WE GUIDE OUR PARTNERS FROM FIRST IDEAS TO DATA CHAMPIONSHIP
At OmegaLambdaTec, we see ourselves as Data Science and AI ecosystem partner dedicated to developing and implementing groundbreaking data-driven innovations. Realizing value from data requires the orchestrated collaboration of specialized competencies.
Our role in the data economy focuses on providing leading expertise and tailor-made DeepTech components in the competence areas of data analytics, artificial intelligence, machine learning, simulations, digital twins, and synthetic training data. The diverse benefits for our partners and clients through our forward-looking Algorithms-as-a-Service business model are evident:
- Faster – from idea to market readiness in under 6 months
- Better – leading algorithms for enhanced value
- More innovative – fresh ideas and Data Science solution approaches for your challenges
- Lower risk – combined experience from over 200 developed Smart Data solutions
- Cost-effective – use our Data Science team instead of building your own
- More focused – every partner concentrates on their core competencies
OUR RANGE OF SERVICES AND APPROACH
Using our established phased model, we guide your company from the initial steps and ideas for data-driven use cases to becoming a leading Data Science & AI champion.
Phase 1: As part of our Data Science & AI workshops, we collaboratively identify, evaluate, and prioritize relevant use cases for your company and create a tailored AI roadmap.
Phase 2: Data exploration and data discovery studies yield rapid results when the feasibility of a solution concept is being investigated and sufficient data quality and completeness need to be validated.
Phase 3: During the prototype and demonstrator development, the entire solution approach is worked out, all necessary algorithms are developed or adapted, a functional version is implemented and tested, and the business potential of the solution is evaluated.
Phase 4: With the complete development of the Smart Data solution, the new application moves into operational implementation and subsequently generates the targeted added value.
Phase 5: As the ultimate challenge for the data economy, we also work with you to develop innovative, scalable, data-driven products and new smart data services.
AGILITY, SPEED, AND EXPERIMENTAL APPROACHES FOR SUCCESSFUL DATA INNOVATIONS
We follow a data-driven, Data Science-based approach using scientific methods and a deep understanding of the problem domain. The approach starts with model and theory-based solution concept (Physical Analytics) and supplements these with machine learning and other artificial intelligence (AI) techniques when they contribute to improving the solution. In general, we combine theoretical-physical solution concepts with rigorous data analyses approaches and advanced simulations. This approach ensures maximum transparency and interpretability of the results, which is often a necessary prerequisite for well-founded business decisions.
Our approach and project implementation is agile, experimental, and iterative, involving the rapid development of prototype algorithms and interim results that are continually validated, reviewed, and improved. All algorithm and software developments take place within the Python framework, making software code and resulting tools universally applicable and scalable.
Implementation phases are divided into agile development cycles typically lasting two weeks. After each development cycle, regular video conference meetings are held to present current development results and align on the next steps with the project team. New development phases typically commence with a kickoff workshop to plan the implementation roadmap with the entire project team. Additional working meetings in smaller groups may be conducted as needed to discuss details such as data status, interim results, or user feedback.
WE TRANSFORM YOUR RAW DATA INTO REAL BUSINESS VALUE
The OmegaLambdaTec team combines unique data science expertise in the fields of data analysis, physical analytics, simulations, machine learning, and deep learning.
We are leaders and award-winners in the fields of advanced analytics:
- data-driven forecasting,
- anomaly detection and classification,
- simulation-based optimization,
- automated image processing,
- digital twin and scenario simulations
Many of the innovative Smart Data solutions combine various of these methodological approaches and core data science topics to achieve optimal results for the specific task and objective. For a large number of our Smart Data solutions, at least the following interconnected development steps are required:
- Evaluation of data sources, data availability, data quality, and automated data correction for raw data.
- Comprehensive representation of all relationships, connections, and boundary conditions relevant to the target questions.
- Theoretical development of a consistent mathematical model and quantitative relationships for the use case.
- Implementation of a suitable simulation or optimization framework for the optimal solution of the use case, considering all relevant dependencies.
- Calibration and validation of the simulation or optimization model using existing historical data.
