The benefits of outsourcing data science

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A growing number of companies are entering the data science industry as part of digital transformation. With data science, organizations can collect and analyze their data to create valuable insights for decision-making. However, it is necessary to understand that this field is complex, and it requires a lot of human intellect and time from the companies. Expert companies in this field can offer companies expertise through the services they provide. The solutions include predictive analytics, building predictive machine learning engines, and creating valuable insights. In this article, you will find out the benefits of outsourcing data science, what advantages it offers, and the risks you need to consider.

How can data science benefit your business?

The application of data science solutions can benefit any industry and accelerate business growth in many ways. Here are a few examples of how companies are using data science:

  • Medical industry: evaluating patient records and improving quality of care for patients.
  • The marketing industry: creating and targeting advertisements. Digital strategy for online businesses is heavily reliant on this.
  • The retail industry: analyzing customer spending and behavior to improve the shopping experience. Optimizing processes for warehouse organization and delivery routes is another example.
  • The finance sector: forecasting market fluctuations and events. The data from stock exchanges can be analyzed with artificial intelligence for reasonable predictions.
  • The telecommunications industry: tracking mobile devices with GPS to optimize their networks, maintenance costs, etc.

What makes data science projects so challenging?

The complexity of data science projects

A lack of data science expertise and experience prevents many companies from achieving their goals. This happens because data science requires extensive expertise, real-world experience, tools, and methodologies. Moreover, data science projects are complex because they need a lot of human intellect and time to complete.

Scarcity of experts

The demand for data scientists exceeds the supply of qualified candidates, making them among the most difficult to hire. Top talent is scarce, and companies are having a hard time finding the best data scientists. Finding and interviewing talents that are so specific or difficult to find could take months.

Data science projects are paved with mistakes – learn from them, or you’ll soon fall behind. Well-organized, experienced teams will already know what trials your organization faces and how they can avoid them.

Why should you outsource your data science tasks?

Accelerated business growth

Data science has become increasingly popular with the development of digital transformation. In other words, if you do not apply data science to your organization right now, your competitors may be far ahead of you.

There are two basic options when it comes to data science tasks: either you can hire an in-house team for this or work with a trusted provider. Having an in-house team provides you with more insight into your employees’ performance and overall productivity. It may, however, take a long time to find the right data scientists. As opposed to that, a service provider will facilitate the work. Working with a trustworthy provider will help you avoid severe problems since they will be responsible for the quality of their services.

Hiring experienced data scientists on the market right now is very difficult. Data science is a new field, and not many people have the required experience to apply it in their organization. That’s why it is easier for companies to outsource these tasks than to hire an in-house team.

Working with a trusted provider also means that they can provide you with data science tools and services in different domains, which means that you will not need to work with other companies. Outsourcing data science tasks will free up your time to focus on what matters. You won’t have to worry about managing a data science team or ensuring they’re all appropriately trained because someone else will take care of that.


A good third-party provider will provide the necessary support, which means that you won’t have to worry about managing a team. They can also manage your entire project, ensuring it stays on schedule or even ahead of schedule. 

A data science project can fail in many ways (see A reliable, tested methodology with real-life experience can reduce risks and help you avoid some pitfalls.

Trusted providers should also provide you with a wide range of services. They may cover all the data analytics domains your organization is working on, which means they will not have to rely on freelancers for additional support. 


Doing some research will help you find a company that guarantees the security of your data. Be sure the company you’re working with has robust security procedures. Ensure that your data is encrypted and can only be accessed by authorized users using cloud-based storage. 

The success of a project depends on good communication within the team. Talk to your project manager about the agreement details and critical milestones regularly.

Time and money

The process of hiring a data scientist is time-consuming and expensive. However, it can be worth it when you commit to the project for more extended periods. Hiring one person will not solve your problems because no individual can handle all of the work by themselves – when they leave, often, everything must be rebuilt from the ground up.

Opportunity to work with experts

Outsourcing companies usually hire data science teams with backgrounds in multiple fields, each with experience on their projects. A good provider will have experts in the key areas that will help your project succeed. They will provide you with consulting services and consultancy on various data analytics and machine learning-related topics.  

What is the best way to choose an outsourcing partner?

