Article
    by Ergin Tuganay, Head of Data & AI, Nortal Finland

    The decision-maker’s guide to building a modern data platform

    For years, organizations have accumulated more data than they can effectively use. Despite the accelerating pace, many still operate with fragmented platforms and disconnected insights. Information is often scattered across teams and tools, making it challenging to form a coherent, real time view of what is happening in the business at any given moment. The systems meant to support those decisions may also remain inconsistent.

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    Data and AI Microsoft

    Unlocking the value of data requires more than better dashboards: it calls for a unified foundation that connects insights directly to everyday workflows. This article examines how organizations are approaching that shift and offers a practical framework for building a modern data platform that enables timelier, confident decision making.

    In many organizations, decisions still rely more on intuition than on evidence – not because leaders lack data, but because the information they need is dispersed, delayed, or difficult to interpret consistently. Some organizations have started to overcome these structural and semantic barriers. In these environments, operational choices – from production planning to service delivery – draw on a shared, up-to-date view of performance rather than manually assembled snapshots. This shift is increasingly enabled by unified data platforms that integrate collection, storage, processing, and analysis into a single, coherent architecture. The result is not just improved reporting, but a fundamentally different way of steering the business.

    In this article, we aim to outline a pragmatic pathway – grounded in real‑world experience – without oversimplifying the complexities involved in modern data transformation. This provides the foundation for examining how unified architectures are reshaping data operations and enabling organizations to move beyond fragmented practices.

    Not just a platform: How unified architectures reshape data operations

    A new generation of data platforms is changing how organizations manage their data. Rather than piecing together separate tools, these platforms emphasize a unified, SaaS‑based data foundation. Instead of maintaining multiple services that need to be integrated and operated separately, storage, data processing, and analysis are delivered within a single, cloud‑native environment that is managed, updated, and scaled as one service. Although the ideas behind them are not new, their integration into a single environment reduces the operational burden that has often slowed data projects. For example, Microsoft Fabric replaces much of the traditional multi‑tool data stack by bringing lake storage, transformation pipelines, warehouse capabilities, and BI into a single SaaS platform. This reduces integration overhead and simplifies how teams build, operate, and govern data solutions.

    As organizations reassess their data estate, three design principles are emerging as particularly influential.

    #1. OneLake Architecture – a unified data foundation means fewer blind spots and a consistent set of facts across different parts of the business.

    Many organizations are moving toward systems that centralize data storage and sharing to reduce fragmentation between teams. In Microsoft Fabric, this takes shape through the OneLake architecture, which shows how a unified approach can simplify even the most complex data environments. By centralizing data in OneLake – where it can be managed, secured, and shared in a standardized way – organizations can bring together information from both operational and IT systems, from industrial telemetry to clinical devices, without creating new silos. This integrated view reduces duplication and gives decision-makers a clearer shared picture, helping everyone work from the same facts instead of scattered local data.

    #2. SaaS delivery model – less operational complexity means faster rollout and experimentation as well as more predictable budgeting.

    With a fully managed approach, organizations spend less time on upkeep and more time using data to drive results. New features can be deployed quickly, expanded as needed, and managed through a single operational layer. For organizations accustomed to long, complicated platform projects, this offers a more reliable way to improve how they use data.

    #3. Integrated AI capabilities – from analysis to applied intelligence – make it easier to detect and anticipate issues, reducing the need for constant troubleshooting and improving resilience.

    As analytical needs become more sophisticated, built-in AI and machine‑learning tools open new ways to spot patterns and anticipate changes. These features help organizations identify anomalies, track emerging trends, and highlight risks that might otherwise be missed. Instead of relying only on specialist teams, AI‑supported insights can be delivered directly to decision-makers, allowing for a more proactive approach

    Taken together, these architectural choices help organizations overcome long‑standing challenges like siloed data, inconsistent information flows, slow reporting, and reliance on local expertise. A unified, managed environment offers the structure needed to expand access to insights and support analysis at scale — without the integration headaches that characterized earlier data systems.

    See how different industries are opening access to data and improving decision‑making.

    Six steps to evaluating a modern data platform

    Experience from implementations across industries shows that organizations benefit from a staged, structured approach when evaluating a modern data platform. Working with real data and use cases early in the process helps teams clarify what the platform can support, where the constraints might be, and how the solution could scale over time. The six steps below summarize the practices that have proven most effective in these evaluations. A step-by-step evaluation helps organizations avoid unfocused transformation programs and ensures that platform choices directly support business priorities.

