Recently, I’ve noticed an interesting trend when discussing data challenges with customers. Despite facing a wide range of data-related hurdles, there's a tendency to dismiss the need for an enterprise-grade analytics platform like Microsoft Fabric, saying things like, “Oh, we already use Power BI or Tableau for all that stuff.”

This response usually stems from a few factors—either a belief that current BI tools cover everything or maybe there's a hesitation about adopting something new. Regardless, it’s become clear to me that many people aren’t fully aware of what a comprehensive data platform brings to the table beyond simply hosting reports and dashboards.

That’s why I thought it would be worthwhile to explore some of the key roles Microsoft Fabric can play in not only consolidating and organizing your data estate but also enabling you to put your data to work to unlock valuable insights. With Fabric, you gain access to a full suite of tools to harness your data’s potential, empowering smarter, more impactful decision-making across the organization.

1. Built-In Reports Aren't Enough

For many organizations, there's an assumption that the built-in reports integrated into core business applications—from entry-level systems like QuickBooks to full-scale ERP systems like SAP—are all that's needed to run the business. However, as soon as you move beyond basic operational reporting, the limitations of these reporting solutions quickly become apparent.

Built-in reports are typically designed for straightforward, transactional data queries and don’t support the more complex, cross-functional analysis that business leaders need to make strategic decisions. What’s more, when you rely solely on the reporting capabilities of a single system, you miss the opportunity to enrich your analysis by combining data from other systems or external sources. This leaves a significant gap in your ability to tell comprehensive, data-driven stories.

Figure 1: Dealing with Gaps in Built-In Reporting Solutions

One of the primary challenges with using built-in reporting is the lack of interoperability with other data sources. Each business system, whether it’s ERP, CRM, or a specialized operational tool, operates in its own data silo, limiting your ability to correlate data across systems. This means you’re only seeing part of the picture, with no easy way to draw connections between, say, financial performance and supply chain data or between customer support metrics and sales performance. With Fabric, we can bring these disparate data sources together so that you have a cohesive view of your business instead of fragmented insights.

While we're on this topic, it's also worth noting that ERP and similar business systems are specifically optimized for online transactional data processing (OLTP), not for online analytical processing (OLAP). This means that when you try to run large, complex queries or data-intensive reports within these systems, you’re likely to encounter performance issues. Analytical workloads, especially when they involve high data volumes or complex calculations, require an industrial-strength platform like Microsoft Fabric that's specifically designed to handle heavy duty processing without impacting day-to-day business operations.

2. Simplifying ERP Data with Business-Friendly Models

Business systems are designed with highly normalized data models optimized for transactional efficiency, not for ease of use in reporting and analysis. While these structures ensure optimal performance for daily operations, they often create a barrier for business users who need direct access to data insights. For example, SAP’s ERP systems are notorious in the way they employ technical naming conventions—often abbreviated in German, such as “VBAK” for sales orders or “KNA1” for customer data—that are difficult for non-technical users to interpret. These obscure, granular data tables may work well in the context of processing transactions, but they present challenges for business users who need an intuitive, easily navigable data model for analysis.

Figure 2: Dealing with Obscure Data Models in SAP Business Suite Solutions

The lack of a user-friendly data model limits the accessibility of enterprise data for everyday reporting, advanced analysis, or even AI and machine learning applications. In order to harness the true value of your data, you need a place to transform this raw, technical data into business-friendly models that are easier to understand and use.

Microsoft Fabric serves as this bridge, enabling you to simplify complex data structures, map them to more meaningful names and formats, and aggregate data across various systems.

By creating business-friendly data models in Microsoft Fabric, you can make ERP data more accessible for everyone, from analysts to business leaders. This approach not only improves the usability of data but also supports a wide range of scenarios—from generating dynamic reports and dashboards to powering AI and machine learning models that drive predictive insights. Fabric allows you to move beyond the rigid structure of ERP data, transforming it into an asset that empowers teams to make informed, data-driven decisions across the organization.

3. Managing Explosive Data Growth

According to a recent study, over 90% of the data in the world today has been created in the past two years. From a global perspective, we’re talking about an astounding 2.5 quintillion bytes of data being generated each and every day.

With data pouring in from smart meters, sensors, and customer interactions, the challenge isn’t just about finding a place to collect all this data — it’s figuring out how to make sense of it.

And it’s not just the volume that’s daunting; it’s also the variety of the data. Today’s data comes in all shapes and sizes: structured data from business systems like ERP or CRM systems, semi-structured data from documents, APIs, or external sources, and completely unstructured data from images, audio, and video. Each type requires different tools and techniques to unlock its value, making it crucial to have a unified approach that can handle the full spectrum of data types.

Within Fabric, OneLake provides a one-stop shop for all your data storage needs. As a managed data lake solution, OneLake makes it easy to bring your entire data estate under one roof: data warehouses, data lakehouses, and even traditional data lakes. With support for domain and group-level security management, Microsoft likes to refer to OneLake as the "OneDrive for all your enterprise data".

