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The Lean Startup Model in Data Science Products

A/B testing in data science

The Lean Startup method, described by Eric Ries in 2011, is inherently the most profitable approach to building your business from scratch. This method comes with several key ideas that help entrepreneurs achieve their goals with the least risk.

The method provides for the development of a minimum viable version of your product — MVP. The MVP is created to check whether a particular problem is available in the market or to verify your choice of a solution. At the same time, your product should remain as lean as possible (hence the name). This will allow you to significantly cut the costs of development. The ultimate tasks of the Lean Startup method are to correctly and quickly test your hypothesis, get feedback, and even gain early profits.

The method’s popularity is so great that there are different software development companies that increasingly focus on full-cycle MVP development. But despite its successful implementation for over 10 years now, there are discussions on whether this method is suitable for all industries. This is an interesting question, and today we will talk about one of these industries — data science.

Does the Lean Startup Method Fit Data Science Products?

A/B testing in data science

Yes, and there are two reasons for this.

First of all, the ideas that underlie the Lean Startup method suit most businesses in the most proper way. They promote customer-oriented enterprise principles, minimization of risks and costs, and quick pivoting in the case of failure. As a result, the business delivers only those products that are in real demand.

Secondly, developing a data science product is like launching any other tech startup. Often, data scientists aim to solve business problems, but the solution is not obvious. Thus, it involves using a trial-and-error method. The success of this solution is not evident too. Lean Startup allows you to switch from an idea to a finished and tested product in the shortest possible time. This approach applies to data science product development perfectly.

Data is volatile, so the speed and dexterity are important here. Engineers can spend half a year developing the ideal data model, only to eventually understand that this model no longer works. That’s why the strategy of validating product features through MVP is so well suited to data science products. This gives the necessary agility, flexibility, and speed.

The Lean Methodology in Organizing Data Science Teams

Managing the data science team is not easy. While developing a product, experts use various tools and layers to obtain new data. Without tight control, the data team can spend too many resources, which may result in big expenses. The Lean Startup method is great for organizing such teams appropriately.

Imagine a team of data specialists as a small startup. Next, identify the hypothesis that this team will test and define the least costly way to verify this hypothesis. Ensure that every team member understands that the key to success is to be as lean as possible. Experiment until you find the best option. For this, analyze the incoming data to determine the right moment for pivoting or full-fledged development.

MVP-Driven Tips For Data Science Product Development

A/B testing in data science

In order to finally convince you that the rules of Lean Startup are suitable for the development of data science products, here are a few more principles that you should follow.

Lean Infrastructure

Before the development itself, you can fall into the trap and spend a lot of money on building the necessary infrastructure for the data team. Here, we are talking about various tools and technologies that you will acquire without knowing why they are needed. An important principle of Lean Startup is to validate the hypothesis at a minimal cost. Use this approach when creating an infrastructure for the data science team. Start small and add new elements only when you clearly see the need for them.

Finally, it can also be appropriate for the team hiring process. It is useful because data science specialists are truly expensive to hire.

Start Small

Data science team members often get ambiguous tasks. It looks like this: “We need a solution that will help to optimize prices for 3,000 items in our online store in real-time.” Such work will take a lot of time and resources, and the final product will most likely disappoint you.

Applying the Lean Startup method here, you will break down the task into small parts. Let’s again use the example of the online store that we mentioned above. You can start by developing a solution that will optimize the price of goods of only a certain small category. After that, in the case of success, the spectrum of the solution can be expanded.

Test, Test, Test!

A/B testing in data science product development is the best way to increase your chances of success. Having developed several MVP models, test them on a real load. This is very important since only the real load will reveal the flaws of a particular model.

Conclusion

A/B testing in data science

The Lean Startup method is perfect for data science projects. What could be more logical than striving to solve the issue in the fastest and least costly way, right?

Moreover, the methodology described by Eric Ries is practically a step-by-step guide on how to minimize your risks, reduce costs, and at the same time, satisfy the user. Isn’t this the surest way to do business?


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Data Science Prerequisites: Skills You Need to Become a Data Scientist

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