AI runs on data. Without high-quality, consistent and up-to-date data, AI cannot perform at its best. The iceberg metaphor illustrates this perfectly: most of the work takes place beneath the surface. But how do you ensure your data is ready for the AI of today and tomorrow?
Why data integration is essential for AI
The information-processing capacity of a model or an organisation depends entirely on high-quality data. Data that needs to be made available. Anytime, anywhere. Without this foundation, AI models cannot function reliably. Just as with reports and dashboards, 80% of the work takes place beneath the surface. Making data accessible, validating it and integrating it form the foundation of every successful AI project. Without these steps, AI remains limited to experiments, while a solid data foundation enables scalable and valuable applications.
Data availability across 4 levels
Data often sits in several layers ‘beneath the surface’, spread across different data sources such as applications, databases, files, sensors, machines or devices. Each source has its own format, access methods, security and definitions. This makes accessing and sharing data complex. The four levels of data accessibility help clarify this challenge:
Level 1: Standalone data (silos)
At the first level, data is confined to a single system or application. It is used only for its primary purpose, such as in a specific machine whose data can only be accessed by a maintenance technician using ‘that special cable’. This limits access and makes the data unusable for other applications.
Level 2: Connected data
Extracting that data and making it available for other applications is the first step in integration. This means creating secure access, choosing a communication protocol and converting data into an accessible, unambiguous format. At this stage, it is important to filter out inconsistent or unreliable data. From this point onwards, the data is suitable for further processing.
Level 3: Integrated data
At the third level, you work with integrated data sources. This is where data starts working for you. One data source feeds another, and applications are integrated so that data is up to date and consistent in every application. Data from a single source is used in different places without being copied. IoT technology enables data from sensors and machines to be processed as streaming data. This makes it possible to monitor the condition of devices and adjust processes in real time. At this level, users of systems and applications are relieved of the most manual work: nothing needs to be retyped or copied manually. There are also no more debates about ‘the truth’, such as “my system says…”.
At this level, so-called business rules are often used. These rules ensure that data is processed automatically. Think of a purchase order automatically being converted into a sales order, or an invoice directly triggering a payment. A barcode scan in one application resulting in a work order in another. Even exceeding a threshold can automatically trigger an alert or action. At this level, you gain the maximum benefit from integrated data.
Level 4: Intelligent or autonomous data sources
At the fourth level, the focus is mainly on experimenting with intelligent or autonomous data. Here, everything revolves around fully automated processes or generating proposals based on data. AI models are used to make predictions, generate new data or adjust the thresholds mentioned above based on previous cases. This includes automatically retraining models so that they continue learning and adapting to new situations.
I say experimenting because it often stops after a proof of concept or an initial version. This is not because AI tools are unreliable or too expensive, but because the data has not been made sufficiently accessible. Without accessible and integrated data, it is virtually impossible to make AI work autonomously.
Successfully reaching level 4: from data integration to autonomous AI
How do you gain insight into the conditions needed to succeed at level 4: intelligent, autonomous and AI-driven?
- Take stock of what you have
Start by reviewing all the data sources within your organisation. This alone often provides a surprising amount of insight into how data is used.
- Identify how each source is used
For each data source, examine the level at which it is used and whether that use is ongoing or occasional. Is data actively shared with one user or with all relevant users?
- Analyse reliability and consistency
Make sure definitions are clear and the right data is used for the right purpose. Also check whether access and use are controlled. This minimises errors and guarantees quality.
- Improve efficiency and effectiveness
Optimise processes by making smart use of standardised access, validation and processing. This eliminates inefficiencies and reduces manual work.
- Ensure and monitor availability
Make sure your data sources are always up to date and provide a sufficient service level to optimally support both your services and AI algorithms.
The importance of a smooth data flow
Ultimately, a smooth flow of data allows you to decide whether to work with static rules or make greater use of active adjustments. AI can make a major difference here, for example by automatically adjusting validation and monitoring based on new insights.
AI can also help optimise manual actions. Think of suggesting actions through defaults or recommendations and assessing when these are adopted. Based on this, you can make a controlled transition to automated or even autonomous decision-making.
80% of AI success revolves around data availability
The work required to use AI on an ongoing basis often revolves around what we call integration and data engineering. This is one of the most underestimated aspects of information processing: 80% of the work involves ensuring data is available in the right format, at the right time and with the right quality. This means only 20% of the time can be spent on actually giving meaning to that same data.
An important addition is that this 80% is not limited to AI alone. Imagine how much efficiency you could gain if data were always processed correctly. How many additional processes you could automate. How much more effectively you could work if you always had complete and up-to-date information at your disposal.
