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.
Perhaps even more stunning is the fact that 90% of the data being produced these days is unstructured. With data pouring in from smart meters, sensors, crew trackers, customer interactions, and so forth the challenge isn’t just collecting all this data — it’s making sense of it all. To truly harness this data, you need a modern platform that can turn this flood of information into actionable insights.
Therefore, in this article, I’ll highlight the key roles that modern data platforms play in driving continuous innovation. While I’ll touch on some examples here and there, this article isn’t really about specific software or vendors. Instead, it’s a deep dive into the core capabilities of modern data platforms and, more importantly, the principles behind their design. Along the way, we’ll look at how these platforms break down silos, consolidate data, and ensure security, all while making powerful insights accessible to drive innovation across the enterprise.
The Problem with Data Silos
As data keeps piling up every day, one of the big headaches for IT departments is figuring out where to put it. But it’s not just about finding space — it’s about handling all the different types of data coming in. You’ve got structured data from databases, semi-structured data like XML or JSON files, unstructured data from emails, documents, and images and even streaming data from sensors and IoT devices.
As “big data” started to become a thing back in 2005, most of the unstructured data was routed to a new wave of data platforms that were tailored to work more efficiently with semi-structured or unstructured data (e.g., solutions like Hadoop or Apache Spark).
Fast forward to today and you have a fragmented landscape where structured data from ERP and other business systems is stored in an enterprise data warehouse and all the unstructured data is scattered hither and yon across the enterprise.
Whether intentional or not, these data silos make it very difficult for data practitioners to bring all their data to bear on solving real-world business problems. As a result, what ends up happening is that you end up with a lot of half-baked dashboards that tell parts of a story. While some heroic data analysts may dump this data to Excel and try to fill in data gaps in a variety of clever ways, it’s difficult to truly innovate this way.
The Power of Data Consolidation
Modern data platforms solve the data silos problem by merging classic data warehouse architectures with modern data lakes to create data lakehouses. With data lakehouses, it’s now possible to manage your entire data estate in one central location.
These data lakehouse architectures are game-changers, making it easier for everyone in an organization — including citizen data analysts — to tap into the full potential of their data. These platforms combine the strengths of data lakes and warehouses, offering a unified space where all types of data — structured, unstructured, streaming — can be stored and accessed seamlessly.
To put all this into perspective, let’s briefly take a look at Microsoft’s Fabric data platform. With Fabric, all data is stored in a centralized data lake called OneLake. As you can see in Figure 2 below, it works a lot like how Microsoft 365 users handle documents in OneDrive.
Just as you can easily access and work on documents from anywhere, OneLake lets analysts pull together and analyze data from different sources in one place without needing advanced technical skills. This setup breaks down barriers, making it possible for more people in your organization to use data to solve problems and make better decisions.
The Need for Searchable Data Catalogs
While consolidation solves many data-related problems, it doesn’t change the fact that data lakes can quickly become data swamps if they’re not managed properly. Organization is key, and it’s important that all stakeholders carry their weight in carefully managing and documenting the data that’s coming into the data lake.
Modern data platforms address these concerns by incorporating product-thinking into their core design. In her seminal book on the topic, Data Mesh, Zhamak Dehghani describes the benefits of this approach like this: “Operational teams still perceive their data as a byproduct of running the business, leaving it to someone else, e.g., the data team to pick it up and recycle it into products. In contrast, data mesh domain teams apply product thinking with similar rigor to their data, striving for the best user experience.”
Instead of just stockpiling data on the off chance that it might be needed someday, it’s important to spend the time documenting what the data is, and perhaps more importantly, what value it brings to the organization.
In an ideal world, analysts should be able to find the data products they need using a simple search interface like the one shown in Figure 3 below. Using glossaries and other documentation tools, it should even be possible for citizen data analysts to find data products even if they don’t speak techy things like databases or SQL.
With careful documentation, the days of asking “Where does this data live?” should be over. Data catalogs should make it possible for everyone to find the data they need. This unprecedented access streamlines development workflows and unlocks faster, more informed decision-making across the board.
Security & Governance: The Foundations of Trust
Another benefit of the data mesh architectures used by modern data platforms is their flexibility in managing security and governance. These architectures allow different business units to take ownership of their own data, letting the people who know the data best handle its security and management. This decentralized approach ensures that governance isn’t just a blanket policy from IT but is tailored to the specific needs and contexts of each domain, making it more effective and relevant.
Additionally, data mesh architectures empower product owners within the business to certify their data products. This means they can implement validation checks and quality controls to ensure the data is accurate and ready for broader use. By doing so, they help maintain data integrity across the organization, ensuring everyone is working with consistent, high-quality data, which is crucial for making informed decisions and driving business success.
Unlocking Self-Service Access
When you put all the concepts we’ve talked about here together, you end up with a flexible data platform that’s set up for self-serve access. Here, while IT may still play a heavy role in integrating/curating core data products, there’s flexibility on the edge to empower teams across the organization to make more informed, data-driven decisions. This unlocks many exciting new capabilities:
- Business analysts can put their vast experience and domain knowledge to work using industrial-strength BI tools that are much more powerful than Excel.
- Report developers now have access to all the data points they need to tell the full story whenever they build reports and dashboards.
- Data scientists of all skill levels have a one-stop shop for accessing both the tools and data they need to run ad hoc experiments or full-scale data analysis.
- AI & machine learning specialists can use all this data to build and train complex AI models that empower advanced forecasting and decision support scenarios.
- Developers of all kinds gain streamlined access to data, reducing the level of effort required to build custom apps and workflows.
Of course, self-service access doesn’t mean a total free-for-all. Building on data mesh principles, modern data platforms make it easy to control access to data, making sure that no one is able to access data that they’re not authorized to see.
Closing Thoughts
In today’s data-driven world, modern data platforms are not just a luxury — they’re essential. They bring together all types of data, break down silos, and ensure that everyone in the organization, from citizen analysts to data scientists, can access and leverage data to drive innovation.
By incorporating data mesh principles, these platforms also offer the flexibility needed to meet advanced security and governance requirements, empowering business units to take ownership of their data while maintaining high standards of integrity and consistency.
If you’re a forward-thinking organization, it’s imperative to note that the journey to AI-driven innovation starts with getting your data house in order. Without a solid foundation, efforts to implement AI and advanced analytics will be built on shaky ground. Modern data platforms provide the structure and tools needed to organize, secure, and optimize your data, making them indispensable for any organization looking to stay competitive in the rapidly evolving digital landscape.