See the big picture and make forecasts for the future. With the Internet of Things (IoT) and Big Data Analytics, this is no longer a vision for the distant future. Use of this type of data analysis has been propelled by process digitization and the development of innovative, data-driven business and service models. These include, for example, the prediction of maintenance requirements for production plants and more reliable seasonal planning. The huge amounts of data processed for applications are no longer provided solely by software but are also obtained from devices or things. Big Data is complemented by the Internet of Things. 

What does Big Data Analytics mean?

Big Data Analytics is the (real-time) analysis of large amounts of data of various types to uncover patterns, correlations and other value-added information. This is done in the following steps:

Data Generation

Data collection via applications or, in the case of Internet of Things, via sensors.

Data Engineering

Platform integration, homogenization and validation of data.

Data Science

Evaluation of data for business purposes by converting information into actions and added value.

In data science, therefore, the decisive step is to generate added value for the business. The division is according to the degree of maturity.

  • Pure analysis: Data is visualized so that correlations are revealed.
  • Descriptive Analytics: Data is aggregated and mined (data mining) to draw conclusions with a view to the past and to answer the question: "What happened?"
  • Predictive analytics: Using statistical models forecasts for the future are created. These models and prognoses are used to understand the future and to answer the question: "What could happen?”
  • Prescriptive analytics use optimization and simulation algorithms to weigh up possible outcomes and answer the question "What should we do?” – potentially even controlling actions.

Classic use cases for Big Data & IoT

Big Data and Internet of Things are used in many business areas and models. For example, building efficiency management can be streamlined by integrating building automation technologies. Other application areas include:

Predictive Maintenance

Microphone-equipped production equipment allows operations to be performed by analyzing sound features. This allows anomalies to be detected and predictions made about maintenance requirements and failures - regardless of the system manufacturer.

Smartification

Big Data Analytics enables comprehensive smartification in many different areas. From a business point of view, planning processes, for example, can be designed more efficiently to enable more reliable seasonal planning. The Internet of Things makes the "Smart City" concept feasible, with such elements as networked parking spaces, offering private individuals the ability to search for a free parking space – and so also benefit from digitalization.

Fleet Telematics

Telematics integrated into vehicles make various data available digitally enabling a more efficient, transparent and safe fleet of vehicles, by early detection of anomalies, for example in routes or vehicle maintenance.

Projections

Use historical data to forecast the development of energy prices in regulated markets or enrich the order management for retailers with branch-specific target figures taking a range of factors (holiday periods, social media trends, ...) into account. 

Combine your skills for success in using Big Data & IoT

Which skills are needed?

Integrations-Know-how

For existing traditional IT as well as for various data sources such as sensors and machines. Edge computing (decentralized data processing at the edge of a network) may have to be used here in order to pre-process data for reasons of security or speed.

Data Engineering Know-how

For data integration within standard environments (e.g. "Data Lakes").

Data Science Know-how

For mathematical models and simulation methods.

Cloud-native Software Design

For application development.

Questions?