Article
    by Petri Rönkkö, Business Area Director Forest, Manufacturing & Services, Nortal Finland

    Data democracy across industries

    Has access to data already crossed the line from privilege to right?

    Service

    Data and AI Microsoft

    The word “democracy” raises varied associations today. While democratic thinking is struggling elsewhere, the push for democracy and democratization in technology, especially in data, is accelerating – not as an optional feature but as a market-driven strategic necessity.

    A cross-sector shift in the data mindset

    For decades, data analysis was seen as the exclusive domain of specialists. Although access to data is not an inherited privilege today, it still must be earned through specific skills, titles, or organizational roles. Highly accomplished experts often act as gatekeepers, leaving business users waiting for reports that frequently arrive too late to inform decisions – or in a format so complex that only the most experienced data specialists can interpret them.

    Such a model is no longer effective in today’s fast-paced business environment. Now, the ability to access and interpret data is becoming a universal right within organizations. The rise of AI-powered tools has made advanced analytics accessible at all organizational levels – provided the necessary data is available. This is where data democratization comes in: making data and insights available to everyone who needs them, not just IT or analytics teams.

    The shift extends far beyond a specific industry. It is transforming energy, finance, healthcare, manufacturing, logistics, and the public sector, among others. Democratizing data enables faster decision-making and continuous innovation, helping organizations maintain a competitive edge. For example, insurance risk managers can analyze claims patterns in real time. Clinicians can combine operational and clinical data to improve patient care. Policymakers can access actionable insights to design better services. Engineers can optimize energy grids and predict outages – without waiting for data scientists. With St1, we developed an AI-ready Data & Analytics Platform that consolidates data and makes it accessible across the organization. It enables real-time decisions and new business models while accelerating the shift toward sustainable energy.

    All these as compelling examples of data democratization empowering those closest to work and the organization to make faster, smarter decisions.

    Three trends driving data democratization

    Three current developments in the business and technology environment make data democratization both possible but also urgent. These forces are reshaping industries and redefining competitiveness.

    #1 Data liberation

    The first trend is the liberation of data. The long-standing divide between Information Technology (IT) and Operational Technology (OT) data is finally dissolving, enabling a holistic view of processes and performance and eliminating data silos. Legacy IT systems in banks, isolated operational platforms in energy grids, and fragmented health records in hospitals are being integrated and made accessible through unified tools for comprehensive analysis. In industrial environments, this shift often means bringing together thousands of previously disconnected data points - from sensor readings to video feeds - into a single, resilient platform. At Valmet, this translated into the development of a cloud-based solution that captures and analyzes data in near real time. The scalable platform improves safety and efficiency on the factory floor while supporting their broader Factory IT strategy for smarter, data-driven operations.

    #2 Real-time analytics

    The second major trend is the rise in real-time analytics, replacing delayed batch-based reporting. Real-time data and analytics empower organizations to make decisions at the speed of business, automating tasks that once required human intervention due to data delays. This shift goes beyond efficiency – in the intensifying global race for customers, it is crucial for competitiveness and meeting clients’ expectations.

    #3 AI for common terminology and digital twins

    Thirdly, structured data models and AI are enabling organizations to overcome terminology gaps across departments that often impede efficient collaboration. Integrated tools powered by generative AI can now summarize, explain, and recommend actions in natural language, directly within everyday workflows. However, large language models work reliably only when datasets are harmonized and sufficiently democratized. Today’s structured data and technologies also enable the creation of digital twins, i.e., virtual simulations of energy distribution networks, hospital patient flows, and financial transaction ecosystems, to name a few. With digital twins, organizations can generate predictive insights and enhance efficiency in ways that were previously unattainable.

    But what does it take to make data a right, not a privilege? It is obvious that data democratization does not happen by default, nor is it guaranteed by any kind of technology. Organizations need a data foundation that unifies data sources across domains, supports real-time access, enables self‑service analytics for non‑technical users, and applies governance transparently across the enterprise. Without these capabilities, access to data may expand in theory, but remain limited in practice.

    Discover how we can help you leverage Microsoft Fabric for real-time, compliant data

    Responding to the trends with modern data platforms

    A key enabler of data democratization is a shared data foundation that acts as a single source of truth, combined with a common semantic layer that makes data understandable across roles and functions. Several modern data platforms aim to address these requirements. One example is Microsoft Fabric, which illustrates how a unified, cloud‑based approach can support data democratization in practice by bringing together data integration, analytics, and governance into a single environment.

    Many organizations are moving toward architectures that centralize data to reduce fragmentation and support IT/OT convergence. In Microsoft Fabric, this principle is implemented through OneLake, which consolidates different data types into a shared environment to improve accessibility and governance. Rather than IT data sitting in enterprise databases and OT data remaining siloed in operational systems, OneLake provides a common space where both can be accessed and analyzed consistently.

    Woikoski transitioned from manual data collection to real‑time analytics at its Kokkola plant, enabling predictive maintenance and significantly reducing downtime. To support this shift, the company implemented a unified data platform built on Microsoft Fabric. Data from diverse sources now flow seamlessly into a unified system, enabling predictive maintenance and significantly reducing costly downtime. As their CFO noted, avoiding a single major compressor failure could offset the entire investment in the pilot, underscoring the operational impact of a more integrated data landscape.

    Modern platforms also aim to address real‑time data needs by enabling continuous access to operational and business data through streaming pipelines. Rather than relying on yesterday’s reports, organizations can monitor current activity and anticipate developments earlier. Unified environments, such as those in Fabric, help ensure that decisions around production, quality, energy use, and maintenance are based on up‑to‑date insights.

    Finally, standardized data models are becoming increasingly important for ensuring shared understanding across teams. Fabric’s Ontology is one example of how a semantic layer can help organizations align terminology and make information more actionable. Combined with AI‑ready tooling for self‑service analytics, these capabilities can reduce dependency on specialist resources while maintaining governance and compliance.

    Together, these capabilities allow organizations to turn AI‑driven data accessibility into meaningful impact, enabling teams to react faster, understand trends earlier, and make decisions with far greater confidence. These are not distant visions but tangible benefits achievable today with the right strategy and modern platform. According to Gartner Insights, organizations that leverage real-time data and advanced analytics achieve up to 80% higher innovation rates than their peers. Modern self-service analytics empowers business professionals across sectors to extract insights independently, reducing reliance on scarce data specialists and accelerating decision-making.

    The steps toward democratic data

    Ultimately, data democratization is not a technology decision alone, but a leadership and organizational choice. Lasting impact comes from aligning data strategy, governance, and culture to ensure that insights are accessible, understandable, and actionable for those closest to decision‑making.

    As organizations evaluate how to enable democratic access to data, unified and AI‑ready platforms play an increasingly important role. The goal, however, remains unchanged: to make data a shared asset — and a practical right — rather than a privilege reserved for a few.

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