Adapt your business successfully today for tomorrow.
Data Science & Machine Learning
With data science and machine learning, you will be able to automatically identify patterns and deeper relationships in your data, enabling you to make predictions about future events with their respective probabilities. These unprecedented insights into your data sets and the predictions derived from them enable you to make better business decisions for the future, allowing you to respond to tomorrow's events today.
By using Data Science and Machine Learning, you can, for example, analyze, interpret and forecast customer behavior or market trends in real time, predict and avoid errors, automate repetitive and manual tasks, control autonomous systems and make decisions and, of course, identify new growth opportunities.
Data Science and Machine Learning comprise a versatile repertoire of advanced analysis methods. In this process, business-relevant data is systematically evaluated using intelligent algorithms. The patterns and trends contained therein can thus be identified and captured in the form of mathematical models. The analysis methods used range from descriptive and inductive statistics and explorative data visualization to readable machine learning algorithms for clustering, anomaly detection, regression and association analyses, and classification methods.
Data Science and Machine Learning comprise a versatile repertoire of advanced analysis methods. In this process, business-relevant data is systematically evaluated using intelligent algorithms. The patterns and trends contained therein can thus be identified and captured in the form of mathematical models. The analysis methods used range from descriptive and inductive statistics and explorative data visualization to readable machine learning algorithms for clustering, anomaly detection, regression and association analyses, and classification methods.
Benefits of Data Science und Machine Learning
Increase productivity
By identifying trends and predicting scenarios, errors or failures can be avoided, quality can be improved, and systems can be better utilized and optimized.
Automate tasks
Enables you to automate repetitive and manual tasks, resulting in greater efficiency and accuracy.
Get predictions
By predicting complex business interrelationships, potential future scenarios can be calculated, which means that you can get today answers to tomorrow’s questions.
Increase innovation
Data science and machine learning support the innovation process by interpreting data through complex patterns in unexpected ways. With the resulting insights, new ideas can be better generated and identified. Prototypes can also be better implemented by using feedback and data in real time.
Customization
Automated user profiles can provide personalized recommendations and services, which can lead to better customer satisfaction, increased revenue, and improved engagement.
Our approach for your Data Science application
When implementing analytical projects, we are guided by proven and adaptable process models (CRISP-DM) that best reflect the iterative and agile nature of such a complex data science process while ensuring the greatest possible transparency and predictability for you.
1
Use case & data understanding
In this step, we evaluate the question with you, identify the concrete use case and define the common goal. This also includes determining the acceptance criteria, which we review in detail during the evaluation of the model. We will also identify the available data and examine it exploratively for data quality, content and processability, and evaluate the technical feasibility. Furthermore, based on your questions and requirements, we will select the appropriate methods and techniques, such as artificial intelligence (AI), in order to find patterns, trends and dependencies in the available data and make them usable. From this, the project plan and schedule for your data science project is defined.
2
Data preparation & data modeling
The data you defined previously is now selected, merged from the source systems and cleaned. This is followed by feature engineering, i.e. the pre-processing and transformation of the data in order to be able to generate meaningful results from the machine learning model. We create the data model(s) according to the selected machine learning method. In addition to high prediction quality, the focus is also on the practicality and robustness of the model.
3
Evaluation & deployment
After the model has been created and optimized, we evaluate the model with you using the defined criteria and decide whether it is suitable for the corresponding use case and leads to sufficient informative value. In addition to the predictive accuracy of the model, we also take a closer look at the overall process - starting with data availability, data quality, modeling, and the insights that can be derived from it. In the final step, we integrate the previously created and trained model into the IT infrastructure or into existing BI applications. In addition, we support you in the regular maintenance and optimization of the model in your IT infrastructure and train your users for a smooth use of the Data Science application in your company.
We support you
in the area & range,
you need.
Data Mining
We support you in the systematic application of artificial intelligence (AI) methods to find patterns, trends and dependencies in existing data sets and make them usable.
Machine Learning
We support you in the automated modeling of statistical correlations based on extensive training data (machine learning).
Predictive Analytics
We support you in the tailored use of advanced analysis and forecasting methods.
Deployment
We support you in the comprehensible evaluation and preparation of the gained insights and prototypical models and the integration into the IT infrastructure or the already existing BI application.
Data Mining
We support you in the systematic application of artificial intelligence (AI) methods to find patterns, trends and dependencies in existing data sets and make them usable.
Machine Learning
We support you in the automated modeling of statistical correlations based on extensive training data (machine learning).
Predictive Analytics
We support you in the tailored use of advanced analysis and forecasting methods.
Deployment
We support you in the comprehensible evaluation and preparation of the gained insights and prototypical models and the integration into the IT infrastructure or the already existing BI application.
the right solution for any problem
Data Science in your company
We show you how you can benefit from Data Science and support you from the development of use cases to the implementation of proofs-of-concept and the integration of the analysis solution into your productive system. Of course, we take into account the process models, reference architectures and software tools already established in your company. From the very beginning, we involve your experts with their knowledge and experience in the Data Science process to think outside the box not only technically but also professionally and to uncover further potential.
Data Science
Application examples
- predictive maintenance: preventive maintenance using machine learning techniques ( real time alert on wear, crack, fault or failure, predicting life expectancy of equipment/machinery, defects and malfunctions)
- logistics: prediction of delivery time and real-time tracking
- inventory management: detection of demand trends, automatic adjustment of stock levels
- customer analytics: elaboration of potential customer groups, products and services
- customer lifecycle analysis to optimize customer relationships
- fraud detection: detection of fraud cases
- topic modeling: classification of texts based on topic areas
- biological and medical research: bioinformatics analysis and evaluation of sequencing data, machine learning in the field of early disease detection