Gathering and analyzing data insights is one of the ways for businesses to stay ahead of the curve in 2024. According to Deloitte, 68% of chief data officers across multiple industries view upgrading the way they use insights and analytics as one of their top three priorities. The increasing focus on data-driven strategies highlights the critical role of data insights in shaping business decisions, driving innovation, and maintaining a competitive edge in the rapidly changing market landscape. In light of this, Data Science outsourcing emerges as a viable option for enterprises lacking the required in-house expertise.

But how do you minimize risks while outsourcing Data Science services? And how to select a trustworthy partner to turn your data into a profitable asset? Let's find out.

What is Data Science outsourcing?

Due to the acute scarcity of expertise in the US, UK, and Western Europe, many enterprises outsource their data science projects to other locations instead of handling them in-house. Outsourcing allows businesses to quickly access top-quality Data Science, AI, and ML with the required skillsets rather than setting up your data science team from the ground up. Data science professionals can assist in establishing processes from scratch (such as developing analytical models or setting up data processing workflows for future projects) or evaluate existing data science strategies and suggest improvements. Here are the areas typically covered under Data Science outsourcing:

  • Natural language processing (NLP);
  • Machine Learning;
  • Computer vision;
  • Reinforcement learning;
  • Chatbots and conversational AI;
  • Predictive analytics;
  • MLOps;
  • Statistical analysis;
  • Data engineering;
  • Data modeling;
  • Data visualization and reporting.

5 key reasons for Data Science outsourcing 

Once you face the need to implement advanced data analytics or embark on complex data-driven projects, consider delegating these tasks to outsourcing firms, as did Pfizer, Netflix, and other global companies. Here are five main reasons why businesses are increasingly turning to outsourcing their data science operations:

  1. Easier talent staffing. Finding and hiring data science experts can be time-consuming and sometimes takes several months. In contrast, you gain access to a ready team of experienced and skilled data scientists by partnering with an outsourcing company. That way, outsourcing can significantly reduce the time and effort needed for talent recruitment.
  2. Top tech expertise. Data science professionals working for outsourcing vendors usually possess better expertise than their freelance colleagues. Such companies often have a collective learning environment that promotes knowledge sharing between team members. In addition, outsourcing vendors invest in the professional training of their specialists, encouraging them to take the latest courses, acquire certifications, and attend conferences.
  3. Improved scalability and flexibility. Outsourcing data science operations allows enterprises to scale their team up or down during the project based on their business needs.
  4. Access to advanced technologies. Companies specializing in Data Science often have access to the latest tools in the field. That way, businesses can leverage these technologies without significant investment in software, hardware, or licensing fees.
  5. Focus on core business activities. By delegating data science tasks to external teams, companies can focus more on their mission-critical operations.

Benefits of Data Science outsourcing

More on the topic: Data science consulting: Process, visualize, and monetize your data

Data Science outsourcing: 6 steps to get it right

Data Science outsourcing is a complex and strategic process that, when done correctly, can significantly boost a company's analytical capabilities and drive business growth. To navigate this process successfully, follow an approach that aligns with your specific business goals. Here are six essential steps to ensure that your outsourced data science project will be a worthy endeavor:

1. Identify your data science needs

The first step of partnering with a data science provider is clarifying your requirements. Understanding what you expect from your data science project is essential to achieving successful project outcomes. The most common requests from enterprises include:

  • Customer insights and segmentation. Uncovering patterns in customer behavior can help enterprises tailor marketing strategies, improve customer experience, and increase loyalty.
  • Predictive analytics for forecasting presupposes analyzing trends and patterns within the historical data to forecast future outcomes. By implementing predictive analytics, you can make informed decisions, boosting your business competitiveness in the market.
  • Operational efficiency optimization is the ultimate goal of many enterprises. Businesses aiming to identify inefficiencies and areas for improvement can leverage Data Science to analyze operational data and enhance productivity.
  • Risk management and fraud detection might be another reason for Data Science outsourcing. You might want to implement ML models to detect and mitigate risks, including financial fraud, cyber threats, or compliance violations.
  • Business Intelligence and reporting. Data science experts can help you create Business Intelligence reports and dashboards that provide actionable insights into various business aspects, aiding in strategic decision-making.
  • Personalization and recommendation systems. Integrating Data Science, ML, and AI to develop advanced personalization and recommendation systems can significantly enhance customer engagement and satisfaction by providing tailored content, product recommendations, or services.
  • Market sentiment analysis. Leveraging NLP and AI to analyze customer opinions and sentiments across various online platforms can provide invaluable insights. This analysis helps understand public perception of your brand, products, or services and can guide marketing strategies and product development.

2. Shortlist large and midsize tech vendors

When choosing among a myriad of Data Science service providers, we recommend opting for midsize and large enterprises. The vendors with 250+ engineers on board usually have access to more tech talent, which ensures quick project launch. In contrast, smaller companies might have limited HR capacities and be unable to scale your team promptly.

