ROBIOTIC

Lexicon

Big Data

At a glance

Big Data uses a variety of data sources to collect a pool of unstructured raw data on a specific topic. The goal is to analyze this data in order to obtain detailed information and insights on the respective topic. A variety of channels and resources can be used as data sources, e.g. social media, public sources (e.g. EU Open Data Portal) or in-house sources (e.g. collected customer data).

Cloud Computing and Big Data

In order for the collected data volumes to be stored sensibly and used within a reasonable time, there must be an appropriate technical infrastructure. Above all, large storage capacities play an important role in securing the ever-growing amount of data. Cloud computing offers the necessary flexibility and scalability: the infrastructure can grow and shrink with demand.

Advantages

The real value of Big Data is not the amount of data it collects - it is the knowledge that can be gained from various analyses. With the right analysis methods, more and more ways can be found to optimize and further develop products, procedures and services.

Understanding processes better
The analysis not only provides new insights and ideas, but also helps to better understand established processes in companies and organizations. For example, the data helps to understand why and under what conditions certain steps lead to problems or why certain approaches are particularly successful.

Application examples

Big Data is useful in virtually every industry to take a closer look at industry-specific processes and draw new conclusions from them.

Industry
In industry, the data collected is valuable for analyzing all the processes involved in the manufacture of products. This is, how processes can be further optimized. And this leads, for example, to an improvement in product quality, production efficiency or production performance itself. The more data available for analysis, the easier it is to identify and solve production problems. It is also helpful for the automation of processes if data (e.g. from the production line) is analyzed continuously - because problems and potential can also be identified in this way.

Trade
In retail, everything revolves around knowing the customer as precisely as possible. Only in this way is it possible to respond optimally to their needs and offer them the services and products they want. In addition, it is interesting for retailers to know which approach works best for the respective customer, i.e. how they can be enthusiastic about a transaction or how recovery works most effectively. With data collected over many years, preferences, expectations and behaviours can be analyzed. This gives retailers the opportunity to develop a more effective strategy for marketing their products.

Further application examples

  • Financial sector: Analysis of customer behaviour and risks
  • Logistics: Optimization of Routes and Fuel Consumption
  • Healthcare: Optimization of patient care
  • Government: Support for administration
  • Research: Analysis of researched data and finding new solutions


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