Outsourcing Data Science Services: Everything You Need to Know

Outsourcing Data Science Services

Most companies deal with multiple data sources. To give a number, in a survey conducted by Matillion and IDG, the average number of data sources per organization is estimated to be 400. Though 400 is average, more than 20% of the companies confirmed drawing data from 1000 or more resources. You can imagine the massive amount of data being gathered per day by companies across departments.

But why do companies collect data? Are they critical to the business?

Data is extremely critical to the success of a business. In fact, they empower companies to better their key strategic initiatives. A survey commissioned by Deloitte Analytics interviewed senior executives at 35 companies in the United States, Canada, China, and the United Kingdom, where 49% of respondents asserted better decision-making capabilities are the key advantage of data and analytics.

This is where data science comes into the picture. It is an interdisciplinary field that deals with vast volumes of data using scientific methods, processes, algorithms, and systems. In simpler terms, companies utilize this domain of study to identify patterns and extrapolate business-critical insights from noisy data sets.

With such benefits and popularity comes a high demand for professional experts (data scientists) in the field. The shortage of skilled talent in the region and cutthroat competition among companies to hire the best data scientists has led companies to outsource data science services.

This article aims to shed light on the benefits, drawbacks, and do’s and don’ts you must consider when outsourcing data science projects.

So without further ado, let’s get started!

How is data science beneficial for your business?

Today’s businesses are awash with data, and they must understand its enormous value to fuel growth and innovation. Thus, making outsourcing data analytics a top priority for businesses. Here are the benefits of data science in your business:

Improves forecasting

Data science enables companies to predict future trends by considering historical data. Data scientists use the company’s historical data to build a mathematical model that captures crucial trends. Later the same model works on the current data to identify trends or suggest actions for the best possible outcome.

The linear regression model and time series analysis are two example models widely used for prediction and forecasting. Such models can immensely improve decision-making processes across the entire organization.

Facilitate adoption of best practices

Data science models can empower employees to extract insights and drive data-driven actions. This ensures the staff is familiar with the enormous value and various utilities hidden behind data. Ultimately leading to the application of best practices and approaches when addressing key business challenges.

Fosters innovation

Data science concentrates on testing and learning; the path between the two lays a good foundation to nurture curiosity through tinkering. Stumbling upon different patterns, phenomena, and anomalies is quite common, which can evoke new insights, questions, or hypotheses.

The thing to note is that data scientists are a curious bunch, and data science, as a field, devices an environment where these professionals can unleash their curiosity and foster innovation.

Identifies new opportunities

No matter the company's size, data science empowers businesses to question the existing processes and improve them whenever needed. It constantly and continuously paves the way to identify new opportunities where companies can invest and get a positive outcome.

In fact, data science ensures companies are up-to-date with the rapid changes in industry trends, internal resource costs, profit/loss expectations, resolving bottlenecks, and improving the performance of the business model.

Supports sales and marketing campaigns

Sales and marketing efforts rely entirely on user behavior. Sales and marketing campaigns would be effective if companies have accurate data on what the users want. With improved efficiency in investments, companies can ultimately improve business performance.

Why should you outsource data science projects?

Effective data science models can also make risk mitigation a breeze, along with the above-mentioned benefits. With the right professionals on board, companies can implement effective measures against fraud and cyber-attacks. They can detect any possible attack and other anomalies with enough time to take actions that ensure strict company security and better performance.

With security being one of the major concerns for companies, data science automatically becomes a priority. However, there are certain challenges that might restrict companies from getting started with their data science projects.

Challenges with data science projects

  • Data science is complex - Without a doubt, data science is complex. Data scientists need to gather data from multiple sources, prepare them to improve quality, ensure security to minimize data vulnerabilities, build models, and implement them across the organization. Even a small bug or error can lead to grievous setbacks.

  • Shortage of skilled talents - Hiring the right data scientist for your team can be challenging. With a limited talent pool and companies fighting to hire the cream of the crop, your options to hire competent and experienced data scientists deteriorates significantly.

The only option to overcome these challenges is through data science outsourcing. Companies can partner with third-party outsourcing service providers with the expertise and resources to take charge of their data science projects.

Benefits of outsourcing data science

It is always a great idea to first weigh all the pros and cons before deciding on outsourcing data science projects to a third-party service provider. In fact, you cannot completely tackle the question “Why should you outsource your data science requirements?” until and unless you don’t contemplate all the for and against considerations for outsourcing.

Let’s first understand the benefits of outsourcing data science:

A great opportunity for business growth

If your goal is to boost the overall growth and scale of your company without having to climb the slow and tedious ropes of managing infrastructure, onboarding in-house teams, and maintaining all the prerequisite. Then outsourcing is might just the key you are looking for.

Even though hiring an in-house team can have its advantages, it can be detrimental to the company. You might even have to spend more time and resources on tasks unrelated to core business functions which can stall business growth.

A chance to work with top experts

By outsourcing data science services, you can simply take the assistance of third-party agencies with decades of experience and expertise in the domain. This can be immensely beneficial if your project is complex and requires experts in the key areas to tackle the challenges.

In fact, before going ahead with an outsourcing partnership, consider discussing and consulting with them on various data and analytics-related topics to understand their competence in leading the project to success.