3. Review the tech expertise of your outsourcing vendor

So, you've selected several reliable Data Science services companies. The next step would be to check out the technological skillset of your potential partners. Your potential Data Science partner must have expertise in the following technologies:

  • Programming languages, including Python, SQL, Java, and Scala;
  • Python libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, Matplotlib, seaborn, SciPy, and PyTorch;
  • Data visualization tools: Tableau, Power BI, Qlik;
  • Big Data frameworks, such as  Apache Hadoop, Spark, or similar frameworks;
  • Machine Learning platforms: Azure Machine Learning, AWS SageMaker, Google Vertex AI Platform, Kubeflow, MLflow;
  • Cloud computing platforms: AWS, Azure, or Google Cloud Platform, including services for data storage, computing, and analytics;
  • SAS, SPSS, or other statistical analysis software
  • Deep Learning frameworks: TensorFlow, PyTorch, Apache MXNet;
  • DevOps and MLOps expertise: Docker, Kubernetes, and Jenkins for development and operationalizing ML models.

N-iX's proficiency in data science technologies

In addition to the basic set of technologies, your project might require expertise in other data science tools. If you are not sure about the set of technologies for your specific case, you might need to conduct a Product Discovery or request aid through consulting. Data specialists at N-iX have extensive experience in these services, so they can help you assess your unique requirements as well as identify the most effective technology stack for your project.

4. Look up the tech partner's industry expertise

Before starting the cooperation, research whether your potential partner has expertise in your or a similar industry. Strong domain expertise ensures that the vendor has a deep understanding of the compliances and regulatory requirements specific to your domain. You might also want to check their success stories to critically evaluate their capabilities in your business vertical.

5. Ensure top-level data security and compliance

Sensitive data exposure can lead to significant financial losses and reputational damage for the business. Since your potential Data Science services company will have direct access to your data, it is crucial that they have a robust data protection policy in place. One of the key indicators of a trustworthy vendor is adherence to international information security standards and regulations, such as ISO 27001 and PCI DSS. In addition, ensure that your Data Science services vendor has a Business Continuity and Disaster Recovery Plan to stay operational in case of any unplanned incidents.

6. Choose a vendor with a robust data science portfolio

Finally, we recommend opting for a tech company with an extensive track record of successfully delivered data science projects. Besides reviewing the success stories on the company's blog, you can consult Clutch, GoodFirms, or other rating agencies to read clients' reviews. Altogether, it will help you gauge the tech company's overall expertise and reliability.

Read more: Here’s how to find skilled Data Science experts and monetize your data

N-iX's success story: Cost optimization and enhanced user experience for a telecommunications enterprise

Let's now explore how N-iX helps enterprises make the most value of their data by looking at our success story. One of our clients, Gogo, is a leading global provider of in-flight Internet that has established partnerships with more than 16 commercial airlines. The company needed to ensure the high speed of the in-flight Internet as well as become able to forecast equipment failures, which led to paying penalties to the airlines.

Our approach

The frequent malfunctions of Gogo's satellite antennas resulted not only in financial losses but also led to significant operational issues. However, when the equipment was examined, no clear anomalies were detected. To help Gogo find a reason for antenna ill-performance, we launched a complex data governance project. First of all, we migrated on-premises data solutions to the cloud, enabling Gogo to gather and analyze a vast amount of data from over 20 different sources. In addition, we implemented a cloud-based unified data platform that collected and aggregated both unstructured and structured data. Following the cloud migration, our team applied the data science models (Gaussian Mixture Model and Regression Analysis) to decrease the no-fault-found rate and predict the antenna malfunctioning. Powered by data science and ML algorithms, we implemented models for predicting satellite antenna malfunctions and monitoring antennas' health. The Data Science solution provided recommendations regarding the antennas' ill-performance and the reasoning behind each recommendation.

Value delivered

  • Decreased operational expenses on penalties from airlines due to minimization of the Wi-Fi services' ill-performance.
  • Antenna failure prediction. Predictive analytics enables the forecasting of antenna failures with over 90% accuracy 20-30 days ahead. This ensures that maintenance of in-flight equipment can be scheduled for the most suitable time, such as when a plane does not have any flights planned.
  • Minimized no-fault-found rate. The rate of no-fault-found incidents decreased by 75%, leading to cost savings by reducing the need for unnecessary equipment removal and servicing.
  • Equipment failure prevention. We identified the reasons behind the antenna malfunctioning, which, in turn, allowed Gogo to mitigate them.
  • Reduced costs on licenses. By transitioning to the cloud, Gogo significantly decreased spending on licenses and on-premises servers.

Bottom line

In conclusion, outsourcing Data Science services offers a strategic opportunity for businesses to leverage expert skills, advanced technologies, and industry-specific insights without the overhead of developing an in-house team. However, enterprises should carefully select the outsourcing partner that would be able to transform their data into a powerful asset that drives business value. With 21 years of cross-industry experience and over 200 data experts, N-iX makes a valuable strategic ally. Here are the other reasons why you should partner with N-iX to boost your data science and analytics:

  • Versatile expertise. N-iX offers a wide range of Data Science services, from deep learning and computer vision to natural language processing, conversational AI, and MLOps.
  • Robust data protection policies. We ensure the strong protection of your sensitive data by adhering to ISO 27001, PCI DSS, GDPR, and other regulations and compliances;
  • Industry recognition. Our dedication to data engineering services was highlighted by ISG, which recognized N-iX as a Rising Star in Data Engineering.

Opting for N-iX as your partner for data science signifies placing your trust in a company that possesses a profound grasp of technological advancements and proven expertise. Let us guide your process of incorporating data science into your operations, transforming your data into actionable insights that drive innovation, optimize decision-making, and unlock new opportunities for competitive advantage in your industry.

Leverage data for your business success now

Have a question?

Speak to an expert
N-iX Staff
Valentyn Kropov
Chief Technology Officer

Required fields*

Table of contents