Enabling Data-driven Decision Making
A multinational company supporting the healthcare industry faced increasing costs and fragmented decision-making due to a reliance on data from disparate processes. Multiple mergers and acquisitions, coupled with rapid product growth, created a complex enterprise architecture with data and technology redundancies. The client sought an enterprise environment that could simplify enterprise data architecture, expand business intelligence capabilities and reduce costs.
As the company expanded, its enterprise data architecture became increasingly disjointed. A lack of appropriate integration of acquired external data architectures and legacy products resulted in non-transferrable data, overlapping data ownership and decentralized control.
The lack of visibility into data across various products led to fragmented teams and silo-based decision making. Data storage and maintenance costs increased exponentially due to large amounts of unused data, compounded by outdated and inefficient products and systems.
To combat these issues, we recommended a detailed plan to consolidate enterprise data architecture, decrease costs and migrate to a data-driven culture.
Our data experts first conducted a current-state assessment to locate data duplications, and design an enterprise-wide system and functional data inventory map. Throughout this assessment, the team identified tactical opportunities to limit data duplications and integrate siloed products and teams.
We then developed a strategic roadmap that outlined and prioritized numerous opportunities for data and architectural improvements.
The roadmap designed by RevGen Partners included recommendations to:
Addressing enterprise data challenges with a simplified business intelligence environment.
The enterprise data architecture roadmap provided clear guidelines for creating an enterprise business intelligence environment with robust tools built on a flexible architecture.
Additionally, the roadmap helped to educate employees on best practice solutions and their benefits, ultimately prompting the organization to buy in to making process improvements that enable better data-driven decision making.
We used data science to help our client qualify more prospective patients without sacrificing their standard of care.
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