USAGE OF ADVANCED DATA ANALYSIS IN AUSTRIAN INDUSTRIAL COMPANIES
Keywords:Big Data, data analytics, predictive analysis, prescriptive analysis, industrial companies
Data has become one of the most valuable resources for companies. The large data volumes of Big Data projects allow institutions the application of various data analysis methods. Compared to older analysis methods, which mostly have an informative function, predictive and prescriptive analysis methods allow foresight and the prevention of future problems and errors. This paper evaluates the current state of advanced data analysis in Austrian industrial companies. Furthermore, it investigates if the advantages of complex data analyses can be monetarized and if cooperate figures such as the turnover or company size influence the answers of the survey. For that reason, a survey among industrial companies in Austria was performed to assess the usage of complex data analysis methods and Big Data. It is shown that small companies use descriptive and diagnostic analysis methods, while big companies use more advanced analytical methods. Companies with a high turnover are also more likely to perform Big Data projects. On an international comparison for most Austrian industrial companies, Big Data is not the main focus of their IT department. Also, modern data architectures are not as extensively implemented as in other countries of the DACH region. However, there is a clear perception by Austrian industrial companies that forward-looking data analysis methods will be predominant in five years.
Andelfinger, V.P., Hänisch, T. (2015). Internet der Dinge: Technik, Trends und Geschäftsmodelle [Internet of Things: Technology, trends and business models]. SpringerGabler, dx.doi.org/10.1007/978-3-658-06729-8.
BARC GmbH (2014). Big Data Analytics. https://www.sas.com/content/dam/SAS/bp_de/doc/whitepaper1/ba-wp-barc-big-data-analytics-2014-2298353.pdf
BARC GmbH (2014). Datenmanagement im Wandel [Data management in the course of time]. https://barc.de/docs/datenmanagement-im-wandel
BARC GmbH (2018). Zeit für eine neue Kultur durch Business Intelligence & Advcanced Analytics [Time for a new culture using business intelligence & advanced analytics]. https://sdv-dialogmarketing.ch/wp/wp-content/uploads/2019/01/bima-studie-2018-abstract.pdf
Bradshaw, L. (2013). Big Data and what it means. Business Horizon Quarterly, 7(1), 18.
Experian (2018). The 2018 global data management benchmark report. https://www.experian.com.vn/wp-content/uploads/2018/02/2018-global-data-management-benchmark-report.pdf
Forbes (2018, May 21). How much data do we create every day?. Forbes. https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#5654930d60ba
Gartner (2014). Industrial Analytics. Powered by the Internet of Things. The next wave of business transformation. Datawatch, (2), 6.
Gorelik, A. (2019): The Enterprise Big Data Lake. O’Reilly.
IBM (2018, May 11). Diagnostic analytics 101: Why did it happen?. IBM. https://www.ibm.com/blogs/business-analytics/diagnostic-analytics-101-why-did-it-happen/
IOT Analytics (2016). Industrial Analytics 2016/2017. https://digital-analytics-association.de/wp-content/uploads/2016/03/Industrial-Analytics-Report-2016-2017-vp-singlepage.pdf
Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. Meta Group Research Note, 949(1), 1-3.
Madhushree L. M., Revathi Radhakrishnan, P.S. Aithal (2019). A Review on Impact of Information Communication Computation Technology (ICCT) on Selected Primary, Secondary, and Tertiary Industrial Sectors. Saudi Journal of Business and Management Studies, 4(1), 108.
Oussous, A., Benjelloun, F., Ait Lahcen, A., Belfkih, S. (2017). Big Data technologies: A survey. Journal of King Saud University – Computer and Information Sciences, 30(4), 433. https://doi.org/10.1016/j.jksuci.2017.06.001
Paganoni, M. C. (2019). Framing Big Data: A Linguistic and Discursive Approach. Palgrave Pivot.
Romeike, F., Eicher, A (2016). Predictive Analytics: Looking into the future. FIRM Jahresbuch, 168-171.
Sampaio, P., Saraiva, P. (2016). Quality in the 21st Century. Springer.
Sappelli, M., Boer, M.D., Smit, S., & Bomhof, F. (2017). A vision on Prescriptive Analytics. Big Data 2017, 45.
Statista (2020, Dec 3). Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2024. Statista. https://www.statista.com/statistics/871513/worldwide-data-created/
Wallner, M (2019). Datenmanagement in österreichischen Industrieunternehmen – Umsetzung, Trends und Hindernisse [Data management in Austrian industrial companies – Implementation, trends and obstacles] [Unpublished master’s thesis]. Mining University of Leoben.
How to Cite
Copyright (c) 2021 Author
This work is licensed under a Creative Commons Attribution 4.0 International License.
The author is the copyright holder. Distribution license: CC Attribution 4.0.