Enterprise Data Architecture Strategy and Roadmap

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Summary

Data challenges emerged when our client, a premier financial utility firm, experienced substantial growth through mergers and acquisitions and by offering their data to external customers as products.

Challenge

Limited standards and unclear data management responsibilities led to the misuse and misunderstanding of data throughout the enterprise, which in turn led to inefficiencies and significant storage costs. The client faced an increase in data demands from multiple business units driven by client and regulatory requirements and wanted to address these needs by aggregating data across platforms and commoditizing data.

Client growth created data siloes that were difficult to integrate, and uneven data quality and multiple versions of datasets across its many subsidiaries limited their ability to meet business needs. The utility firm wanted to enhance its client offerings and client experience by taking a data-driven approach to introducing new products, but its limited data understanding and unclear data lineage was hindering this strategy.

Solution

The Sagence team provided the client with a detailed view of the current state of enterprise data architecture through research, analysis, documents and interviews with key stakeholders across the enterprise. We focused on solutions to address challenges in data technology guidance and patterns, data discovery (metadata) architecture, data management architecture, and data utility blueprints.

Sagence identified and communicated gaps with existing architecture and provided a target enterprise data architecture, as well as its impact on in-flight projects, programs, and initiatives. We developed a “90 day plan” and charters for projects to establish enterprise data architecture concepts and patterns.  Additionally, we created operating principles for the enterprise data architecture function and the stakeholders responsible for adopting enterprise data architecture’s future state.

Results

Data technology management and implementation formalized the lifecycle deliverables to resolve ambiguity on redundant tools/technologies and raise awareness of available capabilities across the enterprise. Data discovery architecture created the foundation necessary to define key data elements consistently and enable data governance processes. Furthermore, data management architecture and data utility blueprints assist with managing data provenance and storage as data is produced and as it matures. The enterprise data architecture team now has the roadmaps needed to position itself as a pattern maker to offer guidance and set standards, which can be referenced across the enterprise.

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