With T-Systems’ big data analysis, companies can generate new business models by analyzing large amounts of data.

Big data with Hadoop from the cloud

Gaining added value from data

  • Big data platform from the cloud
  • Processing large quantities of data
  • Analyzing unstructured data
  • Consultancy, planning, implementation and optimization provided by T-Systems
Uwe Quattländer
Uwe Quattländer

Consultant SAP Solutions

Step by step to a big data strategy

As a survey of more than 700 companies carried out by the IT industry association Bitkom revealed, four out of five companies in Germany now make relevant decisions on the basis of data analyses. However, only a third of companies rely on big data technologies. According to the study, more widespread acceptance is inhibited by the supposedly high level of investment required as well as security concerns and a lack of personnel.
The solution: the T-Systems offering, comprising consultancy, planning, implementation and the optimization of big data analysis solutions and processes with transparent cost calculation. A workshop identifies potential deployment scenarios within the company. As part of a feasibility study (proof of concept), T-Systems provides the customer with a Hadoop ecosystem in order to test the first promising big data scenarios on selected business processes. During this evaluation phase, possible technical use cases such as better reporting, real-time scenarios or the processing of unstructured data, e.g. social media data for marketing campaigns, are investigated. T-Systems then works with the company to develop a tailored big data launch strategy and implements a suitable, customized solution.
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How can my company benefit from big data analysis?

  • Managing and using a growing quantity of unstructured data
  • Processing thousands of transactions per minute
  • Analyzing machine data in real time
  • Converting data into sustainable company assets
  • Consultancy, planning, implementation, optimization
  • Big data innovation workshop
  • Big data assessment
  • Big data Proof of Concept
  • Strategy and solution consultancy
  • Large quantities of data can be processed quickly
  • Processing polystructured data from various sources as well
  • Optimized business planning through Advanced plus Predictive Analytics
  • Improved product development process
  • New insights into consumer behavior and market
  • Reliable and secure data management

How big data with Hadoop leads to new business models

Whether it be IoT solutions, M2M communication or digital connectivity: the T-Systems big data platform from the cloud is dynamically scalable and can cope with all demands. It is based on the Hadoop Distributed File System (HDFS) from Apache, whose framework is suitable for collecting, storing and processing large quantities of unstructured data.
During data streaming with Hadoop Spark for example, the data is processed in real time when it is saved. Hadoop can also be combined with SAP HANA® to create a comprehensive big data platform for smart data management. T-Systems provides the suitable analytics setup for each use case as a platform-as-a-Service – inter alia with the help of partners such as Cloudera or Hortonworks. The customer’s on-premises solutions or existing private clouds can also be integrated in the process. Three examples show how big data solutions from T-Systems can turn data into company assets:

1. Predictive maintenance in automotive after sales

For an automotive manufacturer, the big data analysis platform from T-Systems consolidates and processes all information from the customer’s after sales process. Vehicle connectivity also allows data such as the current mileage or engine details to be collected and evaluated. Problems can be identified early on and the driver is notified well in advance if they need to visit the car workshop.

2. Efficient fleet management thanks to big data

For a fleet manager, the central analytics platform processes and analyzes all critical parameters such as freight, CO2 emissions, fuel consumption or the position of the vehicle. All vehicle profiles with standard assessments for fuel consumption can be managed centrally. In addition, mobile devices transfer logistics data in real time. A sustainable reduction in fuel consumption and optimum fleet management are the result.

3. Predictive rail maintenance

For a transport company, an onboard unit with SIM card collects the locomotive’s sensor data and transmits them via the mobile communications network to the Hadoop platform in a T-Systems data center. Once the data have been prepared in the cloud, the company can predict with a high degree of certainty when a locomotive needs to go into the depot for maintenance instead of having to follow the prescribed maintenance cycle. This saves money.

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