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Sicredi is building a unified view of enterprise data spread across 116 credit unions. The Denodo Platform’s logical data layer has enabled faster data delivery, self-service analytics, data governance, and is one of the key enabler for data democratization at Sicredi
Aislan Kleemann
Senior Data Architect

Sicredi, comprised of 116 different credit unions, is the biggest credit union in Brazil. As a cooperative system, Sicredi enables financial solutions to increase income and contribute to improving the quality of life of its members and society. Sicredi has more than 6 million associates spread across almost every state of Brazil, who participate in its business decision-making process. Overall, the union has more than $29 billion in assets and an employee base of more than 35 thousand.

Weaving a Logical Data Fabric

Being an organization of different unions, Sicredi managed a siloed data landscape, comprised of multiple heterogeneous data systems and reporting applications. These siloed data systems hindered Sicredi from leveraging connected data for informed decision-making and improving its time-to-market to serve its customers better. Data analysts were relying on outdated extract, transform, and load (ETL) processes to consolidate data from these data systems, which consumed a lot of data-analyst time and required additional storage systems.

Sicredi deployed the Denodo Platform, which uses data virtualization to create a logical data fabric (LDF) that consolidates data from the data warehouse, data lake, flat files (CSV/XLS), web services (internal and external), and other sources. In the long run, the LDF is expected to be the main data source for reports and dashboards, as well as for data delivery, through APIs, to several internal and partner applications. Additionally, Sicredi is leveraging the Denodo Platform’s data catalog to tag all data with business terms so that users can easily find the relevant data for themselves. The Denodo Platform has been instrumental not only in monitoring, but also in analyzing data traceability through its data lineage features, and in building data-quality validations.

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