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Data Solutions

Can Data Science be outsourced?

date: 19 July 2022
reading time: 6 min

Big data analytics is closely related to data science as it too involves studying large sets of data to find insights through hidden patterns. Both of these processes can be done with one of a number of methods, such as machine learning, natural language processing and statistical analysis.

What is data science?

Data science is a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today’s organisations.

Data science involves analysing data and transforming it into usable knowledge. Data is first acquired, then it is cleansed and prepped. After this, it is modelled in order to explore the data, and finally, the results are collected. The main goal of this process is to extract insights that help companies to make good decisions.

What is Data Science Outsourcing?

Data science outsourcing is when a company hires an outside third-party organisation to come in and manage some (or all) of the data science initiatives. These could include tasks such as data collection, modelling, cleaning and interpretation.

What is a data scientist?

Typically, they are computer scientists or statisticians whose role in an organisation involves data analytics. They use statistical analysis to create solutions to a company’s problems in a logical and mathematical manner.
While this can be highly valuable for a company, they can also be very expensive to employ, which is a big reason why many organisations prefer instead to outsource data science instead.

Data science outsourcing can be a really good option for companies to gain access to a wide range of skills and expertise without having to fork out on an in-house data scientist

As the market continues to grow, the benefits are huge and make this a valuable industry. However, there are some potential drawbacks that you need to take care of, too.

What are the benefits of outsourcing data science?  

There are numerous benefits to outsourcing data science, including: 

  • More access to specialist knowledge – By using a professional company, businesses are able to access expertise that they may not have in-house. This allows for more complex projects and better solution to your business needs.  

  • It is often more cost effective – Hiring in-house data scientists can be expensive, not to mention not always necessary. Using data science outsourcing services places a lower financial burden on companies, as the service provider has the responsibility for hiring and maintaining the necessary personnel to get the job done. 

  • It allows companies to scale – Having access to outside data science solutions gives companies the opportunity to scale their operations up (or down) as needed. A good provider is able to expand or reduce their operation in accordance witrh their client’s needs, making the whole process extremely useful. 

  • It is highly efficient – Being able to work with external specialists who have knowledge and expertise far greater than your in-house team helps to make everything more efficient. 
  • Projects are completed faster – It goes without saying that being able to hand over very long, technical aspects of a project to a fast and efficient outside team means that you will have a much quicker route to market! 

  • It offers more flexibility – Using an outside data science team means that you can be a lot more flexible when it comes to choosing which aspects of the project to focus on. It might be that you need a strong focus on data science for one aspect of a project, but having that team available means that you can hand it over to them and put your attention on more pressing matters, safe in the knowledge the job will be done. 

What are the disadvantages of outsourcing data science?  

Unfortunately, there are some disadvantages of outsourcing data science that need to be taken into account. Some of them include: 

  • Your control is limited

    Naturally, when handing over part of your operations to an outside organisation, you will not necessarily have the same complete control that you enjoyed previously. Although this is largely the exact reason that you chose to outsource your data science tasks, it can develop into an issue if your service provider isn’t performing the task to your satisfaction.  

  • Data security can be compromised

    Security is a key issue to take care of when outsourcing data science. Companies will very likely have sensitive information that they do not want being leaked or exposed, so they need to make sure that the outsourcing company has appropriate measures in place to guarantee that this information is secure. 

  • Difficulty with integration

    It can be tricky to integrate a company’s data effectively with their data science organisation. Workflows and business processes may need to be changed, which will likely delay the project and increase its cost. 

  • It’s not always cost effective

    While it is often the case that outsourcing data science is more cost effective, this is not necessarily true all the time. For example, when a company seeks to run a very large data science operation with a firmly set group of goals with a short deadline, this could really spike costs and you risk them rocketing up so much that it is actually more cost effective to have your own in-house team after all. 

  • Workplace culture and communication

    Communication can be tricky as the outsourcing staff will be working away from the client company. The physical distance creates delays and opens up room for miscommunication. In addition, even if both companies hail from the same country, their workplace culture may differ greatly when it comes to communication frequency, working times, attitudes and approaches to problem. 

How to choose a good data science company to outsource your project to?  

Once you have taken the decision to work with another organisation to manage your data science, you will need to find the best outsourced data scientist company for your needs. Here are some tips of choosing the best outsourced machining learning company: 

  1. Make sure that the outsourcing company has the right skills

    Some companies may specialise in specific areas and not all outsourcing companies will be on par with each other. It is therefore important to find out what their skills are and how their expertise and knowledge lines up with your company’s needs. 
  1. Make it clear what ‘success’ looks like beforehand

    Before signing any contracts, clearly define what success looks like for your project in terms of timelines, performance and quality. These KPIs (key performance indicators) are critical in tracking and monitoring progress for you and the outsourcing company. 
  1. Look at their portfolio

    It is important that you have a look at previous projects that they have collaborated on before committing to a specific company. This will give you a good idea of what to expect when collaborating with them. 
  1. Confirm what level of support to expect

    It is crucial that you clearly understand how much support you are likely to receive, and who is there to help you when you have an issue. This increases trust and efficiency between both parties. 


Like anything in life, there are always downsides to any initiative and outsourcing your data science is no different. Nevertheless, there are many very clear benefits that can be taken from entering into the right collaboration, not least the cost saving potential and access to a much bigger repository of skills and experience that you simply don’t have in-house.

Outsourcing your data science allows you to focus on areas of your business that need your attention the most, and if done well, is a fantastic data science solution. 

Reach out to us today and we can discuss your needs and we will create a custom software development strategy unique to your business needs.  

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