Data contextualisation: a brief history, key aspects, and best practices

Data contextualisation: a brief history, key aspects, and best practices

data contextualisation

Contextualising data is a hot topic in the industrial sector. Although it’s not a new concept, it’s gaining attention as industries face growing challenges that can only be solved through contextualisation.

This article provides insights into data contextualisation in the process manufacturing industry. It covers the following topics:

  • A brief history of contextualisation in light of Big Data, Unstructured Data, and AI.

  • A concrete explanation of what data contextualisation is.

  • The key aspects to consider for contextualisation to succeed in industrial digital transformation.

Contextualisation: A Bit of History

Before diving into what contextualisation is, let’s take a brief look at the past.
The first contextualisation approach was introduced by historian softwares several decades ago, mainly through the use of asset frameworks to provide context to data from an equipment standpoint. And that was the extent of it.

The Era of Big Data and Unstructured Data

When we started Optimistik in 2015, it was clear from our industrial experience that contextualisation was the key to addressing the challenges of data in process manufacturing. At that time, the hype was around Big Data and unstructured data. Many thought that contextualisation was unnecessary, but we worked hard to build standard data models (physical meaning, traceability, genealogy, events, assets, etc.) in our solution to contextualise data from our customers. Those who followed the hype lost a lot of time and money, and are now considering how to structure data for their use cases.

The Era of AI

Then came Artificial Intelligence with all its promises. At Optimistik, we were convinced from day one that Machine Learning and Data Science could be used to help the industry become more competitive and sustainable. Many companies have been offering solutions or services around AI in the past decade, but the number of use cases implemented in industrial production processes remains limited. We have identified two obstacles:

  • First, compiling clean and relevant datasets to build an AI model.

  • If an AI model is successfully developed, implementing it on the shop floor is not straightforward. This involves establishing a clean data flow, performing proper data preprocessing, running the model code in an industrial environment, and deploying and managing it at scale.

Once again, contextualisation is a key enabler for removing these obstacles.

So, what is contextualisation?

In simple terms, contextualisation refers to embedding industrial knowledge into digital infrastructures so that raw data can become information. To be more precise, it involves integrating new data that will provide context for the data being manipulated. Here are a few examples:

  • On raw numerical data: what is the unit, the origin, the physical meaning of the data, where and when its as been collected…

  • Production Traceability: when and where a specific production took place, what are its caracteristics, details of the productions phases…

  • Production Genealogy: for raw material or when intermediates products are being uses, in which production they were used…

  • Assets: what are their physical caracteristics, the data attached, their localisation…

  • Assets traceability: what are the equipment cycles, the consumable parts cycles…

  • Events context: what happened, where, when…

But it doesn’t end there. Contextualisation makes sense when you can automatically combine this data to generate the proper information, such as:

  • How much energy did I use to dry each batch of powder?

  • What is the variability of my filtration time between two cycles?

  • How do my different reactors behave compared to each other?

  • Do I see differences between batches produced on different reactors?

Are every approach equals in term of contextualisation ?

To be clear, not all approaches are equal when it comes to implementing contextualisation. There are several factors you should take into account when considering such an implementation.

Contextualisation must be at the core of industrial digital infrastructure

To be relevant, contextualisation must be at the core of your digital infrastructure. It bridges technology (databases, data treatment capabilities) and your business. This is intricately done as use cases drive the requirements for data models and infrastructure performance. Additionally, business applications must be fully integrated with contextualisation to offer a fluid and comprehensive user experience.

Contextualisation solution must provide standard yet adaptable data and context models

If you find yourself needing to rethink how to handle data within a specific context and build the data model and processing from scratch, it may indicate a dead end. Your solution should offer predefined data models, context models, and combination functions, alongside optimized data structures, storage technologies, and treatments. The data and context models should be configurable to suit your specific use cases. The goal is to accelerate the deployment of your contextualisation framework by using standard, pre-defined templates, while also capturing your business specificities through proper setup.

Contextualisation is a matter for business experts

Do not ask the IT or OT department to set up data contextualisation, as it requires the expertise of your business experts. To achieve contextualisation set up by business experts, you need a solution that provides predefined data and context models that are business-centric, as previously mentioned. You also require context setup tools that are designed for business experts, not IT or data experts. A no-code or low-code approach with rich documentation is essential for the adoption of contextualisation by business experts.

Contextualisation must be agile, flexible, and traceable

As plant processes and equipment continuously evolve, the contextualisation framework needs to adapt accordingly. Its structure should be able to handle modifications while keeping historical data in a usable form. Additionally, it should keep track of every modification to ensure coherence and compliance.

Contextualisation must support your data governance

Roles and responsibilities are essential to maintain coherence in your data approach within your corporate organisation and remain aligned with your data governance strategy and rules. At the same time, you need to delegate data contextualisation to the business experts who know the data best. This requires a contextualisation solution that offers fine-grained role and access management and a proper audit trail.

Contextualisation solution must be open

Another important aspect is openness. A contextualization solution should not only be able to collect data, but also to share data with other corporate systems. Particularly it should enable you to provision Data models and Context model from other corporate system.

Key takeaways

  • Place contextualisation at the core of your industrial data architecture.

  • Choose proven solutions that provide standard data models and context models that can be configured to adapt to your specific industry and needs.

  • Consider vertically integrated solutions that offer business applications and advanced analytics tightly integrated with the contextualization engine.

  • Involve business experts thoroughly and give them control of contextualisation.

  • Don’t forget to use open solutions to enable long-term integration with your corporate IT.

And this is what we do at Optimistik

Since the first version of OIAnalytics, our Operational Intelligence solution, we have focused on providing the best contextualisation engine for handling data from process manufacturing industries. While other actors were following marketing hype of the moment, we stayed committed to our goal.

We have gained extensive experience in this field through the implementation of our contextualisation engine in many process manufacturing industries and various use cases, including quality, process efficiency, energy, traceability, and asset reliability. This experience has allowed us to build a standard contextualisation engine with optimized data and context models that can handle a wide array of use cases while requiring only configuration by business experts.

Our contextualisation engine has been proven in large-scale corporate deployments, with widespread adoption, particularly by process engineers.

We continuously improve and enrich our contextualisation engine to extend its range of use cases. Our focus is on offering better ease of implementation, maintainability and scalability. In a word, keeping it best in class.


Mathieu CURA

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