    #1 Scoping and use case mapping:

    A clear scope is essential for any meaningful evaluation. This begins by identifying the decisions, processes, or outcomes the organization wants to improve, along with the users who rely on them. A design‑led perspective ensures that data and technology choices reflect real operational needs rather than abstract technical preferences. Industry priorities vary: manufacturers may focus on equipment reliability, retailers on inventory flow, and public sector agencies on citizen services. For example, Compass Group Finland aimed for faster, more coordinated decisions by bringing operational, financial, HR, sales, and procurement data into a shared environment that reflects how managers run the business day to day.

    #2 Data modelling and storage design:

    A well‑considered data model helps organizations see the big picture – understanding core business entities, their relationships, and how they connect to organizational processes. With this clarity, it becomes much easier to identify the data sources needed to support the model. In practice, this guides the first concrete steps: integrating source systems, setting up governance for future growth, and designing an architecture that supports both reliability and analytical depth. Organizations such as Valmet have used this phase to unify OT and IT data into a single cloud-based environment that can handle everything from sensor streams to video. Public institutions like the Estonian Unemployment Insurance Fund have applied similar principles to combine sensitive registries in a way that meets strict protection requirements while enabling advanced analytics.

    #3 Environment setup:

    A well-organized environment structure, typically separating development, testing, and production, creates the conditions for controlled iteration. This approach lets teams try new capabilities, refine architecture, and adjust workloads without disrupting everyday operations. By the end of the evaluation, organizations have a working data platform, with workspaces matched to their structure and an initial reporting layer tailored to the use case. Skipping environment separation early on is a common pitfall that can slow later development. This structure helps avoid that.

    #4 Testing:

    Testing is essential for understanding how the platform performs under real conditions. Evaluations usually look at how well integrations work, how users interact with reports and tools, and whether performance meets operational needs. This phase offers early insight into where improvements may be needed before broader adoption.

    #5 End-user training:

    Involving users often determines whether the platform becomes a part of everyday work or stays on the sidelines. Practical, scenario-based training helps people see how the new features support their roles and reduces the need for external experts. In one global manufacturing project, significant operational gains – such as an 18% drop in downtime and a 30% boost in operator efficiency – came only after combining real-time insights with practical training tailored to the shop floor. This shows that technology alone isn’t enough; real value comes from adoption.

    #6 Documentation and operational readiness:

    Documenting architectural choices, data flows, and support mechanisms ensures that the organization can maintain and expand the solution independently after the pilot. A clear operating model that covers responsibilities, processes, and change management helps teams scale the platform in a controlled and sustainable way.

    Taken together, these steps provide a clear path for assessing whether a unified data platform fits the organization’s needs. By basing the evaluation on real use cases and actual data, teams can show value early, identify key design considerations, and lay the groundwork for broader adoption. As organizations scale, attention often shifts from technology to operating models, governance, and cost – areas that benefit from a thoughtful, ongoing approach.

    See what it takes for organizations to move from intuition‑driven decisions to true data leadership.

    From information to insight: Navigating the shift

    Many organizations find that while data is plentiful, the insights needed for timely decisions are not always readily available. Modern platforms such as Microsoft Fabric address this by providing a consistent environment for managing and analysing data across the enterprise. Their strength lies not in any single capability but in how they bring formerly separate components into a single operational model that can be adopted gradually and scaled as needs evolve.

    For organizations already using Microsoft Power BI, elements of this transition are beginning by default as capacities shift toward Fabric. This brings access to a broader set of data engineering, data science, and AI capabilities that extend far beyond visualization. At the same time, it introduces practical questions about capacity allocation, governance, and workload design. Preparing for this evolution early helps minimize disruption and ensures new capabilities can be smoothly incorporated into existing processes.

    Structured evaluation helps organizations think ahead about future needs, assess where unified data platforms can reduce complexity, and determine which capabilities will deliver the greatest impact over time. Successful pilot projects focus on testing ideas with real data, understanding how the platform works in everyday settings, and pinpointing where it can add the most value. This structured evaluation helps organizations think ahead about future needs – from cost management and user adoption to the governance and operating models needed to avoid unchecked growth – before formalizing long‑term commitments.

    Viewed through this lens, the move from information to insight is more than a platform upgrade. It creates the foundation for aligning data, processes, and decision-making across the organization. Backed by a clear plan and practical testing, the transition helps new capabilities take hold sustainably, shaping how organizations plan, operate, and adapt to change.

     

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