/ Case Studies / Data Platform Assessment Creates a Clear Path to Modernization Case Studies Data Platform Assessment Creates a Clear Path to Modernization How a Manufacturing Organization Used a Focused Assessment to Identify Reporting Risks, Select a Future-State Architecture, and Define Its Next Investment Critical Issue A manufacturing organization was evaluating how to modernize its reporting, analytics, and data environment. Business users were experiencing significant reporting performance issues, creating friction for teams that depended on timely information for operational and management decisions. The organization recognized the need for modernization but lacked a clear understanding of the underlying problems, technical dependencies, platform options, and most valuable place to begin. Moving directly into implementation could have resulted in the wrong technology decision, an overly broad scope, or an investment that addressed symptoms rather than the underlying architecture. Concurrency conducted a focused Data Platform Assessment to examine the organization’s data sources, reporting workflows, semantic models, performance concerns, security requirements, business priorities, and analytical use cases. The goal was to give leadership clear answers to three questions: What should the organization build first? What is creating the current problems? What should the future data platform look like? Customer Profile A manufacturing organization that relies on business intelligence and operational reporting across multiple systems and business functions. Its reporting environment included Power BI reports, semantic models, data preparation processes, manual workflows, and business rules that had developed over time. Key Problem The organization knew its reporting environment was slow, fragmented, and difficult to scale, but it did not have enough evidence to confidently select a platform or define an implementation plan. Leadership needed an assessment that connected business priorities to technical findings, identified the root causes of reporting problems, evaluated future-state platform options, and translated the findings into a practical modernization roadmap. BUSINESS CHALLENGES Reporting Performance and Reliability Business users were experiencing reporting performance issues that affected timely access to information. The assessment needed to distinguish among report-design problems, semantic-model issues, source-system performance, refresh patterns, platform constraints, and issues requiring further investigation. Limited Visibility into the Data Landscape Data sources, reporting dependencies, manual preparation processes, business rules, ownership, security requirements, and analytical workflows were spread across the environment. Without a clear view of those relationships, the organization could not accurately determine the scope or sequence of modernization. Platform and Investment Uncertainty The organization was evaluating potential data platform technologies but lacked a clear architecture recommendation. Leadership needed guidance grounded in actual business use cases, technical dependencies, security requirements, and long-term goals before committing to a larger investment. Outcomes Clear Current-State Findings Created an evidence-based view of reporting issues, data dependencies, business priorities, security needs, and implementation risks. Confident Platform Direction Established Microsoft Fabric and a medallion-style architecture as the recommended path for future data platform modernization. Actionable Implementation Roadmap Converted assessment findings into a prioritized, Sales-first implementation plan with defined scope, dependencies, decision gates, and future phases. our solution Concurrency delivered a focused, workshop-driven Data Platform Assessment that connected business priorities, reporting challenges, technical architecture, security requirements, and platform options. The engagement was intentionally designed as an assessment and planning initiative rather than an immediate production implementation. Business Priorities and Use-Case Assessment Confirmed the business decisions the assessment needed to support. Identified priority reporting workflows, analytical use cases, business questions, and key performance indicators. Evaluated current pain points and modernization opportunities. Connected technical recommendations to business value and decision-making needs. Current-State Data and Reporting Review Examined representative data sources, reports, dashboards, semantic models, workflows, and analytical use cases. Reviewed how data moved through the environment and supported existing reporting. Identified performance patterns, reporting dependencies, manual data preparation, and known data issues. Categorized concerns across reports, semantic models, sources, refresh processes, and platform constraints. Security, Governance, and Operational Readiness Assessed role-based access, row-level security, sensitive-data considerations, and user-access patterns. Reviewed data ownership, stewardship, shared metrics, and business definitions. Evaluated workspace structure, development and production separation, deployment practices, monitoring, support ownership, and capacity considerations. Identified governance and operating-model requirements that could affect future implementation. Future-State Architecture Recommendation Evaluated potential data platform and analytics patterns. Recommended a future-state architecture based on business priorities, data dependencies, security requirements, and performance