Planning ahead

When you’re looking for a suitable outsourcing partner, defining your expectations before the project starts is crucial. Plan each step of the project and review it with your provider. You should ask yourself: Do they have a proven track record of completing successful projects? What steps do they take to ensure the success of this project?

During the project, many problems will arise from data extraction to big data processing, research, production, and monitoring the system over time. This way, you ensure that there will be no gaps and everything should run smoothly.

Is the provider’s knowledge in our industry up-to-date? Does the provider have access to current market research and advanced technologies? Does your provider have demonstrable experience in providing actionable insights?

Don’t just look at the price. It would be best if you also considered how much value that money will bring to your organization.

Agile methodology

Some providers work in an Agile methodology, which means they sustain high communication and constant feedback during every phase. How will my team collaborate with them? Do they have people who can communicate in our language and share our knowledge? What are the limits of their collaboration?

Is the provider able to receive feedback and act on it? Does the provider have its resources, or does it outsource some tasks as well? What is their work priority? How will features be prioritized?

Experience in the real world

Choose a vendor with experience in dealing with real-world data science problems. Good service providers will have expertise in all project phases, from basic data engineering to building and deploying production-ready solutions and monitoring them over time. Domain expertise, proven Agile-friendly methodologies, the latest technology, and industry market research are crucial to the success of any project.

Verifiable experience

Check the experience of potential external partners. Seek out examples of colleagues who worked with the firm. Would they be able to provide you with case studies and research? Have they delivered similar projects to yours? 

The outsourcing provider should have relevant industry experience. Additionally, they should keep abreast of the latest trends in machine learning and AI – as these are constantly developing in our industry. 

Be open

Don’t be shy about telling your outsource provider the truth. Setting and discussing your goals and expectations from the beginning will ensure that the solution they develop is tailored to your needs. Ask questions if you feel that things are not going according to plan. Discuss what went wrong and how it can be improved in future projects.

An outsourcing provider should be involved from the start of the project in order to achieve the desired results. As a result, you will save time and money, which would otherwise be spent on getting up to speed.

Focus on delivery

Ideally, a provider should be ready to help the moment you sign the contract. They know what they’re paid to do and that it’s your money, so they’ll deliver on time, if not earlier than expected.

Delivering a quality product requires a can-do attitude. 

Knowledge transfer

An outsourcing company will work on your project with its resources. Ensure sufficient knowledge transfer so that your new team members can work independently on similar projects in the future. Your success will be the success of your provider, and vice versa – so take time to plan a transparent knowledge transfer process.

A good outsourcing company will become an extension of your team, and its expertise should be available for you whenever you need it. 

Giving up control

Working on teams, not within your department, can feel like giving up control. There should always be clear communication between you and your provider. You must know exactly where your project is at all times and how it’s going to be delivered. You can expect a good outsourcing company to inform you when there are delays that might cause issues in delivering the project on time.

Domain expertise

A good provider should have a well-tested methodology to interact with your domain experts. No one is likely to know your business better than you, but to uncover your knowledge and insights and transform them into machine learning models and subsequently to predictive engines takes many skills, knowledge, and expertise.

Reports and communication

Pick a company that communicates clearly and transparently when outsourcing data science work. A good data science outsourcing provider will provide you with a comprehensive report containing all your project details. 

Engage members of your team

While outsourcing data science work, it’s always a good idea to let the members of your company know that an external provider is working on their project. Take advantage of this opportunity to keep your team informed and engaged. For them, this is an opportunity to learn new methodologies and tools.

To summarize, here are some tips to help you outsource data science:

  • Work with a company that has the skills needed to complete your project. This may take some research on your part.
  • Choose an outsourcing company that can work independently and delivers top-level solutions. Maintain good communication between you and your provider; this will help you remain informed about all process stages. 
  • It’s essential to make informed decisions when outsourcing data science work; otherwise, there may be significant delays and extra expenses incurred due to poor management.
  • It’s a good idea to keep the members of your team involved in the process. This way, they will be more engaged in and excited about the project. 
  • The goal is to get as much insight from data before making business decisions or investing money into any solutions that may not necessarily bring returns.
  • A good outsourcing company will become an extension of your team, and its expertise should be available for you whenever you need it. 
  • Pick a company that communicates clearly and transparently when outsourcing data science work.

We would love to hear from you. For more information on data science outsourcing, click here.

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