- Examination of all relevant solution scenarios, evaluation of the result quality, and quantification of the business case.
OmegaLambdaTec’s mission is to develop and implement leading, customized Smart Data and AI solutions for the data-driven use cases of our corporate clients and partners. Our strengths and unique selling points become particularly evident when innovative use cases place the highest demands on data science methodology, require novel approaches, involve complex interdependencies, or entail challenging data limitations.
COMBINED DATA SCIENCE AND AI METHODS FOR OPTIMAL RESULTS
Overview of Data Science Methods:
Top-left figure:
Physical analytics model for analyzing the temporal evolution of drinking water temperature in the network.
Figure (top right):
Machine learning model for the automated prediction (blue curve) of the gas price (black) for the next day.
Bottom-left figure:
Simulation of synthetic fill-level data from used-clothing containers to evaluate the business case and solve the route optimization problem.
Figure, bottom right:
Deep learning method for the identification and extraction of tunnel section information from hand-drawn maps.
Examples of advanced analytics approaches for fully automated, real-time data processing:
Top right figure: Real-time optimization of decentralized energy systems.
Figure top middle: Correction algorithm for sensor data with some incorrectly transmitted measured values.
Figure top right: Real-time optimization of the entire production process for green hydrogen.
Bottom-center figure: Anomaly identification in the data stream.
Inspiration and insights from data science and AI pioneers
Naturally, we are also happy to provide—upon request—keynotes, presentations, seminars, or webinars that offer inspiring insights and outlooks regarding the present and future of data analytics and artificial intelligence. Our presentations deliver a concise, expert, engaging, and practical overview of how data science and AI can be applied across various industries and subject areas. The focus is always on concrete, topic-specific use cases, best practices, success stories, and real-world experience. Please feel free to submit a no-obligation inquiry regarding topics, content, and terms for a presentation at your specific event. (àContact form)
DATA SCIENCE EXPLAINED – FROM RAW DATA TO SCALABLE BUSINESS SOLUTION
Additional videos on the subject of data science solutions
Where does the name OmegaLambdaTec come from?
OLT phase model
Data Discovery Phase
Demonstrator and Prototype Development
Range of Data Science Methods
Blackbox, Greybox, Whitebox.
Data.Science.Business.
Amount of data required for good results
How do we deal with poor data quality?
The role of uncertainty and variance in planning
What is a precision measurement?
What are the connections between astrophysics and image processing?
GLOSSARY OF KEY TERMS
Big Data: Big Data refers to datasets that are too large, too complex, or change too rapidly to be analyzed using manual or traditional data processing methods. Big Data is characterized by at least one of the “4 Vs”: large (data) volume, high complexity (variety), rapid speed (velocity), and/or questionable quality or truthfulness (veracity).
Data Science: Data Science generally refers to the extraction of knowledge from data.
Deep Learning (DL): Deep learning refers to a machine learning method that employs artificial neural networks (ANNs) with numerous intermediate layers (hidden layers) between the input and output layers, thereby developing a complex internal structure. Deep learning is a specialized method of information processing.
Artifical Intelligence (AI):Artificial intelligence (AI) is a subfield of computer science concerned with the automation of intelligent behavior and machine learning.
Machine Learning (ML):Machine learning is an umbrella term for the “artificial” generation of knowledge from experience: an artificial system learns from examples and is able to generalize from them once the learning phase is complete. To achieve this, machine learning algorithms construct a statistical model based on training data. This means the system does not simply memorize the examples but instead identifies patterns and underlying principles within the learning data. Ideally, this enables the system to evaluate previously unseen data as well.
Physical Analytics: Physical analytics refers to the integration of data analysis and physics—specifically, the combination of theory-based physical models with statistical data analysis methods. The better the theoretical physical understanding of a data-driven problem, the more transparently the results can be explained, while simultaneously significantly reducing the requirements regarding the quantity and quality of available training data.
Smart Data: Smart Data represents the full extractable information content and added value derived from raw data; this can be harnessed by applying the best combined analytical methods and algorithms to all available data sources relevant to the specific task at hand.
