The week's pick
Reseach Article
DataOps in Manufacturing and Utilities Industries
| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 12 - Number 23 |
| Year of Publication: 2019 |
| Authors: Prabin Ranjan Sahoo, Anshu Premchand |
10.5120/ijais2019451814
|
Prabin Ranjan Sahoo, Anshu Premchand . DataOps in Manufacturing and Utilities Industries. International Journal of Applied Information Systems. 12, 23 ( August 2019), 1-6. DOI=10.5120/ijais2019451814
Abstract
The concept of DataOps and its adoption across industries is gaining momentum. This paper draws a parallel between DataOps and DevOps concepts. It then focuses on the relevance of DataOps in manufacturing and utilities industries. The paper then outlines the dataOps process and platform as well as the data challenges in manufacturing & utilities industries. Various DataOps strategies for these industries are also discussed along with the importance of adoption of advanced analytics via DataOps for achieving business benefits.
References
- Lismont, J., Vanthienen, J., Baesens, B., Lemahieu, B.: Defining analytics maturity indicators: A survey approach. International Journal of Information Management 37(3), 114–124 (2017)
- Knabke, T., Olbrich, S.: Capabilities To Achieve Business Intelligence Agility – Research Model And Tentative Results (Research-in-progress). In: Proceedings of the 20th Pacific Asia Conference on Information Systems (PACIS), p. 35 (2016)
- Baars, H., Ereth, J.: From Data Warehouses to Analytical Atoms – The Internet of Things as a Centrifugal Force in Business Intelligence and Analytics. In 24th European Conference on Information Systems (ECIS) Istanbul, Turkey (2016)
- König, L., Steffens, A.: Towards a Quality Model for DevOps. In Continuous Software Engineering & Full-scale Software Engineering, p. 37 (2018)
- Mishra, A., Garbajosa, J., Wang, X., Bosch, J., Abrahamsson, P.: Future directions in Agile research: Alignment and divergence between research and practice. Journal of Software: Evolution and Process 29(6), p. e1884 (2017)
- Pinkel, C., Binnig, C., Haase, P., Martin, C., Sengupta, K., Trame, J.: How to best find a partner? An evaluation of editing approaches to construct R2RML mappings. In: Presutti, V., d'Amato, C., Gandon, F., d'Aquin, M., Staab, S., Tordai, A. eds. ESWC 2014. LNCS, vol. 8465, pp. 675---690. Springer, Heidelberg 2014
- Evelson, B., Kisker, H., Bennett, M., Christakis, S.: Benchmark your BI environment. Technical report, Forrester Research, Inc., October 2013
- Dimou, A., Sande, M.V., Colpaert, P., Verborgh, R., Mannens, E., Walle, R.V.D.: RML: a generic language for integrated RDF mappings of heterogeneous data. In: LDOW 2014
- Knap, T., Kukhar, M., Macháa , B., Škoda, P., Tomeš, J., Vojt, J.: UnifiedViews: an ETL framework for sustainable RDF data processing. In: ESWC Posters & Demos 2014
- Wynne, M., Hellesoy, A., Tooke, S.: The cucumber book: behaviour-driven development for testers and developers. Pragmatic Bookshelf (2017).
- Schwaber, K., Beedle, M.: Agile software development with Scrum. Prentice Hall, Upper Saddle River (2002).
- Beck, K., et al.: Manifesto for agile software development. http://agilemanifesto.org/, last accessed 2018/07/29 (2001).
- Zimmer, M., Baars, H., Kemper, H.-G.: The impact of agility requirements on business intelligence architectures. In: Proceedings of the 45th Hawaii International Conference on System Science (HICSS), pp. 4189–4198. IEEE (2012).
- Krawatzeck, R., Dinter, B.: Agile Business Intelligence: Collection and Classification of Agile Business Intelligence Actions by Means of a Catalog and a Selection Guide. Information Systems Management 32(3), 177–191 (2015).
- Larson, D., Chang, V.: A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management 36(5), 700–710 (2016).
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