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The human element in digitisation- When Your Employees Make Use of Industry 4.0 Data

Not only scientific studies but also personal experience have shown, however, that the new data sources are frequently integrated into everyday work at only a rather slow pace. According to a study by the German Digital Analytics Association, 60 percent of industrial companies feel they are efficient at collecting sensor data but only 32 percent say they are also efficient at getting the right insights from the sensor data [1]. And a current survey revealed that only one out of two employees and only one out of three managers are aware of the potential of data analytics [2].

Managers in many companies firmly believe that they need to focus on AI and predictive analytics. In light of extensive coverage and a few "success stories" in the media, the issue of digitisation is often used as a synonym for the widespread employment of AI. Businesses will certainly not be able to escape this trend for long.

Nevertheless, the greatest success will go to those companies that know how to exploit another – but no less important – aspect of digitisation: the human element. In other words, they need to efficiently combine and integrate their extensive understanding of processes, the capabilities of their current employees and the potential of new data sources in their everyday work.

How much business benefit could we generate, after all, if our highly qualified employees in R&D did not need several days to process data in Excel or create graphics for a report? If it only took a few minutes to analyse problems in our plants? If the questions of our quality assurance departments were answered as quickly as they turned up instead of spending hours in the ERP program's query masks? If we were able to generate new know-how on the potential for optimizing processes and products from hardly used data sources we already have?

When domain experts have the right tools to make use of their data

Putting this vision into practice requires modern and efficient tools. It is neither possible nor does it make sense to outsource every single analysis of large data sets to data scientists. All the more so as gaining any significant insight often requires domain experts because their process know-how enables them to check results on their relevance. What we need are tools that provide reliable, business-relevant results without needing in-depth expertise in data science or programming skills. And we need to be able to apply such tools to large sets of data, as we already know them from such areas as IoT, smart metering or digitized production lines.

That is what Gartner called a "citizen data scientist" years ago. The term describes domain experts that are empowered by means of training and suitable tools to use advanced diagnostic analytics. According to Gartner, citizen data scientists bridge the gap between mainstream self-service data discovery by business users and the advanced analytics techniques of data scientists. They are able to perform sophisticated analytical tasks and deliver advanced analyses that would previously have required a data scientist with many years of training.

The global number of citizen data scientists amounts to an estimated 30 million around the world. This group offers enormous digitisation potential: domain experts generating more and better output based on data. In the specific context of industry 4.0, that would mean:

  • gaining new insight from machine data on process optimization, such as reducing emissions and saving energy as well as other resources;
  • cutting short development cycles in R&D by means of efficient analyses of experiments;
  • reducing unplanned downtimes in plants through in-depth problem analysis.

Such issues can often not be automated but are the everyday business of engineers from process engineering, quality management, R&D or maintenance. This everyday business makes it necessary to unleash the full potential of new data sources.

Visplore – top research from Austria turns engineers into citizen data scientists

That is precisely the starting point of Visplore. The company traces its origin to the research area of "visual analytics", the intuitive visualisation of large sets of data based on methods derived from data mining and artificial intelligence. Austria's top-notch research groups have been assuming a leading international role in this field for many years. Visplore itself was founded as a spin-off from the research center VRVis, where it was developed in practice-related research projects together with innovative, leading Austrian companies, such as AVL List, Austrian Power Grid and RHI Magnesita, where it has already become the corporate standard software. Moreover, Visplore applies research results published in top international specialist journals and awarded as "best papers".

Putting the whole story into a nutshell: Visplore turns engineers into data scientists. Visplore (www.visplore.com) strives to empower domain experts beyond data science to deliver better results with significantly less effort put into data processing and analysis. In contrast to business intelligence software, such as Tableau or Power BI, Visplore focuses on engineers dealing with large sets of measurement and sensor data. The software offers graphic solutions to comparing production plants, analysing the correlation between quality and process data, analysing plant problems or monitoring forecasting models. These solutions are designed as "cockpits" and may be applied to data from individual files, databases or such environments as Matlab and Python in seconds without any programming or configuration effort. A key feature of Visplore, particularly for high-resolution sensor data, is its performance. Visplore puts the original question back into the limelight by allowing for a dynamic analysis even when visualizing millions of individual values.

The Vienna-based company Visplore GmbH is on a growth trajectory thanks to funding by the renowned European VC investor BtoV in February 2021. The customers of Visplore are primarily found in the industry, energy economy and R&D, including such leading Austrian enterprises as the paper manufacturer Mondi, the foundry Georg Fischer, the power producer Verbund, and the research service provider AIT.

These and many other customers appreciate Visplore for its capability to significantly reduce the time and effort companies need to invest for plausibility checks, processing and analysis – sometimes from weeks to hours. In consequence, the customers are able to use more data even better and successfully complete data projects in less time [3]. Moreover, customers also indicated that considerably more employees work and make their decisions based on data thanks to Visplore. That is how they were able to achieve tangible improvements, such as increasing energy efficiency and the extension of maintenance cycles from 19 to 49 days [4].

Conclusion

When it comes to the issue of digitisation, we should keep in mind not only such trends as AI and clouds but also the everyday work of those employees with knowledge about processes. The future belongs to those companies where the efficient processing of today's amounts of data has been integrated into the everyday work of their employees.


[1] https://digital-analytics-association.de/wp-content/uploads/2016/03/Industrial-Analytics-Report-2016-2017-vp-singlepage.pdf
[2] https://blog.wiwo.de/look-at-it/2021/04/29/nur-jeder-zweite-mitarbeiter-sieht-potenzial-durch-data-analytics-und-nur-jeder-dritte-manager/
[3] https://www.report.at/energie/18749-geschaeftsquelle-daten
[4] https://www.oevia.at/publikationen/visplore-use-case

 

Learn more about digitisation in Austria. 

Dr. Harald Piringer

CEO Visplore GmbH
hp@visplore.com

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