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December 21, 2021

Proof of Concept (PoC) in Data Science Projects


Edwin Lisowski

CSO & Co-Founder

Reading time:

6 minutes

Experimentation and innovation can take your company’s data to the next level. However, the implementation of a new data science project may seem uncertain and even risky to some shareholders. No organization wants to waste funds and time, especially for their first AI project. Thus, the best practice is to begin with a proof of concept (PoC).

Proof of concept involves going step-by-step and beginning with lightweight, simple experiments that show tangible results. Consequently, the organization can learn and understand the value brought by the particular data science PoC project. As they gain confidence and assurance, they can proceed with implementation.

In this guide, we dissect what a PoC is, its value, and how to launch a successful one. Read on to gain more insights.

proof of concept in data science project

What is a Proof of Concept?

The idea behind proof of concept is to establish the viability of a product, service, or system to ensure it satisfies particular needs or pre-set requirements. Proof of concept originates from 5 objectives and needs[1]:

  • To guarantee that the end value is satisfactory and to justify the effort and investment in the data science PoC project.
  • To ascertain whether the selected workflow plan is correct
  • To establish any possible limitations that the solution may encounter while solving the specific problem
  • To make sure the solution is technically viable to avoid likely technical challenges in the future
  • To find out whether the suggested solution meets expectations

A Proof of Concept re-scales the entire project into a workable plan to realize these objectives. What follows is the testing of particular assumptions to confirm whether the original idea is practicable and applicable when fully implemented.

As far as the assessment of data science solutions is concerned, Proof of Concept should go further than just proving that a solution resolves a particular issue. Ideally, it should confirm that the system, product, or service will be of value to the organization. And in this situation, data science PoC projects should demonstrate that they have the ability to bring forth data-driven insights to various business strategic objectives.

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Launching a successful data science PoC

Here are important elements that are required to guarantee the success of a data science PoC.

data science project

A practical, relevant use case

Picking a use case is the first step towards running a viable data science PoC. Without it, there cannot be a proof of concept. When planning to adopt data science for a specific use case, it’s important first to figure out the business case behind it. Here are a couple of questions you should mull over to help you launch a successful PoC project:

  • Will the right prediction model provide business value for your particular use case?
  • Does the use case align with the objective of the business or end-users?
  • How much effort does it take to figure out the use case?

Brainstorm a list of important business problems from which to pick and solicit ideas and suggestions from teams across the organization for multiple use cases. It would help if you also looked at the existing processes to determine whether the application of data science and machine learning methods could expressly enhance them.

Set a realistic timeframe

Set aside up to 60 days to implement the data science PoC project. This strikes a perfect balance because it allows adequate time for in-depth analysis without taking away too much time for the personnel who are also handling other projects.

Define success

With the presence of well-defined deliverables, you can restrict a data science PoC within a realistic timeframe. Thus, you need to define what success looks like. Examples may include:

  • Reduce ETL workload from A to B
  • Meet the needs of the user base exhibited by Y score on user reviews
  • Increase analytical potential between A and B via the integration of the new data

team, work, meeting, happy faces

Testing plan

Next, you need a thorough testing plan. Answer yourself a few questions:

  • What is the required number of ETL turns, and at what time should you launch a baseline runtime?
  • What functionality or metrics should you execute to show expanded analytics?
  • What’s the target number of users to gather feedback from? How do you conduct surveys on the users?

Assembling your team

A data science PoC can fall short by having too many or few persons involved. That’s why you need to assemble your team wisely. Ideally, settle on a team of not more than 10 people. A crowded setting may bog down what’s meant to be a nimble process. Create a team that will consist of:

  • Data scientists
  • Data analysts
  • The IT team
  • Business executives

Assembling a team including members from various departments of the organization helps in defining a clear deliverable that speaks to the interest of every stakeholder.

meeting, discussion, team work

Estimate budget expenditure and timeframe

After defining the success criteria and formulating a testing plan, you can now estimate expenses more accurately. The initially suggested timeframe can now be refined to encompass the tasks and testing plan.

Formulate a roadmap

If the PoC turns out successful, it’s important to have a well-defined roadmap to move the solution into production and not be stuck in the prototyping stage. You should ask yourself these questions:

  • What are the required steps to go into production?
  • Which data sources or business units will be implemented at first?
  • The existence of a production implementation roadmap can speed up budgeting and support from an executive sponsor.

What happens in the case of a failed PoC?

Not all[2] proof of concepts turns out to be successful. Several reasons could be behind the failure:

  • The data might be jumbled and unfeasible.
  • The ETL workload may be extremely costly at scale.

Regardless of the reason, failed proof of concept shouldn’t be seen as a letdown but a learning opportunity for future successful ideas.

Final thoughts: Data science PoC project

Proof of concept is critical when implementing a data science solution. It seeks to provide a working prototype in order to measure the effect of recommendations and learn more about the technology and concepts. When an organization is confident about the project, it can move into full production without any qualms.

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[1] What is a proof of concept and how to make the most of it. URL: Accessed 15 December, 2021
[2] Launch a successful data analytics proof of concept. URL: Accessed 15 December, 2021


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