Figure 3: Separating Data from Compute Resources with OneLake

With OneLake, Microsoft separates low-cost data lake storage from the compute resources in Fabric needed for data analysis. This separation of concerns makes Fabric capable of efficiently managing both structured and unstructured data at scale, empowering you to deeply analyze your data without burdening your transactional systems. This makes Fabric an ideal platform for long-term data growth.

4. Data Consolidation for a 360° View

As noted earlier, one of the biggest limitations of built-in reporting solutions is that they’re siloed. These solutions are self-contained, leaving you with fragmented insights and blind spots when you zoom out and start to look across the organization. Without a way to consolidate all your data, it’s impossible to truly understand your business operations from every angle.

Microsoft Fabric changes the game by providing a unified platform to bring your entire data estate under one roof. It integrates data from ERP systems, CRMs, spreadsheets, IoT devices, and more, creating a single source of truth for your organization. With all your data in one place, you can uncover trends, correlations, and opportunities that would otherwise be missed.

Figure 4: Data Consolidation with Microsoft Fabric and OneLake

Although it's technically possible to utilize BI visualization tools like Power BI to combine data in one-off reports, it's worth noting that this approach generally does not scale very well:

• Workarounds that utilize tools like Excel or Access to "stage" data from external systems are brittle and prone to error.

• Connector-based access can also be brittle and can lead to data refresh issues.

• Performance can become a real problem when you start dealing with larger volumes of data.

For a variety of reasons, you're much better off if you bring these data sources onto a platform like OneLake/Fabric so that all the data's there at your fingertips and easy to manage. And, as data volumes grow, you also have an ability to scale up your compute resources to maintain optimal performance.

5. Connecting the Dots: Telling Stories With Your Data

One of the main reasons to consolidate your data is to make it easier to fill in the gaps when telling stories with it. To put this concept into perspective, let’s say you want to analyze your field operations to pinpoint resource bottlenecks and identify areas for productivity improvement.

You might start by looking at work order history in your field service management system to spot patterns in service delays or recurring issues. While this data provides you with a starting point, it really doesn’t tell the whole story. To truly understand what’s happening, you’d need to bring in other data sources—like crew work schedules to evaluate resource allocation, geospatial tracking data to analyze crew movements, and customer feedback to measure service quality.

Figure 5: Telling Complete Stories with Data

Without consolidating these datasets, you’re left with disconnected fragments, making it difficult to uncover actionable insights. On the other hand, if we bring all this data into Fabric, it's much easier to see the bigger picture. Patterns emerge, such as identifying regions where service times lag due to inefficient crew routes or spotting scheduling conflicts that create resource bottlenecks.

With all the dots connected, you can tell a cohesive story about your operations, making it easier to pinpoint issues and take targeted action to improve efficiency and customer satisfaction.

6. Creating a Safe Space for Analysis & Experimentation

Data engineers and data scientists need more than just access to data—they need a dedicated space where they can analyze, experiment, and innovate without fear of disrupting production systems. Lab environments like this provide a sandbox where ideas can flourish, mistakes can be made safely, and breakthroughs can happen.

In these environments, you can stage wholesale data cleanup efforts, ensuring your datasets are accurate and reliable before they’re deployed for broader use. You can also run simulations to predict outcomes, test hypotheses, and model different scenarios to see how changes could impact your business.

Beyond cleanup and simulations, a lab environment is also the perfect place to experiment with your data. For example, imagine that a manufacturing company wants to utilize weather and customer demographic data to optimize rebate promotions for distributors and installers that might be mobilizing. In this scenario, Fabric provides a perfect platform to bring all this data together, analyze it with Azure AI Studio, and then feed Salesforce with optimized rebate promotions.

Figure 6: Incorporating 3rd-Party Datasets into the Analysis Process

7. Data Governance & Retention

Strong governance and thoughtful retention strategies are essential for effective data management, and Fabric provides valuable support in achieving both. Fabric works seamlessly with data protection solutions like Microsoft Azure and Microsoft Purview to give you centralized data security and governance across your entire data estate. This means your data stays accessible, well-protected, and fully compliant, with policies you control to manage access, ensure compliance, and track usage.

Figure 7: Data Governance with Fabric and Purview

OneLake’s low-cost, scalable data lake storage also provides an ideal foundation for building out your data archiving strategy. By offloading historical or rarely used data to OneLake, you can greatly reduce the database size of tier-one business systems like SAP. This approach helps lower storage costs, improve system efficiency, and keep archived data readily accessible for analysis when needed.

8. Creating a Beachhead for Managing Software Upgrades

Software upgrade projects—especially those involving complex ERP or financial systems—often bring a host of challenging data issues. From cleaning up master data to requirements for re-mapping legacy data to work in the to-be organizational structure, you need an environment where you can stage all this data and have access to the various tools you need to analyze, transform, and prepare data for importing into the new system.

