A solid data infrastructure is the key to future-oriented business development. It facilitates everything from the creation of new value chains and well-founded decision-making using business intelligence to product personalisation and the use of artificial intelligence.
Opportunities for collecting data have increased rapidly in recent years. Almost any interface with a customer, a machine, or a product is also capable of capturing data. Maintaining an overview in this ocean of information requires concepts and competencies from a wide variety of areas that go beyond traditional software engineering.
To this end, therefore, we provide more than just the data streaming architecture. Organising, provisioning, managing, and integrating huge volumes of data requires intelligent data engineering which also takes into account testing, security, monitoring, and data quality.
To make our data engineering projects even more successful, we employ best practices from software engineering. This results in higher quality, robust data products which form the cornerstones of sustainable success.
When a major brand like dm strategically enters the online market, it creates high expectations. For this reason, their IT subsidiary FILIADATA, which has been responsible for all dm’s IT systems since 1988, launched a project in 2013 which was aimed at laying the foundations for reliable, fail-safe IT operations for dm.de. These included creating a large, fully automated Linux infrastructure in the data centre on which sophisticated web services like the online shop can be operated.
READ CASE STUDYinovex and REWE’s collaboration in the area of supply chain optimisation has focused particularly intensively on REWE’s IT subsidiary, REWE digital.
This particular project involved developing demand forecasting for REWE’s delivery service, leveraging big data technologies to enable easy scalability.
mobile.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 STUDYarvato Financial Solutions is collaborating with Microsoft, Cloud and Big Data specialist inovex GmbH, and three pilot e-commerce customers on an innovation project to create a Big Data architecture based on Microsoft Azure. The team will use the project to evaluate how the combination of Cloud Computing, Big Data and advanced analytics can improve fraud prevention and facilitate the development of new financial BPO services.
READ CASE STUDYAs in classic software development, the architecture is also decisive for the success of a project when designing a data lake. With the right design, the data platform is not only stable, but also flexible enough to meet new use cases, such as in the areas of reporting or machine learning. At the same time, operating costs remain low.
With a cleanly designed architecture, we enable our customers to master challenges such as the DSGVO.
In projects, we gear our use of technology entirely to the requirements of our customers. In most cases, we accompany them from the conception phase through to implementation and further development.
We gained experience in the field of Big Data at an early stage and are particularly experienced in setting up, designing and maintaining on-premise Hadoop distributions. Accordingly, we know the aspects of a cloud migration in detail and can evaluate its advantages and disadvantages individually and comprehensively.
During a migration, we ensure that the infrastructure as well as the data systems and use cases are implemented “cloud native”. In doing so, we fully exploit the advantages of the cloud. It is important to us that the migrated products can be used by our customers with the new technologies as before.
The EU General Data Protection Regulation (GDPR) is an important factor for the development of new solutions.
This has a massive impact on existing and new data platforms – starting with the compliant storage and provision of data and extending to corresponding authorization concepts and documentation.
A subsequent conversion is time-consuming and associated with additional costs if these requirements were not considered when designing the architecture. For this reason, we consider which regulations apply and how they must be applied in the individual solutions.
A high-performance data platform is the basis for successful data value creation. It enables teams to develop new products by giving them flexible access to the information and allowing them to expand it as required. In this way, a common platform also results in cross-team symbioses that can offer the company new insights.
We help teams with continuous quality monitoring so that they can work successfully and reliably load their data into the platform.
In addition, we enable flexible analysis of data by giving classic reporting solutions access to the data lake. This allows analysts to create reports and evaluations on the data. Complex machine learning products can also take advantage of a data lake, which can also store unstructured data such as images.
We gained early experience in the field of big data, and we are particularly experienced in setting up, designing, and maintaining on-premises Hadoop distributions. We therefore understand exactly what is involved in a cloud migration and can evaluate the advantages and disadvantages of each case both individually and comprehensively.
During a migration, we ensure that both the infrastructure and the data systems and use cases are implemented cloud natively in order to fully leverage the advantages of the cloud environment. We believe that our customers’ working methods should be altered as little as possible by their cloud migration, despite the addition of new technologies.
The GDPR is an important factor in the development of new solutions. Not only does it usher in new technical requirements, but it can also mean severe penalties for companies which violate them.
This has a tremendous impact on both existing and new data platforms, as it affects everything from regulation-compliant data storage and provision to the applicable authorisation concepts and documentation.
If these requirements are not taken into account during the design phase, the subsequent conversion process is both time-consuming and expensive. When designing our projects, therefore, we determine exactly which GDPR provisions must be applied and decide how to incorporate them into each individual solution.
A high-performance data platform is the basis for successfully extracting value from data. It enables teams to develop new products by allowing them to flexibly access information and to expand it as they see fit. Using a common platform also facilitates the development of symbiotic cross-team relationships, which can provide companies with new insights.
We support customer teams by providing continuous quality monitoring, enabling them to set up their platforms so that they can work successfully and load their data reliably.
We also enable companies to flexibly evaluate their data by giving them access to their data lakes through traditional reporting solutions. This enables analysts to use the data to create reports and evaluations.
Traditional reporting, however, is not the only business tool which can benefit from the creation of centralised data platforms. Complex machine-learning products can also be used to mine data lakes containing unstructured data, such as images.
The collection, analysis, and evaluation of big data help us to understand existing business models better and in greater detail, to digitise them, and to establish new digital products. Highly scalable data management solutions form an indispensable foundation for many of the processes associated with data science, machine learning, and artificial intelligence.
Companies can use performance-oriented BI solutions to analyse, evaluate, and visualize their business data independently and in real time. They can then use this aggregated information to draw solid conclusions and determine specific options for action.
A solid cloud infrastructure is an important factor in analysing and processing data in scalable and cost-efficient manner. While traditional data centres require a higher level of manual effort, cloud solutions can be more flexibly adapted to changing needs. Building on this, additional automation steps ensure that we can now implement cloud infrastructures that are precisely tailored to the requirements of the digital transformation.
Data science goes hand in hand with data engineering. Once data has been collected and processed, the next step is to leverage its benefits. Data-driven models ensure that the information gathered makes its way into production.
Data has the potential to offer companies great added value. The challenge, however, is to be able to draw the right conclusions from it: The sheer volume, complexity and variety place high demands on tools, people and methods until relevant insights can be gained. Data Mesh, as an analytical data architecture, can create the prerequisite for effective data value creation.
The aim of the KOSMoS project, which is funded by the Federal Ministry of Research and Education, is to connect manufacturing companies with one another, thereby creating a secure, digital value network that transcends company boundaries. Within the consortium of nine project partners, inovex is the expert in Data Management and Analytics.
The innovative technology of imaging mass spectrometry is about to complete its move from its primary application in research to commercial use in the clinical environment.
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