Learning from hands-on experience

In theory, theory and practice are the same. In practice, they are not

Based on our real-world experiences across more than 300 projects, we've identified common challenges in this transition and developed actionable insights to address them. Our experience has revealed consistent patterns in the journey from concept to implementation.

We have observed three persistent challenges in digital transformation: insufficient cross-functional collaboration, mismatched expectations about implementation requirements, and compounding technical compromises driven by budget or timeline constraints.

Let's dig in…

Key Learnings

1. Collaboration with IT: a non-negotiable priority

While we focus on solving business challenges, we must always acknowledge the critical importance of IT considerations.

Involving IT teams early and consistently is essential for successful industrialization—their expertise in infrastructure, best practices, and compliance ensures a smooth transition from prototype to production-ready system.

• Challenges: often IT engagement gets delayed, leading to misaligned expectations and technical hurdles.

• Solutions:

  • Identify key IT stakeholders early (e.g., data engineers or enterprise architects).

  • Schedule regular meetings or reviews to keep them informed.

  • Avoid vague invitations—organize structured discussions with clear agendas.

2. Industrialization requires separate planning

The transition from PoC/prototype to MVP is not merely a renaming exercise; it involves substantial technical evolution. Code developed for prototypes often lacks the structure needed for scalability and maintainability.

• Challenges: Prototype code often becomes bloated with features added hastily to demonstrate value, leading to bugs and poor documentation.

• Solutions:

  • Conduct external code reviews to validate assumptions and improve structure.

  • Allocate time for refactoring code once feasibility is demonstrated.

  • Clearly distinguish between PoC/prototype phases and MVP development in planning the project.

3. Budget realities: transparency is crucial

Organizations often underestimate industrialization costs, mistakenly expecting them to match prototype expenses. This misconception leads to frustration when the project requires additional resources.

• Challenges: Budget constraints often arise due to unclear communication about industrialization costs upfront.

• Solutions:

  • Educate all stakeholders early about the distinction between prototype development and industrialization.

  • Include an indicative budget for industrialization in initial proposals.

  • Reinforce this distinction during project kickoffs and milestone reviews.

Practical tips for smooth industrialization

1. Document business processes thoroughly

   Never assume—validate every detail with stakeholders. For example, seemingly minor details like unexpected file formats in attachments can disrupt workflows if not captured during initial planning.

2. Create representative test data sets

   Datasets should be validated to ensure they reflect real-world scenarios accurately. They are invaluable for benchmarking KPIs and retraining models.

3. Define clear code structures

   Invest time in brainstorming ideal code architectures before scaling up development. This reduces complexity during maintenance and facilitates smoother handovers.

4. Plan for robustness features post-prototyping

   Features related to resilience or scalability should be deferred to the industrialization phase rather than being rushed into prototypes.

Industrialization is more than deployment

Industrializing a data product involves far more than pushing code into production environments. Key considerations include:

• Containerizing code for scalability

• Ensuring security compliance

• Establishing monitoring practices

• Tailoring documentation to the audience's technicality

For example, during industrialization, the initial architecture often evolves dramatically to include subnet management and virtual environments—components not present in the prototype phase.

Conclusion

Until it's in production, what we develop remains useless.

The success of an industrialization depends on three key factors: close collaboration with IT teams, dedicated industrialization planning, and transparent cost discussions.

These lessons remind us that prototypes, while demonstrating potential, are merely stepping stones toward production-ready solutions that deliver tangible business value.

PoCs remain essential to de-risk innovation and clarify the RoI calculations of data initiatives. When organizations keep common sense industrialization in mind, their innovations can truly flourish in real-world conditions—becoming scalable, robust, and ready to make an impact.

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If you are looking for further project inspiration, check out our recent case studies!