Data ecosystem: do SMEs have a real chance?

To answer right away: yes, SMEs have a real chance in a data ecosystem. They do not need to build their own platforms or compete with the largest companies on budget. Their advantage can come from a well-described set of data from production, quality, service, energy, or the supply chain. The condition is simple: data must be collected consistently, have an owner, and be suitable for secure exchange.

Read on to see how you can grow digital transformation and Industry 4.0 without taking on a project that is too big for your team. First, let’s go back to the beginning and start with a short definition of a data ecosystem.

A data ecosystem is a network of companies, systems, people, and rules that lets data move securely between process participants.

In manufacturing, this can mean data exchange between a plant, a customer, a supplier, a machine service provider, an integrator, or a logistics operator.

The five most important elements are:

A data ecosystem is a way of working in which company data does not end its life in a spreadsheet, an email, or a machine panel. It starts helping you make everyday decisions.

According to PARP, in 2024 there were 2.37 million active non-financial enterprises in Poland, and the SME sector represented 99.8% of all companies in the country.

Effective data ecosystems cannot be built without SMEs. A large company may have systems, but it often needs information from smaller suppliers, component manufacturers, service companies, and operators of local processes.

That is where the opportunity appears. An SME has data that a larger partner does not have in-house: data from a specific machine, batch, process, and place in the supply chain.

The European Union wants more than 90% of SMEs to reach at least a basic level of digital intensity by 2030. In 2024, 73% of SMEs in the EU had reached that level, compared with 98% of large companies.

Cloud adoption is also growing. In 2025, 52.74% of EU companies with at least 10 employees used paid cloud services. Among small companies it was 49.3%, among midsize companies 66.78%, and among large companies 84.67%.

AI is growing faster, but Poland still has ground to cover. In 2025, 19.95% of companies in the EU used at least one AI technology. In Poland, the share was 8.36%.

The conclusion for SMEs is clear: you do not have to start with AI. First, it is better to organize the data that already exists in the company.

The greatest opportunity for SMEs lies where the company has data that other process participants do not have. The data does not have to be large in volume. It is often enough for it to be accurate, current, and linked to a specific order, batch, machine, or product.

For many manufacturing companies, the data ecosystem starts with organizing shop floor data. That is when you can see which information supports production planning, which data customers need, and which data can become the basis for better service or quality reports.

A midsize company manufactures parts for a larger customer. The customer expects on-time delivery, traceability, and fast information about the risk of delay.

Data from MES, ERP, and quality control can connect the order, material batch, machine, process parameters, and inspection result.

Benefit: less manual explanation during complaints and faster response to plan changes.

Here, the batch, recipe, temperature, time, raw material, and quality documentation matter most. Data is often spread across the shop floor, laboratory, warehouse, and sales.

Once it is organized, you can check faster what the batch was made from, where it went, and which process parameters applied to it.

Benefit: shorter response time when a nonconformity appears and better order in documentation.

A company delivers a machine to a customer’s plant. After the sale, it often loses access to information about how the equipment works and which parts wear out fastest.

If it defines access rules for service data, it can offer maintenance based on actual machine usage, faster diagnostics, and shorter technician response time.

Benefit: a new service revenue stream and fewer support requests without a full problem description.

The Data Act has applied in the EU since September 12, 2025. It covers, among other things, access to data from connected products, data exchange between companies, protection against unfair contractual terms, and interoperability.

For SMEs, two points matter most:

When buying a machine, line, or system, it is worth asking:

Trying to describe the entire plant at once will likely end in failure. Choose one process, one problem, and one result to verify. Below is an example process for one area.

Start with a question that affects cost, deadlines, or quality:

For the selected process, write down:

This step often shows why reports do not match.

In manufacturing, data is created on the shop floor, but decisions are often made in the office. That is why you need a bridge between machines, controllers, SCADA, MES, ERP, and reporting.

A natural starting point is often a manufacturing MES system, which organizes data about orders, downtime, quality, workstation load, and machine operation.

Before exchanging data with a customer, supplier, or service provider, define:

Your company is closer to readiness if:

If most of these points are not yet true, treat this list as the first work plan.

OECD points out that smaller companies remain behind in digital transformation mainly because of low awareness of benefits, limited internal resources, skill gaps, and financial barriers.

The most common mistakes are:

Yes, but not by copying their scale. A large company has a bigger budget, more systems, and a larger IT team. An SME has a shorter path from problem to decision. When the owner, production, quality, and IT sit at one table, it is easier to choose the first data area and verify the result.

The most important question is:

which part of our data would be useful for a customer, supplier, or service provider, while still being safe for us?

To check whether your production data is ready to be exchanged with a customer, supplier, or reporting system, start with a short audit of one process. At explitia, we can help you assess where data already exists, what is missing, and which first data flow can bring the fastest business result.

No. At the beginning, an organized data flow between a machine, MES, ERP, report, and one partner is enough.

No. You can share only selected data, for a defined purpose and for a defined period.

Start with data that affects costs and deadlines: downtime, order statuses, cycle times, quality results, material batches, complaints, and energy use.

It can help, especially when a company uses connected machines or devices and needs access to data generated during their operation.