Data Management on Cloud

Structured and unstructured data of all formats must be able to be recorded and processed without any problems. This requires maintenance-free native interfaces for the efficient connection of source and target systems. 

For further processing of the data, the transmission speed and computing power must match the data volume.

Furthermore, the stability of the operation must be guaranteed. This can be achieved through a homogeneous technology stack and coordinated, automatically maintained services.

An ever-growing volume of incoming data must be securely stored and efficiently made available for job execution in seconds and for analysis workloads. The storage infrastructure has to adapt intelligently to the growing data volume. 

Partial data sets should be stored at optimal cost depending on the respective access frequency, without additional maintenance or operational effort. 

It must be possible to analyse and evaluate data holistically and coherently, including machine learning and AI technologies. If this requires large data memories, these should only be available temporarily for the duration of the analysis in order to save costs. 

In order for data scientists to work efficiently with the latest versions of the tools for business insights and analysis, these should be available in environments suitable for collaboration. Without a maintenance window for installing patches or updates. 

Um den Aufwand von Datenanalysen, Prototypen und das Ausführen von datengesteuerten Anwendungen zu minimieren, bedarf es einer integrativen Umgebung, wobei die Entwicklung und Dokumentation mit den für Data Scientisten gängigen Sprachen und Werkzeugen erfolgen muss. Hierüber sollten auch interaktive Visualisierungen und Business-Intelligence Funktionen über alle Datenquellen entwickelt und verwaltet werden können, so dass Benutzer im Self-Service eigene Dashboards und Reporte erstellen und für interne und externe Konsumenten publizieren können. 
 

An increasing number of stakeholders must be provided with simple yet secure and authorized access to the right data. At least the same level of data protection and data security should be applied as in an on-premise solution. In cloud environments, large cypersecurity teams operate state-of-the-art security controls down to hardware and firmware level to provide the highest level of data security. 

Date lifecycle management must ensure that changes and deletions of personal data are logged in compliance with DSGVO and passed on to downstream systems. 

Redundancy must ensure that the data is available and can be restored in an emergency in case of temporary hardware failures. To provide a further level of protection for local emergencies or natural disasters, it should be possible to replicate data across data centers or geographical regions.