Collecting and evaluating the data is, however, only part of the challenge. A company’s success hinges on being able to use the knowledge thus gained in their daily operations and to bring data-driven models into production: From Data Science to Production.
We support our customers holistically on their journey from analysis to completed solution. We do this by helping them to create new products which offer real added value to both companies and their customers.
ProSiebenSat.1 is greatly interested in the specific contributions made by TV ads to the added value of the e-commerce company advertised. Exactly how many visitors visit the e-commerce website because they saw the TV ad? How much revenue is generated by those visitors proven to have come to the website from the TV ad? The challenge was that standard solutions for analysing web traffic cannot explain the correlation between TV ads broadcast (events outside the online world) and the online behaviours they trigger.
READ CASE STUDYIn optimising the group’s supply chain, inovex has worked with REWE’s IT subsidiary, REWE Digital. This arm of the company is responsible for all the REWE Group’s strategic online activities and aims to become the leading provider of online solutions in all REWE’s associated supermarkets and supply warehouses. These include those distribution warehouses that REWE set up specifically for their home delivery service, as well as the group’s in-store delivery and collection points all over Germany.
READ CASE STUDYbeyerdynamic has stood for high-end headphones, microphones and conference systems since 1924. The products manufactured by the company, which is headquartered in Heilbronn, Germany, deliver audio technology with outstanding sound quality. As a leading audio brand, beyerdynamic integrates innovation into both its audio-technology products themselves and its production processes. It was for one of their innovative production projects that beyerdynamic leveraged inovex’s expertise.
READ CASE STUDYmobile.de is a marketplace for the buying and selling of vehicles. Every month, the web platform draws 13.5 million visitors who can choose from the more than 1.6 million vehicles on offer. Each visit to the platform creates a stream of data which contains information about the demand for particular vehicles, the quality of the vehicles for sale, and user requirements. mobile.de wants to use this data to continuously improve the user experience for both vehicle sellers and purchasers.
READ CASE STUDYWe often start out by performing an exploratory analysis and evaluation of all the company’s data and by defining common objectives taking into account its business model.
At inovex, we combine proven academic and scientific analysis methods with many years of experience in IT and software engineering. This enables us to fully explore the opportunities offered by data science with our customers and to create an end-to-end solution which covers everything from data analysis to practical operation and maintenance.
We will assist you in meeting every challenge that arises along the way, from creating complex, scalable analysis methods to integrating them into productive system environments, right through to incorporating feedback mechanisms, security & legal considerations, and changing business models.
This enables us to use tailor-made algorithms to extract valuable information and knowledge from tremendous volumes of data, enabling practical applications to be developed.
We help you identify the most promising data and AI use cases in your business model. Together we work out how to create the conditions for the meaningful use of data, methods and modern technologies at your company.
With data-driven services, you use your data profitably and expand your offering to secure your unique selling proposition in the long term and develop new solutions.
For years, inovex has employed a versatile team of highly qualified data scientists and data engineers who make the latest methods of data analysis and machine learning functional for our customers. Our teams have extensive experience in implementing data science products in different industries – from selecting algorithms to integrating models into productive environments and developing them further in live operations. Our experts combine a broad spectrum of scientific data experience in their specific fields – which include biology, mathematics, physics, linguistics, and computer science – with the use of highly efficient algorithms to create our wide range of services.
Our data science team relies on established open-source technologies which enable both high-performance batch processing and the complex real-time analysis of data streams. Appropriate technologies are selected based on the actual requirements for each use case, taking into account the system landscape and the requirements for models and algorithms, as well as any maintenance aspects. Our open source approach enables us to cover all common methods, including regression, decision trees, support-vector machines, and neural networks from a single source.
We’ll use our expertise in the Internet of Things (IoT), data engineering, and data science, as well as mathematical modelling, to develop predictive maintenance solutions and integrate them into your operational systems – from edge computing to cloud and hybrid solutions.
Machine Learning Operations (MLOps) practices enable data products and machine learning (ML) applications to run reliably and efficiently in productive environments. They do this by taking DevOps concepts and expanding them to work with machine learning processes.
Mathematical modelling is the art of turning a business problem into a clear mathematical formulation. This abstraction and its subsequent algorithmic implementation generate insights, answers, and understanding in order to enable knowledge- and data-driven decision-making.
We put data science models into production. To ensure that the information from the data collected doesn’t remain merely theoretical, our teams, supplemented by IT engineers, translate the models into practical applications. Sophisticated concepts for integrating our solutions into production environments and for ensuring the maintainability of analyses are decisive factors in our solutions’ success.
Data science projects are perfectly suited to the cloud. While the platform products provided by cloud operators give data scientists a jumpstart when exploring or creating new machine-learning models, there are no one-size-fits-all solutions. Our interdisciplinary teams therefore include cloud engineers to help our customers find their optimal cloud setup and put it into practice.
Personalisation is a key factor in online success, and recommender systems or recommendation engines are the technical facilitators behind it. These systems use data to learn and aggregate user needs in order to customise and optimise content and its presentation.
We provide our customers with custom-tailored sales forecasting solutions which can be integrated into existing systems and which take into account key performance indicators (KPIs). Our customers also receive manual intervention options and comprehensive monitoring tools to enable them to keep an eye on the models and their predictions.
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