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A data science dilemma
One of the biggest dilemmas for entrepreneurs and decision makers in any company is whether they should build an internal team, or whether they should outsource the technical development to a third party. This is an important decision that can affect everything, from your costs to the development time.
In this post we will analyse the pros and cons of each choice.
In-house data science teams
There are many benefits to an in-house data science team. The greatest benefit is that the team gets to know the business and its culture over the years. As the years go by and the team developers models and algorithms, it is gets more and more integrated into the business. This helps the data scientists being come up with new valuable suggestions for how data science could work within the company. This is way more difficult to achieve for third parties, which often need to be instructed on what to do specifically.
Another benefit of building an in-house team is that the team “owns” the solutions that it develops. Contractors sometimes might have a short-sighted mentality. In-house teams are forced to think about the long run from the beginning.
That being said, building an in-house team is not an easy task, especially if a company does not have any prior experience in data science. Hiring some full-time is not a small deal, and the decision maker needs to make sure that they are making the right choice. Hiring is a topic I have discussed many times, in my book and in the Tesseract Academy‘s workshops.
Outsourcing data science
Outsourcing data science is a very efficient choice if you know exactly what you are after. This choice can help you save time and money. However, contractors tend to me less involved in the work, after it is submitted.
This is why you need to make sure that:
- The milestones are clear.
- You get support after the software is delivered.
This is where it also really pays off to work with experienced contractors, who understand how to manage risk, and set expectations in the right way.
That being said, using a third party makes it more difficult to build the relationship that you build with an in-house team over time. So, it can be a great choice to start, but eventually, a company should focus on developing its own in-house team.
The only exception is when the requirements are really one-off. For example, let’s say that you want a recommender system. Once this is built, and tested, then there is no further work, other than updating it every quarter or so. In this case, a third party is a cheaper alternative than a full-time in-house team, since the team might not even have enough work to do the rest of the time.
Data science teams
Building the right data science team is not easy. This is why you need to make sure you really understand your requirements, and design a data strategy that will help guide you moving forward. If you want to know more about how to do that, make sure to check out the Tesseract Academy‘s website, or simply get in touch.