How to increase the value of data with data science

Data Science is a discipline that has been around for a long time. Many have heard of the term but find it hard to define, since it combines various fields such as statistics, scientific methods, artificial intelligence and data analysis. The data is first prepared, then cleaned, aggregated and processed. Subsequently, an extended data analysis is performed. The results can then be reviewed and patterns can be identified from which insights can be drawn. Data Science is also closely related to data mining, machine learning and big data.

Data Science is also a branch of engineering and mathematics and is therefore important for TEWS. At TEWS, Data Science is applied to perfect the instruments and their processes.

TEWS instruments can be used to determine the moisture and density content of products in the food/feed, paper, pharmaceutical, chemistry and tobacco industry. The data produced by TEWS instruments must be analyzed and interpreted using statistical methods and can then be displayed graphically.

A single moisture value may not say much at first glance, but it hides a great deal of information: With the help of Data Science, it is possible to make statements about this value and draw conclusions for future action. For example, if a moisture value has changed significantly over the last few months, or if it has fluctuated greatly, it is possible to deduce what problems might exist in the costumer’s production process.

Three steps are essential for Data Science and form the foundation for further action:

  1. Domain Expertise & Business Understanding

First of all, we need to understand the customer’s production process and its challenges. Because often customers approach us with the question of whether we can measure the moisture content or density of their product at a certain production step and in the end it turns out, based on our decades of experience in a wide range of industries, that the measurement at another point makes more sense. So before we can focus on determining how the moisture or density value can be used we have to understand the customer’s process and define the measurement problem, first.

  1. Engineering

During the engineering phase, we focus together with the customer on the correct installation, setup and adjustment of the suitable measuring system. Once the measurement system is calibrated and running properly, it can generate, process and store data.

  1. Data Interpretation

In the third step, the data from the moisture and density measurement instrument is analyzed and interpreted using statistical methods. For example, mean values, noise and fluctuations can be determined. The identification of correlations and trends as well as of recurring patterns are important for drawing further conclusions: How has the moisture value developed over time, , what conclusions can be drawn from this and are there measures that must and can be taken against it?

The domain expertise, i.e., the knowledge that exists about the customer problem, is enhanced with the help of engineering methods. This results in approaches that are then interpreted and evaluated.  TEWS uses data science methods to practice co-improving: TEWS is a solution provider with the goal of finding scientifically sound, precise, reliable and robust solutions to the customer problem.

So Data Science can help us….

  • Understand customer problems
  • Quantify previously qualitative problems
  • Develop appropriate customer-specific solutions
  • Improve customer processes
  • Connect our MW systems

To find our more about data science and how it can used to perfect your processes, please contact Hendrik Richter, TEWS data science expert, hendrik.richter@tewsworks.com

 

Ready to use data science? Get in touch with us!

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