How DayBlink Consulting designed a unified data model and lightweight data governance
Read the full case study here: Data Modeling for Asset & Software Management
Introduction
A Fortune 100 communications client faced critical challenges in maintaining visibility across its expansive infrastructure. It maintained and operated hundreds of thousands of assets, including physical devices and private and public cloud resources. With countless applications, microservices, operating systems and other software running on these assets in various environments–many with known security vulnerabilities affecting both hardware and software components, the client struggled to have confidence that it was meeting compliance targets due to fragmented data management. DayBlink Consulting was engaged to design and implement a comprehensive data model that would establish a unified view of their complex technology ecosystem. We also designed a lightweight data governance framework to enable change management. Our team conducted extensive data analysis across dozens of disparate systems to design a target state data model and implementation roadmap to greatly improve data integrity. The solution enabled the client to connect previously siloed information, build a foundation for compliance reporting, and have confidence that its data produced reliable analytics for the entire portfolio of network, cloud, and software assets.
Problem
The client struggled to connect fragmented infrastructure data to software and security information for tens of thousands of assets.
The client faced critical business risks stemming from its inability to maintain comprehensive visibility across the massive network infrastructure. With the combination of physical hardware and virtual cloud-based assets–all running a variety of applications, microservices, and varying system versions–the permutations of relationships between these data nodes created a challenging data management problem that had proven impossible to solve using traditional approaches.
Our client was maintaining dozens of datasets that, when combined, included millions of records. This created a web of interdependencies defying conventional data modeling approaches. A single piece of hardware might host multiple virtual machines, each running different applications with various versions, all potentially affected by different security vulnerabilities. These many-to-many, dynamic relationships between hardware, software, applications, services, and security data could not be effectively represented or queried using rigid relational database schemas.
Traditional data models forced artificial constraints that either oversimplified critical relationships or created unwieldy, performance-killing join operations. Manual processes for understanding and maintaining these relationships were not only error-prone but fundamentally impossible at scale, creating dangerous gaps in infrastructure visibility and control.
The complexity and scale of the problem created significant business risks across compliance, operations, and strategic planning. Without understanding how infrastructure components connected and depended on each other, compliance and security teams struggled to assess the true cybersecurity risk of associated vulnerabilities or demonstrate adherence to targets. Operations teams wasted many hours determining the downstream impact of changes or failures, leading to costly outages and inefficient resource allocation. Strategic decision-makers lacked the visibility needed to plan infrastructure modernization or evaluate the business impact of technology investments.
Solution
Our team unified disparate infrastructure and software data across the environment through a flexible graph-based data model
Data Analysis and Model Design
Our team conducted detailed analysis of the client’s many disparate systems to understand the complex relationships between hardware, software, applications, microservices and security vulnerabilities. Rather than forcing these diverse data sources into rigid schemas, we designed a graph-based data model that could flexibly represent the intricate connections and dependencies across different attributes or nodes. This graph approach proved essential for handling the complexity of different software architectures, run-time deployments, and dynamic security vulnerability relationships that traditional relational models couldn’t effectively capture.
The graph model’s flexibility enabled us to accommodate the existing reporting and analytics challenges our client faced today while adapting to queries they might dream up in the future.
Data Governance and Implementation Roadmap
To ensure the success of our graph-based model, we developed lightweight data governance practices that established standardized definitions and metadata specifications for the data elements in the model. We also created a strategic implementation roadmap that outlined how to systematically connect existing data sources to the new model while maintaining data quality and consistency. These governance and roadmap components provided the necessary foundation for sustainable adoption of the new data architecture.
Outcome
We uncovered $20M – $40M in risk reduction opportunities and enabled another $1.5M in avoided labor costs
For the first time, the client achieved a unified view connecting physical hardware, software deployments, version information, and security vulnerabilities across the entire environment, enabling better management of the vast network and cloud infrastructure. This transformation eliminated dangerous blind spots that had previously plagued compliance efforts and security posture management.
The standardized data model enabled automated correlation between vulnerability scans and specific assets and software deployments, allowing security teams to quickly identify affected systems and prioritize remediation based on actual impact. With tens of thousands of assets now properly mapped and categorized, compliance reporting turned from a months-long manual process to automated dashboards that provided real-time visibility into security posture across the full network.
The data aggregation pipelines enabled daily monitoring of data quality and automated detection of configuration changes, version inconsistencies, and newly discovered assets. This proactive approach significantly improved the ability to identify when datasets were drifting and required reconciliation while reducing the risk of security incidents caused by unknown or unmanaged infrastructure components.
Most importantly, the unified data model provided a foundation for strategic planning and network optimization. The client can now make informed decisions about technology refresh cycles, security investments, and capacity planning based on comprehensive, accurate data about the entire technology portfolio.