Why you should stop building in house solutions

Published March 28, 2019·Written by Wonderflow

What sounded like the best alternative, might end up being a nightmare. In today’s video, our CEO Riccardo Osti will explain why corporations shouldn’t build in house solutions. If you believe it will save you time, money, and will allow you to have a better product, you might be terribly wrong. In fact, outsourcing solutions may prevent you from entering in a field where you don’t have any experience and saving time and money from the learning curve.

Would you like to know why? Go ahead and watch the video!



Hello, everyone, I am Riccardo Osti and on a daily basis I help the world’s best brands become more profitable by investing in the consumer experience

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In today’s video, I will tell you why, if you work in a corporate, you should not build in-house solutions.

So guys, let’s make it personal…if you ever tried to sell something to a corporate, there are very good chances that the counterpart responded something like: “we do it in house”…or the sentence nobody ever wants to hear…” we already do it”.   When you hear that, I know how you feel, so today I will put together a small video that you could potentially send to your contacts, and explain why they should give you a chance.

During these years I have seen many large enterprises hiring huge teams to develop all kinds of solutions in house, and in most cases, I recognized the following issues:

Number one: alignment of business and technology

If you work with consumer data you have the tough role to translate numbers into actionable insights for other stakeholders like marketing, design or sales. This means that you need to have deep technical knowledge as well as business understanding. You won’t believe how difficult is to find these two skills in the same team.    Ideally, business people should be able to leverage technology in a simple way, but the reality says technology is too often a barrier.

Number two: lack of experience

Even the largest brands are fairly new to data science and AI. This means that teams have to build many things from scratch, which exposes them to mistakes and time-consuming iterations. On one side, this gives you the freedom to build what you want, but on the other side it opens up a world of complexities and questions that are hard to answer. So, here you already see two things piling up, as you would need tech people, with business understanding, being able to find the right answers while dealing with the uncertainty of new things.

Number three: different speed requirements

Research and experimentation require time. If you put together a data science team to solve business challenges and help the organization become more successful, then you need to accept that for months or years, this team won’t pay you back. We all know that today’s markets move so fast…so the question is…can your company really afford this misalignment between the speed of research and day-to-day business?

Probably the most important thing,

Number four: market validation

Specialized companies usually have developed their products over the years, gathering feedback from global clients and transforming them into valuable features. If you develop your product in house, you cannot really know if you are developing the right thing or not. In many cases what companies build doesn’t make too much sense…and when it really makes sense…it was already available in the market, at a cheaper price point, from external vendors.

So, if you plan to “do it in-house”…or if you hear someone who plans to do so…now you know that is not going to be easier, cheaper or faster…but still…it could be a great and expensive learning opportunity


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