Data science and analytics for industry2020-05-29T14:12:38+02:00

Data science and analytics for industry

Data science and analytics take industrial services to a new level

In industry, fast data analytics offers a lot of potential to continuously improve services. In addition, data science is already providing support here in the form of various practical applications with the aim of accelerating, automating and optimizing processes. Particularly in the case of critical processes, such as those involving chemicals or food, data science can help to detect irregularities immediately and thus initiate reactions and counteractions quickly.

84% Time saving
18% Better data
98% Operational Excellence

Case Study: Machine learning improves data quality and forecasts

In one of our projects with an infrastructure provider, it quickly became clear how much the combination of different data science models can improve business processes. In the industrial park, toxic wastewater is transferred to a sewage treatment plant via a sewer system. In order to safeguard the water quality, counteractions must be initiated as quickly as possible in the event of irregularities. Regular samples of water temperature, quantity, conductivity or pH value are taken by sensors. With the help of critical anomaly detection, it was possible to directly identify data outliers..

After our data scientists were introduced to the company’s complex data structures and operational processes, they developed an algorithm as part of a proof-of-concept that alerts in the event of irregularities. With the help of Unsupervised Learning, outliers in data quality could first be identified and then, based on this, it was possible to predict when a worrying wastewater value will occur using target values with the help of Supervised Learning.

0% faster reaction time
0days time-to-value
0terabyte data volume

Data Cleaning: Pitfalls and Solutions

As the interest in machine learning and artificial intelligence grows, companies regularly find themselves confronted with the dissatisfying quality of their data. This discovery is either made early-on with a structured approach, or a lot later, when poor data quality is identified as the root-cause of poorly performing models. In either case, the next step should be a methodical exploration of the available data, followed by a series of steps to remedy the identified issues. In this article, I will [...]

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