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Currently, most organizations outsource their data science service needs since it allows them to expedite their response to changes in the marketplace or the target demographic. For a growing enterprise, catering to the rising data needs is challenging for the in-house team. Turning to data and analytics outsourcing companies lets them leverage the skills of qualified data science experts and the latest tools and technologies. However, some enterprises still prefer to have an in-house team.

Let's look at some of the pros and cons of both solutions to determine if outsourcing your data science service requirements is the right option for you!

Advantages of In-housing Data Science Services

  1. Intellectual Property

    Intellectual property is an irreplaceable asset for a business. Working with an in-house team can be more feasible for companies that are especially keen on protecting their intellectual assets. With an in-house team of data scientists, managing your intellectual property is easier. With an outsourced team also, you can safeguard your intellectual properties as long as are careful and willing to go the extra mile before deciding on a service provider.

  2. Self-Reliance

    You can work and communicate directly with your team, and not depend on anyone else's knowledge and experience. There will be no time difference and no language barrier. An in-house group operating from your workplace is an easy answer to data science solutions; one that offers independence and comfort.

Disadvantages of In-housing Data Science Services

  1. Time and Cost

    Keep in mind that hiring a data scientist doesn't solve the problem - you can't rely on one person to do the team's work. Hiring a team of data scientists is an expensive and time-consuming process, so it makes sense only if you are committed to keeping the team long-term.

  2. Scarcity of Experts

    The demand for adequately trained data scientists far outstrips the supply, thus making outsourcing an attractive option. Some data requirements are so niched that it could take an organization up to a year to fill up the job vacancy. Also, fewer specialists will have the right mix of technical skills and business understanding to deliver the expected outcomes.

  3. Difficulty Assessing Skills

    If you're new to AI and you're not a data scientist, how do you know if the person you're talking to is a true AI expert? Without the technical know-how, you may not be able to accurately assess the skills of the candidates.

Advantages of Outsourcing Data Science Solutions

  • Verifiable Experience

    By outsourcing data science solutions to an external partner, you can breathe easy. But before hiring a third-party vendor, make sure you check out their portfolio, read their case studies, and enquire into their team's credibility. It's a good idea to review their services to ensure it meets your expectations before bringing them on board.

  • Opportunity to Work with Specialists

    Outsourcing companies hire candidates from diverse backgrounds, who have experience in various technologies and projects. By working with a reliable service provider, you get access to highly qualified professionals with global expertise, without exceeding your budget endlessly.

  • Cost Effectiveness

    By offshoring your data entry requirement, you can save costs on hunting for the right resources. Furthermore, you save costs on maintaining resources since the extended team would work with you on a project basis. Moreover, these team members are not your financial responsibility, so you can save costs on benefits on allowances since they are not on your payroll.

  • Flexibility

    If you need a customized solution, the data analytics team of the service provider could handle it better. They have a better understanding of the data science requirements of businesses, and therefore will be able to create custom solutions.

Disadvantages of Outsourcing Data Science Solutions

  • Lack of Niche Expertise

    When you are looking for an outsourcing company, you are not sure if they have the industry experience you need. Therefore, you need to vet your service provider carefully and let the extended team to understand your business.

  • Giving Away Control

    Working with a team that is not yours can feel like you are surrendering control. Schedule calls to track progress and stay in touch with the outsourcing team. It can be uncomfortable at first, but you need to learn and adapt.

The Effort That Goes into Choosing a Service Provider

While data is the need of the hour for any enterprise, understanding the intricate nuances of data science is not possible for all entrepreneurs. In that case, you need a service provider with extensive knowledge and understanding of the field. They should be able to fully comprehend your business requirement and work effectively on a limited brief. Finding the right outsourcing partner is a lengthy and tiresome process and must be done with due diligence.


By outsourcing your data science needs, you'll work with a productive team that can start working on your project right away. You'll gain the skills and expertise to help create cutting-edge technologies while saving time and expenses. If you try to manage data science in-house, it can turn out to be a more complex process, one that is not free of error.

Depending on your specific business needs, you can choose to carefully weigh your options between in-house or outsourced solutions and decide on the one that is the best solution for you.

Outsource Data Science Services to Flatworld Solutions

At Flatworld, we offer comprehensive data science solutions to businesses. We can help convert business data into actionable insights by leveraging AI-based solutions. We enable our clients to track industry trends and make better business decisions with our services.

Want to become a data-first organization? Reach out to our experts now. We work closely with your in-house team and offer you data science solutions that are tailored for your enterprise. To know more about our data science offerings, contact us now!

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