Data Platform Decentralization - Case Study

The Objectives

At Agile Lab, we delegated a team of ten consultants and engineers to work with a large enterprise insurance customer who required strategic guidance and technological expertise to navigate their organization towards a decentralized data architecture and integrate an effective data management platform.

Their primary objectives were to; harmonize their data delivery and governance process, automate the development lifecycle and data product deployment, achieve third-party data catalog integration, establish data contracts between data producers and data consumers as well as have the ability to reuse an architectural blueprint while building new data initiatives.

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Our team carried out a structured, layered approach to address the customers' data challenges. This included;

Developing a deep understanding of the overall business and functional requirements.
Identifying the recurring patterns across various data initiatives that were causing inaccuracies and inefficiencies.
Selecting the appropriate technology stack for the customer's unique internal architecture.
Outlining the roles and responsibilities of the data platform team, data factory team, data consumers and data governance team.
Defining each team's impact and responsibilities on data stewardship, data product deployment, delivery and governance.

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ACCELERATED DECISION-MAKING

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COST SAVING

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DATA QUALITY IMPROVEMENT

Accelerated Decision-Making

Data-driven organizations are three times more likely to report significant improvements in decision-making speed, helping them to respond faster to market changes

(Source: HARVARD BUSINESS SCHOOL)

Cost Saving

Data Platforms can allow companies to realize cost savings of up to 15% through minimized redundancies, optimized resource utilization and streamlined processes.

(Source: McKinsey&Company)

Data Quality Improvement

Companies focusing on structured data management can improve data accuracy and consistency by 10-20% through centralized data platforms

(Source: McKinsey&Company)

Working with the customer, we were able to achieve the following that addressed critical data challenges, leading to transformational changes in efficiency and data discovery:

Reduced the cognitive load on development teams, allowing them to focus more on the relevant and differentiating aspects of their data workload.
Defined data standards and best practices that data teams must adhere to as part of the customers' new governance protocols.
Evolved the data governance framework that outlines the roles, responsibilities and policies for data management within an organization. The framework that we implemented included data quality, privacy, security, cataloging and data lifecycle management.
Developed and evolved data products for the client's internal company groups and related business domains.

Cost Saving

Our approach resulted in lower storage, data integration costs and data transaction costs. This reduction in expenses has enhanced the organization's financial efficiency and resource allocation.

Efficiency

We achieve streamlined Data Management processes and improved Governance by implementing structured guidelines and technical solutions. This led to smoother operations and better utilization of resources across the organization.

Stakeholders Confidence

Demonstrable improvements in Data Management increased stakeholder trust. This support was crucial for securing ongoing investments and resources for future Data Management initiatives.

Data Platform Enablement and Decentralization for a Leading Multi-Billion Euro Insurance Company

The Objectives

Powering Digital Transformation through Data Platform Enablement