The Crucial Difference Between a POC and MVP in Data Science Projects
Working with clients, two terms are frequently used. One is “ Proof of Concept” (POC) and the other is “Minimum Viable Product” (MVP). Have you ever wondered which is which and when to use one over the other? Businesses looking to develop data-driven solutions need to understand the difference between these two approaches to make informed decisions. In this article, we will explore the distinctions between a POC and an MVP in the context of data science projects, their benefits, and why choosing the right one is essential for success.
Watch our YouTube video with Jonas Stray, lead data scientist at Pepkor, where he discusses this topic with Pierre le Roux
The Proof of Concept (POC):
A POC, or Proof of Concept, is a preliminary demonstration of a project’s feasibility. It aims to validate that a proposed solution can effectively address a specific problem, challenge or big risk. In the data science context, a POC often involves working with a subset of the client’s data or limiting the problem scope to showcase the solution’s potential.
However, it’s important to note that even within a POC, the entire problem must be solved. There’s no escaping the chain of reasoning that connects the problem to the solution. While a POC may appear to be a cheaper and quicker option, it still requires a significant amount of effort and resources to develop.
Benefits of a POC in Data Science Projects:
Risk Mitigation: POCs can help businesses identify potential challenges and risks early in the development process. By focusing on a specific aspect of the problem, businesses can assess the feasibility of the entire solution and determine whether it’s worth investing further resources.
Stakeholder Buy-in: A POC can serve as a convincing demonstration to stakeholders, showcasing the potential of the proposed solution. This can help secure the necessary support and resources to proceed with the project.
Learning and Iteration: Developing a POC allows teams to learn and iterate on their ideas before committing to a full-scale project. This can lead to more efficient development processes and better overall solutions.
The Minimum Viable Product (MVP):
An MVP, on the other hand, is a more refined product that can be used by the client to generate revenue. It is built with the essential features needed to solve the problem while omitting any non-critical elements (the nice-to-haves). An MVP provides a functional solution deployed within the client’s business.
Benefits of an MVP in Data Science Projects:
A viable system: An MVP provides a functional product that can be deployed within the client’s business, generating immediate value and revenue. This can help businesses recoup their investment faster and justify further development.
Market Validation: An MVP allows businesses to test their solution in the real-world, gathering valuable feedback from the real-world impact. This information can be used to refine the system and make data-driven decisions about future development.
The Crucial Difference:
The main difference between a POC and an MVP lies in their intended outcomes, scope, complexity, and development time and cost. A POC aims to prove the feasibility of a solution, while an MVP provides a functional solution that can be used by a client. Just because it is called a POC does not mean it will be cheaper than an MVP. Both require a thorough understanding of the data, experimentation and the development of a solution. The POC just addresses a specific risk or issue, while an MVP is a usable system, but not the full solution.
However, a POC in data science could be a stepping stone towards developing an MVP. By first tackling one aspect of the problem and demonstrating the solution’s effectiveness, businesses can then expand their focus to other areas, ultimately building towards a full solution.
Let’s say you are a retailer and you wish to develop an improved markdown system. The biggest risks are:
- Is there enough data to create models that can forecast sales accurately enough to be useful.
- How the team will be using the outputs of the model.
- Whether the team will trust the model.
The first POC would be to gather the available datasets in a single data-dump, applying exploratory data analysis and building the baseline model. This will provide enough information as to what the next step would be, be it building a stronger model, finding more data, using the baseline model or accepting that the data is not good enough.
Assuming all went well, and a few model versions in the model is acceptable. Then the data scientist can run what we call a “concierge” service, where they sit with the team, and deliver the insights to them. As time goes by, the team will start to trust the outputs of the model, have tested the effects. Next a software developer can put together a rudimentary MVP that incorporates the model and allows the team to work independently from the data scientist.
If budget allows and there is a need to scale the system up and empower more users with better features, then the development of the full end-to-end system will start.
Note that from the POC or the first few models, the business can already track the potential impact and improvement to the business. It will already generate a level of savings. The next steps enable a bigger impact, by empowering more users and building improved models over time.
In data science projects, the terms POC and MVP may sometimes seem interchangeable. However, understanding their distinct purposes, benefits, and outcomes is crucial for businesses looking to develop data-driven solutions. While POCs can provide valuable insights and validate a solution’s feasibility, moving towards MVPs offer a functional product that can be deployed within the client’s business.
Reach out if you are in need of a data solution. We follow a lean approach, identifying and providing business value early through the use of POCs, and expanding towards bigger solutions as the business value gets delivered.