IT outsourcing for data analytics and ML: The trend driving smarter decision-making

Data is no longer something abstract or symbolic for today's companies: data is practically gold, the difference between success or failure in many strategies today.

For example, Google announced in 2025 that it plans to invest $5.8 million in Germany to build a data center. “Demand for European data centers is expected to increase by more than 50% between 2023 and 2027,” noted an article from Finimize, which highlights the growing importance of data in today’s world.

Alongside this reality, there is a shortage of trained or expert personnel in the data field. According to a McKinsey survey conducted in 2024, 77% of the companies surveyed stated that they lack the necessary data skills and talent to perform the required tasks in their organizations.

This leads us to the most attractive option for finding data experts quickly and with a business-appropriate investment: IT outsourcing.

Let’s start by explaining what IT outsourcing is

IT outsourcing is the practice where a company hires an external provider to handle technological tasks, projects, or services that would normally require an internal team.

These services can include: software development, data analysis, machine learning, infrastructure management, cybersecurity, technical support, DevOps, among others.

The goal is to quickly access specialized talent, reduce operational costs, accelerate projects, and allow the company to focus on its core business.

Example of IT outsourcing

Imagine a retail company that wants to implement a personalized recommendation system based on machine learning but does not have an internal team of data scientists.

Instead of hiring full-time staff, which can take months and require a large investment, they decide to hire an IT outsourcing company that already has specialists in data and ML.

This external company:

• Evaluates the retailer’s data
• Builds predictive models
• Implements the system in production
• Provides maintenance when necessary

The retailer gets the project completed faster, with optimized investment, and without increasing its permanent staff.

Key benefits of IT outsourcing in data and machine learning projects

IT outsourcing has become one of the most effective strategies for companies looking to develop data analysis and machine learning projects without facing internal talent limitations.

“Companies are increasingly prioritizing the outsourcing of data centers to improve agility, profitability, and sustainability of their IT systems,” stated an article published by Globe News Wire.

Below, we present the most relevant benefits that explain why this model has become a competitive advantage in today’s market.

Immediate access to specialized talent

The data field requires complex profiles — data scientists, data engineers, cloud architects, machine learning engineers — that are difficult to attract and retain.

With an IT outsourcing provider, companies can access already assembled and highly skilled teams without long recruitment processes. This reduces risk and accelerates the development of any analytical or AI initiative.

Reduced operational costs and greater efficiency

Hiring internal talent involves high fixed costs: competitive salaries, benefits, ongoing training, licenses, and tools. In contrast, outsourcing allows those expenses to become variable costs depending on project needs. This enables scalable and optimized investment, especially for companies with tight budgets or phased projects.

Faster project execution

Data analysis and machine learning require clear methodologies, experience, and mature processes to avoid delays. Specialized providers already have proven frameworks, structured data pipelines, and best practices that deliver faster results. For companies needing immediate outcomes, this factor can make a significant difference.

Constant technological updates

The evolution of data and ML tools is rapid. With new APIs, advanced models, cloud solutions, and modern architectures, staying up to date internally can be a challenge.

Outsourcing ensures that projects are driven by modern technology and talent that stays constantly updated, without requiring additional time or resources from the company.

Risk reduction and higher quality deliverables

Mistakes in data projects can be costly: poorly trained models, inefficient architectures, security gaps, or wrong decisions based on incomplete data. An expert provider brings quality standards, security practices, robust documentation, and multidisciplinary teams capable of anticipating errors. This increases the reliability of business outcomes.

Flexibility to scale based on demand

Data projects can grow or need adjustments at any time. With IT outsourcing, a company can increase or reduce the number of specialists according to the project phase, avoiding oversized teams or delivery delays. This agility is especially important in machine learning projects, where needs evolve as the model develops.

Altogether, these benefits make IT outsourcing a strategic tool for any company looking to efficiently harness its data potential and accelerate digital transformation.

How to choose an IT outsourcing provider specialized in data

Choosing the right partner can determine the success or failure of a data analysis or machine learning project. It’s not enough for the provider to have general technology experience — they must demonstrate deep expertise in the data ecosystem, modern methodologies, and the ability to adapt to business pace. 

Here are the key criteria for making an informed decision:

1. Proven experience in data and ML projects — Review their portfolio, success stories, and previous clients. A good provider should have a track record in building data pipelines, developing predictive models, and modern cloud architectures.
2. Available multidisciplinary talent — A data project cannot be executed by a single profile. Ensure the provider has data scientists, data engineers, cloud architects, ML engineers, and DevOps experts for comprehensive support.
3. Clear methodologies, frameworks, and best practices — The provider should work with mature processes: CI/CD for ML, MLOps, technical documentation, continuous testing, model monitoring, and proper data versioning.
4. Updated infrastructure and technologies — Assess whether they master modern tools like Spark, Kubernetes, Airflow, BigQuery, Redshift, Databricks, TensorFlow, or PyTorch, among others. This ensures scalable, long-term solutions.
5. Data security, compliance, and governance — Data projects require strict controls. Your provider should demonstrate high security standards, regulated data handling, GDPR/ISO compliance, and experience in data governance.
6. Service flexibility and scalability — Every project evolves. Your provider should be able to scale the team as needed and adapt to changes without delaying delivery.

Choosing a provider that meets these criteria ensures solid, predictable development aligned with business goals.

Use cases where IT outsourcing accelerates analytics and ML

IT outsourcing not only covers the talent gap — it also enables new scenarios where companies can adopt advanced analytics and machine learning at a pace that would be impossible with small internal teams. Some of the most relevant use cases include:

Recommendation and personalization systems — Retail, e-commerce, and content platforms use outsourced teams to develop recommendation engines that increase conversions and engagement.

Process automation through ML models — From credit scoring to anomaly detection, specialized providers can build robust models that reduce risks and accelerate decision-making.

Predictive analysis for demand and operations planning — Industries such as logistics, manufacturing, and energy turn to outsourcing to create models that anticipate trends and optimize resources.

Building data lakes and modernizing the data ecosystem — Companies often need external support to migrate infrastructure, integrate disparate sources, or create efficient data pipelines.

Implementation of chatbots, intelligent assistants, and generative models — Outsourcing enables the integration of conversational AI and generative models without having to build internal AI teams from scratch.

Monitoring and maintaining models in production — A model’s lifecycle doesn’t end at deployment. Outsourced teams also handle observability, recalibration, and continuous improvement.

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