In this day and age, data is a vital resource for the success of any organization. A staggering amount of 3.5 quintillion bytes of data is generated each day, a testament to the unprecedented information age in which we live. With a vast amount of data becoming available to organizations, a growing number of tools emerged to solve the problem of big data gathering, sorting, and interpretation. The rise of data-driven strategies has also spurred the emergence of data science services, including data science outsourcing. Given the fact that not all organizations have the necessary resources and tools to effectively conduct data analysis, they may turn to these services aimed to help business leaders understand how to effectively leverage these tools to make a sense of their data.  

At nCube, we build teams of data science developers in Eastern Europe and Latin America, which allows us to stay on top of the latest trends in the field. While many businesses in the USA, the UK, and Western Europe suffer from limited access to data science experts, the talent pools in Central and Eastern Europe, as well as LATAM can serve as a valuable resource to compensate for this shortfall. You can easily hire a provider of data science as a service like nCube to build your Data Science Team relying on a vast network of engineers. Additionally, these regions offer a cost-effective solution to address talent shortages in your business, without putting a strain on your budget.

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Before you outsource data science with us or any organization of your choice, let’s take an in-depth look into big data outsourcing and how it works. In this post, we review:
  • Data science outsourcing definition;
  • Data science services: A market overview;
  • Benefits and downsides of data science outsourcing;
  • Tips on how to make the most of outsourcing data science.

 

What is data science outsourcing?

Data science outsourcing is the practice of hiring an external provider of data science services to work on all or part of your data-related activities. If you add data science experts to your team, they will handle such tasks as data collection, cleaning, modeling, interpretation, and other data-related activities. 

Another area of expertise that’s gaining popularity is data science management consulting. This involves partnering with experienced data science professionals who can offer you guidance on how to manage and optimize your data operations and build data-driven strategies. 

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Another important question that will help us define data science outsourcing is – What motivates companies to delve into it? For many companies, outsourcing data science means killing two birds with one stone –  access to the desired skills without having to hire a full-time data scientist or train someone in-house and optimizing the budget. With that in mind, data science outsourcing can add a lot of value when it comes to reaping the benefits of your data. 

Outsourcing data science: Market overview 

The report by Grand View Research shows that the global market for outsourcing data analytics is set to grow at a breakneck speed, with a CAGR exceeding 22.8% from 2018 to 2025. This tendency demonstrates that businesses have a vested interest in data science outsourcing. However, the big stumbling stone is access to top AI experts and data scientists, which is outstripping the supply by far, making data science outsourcing a lucrative option. 

The US Bureau of Labor Statistics predicts that data science will outgrow all other fields by 2029. They anticipate that data scientist jobs will witness a whopping 36% growth from 2021 to 2031. The US Bureau of Labor Statistics predicts that by 2026, data science will generate an estimated 11.5 million job opportunities. The big question is whether the supply of data science experts will be able to keep up with the increasing demand for them, or if there will be a gap between the two, creating difficulties for companies looking to fill these positions. 

A recent search query on Indeed.com listed 11,642 job openings in the USA for data scientists experts, which upholds the assumption about the growing demand for this type of talent. 

The highlighted figures indicate that data science outsourcing is going to become more and more prevalent in the future. With that in mind, it makes sense to take a look at some pros and cons of data science services as a potentially commonplace offering in the future.

What are the benefits of data science services?

Data science outsourcing comes with many advantages, including:

Budget-efficiency: Outsourcing data science is in many cases a more cost-effective solution than building an internal data science team. By handing it off, you can take advantage of the service provider’s skills, experience, and resources while reducing the costs associated with recruiting, training, and managing employees. 

Access to expertise: Data science outsourcing can provide access to specialized skills and technologies that may not be available in-house.   The provider of data science services will provide a team well-versed in the latest technologies and advancements in data science. The provided team will quickly apply those skills to the project at hand, which includes:

  • Python, R, SQL, Java, and other programming languages;
  • TensorFlow, PyTorch, and Scikit-learn as the most common ML frameworks;
  • Hadoop, Spark, Kafka, and other big data technologies; 
  • Tableau, Power BI, and matplotlib as the most popular data visualization tools.

Scalability: By outsourcing data science, you can increase or decrease operations according to your needs. The provider of data science service provider like nCube will give you the elasticity to adjust the number of resources used to meet the changing demands of your business.

Focus on your goals: Thanks to outsourcing data science activities, you and your core team can dedicate more attention to your main activities, whereas the service provider will handle the technical aspects of the job.

New opportunities: By tapping into the expertise offered by data scientists, your business can gain insights that can help to make better decisions, increase sales, and improve customer loyalty as well as improve customer experience and create better marketing strategies.

Start your project right away: Outsourcing your project lets you launch your data science team fast. Instead of spending time on recruitment interviews, you can hand off all the administrative work to third-party providers and focus on what matters most to your business. 

Risks of data science services

Although outsourcing data science has numerous benefits, it’s important for businesses to be aware of the potential risks involved. By understanding these risks, you can take proactive measures and work together with your provider of data science services to mitigate them.

Reduced level of control over your project: One of the biggest concerns companies have when it comes to outsourcing data science is giving up control over the work being performed with their data. However, there’s a way to overcome this risk. With a provider like nCube, you will maintain full oversight of your project, retain control over your team in full and communicate with team members without any middlemen. 

Cultural differences: When businesses use data science services, some cultural barriers may arise, given that the service provider is located in a different region and stems from a different culture. Both parties need to understand each other’s needs and requirements effectively. We at nCube are a global company with expertise in establishing effective communication channels between distributed engineering teams. Additionally, we only add confident English speakers with experience working in diverse teams to ensure seamless collaboration.

Data protection: Data science outsourcing can raise security concerns as the service provider typically has access to sensitive company information. To mitigate these risks, businesses must ensure that they understand the security measures implemented by the service provider. At nCube, we will adhere to your company’s data protection standards and are ready to sign any necessary NDAs. 

Tips to hire data scientist services efficiently

When using data science as a service, a few tips can make the process smoother and more effective.

Determine your project needs. Before you kick off the search for a data science outsourcing provider, it’s essential to conduct thorough research to gain a comprehensive understanding of the skillsets your project needs. This will enable you to establish project requirements precisely and ensure your provider will help you build the right kind of data science team.

Focus on providers of data science outsourcing with the skillset. It is important to note that not all providers of data science outsourcing possess the same level of expertise. Therefore, it is crucial to seek out a provider with actual experience in building data science teams, which you can find by analyzing their portfolio and success stories

Inquire about processes and support during the project. It’s best to collaborate with a data science outsourcing provider that can help you establish the right kind of processes and communications. At nCube, we adopt a 100% commitment approach your provided data science experts function as an integral part of your core team, much like in-house employees. As your data science development provider, we prioritize team comfort and retention to ensure you work with the team for as long as you need it. 

Final thoughts

Data science is (and will remain) the lifeblood of modern business operations. By outsourcing data science, you can gain a suite of benefits, including budget optimization, access to large pools of tech talent, and a quick project launch, to name a few. We at nCube can help you source data science experts based in Central and Eastern Europe and Latin America and build your own remote team, as we did for such companies as AstraZeneca, doTerra, CrossEngage, Life360, and more. Contact us. 

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