From dashboards to data platforms: the foundation of successful AI

Data platforms and AI are delivering increasing value to organizations. Companies are saving costs through predictive maintenance, where sensors detect when machines need servicing, helping prevent unplanned downtime. Automation increases productivity as systems take over tasks previously handled manually. Some companies are even creating new revenue streams by offering smart services to customers, based on insights from their own data and processes.

But before any AI system can even function, all data must first be accessible and easy to find. And that’s often the biggest challenge.

Ready-to-use components

Companies collect data from countless sources: files, sensors, IoT devices. A data platform brings all that information together into a complete picture. Unlike traditional systems, it doesn’t store data in separate silos. Instead, it unifies it all: ERP, CRM, machine sensors, quality measurements, maintenance logs. In one central environment, AI can get to work right away.

A major advantage of modern platforms is that many components are readily available out of the box. This makes developing a data product far more efficient. A central data platform also ensures that data within the organization reflects a single source of truth.

These components work best when the underlying data is well-organized. Organizations collect data from different systems and create strong internal data models, allowing them to analyze data from multiple perspectives. With modern tools, it’s even possible to use the data model, or parts of it, for an AI agent that can answer specific questions based on that particular business data.

Cloud platforms offer scalability without the need to maintain your own infrastructure. You only pay for what you use, and can scale quickly when needed.

Organizations already using many Microsoft products often go for the Microsoft stack: Azure, Databricks and Power BI. Microsoft offers industry-specific solutions, such as Factory Operations Agents that use natural language to query manufacturing data. The Azure ecosystem also includes AI services and Power Automate. Alternatives include Factory Thread for manufacturing-specific data virtualization, Amazon Web Services with Glue, or Google Cloud Dataflow.

Data quality: garbage in, garbage out

Even the best data models and AI agents will fail if the underlying data is poor. Data quality plays a crucial role in AI implementation. An AI solution only works properly if the data is accurate, consistent and complete. Accuracy is often the biggest challenge: content errors are common, especially when humans are involved in providing the data. People make mistakes.

For example, manual data entry can lead to inaccuracies. Take maintenance logs where technicians record service activities. If someone forgets to register a repair or logs the wrong machine, the AI system might learn that certain equipment requires less maintenance than it actually does.

These problems can’t be solved by building better systems. The solution lies at the source: that’s where data quality must be ensured. Platforms can help by making the quality of data visible and clarifying how the data can be used. In combination with governance processes, this ultimately leads to a reliable AI platform.

It’s not just about the technology, but also the processes around it: who checks the data, what gets checked, and who is responsible?

IoT data: always on

In manufacturing, IoT data plays a special role. Machine sensors continuously transmit data: temperature, vibration, pressure, energy consumption. This data streams in real time, often multiple times per second.

That makes IoT data fundamentally different from traditional business data. While ERP systems are updated a few times a day or week, sensor data is constant. This streaming data requires a different approach: systems that can process millions of events per second and respond immediately to changes.

The advantage of sensor data is that it tends to be high quality. It comes from technical devices programmed to take specific measurements. Occasionally a sensor might malfunction and generate outliers, but this can often be explained technically.

Modern data platforms not only collect data, they also send information back. When a data point comes in, the system can respond immediately. A pump, motor or heating system reacts automatically to the new information.

When it’s important to minimize latency, edge computing comes into play. Here, data is analyzed close to the machine, and only relevant insights are sent to the cloud. This not only reduces latency, it also helps cut costs.

Security: protecting sensitive data

As data platforms begin to store more sensitive information, security becomes increasingly important. Manufacturing is one of the most vulnerable sectors to cyberattacks, especially as operational technology becomes more connected to IT systems.

A secure platform must address several key areas: data classification, access control, network security and data masking. These requirements are captured in security guidelines, which are then implemented in the platform alongside best practices and security principles.

Communication between systems is often the weakest link. That’s why platforms ideally operate within the organization’s own network, reducing the risk of external access. Additional layers of security for authentication, authorization and encryption are essential.

Working with data platforms also often involves handling privacy-sensitive information. Key questions include: are you allowed to share certain data within the organization, and who has access to what information? Raising awareness about these issues is crucial.

Getting the foundation right

For manufacturers, using data platforms is becoming essential to stay competitive. The problem is that many companies don’t have their data in order yet. They lack even a basic catalog, making it hard to locate the right information.

To build a solid foundation, organizations must develop three pillars in alignment: technology, processes and people. The technical side is about which systems you choose and how you implement them. The process side focuses on who is responsible for data governance and the associated procedures.

Equally important is the human side. Employees need awareness of what AI can and cannot do. They need training to use new tools effectively. And most importantly, they need to feel ownership over the data and processes they work with. Without that engagement, even the best technology will fail.

A data platform is not a magic solution, but it is a critical enabler. Companies that invest in a strong data foundation create the best conditions for the future. For technologies like generative AI, digital twins and autonomous agents, that foundation is more important than ever.