Data-driven companies are more successful on their way through digital transformation. The right data, effectively recorded and processed can for example help to predict energy demand in industrial plants. It can also help to adapt the behavior of IT applications to real user experiences or generate groundbreaking data products for new business models. In short: data creates benefits and reduces costs.
Pilot phases for data products often run too long and focus too much on concept level. However, data projects live from trying, verifying, falsifying and adapting theses. Here it is always important to work closely on the data, to quickly find a feasible solution or to stop the pilot phase in a controlled manner if it is not feasible (fail fast).
The even greater challenge emerges as soon as a successfully piloted data product is targeted to go into production as MVP. Overnight, the data-focused pilot team finds itself in a full-blown software engineering project. This project must now be integrated with modern DevOps approaches, bringing new functionality and embedding new data streams into grown process and application landscapes.
By driving sound cycles according to CRISP-DM, we evaluate the feasibility of a data product closely and in minimum possible time. At an early stage, we manage the expectations of product benefits and give you necessary foundation to decide for a go or no-go.
We design and control development and deployment of a data-driven MVP primarily as a data-driven software engineering project. We rely on our many years of expertise as a proven DevOps partner for small and large custom-made IT systems and solutions.
It all starts with the right data. About 80 percent of the effort for a viable data product lies in extracting, processing, merging, and transforming the data. You do not want to invest this effort manually every time but strive for an appropriate degree of automation by deploying reproducible and robust data pipelines.
Classic ETL for data warehouses or ELT for data lakes, structured, semi-/unstructured or stream-based data, in batch or in real time.
From raw data storage, single-node databases, and big data to analytics-ready data representation in classic data schemas or agile data vault models.
Attack protection, CIA, GDPR, procedural and technical sound integration of data sources, data ownership, data usage, and data sovereignty in productive data pipelines.
As a DevSecOps partner, we provide mobility data for further analysis in 25 markets and for many millions of automotive vehicles.
We provide essential product and market data at the interface between product development and sales.
For an installed base of over 200,000 devices, we collect and deliver the necessary data for smart services in real time.
We develop apps that are supplied with real-time data, e.g., for safe driving and healthy baby sleep.
The data relevant for an application context must be provided, analyzed and visualized. We distinguish between two variants: reporting and exploratory analysis. Here, too, there are many recurring tasks that should be robustly automated so that your users can concentrate on interpreting reproducible results.
Avoid vendor lock-ins through flexible and modular interfaces that provide company-wide and domain-specific master and transaction data.
Integration of analysis tools from open source components, cloud services, or in-house developments in order to continuously update KPIs, reports and dashboards.
Provision and configuration of analysis playgrounds with a high degree of freedom to discover and save new relationships and metrics in self-service.
We equip a global platform for location-based services with functions that record and analyze customer experiences.
Based on extensive sensor data from the shop floor, we determine optimization potentials for the use of resources.
Using our award-winning IoT Bike platform, we analyze the degree of usage and the quality of municipal cycle path networks.
We develop descriptive analysis models for energy management of photovoltaic systems on an industrial scale.
Data analytics helps us to better understand causal chains of the past and present. With data science we recognize recurring patterns and can make predictions. With the right preprocessing to adequately smooth out the application domain's data volatility, we keep predictions robust, reliable, and traceable. With MLOps, we integrate data science workflows seamlessly and automatically into your enterprise IT.
Design thinking workshops for multi-dimensional problem analysis and derivation of hypotheses. Joint selection and definition of suitable decision-making criteria.
Selection of suitable ML platforms and AI services. Automation of data science workflows with versioned models, data, and environments. Reproducible DevOps integration into your company IT.
Objective test procedures to validate your ML models, scientifically proven compensation concepts, and whitebox-driven traceability of predictions.
We enable our customers data scientists from model and data management up to automatic deployments.
Using biometric and telematic factors, we make secure access control a seamless part of the customer journey.
We evaluate, build, and integrate the state-of-the-art components for gesture controlled human-machine interaction.
We develop cobot-based workstations for the production shop floor and test them using automatic end-to-end test frameworks.
For Munich call Marco Pötke on 0151 544 22 204