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Formal Assessment and Measurement of Data Utilization and Value for Mines

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Abstract

Most contemporary mines have considerable amounts of data. Structured and unstructured large data sets are colloquially named big data. Much of the data in mining operations come from a variety of systems, each with different databases and reporting environments. Standard technology deployments create a “silo-ification” of data leading to poor system benefit. Through modern server monitoring and systematic approach, data utilization and value can quantifiably be measured. The Data Utilization and Value Index (DUVI) can quantify business intelligence best practices and user interaction. This study seeks to provide a data management tool to measure data utilization across the process of converting data into action.

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References

  1. Applegate LM, Austin RD, McFarlan WF (2007) Corporate information strategy and management. McGraw-Hill/Irwin, New York

    Google Scholar 

  2. Bartos PJ (2007) Is mining a high-tech industry?: investigations into innovation and productivity advance. Resour Policy 32(4):149–158

    Article  Google Scholar 

  3. Basçetin A (2004) An application of the analytic hierarchy process in equipment selection at Orhaneli open pit coal mine. Min Technol 113(3):192–199

    Article  Google Scholar 

  4. Bradley RV, Pridmore JL, Byrd TA (2006) Information systems success in the context of difference corporate culture types: an empirical investigation. J Manag Inf Syst 23(2):267–294

    Article  Google Scholar 

  5. Burrus D (2014) “The internet of things is far bigger than anyone realizes” Retrieved from wired: http://www.wired.com/insights/2014/11/the-internet-of-things-bigger/. Accessed 12/2017

  6. Chaulya SK, Prasad GM (2016) Sensing and monitoring technologies for mines and hazardous areas monitoring and prediction technologies. Elsevier, Amsterdam, p 419

    Google Scholar 

  7. Davis F (1989) Perceived usefulness, Perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340

    Article  Google Scholar 

  8. De Lemos D (2013) Surface mining technology: managing the paradigm shift. Min Eng 65(12):36–40

    Google Scholar 

  9. DeLone WH, McLean ER (2003) The DeLone and McLean model of information systems success: a ten-year update. J Manag Inf Syst 19(4):9–30

    Article  Google Scholar 

  10. Dessureault S (2002) Tactical mine management, Ph.D. Thesis, University of British Columbia

  11. Dutton E, Afuang A (2015) Robotics, control and virtual reality: why digital transformation is critical for mining, Retrieved 8 25, 2015, from http://www.idc.com/getdoc.jsp?containerId=prSG25858815. Accessed 12/2017

  12. Eppler MJ, Mengis J (2004) The concept of information overload: a review of literature from organization science, accounting, marketing, MIS, and related disciplines. Inf Soc Int J 20(5):324–344

    Google Scholar 

  13. Erkayaoglu M, Dessureault S (2018) