Data Analytics Services

The analysis of data often forms the basis for new business models. With our data analytics services, we help you to correctly evaluate internal and external (real-time) data with the help of artificial intelligence and to develop new products and services for a data-driven business. Machine learning is an important building block in this process. 
Using data analytics and building machine learning models, we offer complex analyses from image recognition to social listening. 
Depending on your requirements, we create models and can automatically analyze huge amounts of data using artificial intelligence. We not only create the models but also cover the entire analysis process from data acquisition to processing and delivery, whether one-time delivery or daily data monitoring.  We fully customize the solution to your needs, whether you want to have your data simply sent as a download link via Slack or Teams, or whether we feed it directly into a visualization tool of your choice. We can analyze web content independent of the source to evaluate the tonality (positive, neutral, negative) of articles related to image and reputation (products and services, innovations, service orientation, sustainability, etc.), or any topic that might be of interest to you. 

Social listening and online media evaluations

That means not only Twitter or news sites, but any text medium that can be found online. The analyses are adapted to general trends, special trends, or selected target groups in the shortest possible time, depending on your needs. 

Which data are you interested in?

You tell us your question and we develop the analysis for it. Our content analytics pipeline is implemented based on AWS cloud services. The model is continuously refined with DevSecOps methods and analyses millions of texts for thousands of customers every day. 

How data becomes knowledge - the increasing complexity of models

Model Building

Data is plentiful these days. Gathering the right combination of data, bundling it into meaningful information, and extracting valuable business insights is an art we have mastered. It takes experience to assess the business significance of data, select the most relevant and combine them into statistical models, verify them as well as refine them.  Verification is followed by implementation, taking into account performance, especially for real-time applications. Subsequently, we constantly re-evaluate and improve each model. In doing so, we take care to avoid the dreaded overfitting by overloading the model with too much data and ensuring its effectiveness. 

Model Design

We develop effective statistical models and you benefit from our experience with various machine learning techniques. 

Model verification

Verifications of models are essential for their practical use. We carry out the validation of machine learning models with care and conscientiousness with a view to further improvements. 

Model implementation and maintenance

We implement machine learning models using techniques that enable continuous development and improvement. 

It is not enough to collect data, it must also be seamlessly accessible

Data Layer Integration

Every company permanently collects a wealth of data. But only a few gain value-added insights from the existing data stock. This is mainly due to the wide variety of different systems, databases and data formats. With our expertise at your side, you can create the prerequisites for using your data in a meaningful way by setting up a data layer. You gain clear competitive advantages by making existing material from internal and external sources accessible, analyzable and manageable for your business decisions by means of the data layer.

From databases and warehouses to data layers

Data Layer Architecture

With the help of a sustainable data layer architecture, you can identify and integrate all relevant internal and external data sources.

Data Layer Implementation

We implement data layers for your needs and enable scalable and future-oriented data management.

Data Layer Management

The operation and management of data layers not only ensures the processing of large volumes of data, but also enables the data integration of other relevant sources.


Data Layer Architecture


Data Layer Implementation


Data Layer Management

Workspace solutions do not guarantee adequate usage

Workspace Monitoring

Workplace tools are a matter of course in digital work environments. But their efficient use is difficult to track. From traditional desktops to browser-based front-end applications to mobile clients, users’ problems tend to be similar. User satisfaction immediately drops as soon as something doesn’t work as expected – regardless of whether connectivity issues are the source of the difficulty, for example, workspace tools are blamed in the eyes of end users. At the same time, workspace solutions are not always used in the way they were intended. It is difficult to draw meaningful conclusions from user feedback, which is often delayed. That’s why workspace monitoring is elemental to qualifying complaints and effectively identifying and fixing application problems in your organization.

Workspace Instumentation

We set up instrumentation that captures data such as uptime and availability, as well as usage patterns and, where applicable, their economic value.

Workspace Intelligence

We create machine learning models to derive meaningful measures from data and increase employee satisfaction.

Workspace Optimization

With workspace monitoring, we enable continuous improvements in workplace functionality and increase employee satisfaction.

Let's start the future together.

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