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In-house Teams vs. DSaaS Providers: Pros and Cons

Nowadays, it’s quite common to perceive data as oil since it’s valuable, but it should be refined to be of benefit. In the world of digitalization, companies and organizations rely massively on data to make smart decisions, enhance business operations, and gain a competitive advantage.  

As a result, the demand for data scientists and data science and analytics services has skyrocketed. When pursuing the goal to meet this demand, businesses have two options. They can invest in building an internal team full of professionals of their craft or partner with data analytics service providers.  

Companies need to decide on what’s best for them. It’s important to consider each approach’s set of pros and cons. By weighing all the important factors, businesses can choose what’s best for them and it’ll significantly impact their success. We’re going to look at essential details and factors.  

In-house Data Science Teams 


  • Tailored solutions. When an internal team works within the company, business leaders can count on their understanding of business and industry nuances. That’s why such a team can create tailored solutions that align precisely with the firm’s distinctive goals and requirements. Whatever a project they work on, they can customize all the details and move forward.  
  • Full control. Whatever is going on inside the data science team, company management can watch over everything. They can hire specialists who are not only professionals in what they do but also understand the business’s core values. A high level of control over every project lets companies work safely with sensitive data and confidential information.  
  • Immediate access. Many projects depend on real-time data. Besides, there’s a high percentage of important changes that can’t wait. When companies have internal teams, they can accommodate any urgent request immediately. Whenever there’s a need for real-time insights or there should be a quick response to changing market conditions, companies don’t have to wait but act accordingly.  


  • Pricey investment. Besides working on projects, companies need to recruit, hire, and maybe even train data science talent in the early process of building an in-house team. Moreover, as the demand for data scientists grows and since their job is quite challenging, companies need to be ready to pay high salaries.  
  • Limited scalability. Project requirements can change fast. Thus, there can be an urgent need to scale. An internal team can be challenging to scale up in a matter of seconds.  

Data Science as a Service (DSaaS) Providers 


  • Cost efficiency. Expenses that are needed to build an external team do not become the burden of clients. Those costs are spread across many clients. That’s why clients only pay for what they need.   
  • Expertise and experience. When choosing data science as a service, businesses can rely not on mediocre talent, but on professionals with a wealth of experience and expertise in data science. Those specialists are highly skilled in what they do. That’s why they can offer the expertise that an internal team might not have.  
  • Flexibility and scalability. In terms of scalability, an internal team might be constrained by the budget and deadlines. However, an external team can change according to the preferences of their clients.  
  • Fast delivery of insights. Apart from hiring data science talent, companies need to provide them with the best tools and equipment that will enable fast delivery of insights. In this respect, budget limitations can impact the access to cutting-edge technology the team may need.  


  • Lack of customization. DSaaS providers can offer a wide range of solutions. However, there might be an issue with tailored solutions. It can take a great deal of time and resources to find a reliable provider that can align with the client’s unique goals.  
  • Data privacy concerns. The process of sharing sensitive data with the provider is inevitable. That might raise some privacy concerns. Thus, it is crucial to establish strong data protection protocols.   
  • Lack of supervision. With an internal data science team, companies can get full control over what they do. However, partnerships with external DSaaS providers can create a level of dependency.   
  • Communication challenges. Any project’s success depends on effective communication within the team. Without using successful collaboration solutions, there might be some misunderstandings and misalignment that can impact results.  


When working closely with data, companies need to decide whether they build their internal team or partner with an external team. Companies with tight budgets can significantly benefit from partnering with data as a service providers. If customization and control play a major part in business operations, it might be a better idea to build an in-house data science team.

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