Advanced Analytics comprises a versatile repertoire of advanced computer-based analytical methods. Data relevant to your business is systemically evaluated by intelligent algorithms. The patterns and trends contained therein can thus be identified and recorded in the form of mathematical models. These analytical methods range from descriptive and inductive statistical methods and exploratory data visualisation to powerful machine learning algorithms for clustering, anomaly detection, regression and association analyses, as well as classification methods.
With Advanced Analytics, you can use models to automatically identify patterns and correlations in your data, enabling you to forecast future events with their respective probabilities. These unprecedented insights into your data and the forecasts derived from them enable you to react today.
Benefits Advanced Analytics
- Pattern recognition in the data
- Identify correlations and anomalies
- Trend spotting
- Predict complex business correlations
- Calculate potential future scenarios
Advanced Analytics in your company
We will show you how you can benefit from Advanced Analytics and support you starting with the development of use cases, the implementation of proof of concepts and the integration of the analysis solution into your production system. In doing so, we naturally take into account the process models, reference architectures and software tools already established in your company and integrate your experts with their knowledge and experience into the data science process at the start, in order to think outside the box and uncover further potential, not only technically but also professionally.
We support you with:
- the use of systematic application of artificial intelligence (AI) methods to find and utilise patterns, trends and dependencies in existing data (data mining)
- the automated modelling of statistical correlations based on extensive training data (machine learning)
- the custom-fit application of advanced analysis and forecasting procedures (predictive analytics, deep learning)
- the clear evaluation and processing of gained insights and prototypical models
In implementing analytical projects, we are guided by tried, tested and adaptable process models (see CRISP-DM), which best reflect the iterative and agile nature of such a complex data science process and ensure the greatest possible transparency and predictability.
Technology stack and methods in the area of Advanced Analytics
- Programming languages: Python, R, MatLab
- Libraries: Pandas, SciKit-Learn, PySpark, TensorFlow, NLTK, Tidyverse, Shiny, ggplot2
- Methods: Regression, Entscheidungsbäume, Support Vector Machines (SVM), Neuronale Netze, Clustering, Zeitreihenanalyse, Forecasting
Use Case in the area of Advanced Analytics
- Preventive maintenance with the help of machine learning methods (prediction of the life expectancy of machines, defects and malfunctions, real-time alarm in case of wear, crack, fault or failure)
- Logistics: delivery time forecasting and real-time tracking
- Inventory management: Recognition of demand trends, automatic adjustment of inventory levels
- Customer Analytics: Development of potential customer groups, products and services
- Customer Lifecycle Analysis for the optimization of customer relations
- Fraud Detection: detection of fraud
- Topic Modeling: Classification of texts based on the subject areas
- Biological and medical research: bioinformatic analysis and evaluation of sequencing data, machine learning in the field of early disease detection
How can we support you?
Do not hesitate to contact us! We look forward to your message.
