4 Use Cases:
These customers realized a future-proof dataplatform with Microsoft Azure

Creating the conditions for customers to get real value out of their AI and Machine Learning initiatives

Introducing 4 Microsoft AI use cases

Where many organizations might still struggle with understanding how AI will affect their business and markets, there are probably even more companies that have already been experimenting with AI and Machine Learning models.

And that's no wonder, since the opportunities of AI driving innovation, efficiency and new business models are huge! However, what we find is that many of these early adoptors sooner or later run into some kind of wall. They can't scale or grow their Proof of Concepts. And the lack of a stable data platform, with quality standards and governance in place is quite often the root cause. That's where our experience and knowledge of the Microsoft Azure AI platform comes in.

Sebastiaan Oude Groeniger is Managing Consultant Data & AI at Motion10 (a HSO Company). In this role he advises clients on data issues and together with his team he develops and implements Machine Learning platforms. And in most cases, AI use cases are preceded by a complex data project. We asked Sebastiaan about stories of clients making significant strides in the field of AI and what was required to help them get there.

"What these 4 use cases have in common is that in all these stories we 'enable' our customers. We create the conditions that allow customers to get real value out of their data and AI initiatives. Because if proper data management, standards and governance fall between the cracks, your AI projects just won't deliver value."

A future-proof Machine Learning platform for Stolt-Nielsen

Stolt-Nielsen, an international player in logistics and shipping, has been using AI technology for some time. For instance, to provide faster price indications and to better match capacity supply and demand.

However, these models were developed both internally and externally, data was hosted and managed at different locations, and that was causing increasing challenges around scalability and manageability. Stolt-Nielsen's question was:

Can you build us a platform on which we can land our various AI initiatives, now, and in the future, including a fixed set of standards and managed within our own organization?

Solution: set the standard 

Based on this issue, we mapped out a new architecture and developed a Machine Learning platform in Azure. On this platform, not only the already existing machine learning use case can run, but it also provides a standard on which new Microsoft AI use cases can be developed.

"An operational data platform is the foundation on which you can leverage the potential of AI. Because without high quality, available data, you won't realize results."

Cloud foundation enables AI-innovation for Agro-Tech Firm

Our customer develops vegetable varieties and sells its seeds worldwide. High-tech research, including AI is highly relevant in order to stay focused and efficient. One of their R&D processes involves very detailed measurement and determination of product properties. And this used to involve a lot of manual work.

The company already started developing an advanced automated process, using a machine learning model for automated image analysis. This Proof of Concept worked, but it was difficult to scale and roll out to other locations. Their question to us:

Can you help us in bringing this solution to the cloud and further automate and optimize our image analysis process?

Solution: Machine Learning for automated image analysis

The image analysis process is now fully automated, with the data and Machine Learning model all hosted in Azure. The implementation was done in such a way that it's easier to develop new machine learning initiatives, using the lessons learned from this project. The next steps are to further improve the data foundation in Azure. Their next AI use case is already in the works and involves a self-learning model for seed and plant development.

Reliable data platform enables effective collaboration at Alliantie Housing

This housing corporation believes that data intelligence can help teams work more efficiently and deliver better services to their tenants.

Use cases that Alliantie wanted to address included fraud prevention, as well as the process of "re-letting". When a tenant leaves, the model can give a good indication of whether the property is ready to be immediately re-rented, or whether maintenance is needed. This saves teams of inspectors a lot of time. The biggest challenge they faced when trying to implement these AI initiatives was the lack and inefficiency of data management and knowledge of Azure. De Alliantie's question to us:

Can you provide a better managed data and machine learning platform that enables our current but also future data and AI initiatives, bringing together both data engineering and data science?

Solution: using AI to drive more effective teams and better services for tenants

One of the outcomes was that the teams of data scientists and data engineers were able to collaborate much more effectively. We were able to onboard both data and the two teams on the platform. This helped in eliminating the boundaries between the teams, improving the workload for both data engineers and data scientists.

MPS Systems: a stable data architecture and continuous data flow

Machine manufacturer MPS Systems designs and builds printing presses for labels and the flexible packaging industry. The main mission of MPS is to enable machine operators to deliver the best quality and productivity. This is only possible if the machines are used to their full potential. And that’s where this Microsoft AI use case comes in.

MPS systems already experimented with IoT on their machines, with the ambition to develop a model that would help educate their customers on the most efficient use of the machines. But they ran into issues around data structure, hosting and management. Their question to us:

Can you set up a new data architecture, that enables existing and future data and AI-initiatives?

Solution: enabling a data-driven way of working and company culture

MPS' architecture and data warehouse is now set up in the Microsoft Azure cloud, with data coming in through a continuous, stable flow from the machines, via Azure Device Agent, ready for further analysis.

Build a foundation for success

Sebastiaan Oude Groeniger, Managing Consultant Data & AI: “What these cases have in common, and what we find at many of our customers, is that they start experimenting with AI themselves first. But sooner or later, they run into some kind of wall. We help them get across those hurdles.”

We help companies strategically plan and build the right data & technology foundation. So they can fully and successfully leverage the latest in Microsoft AI, allowing them to:​

  • Become more productive​
  • Enable better decision-making​
  • Develop new paths to competitive success

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