Different industries – same challenges

How can Data Science help you?

Even if the technical requirements vary by industry, the technological challenges can still be compared. Here we show typical application areas where Machine learning is used.

By "predictive process optimization" we understand analyzing and improving business processes with the help of data mining and predictive methods of Machine learning. This includes not only technical industrial processes, but also non-technical topics such as purchasing, marketing or administration.

The goal is to develop learning systems capable to generate recommendations of action from historical and current data. This helps experts to optimize the quality, efficiency or resource utilization of their processes.

For the modeling stage, different data sources can be integrated:

  • Internal operating data
  • External data sources
  • Measurements (for example, sensors on machines, RFID tags)
  • Experience of the experts

The term "predictive maintenance" refers to the set of techniques used for planning intelligent maintenance. Such a flexible maintenance plan leads to cost savings because machines and their parts are serviced only when needed and downtime can be minimized.

Using machine status data, measurements and various machine learning algorithms, forecasts provide the right time for a maintenance task and the recommended actions for technicians and experts.

According to various estimates, up to 70% of corporate knowledge is stored in the form of unstructured text – reports, profiles, documentation, logs, customer contacts, and other documents. Relevant content is difficult to find and analyze.

Techniques of text mining and machine learning make it possible to access this information again.

When developing data science projects, we use current and free technologies. We rely on state-of-the-art languages, frameworks and libraries starting with the data connection and preparation stage, to the presentation and deployment of the created models and solutions.