Cost efficiency is the key reason companies outsource. After all, the bottom line of any business is to gain as much profit as possible. When you outsource data science, you significantly simplify the team scalability process. Since you won’t need to manage the challenging and time-consuming recruitment and resource management tasks, the company can save money in the long run.

As per a study commissioned by Fayrix, companies can save up to 50-60% of their budget when outsourcing data science.

Improved efficiency and scalability

As mentioned earlier, outsourcing data science projects entails access to experienced experts in the field. Consequently, this improves efficiency, which can be highly beneficial for the company.

Furthermore, the outsourcing partner can extend or lower their resources to align with the goals and requirements. This means companies can scale up and down their operations as per the project requirements.

Focus on core activities driven by data

Outsourcing data science can considerably free up your schedule, which you can utilize to concentrate on core business activities. In fact, companies can seamlessly integrate data-driven insights into business processes to boost growth and attain a sustainable future.

Drawbacks of outsourcing data science

Despite there being unsurmountable benefits of outsourcing data science projects, you don’t want to go down this path until you understand the disadvantages that come with it. Here is the list of potential drawbacks of data science outsourcing:

Selecting the wrong service provider

This is the most crucial point that companies must be mindful of before committing to any type of outsourcing engagement. In fact, one wrong decision in selecting the outsourcing partner can lead to the project's failure. Many touts being the best in the industry, but the only way to succeed here is by being strategic. Even though the cost is the major factor, ensure to include cultural fit, communication skills, tech stack, and other relevant elements into your vendor selection process.

Communication gaps

Communication ensures both parties effectively collaborate. Even a small gap can lead to delivery delays, unresolved errors, and poor performance. That’s why when looking for data science outsourcing partners, assess their communications infrastructure during the negotiation stage. Ensure the communication flow is according to your standard, and beware of communication gap signs even after signing the contract and working together.

Lack of control

You must tread carefully when outsourcing data science projects. You are farming the work required to make your project successful, and you lose control over how these tasks are accomplished and monitored. However, the right strategies in place can help you overcome this challenge:

  • Know what to delegate
  • Keep detailed and precise documentation
  • Encourage active communication and transparency

Risk of data protection and compliance

Data scientists work with a large volume of data, and as data grows, so does the risk of vulnerability and non-compliance. That’s why before outsourcing data science projects, ensure to validate if the vendors have robust security practices. It is always a good option to make sure they meet your company’s internal security requirements.

Cultural and timezone differences

Without a doubt, software development is a communication-intensive industry, and lack of cultural compatibility, timezone differences, and language barriers can impact the performance of your outsourced data science project. The only way to address this challenge is by fostering an intelligent culture trend. You can do so by promoting international openness with the internal team and managing expectations and visibility with the service providers.

The do’s and don’ts of outsourcing data science

It is crucial to establish guidelines in order to successfully manage your outsourced service provider. Here are some do’s and don’ts that you must heed when outsourcing data science projects:


Do: Ask the right questions

Given the plethora of service providers, selecting the right one can be highly confusing. To minimize risks and potential costs, you must ask the right questions and make informed decisions. Here are a few questions that you can begin with:

  • Whether or not they have a positive track record in the industry?
  • Do they have skilled and experienced teams who can successfully lead your project?
  • Do they have any portfolios and references?
  • What tech stack they’re using?
  • Does their company culture aligns with yours?

Do: Protect your data

Data is critical and can be damaging to the company if compromised. Implement best practices and guidelines for data breach prevention. For instance, execute regulatory compliance, involve legal backup, invest in advanced security solutions, lay down administrative measures, and conduct regular security audits.


Don’t : Concentrate entirely on the cost

You must not automatically select the cheapest option. After all, quality work comes with a price. You must do your due diligence and look at all the aspects before you decide on partnering with a particular data science outsourcing service provider.

Don’t: Block access to crucial information

You will have to proceed with caution when outsourcing data science. Find the right balance between protecting your data and providing access to essential information. Establish the right safeguards and form an exit strategy once you end the partnership.


Data science outsourcing can result in significant cost efficiencies amplified by developing an adaptive, long-term, and iterative relationship with the service provider. However, understand that a successful deployment of data science outsourcing requires careful consideration of all aspects related to it. Furthermore, you must ensure the organizational readiness for change and the risks and rewards associated with the outsourcing model.

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  • Anupriya Singh

    Anupriya Singh

    Anupriya is a content writer well-versed in researching and writing on an array of topics. She works closely with businesses and helps them get rapid and organic growth through compelling digital marketing content. When not working, you can find her reading or sketching.

Frequently Asked Questions

Ensuring your data science projects are being handled by someone who spends their time immersed in the field will give you the desired result. So, if your team cannot handle such specialized tasks, it only makes sense to outsource your projects to such industry experts. Hiring in-house employees are expensive, so delegating your tasks to outsourced teams can significantly cut operational expenses.

Here are some of the benefits of outsourcing data science:

  • Business growth
  • Work with industry experts
  • Cost-effective
  • Improved efficiency and scalability
  • Focus on core business activities

Data science enables companies to create reports, generate forecasts, analyze industry trends, and develop user-centric products/services with the help of data. Experts in the field collect and assess business data and develop models to improve performance and boost growth.

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