Software upgrade projects—particularly those involving complex ERP or financial systems—often present significant data challenges. From cleaning up master data to re-mapping legacy data to align with the to-be system, you need an environment where you can stage all this data while also providing access to the tools needed to analyze, transform, and prepare it for seamless import into the new system.

As you can see in Figure 8 below, Microsoft Fabric can play the role of data fabric within Gartner's composable ERP strategy, providing the data foundation for upgrades across your entire business landscape.

Figure 8: Microsoft Fabric as the Data Fabric in a Composable ERP Strategy

On a semi-related note, Fabric can also be a place where you can timebox data to represent the structure and/or operation models of your business at a particular point in time. This approach provides a nice abstraction for data consumers of all types that may not be aware of fundamental data changes such as:

• Changes to the way business processes work

• Organizational structures (e.g., before or after mergers or acquisitions)

• Changes in software packages and/or software functionality (e.g., we used to run vendor A's product for our financials, but then we switched to vendor B's solution and that required us to change our strategy for building out our chart of accounts, etc.)

With Fabric, we can use tried-and-true data modeling techniques to abstract and/or map historical data in such a way that data consumers don't have to keep track of subtle changes that take place over time.

9. Empowering Teams for Self-Service Data Access

Every BI project I’ve ever worked on has started with the same straightforward question to the business: “What do you want to report on?” And without fail, the answer is always the same: “Everything.” It’s a common starting point that highlights both the ambition of the business and the challenge of turning raw data into actionable insights.

From an IT perspective, the challenge with delivering “everything” is that it often leads to BI solutions that consume significant time and resources, only to become outdated before they ever even see the light of day. This cycle not only wastes effort but also adds to an ever-growing backlog of reports, leaving BI teams perpetually struggling to keep up.

Figure 9: Keeping Pace with Ever-Growing Business Demands

While there's no foolproof solution to address these types of challenges, Fabric's foundations with data mesh and data fabric design principles makes it easier than ever for BI teams and business teams to meet in the middle when it comes to achieving self-service analytics. Here, BI teams can focus on curating flexible data models and then turn the business loose to slice and dice their data to their heart's content.

Figure 10: Achieving BI Nirvana

As you can see in Figure 11 below, Fabric's separation of data in OneLake to various runtime engines makes it easy for personas across the entire organization - from IT specialists to citizen data scientists and business analysts - to securely access data and put it to work solving real-world business problems. With OneLake, all the data's there under one roof. Plus, with Microsoft Purview, we can define access policies, searchable data catalogs, and even facilitate self-service access via online request processes.

Figure 11: Empowering Users for Self-Service Analytics with Fabric

10. Unlocking New Business Models & Revenue Streams

The last but arguably most compelling reason for adopting a modern data platform like Fabric is that it provides you with the foundation you need to put your data to work to unlock new business models and/or revenue streams. In the information economy that we live in today, data is extremely valuable.

If you want to utilize recent innovations in AI and machine learning (ML), data is the fuel that makes these technologies run. Once you bring your structured, semi-structured, and unstructured data under one roof in Fabric, you can easily build AI and ML operation pipelines that leverage this data to solve all kinds of problems.

For example, in Figure 12, you can see how disparate data points are being brought together to build a knowledge base that field workers can access via chatbots (think ChatGPT). When you think about the amount of time field workers spend each week searching for information (e.g., work order statuses or repair instructions), a solution like this can save hundreds of hours per year and that can translate to big savings.

Figure 12: Building Knowledge Bases Using Disparate Knowledge Resources

This one-off example is just the tip of the iceberg. With monumental breakthroughs in generative AI and other AI technologies, there are many compelling opportunities to derive real value from your data.

Depending on your line of business, it might be possible to even sell your data. In their book, Bits, Bytes, and Barrels: The Digital Transformation of Oil and Gas (MADCann Press, 2019), the authors talk about an emerging service in the oil and gas industry called digital oil recovery. In this scenario, digitally-savvy operators are working with traditional oil companies to apply digital thinking to discover ways to gain more value from underperforming assets. In this scenario, both parties win by sharing in the proceeds from the improved production output.

These kinds of big picture plays are never easy, and it requires a lot of horsepower and brain power to derive real value. Fabric helps on both front fronts by delivering a scalable foundation to process data while also providing resources from across the business the tools they need to perform the necessary data analysis.

Closing Thoughts

Hopefully this deep dive into the benefits of Microsoft Fabric has helped you to better appreciate its overall value proposition. Naturally, there are plenty of choices out there when it comes to modern data platforms and while we're somewhat biased towards Fabric, you certainly can't go wrong with products like Snowflake or Google BigQuery. Whether you choose an all-in-one, SaaS-like solution like Fabric or opt for a best-of-breed approach, one key takeaway remains: modern data platforms are no longer a luxury—they're a necessity.

If your goal is to improve decision-making, foster collaboration, or stay ahead of the competition, Fabric offers the tools and flexibility you need. It’s more than just a platform—it’s a foundation for innovation and growth in today’s data-driven world.