AI Open Space

How Much Does It Cost to Join a Data Space? Costs, Timelines and Return on Investment

“It sounds interesting, but how much is it going to cost us?” This is the question any SME manager asks when someone proposes joining a data space. And it is a legitimate question: before investing time and budget in sharing or consuming data with other organisations, you need to know what costs are involved, how long it will take to deliver results, and whether the effort is worthwhile compared with continuing to do things as before.

The usual difficulty is that explanations of data spaces often remain at a conceptual level —interoperability, data sovereignty, federated catalogues— without addressing the issues that really matter to a financial manager: cost items, implementation timelines and return-on-investment calculations. This article aims to fill that gap with a practical approach, avoiding invented figures and focusing on the factors that determine the actual cost in each case.

If you run an SME in Castilla y León and are considering taking the step, this guide will help you understand what cost items are involved, what timelines are reasonable, what public funding opportunities may help reduce the initial investment, and how to estimate whether the return is worthwhile.

The actual cost items involved in joining a data space

The cost of joining a data space is not a single figure, but rather the sum of several cost items that vary depending on each company’s starting point. At least four areas should be distinguished:

Infrastructure: the connector and associated components can be deployed on the company’s own servers or in the cloud. An SME without existing infrastructure will face higher setup costs than one already operating in a flexible cloud environment.
The connector and its integration: installing the connector is only the first step; the real effort lies in connecting it to internal systems (ERP, databases, sensors, custom applications) so that data can flow without constant manual intervention.
Data and catalogue preparation: internal data is rarely ready to be published as-is. It usually needs to be documented, cleaned to some extent and described with metadata so that it is useful in the federated catalogue.
People and expertise: someone within the organisation needs to understand which usage policies to apply, how to interpret agreements and how to maintain the integration over time. This may involve a part-time commitment from an existing employee or require specific training.

The relative weight of each cost item depends heavily on the company’s existing digital maturity. A company with well-integrated systems and reasonably structured data will mainly face the cost of the connector and integration. A company starting from scattered spreadsheets and manual processes will first need to invest in organising its own data, and only then in connecting it to the data space.

Hypothetical cost breakdown

Let’s consider Cárnicas Esla, a fictional meat-processing SME in Castilla y León with an ERP production management system already in place. Its deployment could involve: a server or cloud instance for the connector (a moderate and recurring infrastructure cost), an integration project with its ERP to automatically export traceability data (a one-off development cost that varies depending on the complexity of the ERP), documentation of the datasets it wants to publish or consume (an internal effort that does not necessarily involve external expenditure), and a part-time commitment from its IT manager for maintenance (a recurring but limited personnel cost). This breakdown is only illustrative: each company should build its own based on its actual starting point.

Realistic timelines: from the decision to the first data exchange

One of the most common mistakes is underestimating the time required and expecting immediate results. For an SME, a realistic timeline can be divided into several phases:

Analysis and design phase (weeks): identifying which data makes sense to share or consume, for what purpose and which usage policies should apply.
Technical deployment phase (weeks to a couple of months): installing the connector, configuring the policy engine and carrying out initial connectivity tests with the data space.
Integration with internal systems (variable, often the longest phase): connecting the connector to the company’s actual data sources, whether databases, APIs or files.
Validation and first real agreement: carrying out tests with a partner or pilot customer before operating with production data.

The total time until the first operational data exchange can range from a few weeks, if the company starts with a simple integration and a clearly defined use case, to several months if legacy systems need to be adapted or complex agreements with multiple partners need to be negotiated. The practical recommendation is to start with a specific, limited-scope use case rather than attempting to integrate all of the company’s data from day one.

Public funding that can help reduce the initial investment

The digital transformation towards shared data models is often supported through different public programmes, both at national and European level. Without referring to specific amounts, which vary from one call for proposals to another, SMEs should consider:

National digitalisation programmes aimed at SMEs, such as initiatives known as Kit Digital, which in its various calls has covered categories related to data management, cybersecurity and connectivity.
European recovery funds and framework programmes focused on digitalisation and innovation, which periodically open funding lines specifically for the adoption of data and artificial intelligence technologies.
Regional programmes in Castilla y León aimed at business digitalisation, which may sometimes include specific funding for data interoperability projects.

The availability, amounts and requirements of each programme change over time, so the recommendation is always to check the calls currently open when planning the project rather than assuming that the conditions of previous calls still apply.

How to estimate the return on investment

The return on investment from joining a data space does not always translate into immediate direct revenue. It should be assessed from several perspectives:

Operational time savings: if data is currently shared with a partner through emails, manual files or phone calls, automating the exchange reduces recurring administrative work.
New business opportunities: accessing data from other organisations can enable services that were previously not viable, such as providing analyses or recommendations based on information that was previously unavailable.
Reduction of errors and rework: manual exchanges are prone to formatting or version errors; an automated and governed exchange reduces this risk.
Value of data as an asset: an SME that publishes well-documented data in a data space can begin to generate value from information that previously remained unused within its own systems.

A hypothetical reference calculation

Suppose, purely for illustrative purposes, that an SME currently spends the equivalent of one employee’s working day each week preparing and manually sending data to three business partners. If automation through the data space reduces that workload to a fraction of the current time, the annual saving in working hours can be compared with the estimated annual cost of maintaining the connector and integration.

If the time savings, combined with the new business opportunities enabled by access to third-party data, exceed the recurring cost of the solution within a reasonable timeframe, the investment can be considered justified. This exercise, adapted using each company’s actual figures, is what any financial manager should carry out before making a decision.

Before deciding, assess your specific case

Every SME has a different starting point, and the cost and return of joining a data space depend directly on that reality. If you want to assess, using real figures, what joining a data space would mean for your company, look for a technology partner with experience in data spaces and assess your specific case together.