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Data Science plays a vital role in securing competitive advantages, yet many organizations have yet to fully harness its potential.
Its core objective is to extract actionable knowledge from diverse data sources — enabling the detection of patterns and the prediction of future outcomes.

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Data Science is not just Science - It's Business Advantage

Most companies collect massive amounts of data in their day to day operations. Using adapted algorithms to unveil the underlying patterns and relations, important information can be extracted efficiently. This gives deeper insight into operation processes in a dimension that would be too complex for a human to process and yields a valuable basis for future decision making.

When applied effectively, Data Science empowers companies to for example optimize products, services, and internal processes, enhance customer satisfaction, and detect fraudulent activities early.

Our data science consulting is therefore tailored to the development and implementation of your use case: from identifying relevant strategies and selecting suitable tools to the final productive implementation of machine learning algorithms within productive systems.

From

Use Cases

Forecasts with Time Series Analysis

Reliable forecasting of future sales and other key performance indicators (KPIs) is crucial for a company's strategic management and planning. This requires analyses of chronologically ordered data (time series). In addition to classic statistical models such as the ARIMA model, complex deep learning models (neural networks) are increasingly being used. Predicting key performance indicators has enormous potential for companies, as the insights serve as a sound basis for decision-making.

Predictive Maintenance

Predictive maintenance helps companies avoid costly downtime by using data to anticipate when equipment is likely to fail. Instead of relying on fixed service intervals or reacting to breakdowns, AI-powered systems analyze real-time sensor data to detect patterns and predict issues before they happen. This enables timely interventions, reduces unnecessary maintenance, extends asset lifespans, and ensures higher productivity—making it a powerful tool for industries that rely on machinery or infrastructure.

Fraud Detection

Many companies suffer from fraudulent activities, some of which result in enormous costs. Fraud detection involves examining transactions to develop a model that identifies patterns that are most likely to lead to fraud. This model is applied to new and real-time data to detect and prevent future fraudulent transactions. Its use can help save avoidable costs.

Client Segmentation

Today, it is becoming increasingly important for companies to understand their customers' individual needs and respond to them effectively. Customer segmentation identifies relevant customer groups and categorizes them into meaningful segments. With knowledge of the similarities and differences between these customer groups, companies can develop targeted marketing and CRM strategies. Segmentation also provides a good foundation for developing further data science analyses, such as churn or sentiment analysis.

Churn Management

When customers churn, companies lose revenue sources and potential for upselling, cross-selling, and referrals. A churn analysis can determine a customer's churn rate, based on historical customer data. Furthermore, the churn analysis serves as an indicator for targeting customers with a high churn risk. This can prevent existing customers from switching to the competition and increase customer satisfaction.

Financial Organization

Through the digitalization of processes, companies have access to vast amounts of data on their customers, markets, and products. Transforming these data volumes into meaningful insights and competitive advantages for companies and fully exploiting the potential of big data requires a well-thought-out digital strategy. Using statistical methods and models from machine learning, we work with you to create the foundation for sound business decisions and faster processes in financial controlling.

Supply Chain Management and Production

The increasing digitalization of production and the supply chain is already generating enormous amounts of data. The intelligent linking and enrichment of data with data science methods results in numerous use cases that generate real added value in terms of transparency, optimization, and automation. We work with our customers to implement high-quality data products in the areas of supply chain management and production.

Recommendation Systems

A recommendation system is designed to suggest the most relevant items to users from a large set of objects (e.g. past purchases and product characteristics). Relevant suggestions are generated by a machine learning algorithm that tracks user behavior. By recommending suitable products, the customer experience is sustainably improved in an online store. Furthermore, a recommendation system can support employees in their daily work, leading to streamlined processes and lower costs by helping them find relevant documents more quickly.

Natural Language Processing

Companies generate a vast amount of text documents every day. This can quickly lead to a loss of clarity. Furthermore, incorrect entries in unstructured text data often go unnoticed. By using text analytics and NLP, companies can efficiently examine text documents, analyze emails and other customer communications, automate form filling, and find relevant information in large data sets. This technology saves costs and allows employees to focus on the relevant tasks of their work.

Sentiment Analysis

Sentiment analysis is a method from the field of natural language processing (NLP). The analysis is used to determine whether customers have a positive or negative attitude toward a company's products or services. Through sentiment analysis, companies gain a better understanding of their customers and can respond quickly and effectively to customer requests or criticism. The results of sentiment analysis can be presented in dashboards and made available to relevant departments. The use of sentiment analysis supports decision-making through customer-based insights.

From Information to Automation

Turn insights into


Once we’ve extracted insights from your data, we help you go further: by building automation that turns those insights into action.

Our systems help you anticipate what’s coming, decide faster, and act sooner — without relying on guesswork or manual monitoring.

Read More about Automated Data Systems
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