concerns. Defined the architecture direction at a level appropriate for implementation planning. Established a foundation for future data engineering, analytics, governance, and AI readiness. Implementation Roadmap Sequenced recommended implementation activities into practical phases. Documented key dependencies, risks, decision points, and priorities. Translated assessment findings into a defined next-phase scope. Created a clear path from discovery to implementation. FROM ASSESSMENT TO ACTION The Data Platform Assessment identified four connected issues within the organization’s sales reporting environment: Duplicated data preparation and transformation logic across Power BI reports. Refresh contention caused by heavy transformations running in overlapping semantic models. Increased maintenance effort created by parallel data preparation pipelines. No shared, governed single source of truth for sales reporting. The assessment helped the organization align around Microsoft Fabric and a medallion-style architecture that would move shared transformations out of individual reports and into a governed data platform. It also helped stakeholders prioritize Sales as the first implementation domain, with other reports and data areas sequenced for future phases. That direction was translated into a follow-on implementation focused on: Establishing the Microsoft Fabric foundation. Creating governed Orders and Order Lines. Preserving critical business rules as documented and testable code. Building a Direct Lake semantic model. Migrating a production Power BI sales report. Creating a prioritized backlog for future data domains and reporting needs. Lessons Learned & Next Steps Assess Before Selecting Technology A data platform decision should not begin with a preferred product. It should begin with business priorities, existing data dependencies, performance issues, security needs, governance requirements, and implementation constraints. A focused assessment helps organizations select technology based on evidence rather than assumptions. Representative Analysis Can Reveal Enterprise Patterns A useful assessment does not always require an exhaustive inventory of every report and data asset. Reviewing a carefully selected group of high-value reports, models, workflows, and use cases can reveal repeated transformation logic, architecture problems, governance gaps, and modernization opportunities. A Strong Assessment Should Lead to an Executable Decision The value of a Data Platform Assessment is not simply a findings document. The assessment should provide a recommended architecture, implementation roadmap, defined dependencies, risk considerations, and practical starting point. In this engagement, the assessment directly informed the organization’s Microsoft Fabric implementation strategy and Sales-first scope. Conclusion Organizations often know their reporting environment is not working as effectively as it should, but they may not know whether the cause is report design, duplicated logic, semantic models, refresh architecture, source systems, platform limitations, governance, or a combination of these factors. Moving directly into implementation can increase cost and risk when those questions have not been answered. Concurrency’s Data Platform Assessment gave this manufacturing organization a structured way to understand its current environment, evaluate platform options, identify its most important modernization priorities, and define an actionable implementation path. The assessment transformed broad concerns into clear findings, a recommended architecture, and a phased roadmap, giving leadership the confidence to move from uncertainty into a focused Microsoft Fabric implementation. Data Platform Assessment FAQs What is a Data Platform Assessment? A Data Platform Assessment evaluates an organization’s current data sources, reporting workflows, analytics architecture, semantic models, performance concerns, security requirements, governance needs, and priority use cases. It converts those findings into a recommended future-state architecture and actionable modernization roadmap. When should an organization consider a Data Platform Assessment? An organization should consider a Data Platform Assessment when reporting is slow or unreliable, teams duplicate data preparation, business definitions are inconsistent, information is fragmented across systems, or leadership is evaluating new platforms without a clear implementation path. Does a Data Platform Assessment require reviewing every report? A Data Platform Assessment can use a representative set of reports, dashboards, semantic models, workflows, and analytical use cases to identify recurring patterns and dependencies. This approach helps uncover architecture and governance issues without requiring an exhaustive inventory of every asset. What should a Data Platform Assessment deliver? A Data Platform Assessment should deliver current-state findings, a representative data and use-case assessment, a recommended future-state architecture, an implementation roadmap, and a clearly defined next phase. These outputs help leadership decide what to build, why it matters, and how to sequence the investment. Can a Data Platform Assessment help select the right technology? Yes. A Data Platform Assessment evaluates potential technologies and architecture patterns against the organization’s business priorities, data dependencies, security requirements, operating model, and future goals. The objective is to recommend a direction based on the organization’s actual environment rather than selecting technology in isolation.
