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Technologies and customer needs are changing rapidly and classic analysis processes are quickly reaching their limits. Our industrial customer has an impressive data history with information dating back up to 200 years. But despite structured management, much of this data remains in silos and is not actively used. Valuable potential for data-based decisions and innovations therefore remains unused.
With Microsoft Fabric, we are creating a central, integrated platform that breaks down these data silos and combines all data sources into a single, uniform data model — seamlessly and scalably. Teams get self-service access to consolidated data and can therefore make decisions faster, more securely and in a more customer-oriented way. Especially when it comes to AI initiatives, Fabric creates the necessary basis for real-time analytics, machine learning and automated insights, all in a toolset that builds on existing Microsoft infrastructure. With Microsoft Fabric, the aim is to finally make existing data values usable, create transparency and accelerate innovation sustainably.
How can data silos be reduced?
How can historical and current data sources be meaningfully linked?
How is a basis created for AI initiatives and innovations?
Together with the customer, we identify goals, challenges and priorities. In structured sessions, we develop specific use cases and evaluate them in terms of costs, benefits and technical feasibility with a focus on analytics, information access and AI applications.
We get an overview of the existing data landscape, including historical data, current systems and silo structures. Relevant source systems are prioritized for integration into Fabric, such as ERP, CRM, IoT, or Excel-based reports.
Based on use cases and data sources, we implement a scalable Microsoft Fabric architecture. We configure the relevant modules such as Data Factory, OneLake, Synapse, Power BI and set up a consistent governance and security model.
We load the prioritized data into Microsoft Fabric, build semantic models and develop dashboards, reports, or ML pipelines. The first quick wins are implemented productively, such as automated reports or AI-based analyses.
We train internal teams how to use Fabric and embed self-service analytics in everyday life. At the same time, we are scaling the architecture to other use cases and business areas, always in line with the developed priority model.
The project with our industrial customer aims to make data usable as a strategic value and pave the way for AI-based innovations. For this reason, a central data platform based on Microsoft Fabric was set up, which significantly simplifies access to information and systematically resolves silo structures. The integrated use of OneLake, Synapse and Power BI enables teams to make well-founded decisions faster and efficiently implement data-driven use cases. This creates a scalable infrastructure that actively supports future growth and technological development.
The industrial company wanted to optimize analysis workflows, reduce data silos and introduce a uniform data platform. The aim was to combine historical and current data sources in a central model, enable self-service analytics and at the same time create the basis for AI initiatives.
Prior to the project, the industrial company had a very large data history, some of up to 200 years, but was unable to actively use this data for decisions and innovations due to distributed silos. Traditional analysis processes reached their limits, and valuable potential from historical and current data remained unused.
taod developed a uniform data platform for the industrial company based on Microsoft Fabric, Microsoft Azure and Power BI. The solution combines data sources in a consistent data model using central fabric components such as Data Factory, OneLake, Synapse and Power BI as well as a governance and security model for secure self-service access.
The introduction began with a use case workshop, in which goals, challenges and priorities were assessed in a structured way. taod then analyzed the existing data landscape, prioritized relevant source systems such as ERP, CRM, IoT and Excel reports, set up the Microsoft fabric architecture, integrated the data and productively implemented initial quick wins such as automated reports and AI-based analyses. Internal teams were then trained and the platform was scaled to further use cases.
The industrial company brought together more than 12 operational and historical data sources in Microsoft Fabric via OneLake, creating a central, searchable data model. Standardised Power BI reports reduced reporting time in selected use cases by up to 70 percent, a governance model standardized data classification, access rights and quality processes for five critical specialist areas, and a first machine learning use case was put into production within just the first 12 weeks of the project.
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