Contact us
Login

Data Science & AI

Use statistics, machine learning, and AI to gain insight into complex challenges and make better-informed decisions.

From data to better decisions

Data is everywhere. Organizations have access to ever-growing volumes of information from processes, systems, machines, customers, and markets. Yet more data does not automatically lead to better decisions.

The challenge is not collecting data, but turning it into insights that help you make the right decisions.

At CQM, we have been using mathematics, statistics, and data analysis for more than 45 years to provide insight into complex challenges. We combine classical statistical techniques with modern data science and AI methods. Not because a particular technique is popular, but because it is the right fit for the question that needs to be answered.

Every situation requires a different approach. Sometimes, a straightforward statistical analysis provides sufficient insight. In other cases, advanced machine learning models, deep learning, or text analytics are needed. The key is to select the right method and translate the results into practical applications.

260605 CM Placeholder

Statistical analysis

Many challenges start with understanding what is happening and why.

Using statistical analysis, we examine relationships, differences, trends, and uncertainties in data. We use techniques such as regression analysis, analysis of variance (ANOVA), hypothesis testing, and experimental analysis to uncover patterns and support decision-making.

Statistical analysis helps organizations with areas such as:

  • quality improvement;
  • product development;
  • process optimization;
  • complaint analysis;
  • risk assessment;
  • fact-based decision-making.

Statistical techniques often provide the foundation for further analyses and models

Machine Learning & Deep Learning

When patterns in data become more complex, machine learning techniques can help uncover relationships that are difficult to capture with traditional models.

Machine learning is used for applications including:

  • forecasting;
  • classification;
  • pattern recognition;
  • anomaly detection;
  • decision support.

For specific applications, such as image recognition and complex predictive models, we use deep learning.

One example is our collaboration with VolkerRail and Inspectation, where self-learning image recognition algorithms were used for rail inspections. This solution made the inspection process significantly more efficient and made an important contribution to infrastructure reliability.

We do not see AI as an end in itself, but as a tool for enabling better decisions.

260605 CM Placeholder

Text Analytics

Organizations have access to large volumes of unstructured information in the form of customer feedback, reports, reviews, emails, and other text sources.

With text analytics, we turn text into actionable insights.

Depending on the challenge, we use techniques such as:

  • sentiment analysis;
  • topic modeling;
  • classification models;
  • Natural Language Processing (NLP);
  • Large Language Models (LLMs).

These techniques help organizations uncover patterns, needs, and risks that would otherwise remain hidden in large volumes of textual information.

260605 CM Placeholder

Big Data

Not every challenge requires big data. Sometimes, relatively small datasets provide all the insight you need.

When the volume, velocity, or complexity of data requires it, we have the knowledge and experience to analyze and model large-scale datasets as well.

We look not only at the data itself, but also at data quality, the origin of the information, and the reliability of the conclusions drawn from it.

For us, big data is not an end in itself, but one of the tools we can use to solve a challenge.

Model development and validation

Models play an increasingly important role in decision-making. They support investment decisions, capacity planning, risk analyses, and operational processes.

However, the value of a model depends on its reliability.

That is why we pay close attention to:

  • model development;
  • validation;
  • verification;
  • monitoring;
  • lifecycle management.

We also conduct independent Model Reviews for organizations that want greater confidence in the quality, performance, and applicability of their existing models.

AI in practice

Successful data science projects are about more than algorithms.

The greatest challenge often lies in combining data, domain expertise, and practical experience. That is why we work closely with our clients to develop solutions that are not only technically sound, but also actually used in practice.

We believe a model only creates value when it leads to better decisions and measurable improvements in practice.

From insight to impact

Data science and AI offer organizations an expanding range of opportunities to better understand complex challenges, predict future developments, and improve processes.

At CQM, we combine more than 45 years of experience in statistics, modeling, and data science with deep domain expertise and a pragmatic approach. This is how we help organizations turn data into insights, insights into decisions, and decisions into results.

Logo CQM