SME follows large companies in creating value from a data model
Setting up a strategy where data guides strategic and operational decisions is not always easy. Retrieving data from an ERP system is usually still a relatively easy step. The challenges become really clear when data from different sources need to be combined. The number of data sources that can provide meaningful information for decision making has increased rapidly in recent years. Think production data, sensor data, meta-data, social media and climate data, depending on the sector."
What key changes do you see in the market?
"Metriqzz works mainly for small and medium-sized companies with a turnover of 30 to 800 million euros and then mainly in the manufacturing, transport & logistics and retail sectors. The logistics sector has traditionally had to deal with low margins and a lot of competition. Then you have to be very sharp. You can be, because with data and AI, the predictive nature in planning is becoming increasingly interesting. The manufacturing industry in particular is facing increasing complexity with data from various business processes. There, too, is the need for reliable predictions. For that, you need to be able to read the right data in the right file format. A customer of ours makes forklifts and wanted to switch to hydrogen propulsion for a new model. There are as many as 2,500 sensors in that piece of equipment. Those generate data every millisecond, but in the MDF4 format, a familiar format from the automotive industry. As a result, that data is not easy to use in general management information systems.
You hear a lot of stories from consulting firms explaining in a great way all they have done in that area. But how do you really get started?"
"Then it's really about taking a client by the hand and being able to show in a short time what is possible with the company's data. We were eventually able to convert that data with Python scripting and create dashboards with it."
"We continue to see companies doing a lot of change in their business model. Companies that have been selling machines for years are transforming into a service provider. That's also called Manufacturing as a Service . Then data about machine usage and wear and tear are needed to plan predictive maintenance [maintenance at the optimal time, ed.]. The power lies in the combination of data sources. Suppose a company supplies automatic entrance doors, but is now switching to a service model where it guarantees that the doors will function 365 days a year between 9 a.m. and 6 p.m. You need to know exactly which parts are broken. Then you need to know exactly what parts might break down, know how much time is needed for replacement and schedule them proactively. You can't do that without a good data model."
How can organizations best approach that change?
"It's important that an organization is convinced of the need to innovate, where they have or want to develop a data strategy. Then quickly - within two to four weeks - it should become clear how customer data can be used to deliver value. We often do such a proof of concept with DOMO, but also with platforms from Databricks or Microsoft. Most companies continue development after such an initial step. Talking is nice, but showing that it works, with data from the company itself, that convinces.
Does AI play an important role in these sectors?
"Absolutely! The data platforms we work with incorporate a lot of AI tools. The whole development of AI agents is very interesting. They can automatically spot trends or threats within complex data streams. That really moves companies forward. The possibilities are not infinite, but they are going very far at the moment.
So AI is not an end in itself, but a necessary support for the new business model. But you have to make sure you establish a solid foundation with a good data model before you apply AI."
What is your goal to offer visitors to the Data Expo?
"We are there together with our supplier DOMO. We want to show people what you can really do with the DOMO Data Platform. In doing so, we focus particularly on the industries I just mentioned. Large companies such as Playstation, DHL, Unilever and L'Oréal are already working with it, but the platform is not yet so well known among slightly smaller companies. However, achieving success with such a platform is only possible if the data quality and data structure are in order. We will tell them that too."
Visit Metriqzz at booth #60. Here they will show you how AI Agents are completely changing the way companies work with data. With real-life examples and concrete solutions that deliver results. Order your free tickets here.
