key Victories

The clients whose many IT projects had failed in the past was able to deliver multiple reporting solutions onto the platform and provide them to the users. We centralised the business semantics reducing the complexity of the reporting solutions and reducing the time for testing as numbers were matching expectations and the users were able to focus on functionality over content. Further strategic investment was scheduled on the platform to bring in more reporting solutions further simplifying the architecture.

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Simon Trewin
Simon Trewin
Head of Solutions Architecture
Other team members
Allan Eyears
Allan Eyears
Head of Delivery

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