A shared understanding of the various stages of development
When interdisciplinary teams from areas such as data product ownership, data science, IT and specialist departments work together to plan a new data product, there are often differing ideas about which stage of development should be reached first. This raises the question: Should the goal be a proof of concept (PoC), a prototype, or an MVP (minimum viable product)?
These four phases each address a different core risk:
A common understanding is crucial for the targeted and resource-efficient implementation of software development projects, especially for data products. Disappointed expectations and wasted time for customers and companies can be avoided only when everyone involved understands the differences and objectives of each phase, from technical feasibility to production-ready pilots.
In this article, we demonstrate how a structured, end-to-end approach — depending on the starting point — from an initial entry point, such as a proof of concept (PoC) or a prototype, through to the pilot project, can help identify risks early on and ensure clarity during implementation. We also explain how companies can effectively use this approach to lay the groundwork for scalable, long-term software solutions.
Unlike traditional software, data products are based on data quality, model performance and user acceptance.
Throughout the PoC → Prototyp → MVP → Pilot phases, knowledge continuously increases while uncertainty decreases. The scope is deliberately expanded at each stage — technically, professionally, and organisationally.
The PoC tests the technical feasibility of a solution. At this stage, the focus is not on design or user-friendliness, but on establishing whether the idea is technically feasible. The aim is to identify risks early on and conserve resources.
Advantages of a PoC
Typical questions
Characteristics
Typical deliverables
For example: A company tests the performance of its data model for real-time analysis in the cloud, even before the software is fully developed.
The prototype focuses on usability and the user experience. Once the technical feasibility of the idea has been confirmed, the prototype can be used to design the user experience and gather initial feedback. The emphasis here is on user feedback.
At this stage, the end-to-end process is simulated to make interactions, user interfaces and decision-making logic tangible. This prevents technically sound solutions from failing later due to a lack of acceptance.
Advantages of a prototype
Typische Fragestellungen
Charakteristika
Typische Deliverables
An example: After a successful PoC, a team creates an interactive click prototype for a dashboard. With the help of this prototype, the navigation flow and layout can then be optimized together with test users.
The MVP is the first version of a data product that can be tested by real users and is based on real data. It contains only the core functions necessary for productive use. This demonstrates whether the technology creates value in a real-world context and provides valuable market feedback.
Advantages of an MVP:
Typical questions
Characteristics
Typical deliverables
Example: A start-up releases a basic version of its app for appointment booking to test the added value of the core “appointment scheduling” feature.
The pilot project is a practical test conducted under real conditions. It assesses the stability, security and scalability of the data product in a production-like environment with a limited user group. The project reduces operational and scaling risks, paving the way for a broad market launch.
Advantages of a pilot
Typical questions
Characteristics
Typical deliverables
For example: A data platform is first piloted in a specialist department to test operational workflows and data interfaces, before being implemented across the whole company.
The following table provides an overview of the detailed differences between the individual stages and includes practical examples:
PoC, prototype, MVP, and pilot are not interchangeable terms, but rather tools that build on each other to systematically reduce technical, functional, and organizational risks. Each of these phases addresses a different risk: technical, user-related, market-related, or operational.
A common mistake in projects is to skip stages or have false expectations of the respective development stage. For example, if an MVP is expected when in fact a PoC is still available, this leads to misunderstandings, unnecessary loops, and avoidable costs. It is crucial to choose the right format at the right time, e.g., PoC for technical hypotheses, prototype for user experiences, MVP for proof of value in everyday use.
This creates a transparent end-to-end process that provides guidance for teams, synchronizes expectations, and lays the foundation for sustainable, scalable software solutions – with further development continuing beyond the pilot phase. This turns a good idea into a scalable data product.
As a next step, our workshop on data product strategy can help determine the appropriate entry format—PoC, prototype, MVP, or pilot—and develop a coordinated roadmap for further implementation.
, Working Student at scieneers GmbH
shinchit.han@scieneers.de
Alexandra Wörner, Data Scientist at scieneers GmbH
alexandra.woerner@scieneers.de
