Success Stories

Enterprise Data Architecture Strategy

Enabling Data-driven Decision Making

Enterprise Data Strategy
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Project Overview

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.

Client Challenge

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 Data storage and maintenance costs increased exponentially due to large amounts of unused data, compounded by outdated and inefficient products and systems.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.

Approach

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.

Solution

The roadmap designed by RevGen Partners included recommendations to:

  • Develop business intelligence capabilities: A plan was developed to create a centralized, enterprise-wide BI environment where both internal and external customers could access information across products in a timely and dependable manner.
  • Simplify enterprise data architecture: The roadmap proposed a flexible, open and extensible master data model with data objects, hierarchies and business rules defined consistently across the organization. This data model eliminated overlapping data and data silos to provide a single version of truth and an aggregated view of information across systems and transactions.
  • Decrease costs: We suggested the definition and implementation of data archiving, storage and retention policies, as well as information architecture and development standards. Additionally, the roadmap outlined goals to enforce the decommissioning of legacy products and systems. These plan objectives helped to reduce the avoidable storage and maintenance costs of duplicate data and unnecessary processes.

Results

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.

 

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