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Data Solutions

Can Data Science be outsourced? Benefits and risks

date: 19 July 2022
reading time: 9 min

Big data analytics is closely related to data science as it also involves studying large data sets 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 is an interdisciplinary field that uses methods from computer science, statistics, mathematics, and other fields to find useful information in raw and organised data and transform it into usable knowledge. It begins with acquiring raw data, cleaning and preprocessing it, and then modelling to uncover patterns or predict outcomes. The main goal of this process is to extract insights that help companies to make good decisions.

You will often hear about data analytics vs data science. While both fields work with data, the key data science and data analytics differences lie in their scope and focus. While data analytics is primarily concerned with analysing historical data to extract useful insights and identify trends, data science goes beyond just examining the past. It involves predicting future outcomes and proposing plans of action based on analysis.

While data analytics is retrospective, data science is both retrospective and prospective.

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.

Having an internal team can provide more control over data and reduce associated risks. It also allows for better insights into the company’s operations, leading to improved Business Intelligence. In-house teams are flexible and can quickly adapt to changes in the market.

However, many companies choose to outsource some data science services, regardless of their size. This may be for specific projects or to complement their in-house expertise.

For example, a business might hire external experts for a particular task or to take advantage of specialised knowledge in areas such as AI PoC Development or Cloud solutions. Partnering with outside experts can be simpler than starting and educating a new internal team from scratch. This approach helps businesses leverage the latest technology and understand how data scientists can help achieve their goals.

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 logically and mathematically.

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 vast benefits make this a valuable industry.

Why consider data science outsourcing: the challenges of building and sustaining an in-house team

Dealing with big data challenges can be a daunting task for businesses. Creating an in-house team implies hiring, training and continuously updating them. There may be times when there are numerous data science projects, while at other times, there could be none. This could lead to the team either wasting their time or getting overwhelmed with work.

Due to these challenges, some companies opt to outsource data science services. This way, they can get the required help only when needed. It’s like hiring experts without having to keep them on the payroll full-time. This decision can save both time and money and also bring in specialised skills for each project.

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 can access expertise they may not have in-house. This allows for more complex projects and better solutions 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 is responsible for hiring and maintaining the personnel required to get the job done.

  • It allows companies to scale

    Access to outside data science solutions allows companies to scale their operations up (or down) as needed. A good provider can expand or reduce their operation according to their client’s needs, making the whole process extremely useful.

  • It is highly efficient

    Working with external specialists with knowledge and expertise far greater than your in-house team helps 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 flexibilityUsing 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. You might 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. 

Read more about outsourcing data science:

Importance of data science in IT industry

How outsourcing data analysis can put your business in the fast lane?

How to choose the best data solution for you?

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 you previously enjoyed. 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 leaked or exposed, so they need to ensure that the outsourcing company has appropriate measures to guarantee that this information is secure. 

  • Difficulty with integration

    Integrating a company’s data effectively with its data science organisation can be tricky. 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 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 problems. 

The future of outsourced data science: trends and predictions

The future of outsourced data science appears to be on a trajectory defined by several key trends and predictions.

  1. Machine Learning and AI: There’s a growing demand for machine learning and AI expertise. Companies increasingly seek external professionals to help them integrate these technologies, enabling better data analytics and automation.

  2. Blockchain services: Expect to see a rise in the need for blockchain services. This technology’s potential for ensuring data integrity and security means more businesses might turn to it.

  3. Cloud computing: Many companies are shifting their IT systems to the cloud for its benefits like cost-saving, scalability, and security. This means more companies will likely outsource data solutions services to cloud providers in the future.

  4. Augmented Reality (AR) and Virtual Reality (VR): As AR and VR technologies grow, businesses might require expertise in processing and interpreting data generated from these platforms.

  5. Quantum computing: Quantum computing, though still emerging, promises unparalleled processing power. As it becomes more mainstream, there will likely be a need for specialised outsourcing in this area.

  6. Specialised data infrastructure: The need for tailored data infrastructures, like data lakes and warehouses, could grow. Companies might seek external expertise to design and maintain these systems.

  7. Automated data cleaning and preprocessing: As data grows in volume, automated methods for cleaning and preprocessing become crucial. Outsourcing services that offer these solutions could see increased demand.

  8. Integration of diverse data sources: With data coming from various sources, integrating them coherently is challenging for a software development company. There could be a rise in demand for data scientists who can merge information from different platforms and formats.

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

Once you have decided 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 for choosing the best data science outsourcing 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 line up with your company’s needs. 

  2. 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. 

  3. Look at their portfolio.

    It is important that you look at previous projects 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. 

  4. 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. 

Summary: is outsourcing data science right for your business?

Like anything in life, there are always downsides to any initiative and outsourcing your data science is no different. Nevertheless, there are many obvious benefits that can be taken from entering into the right collaboration: 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. We will create a development strategy unique to your business needs.

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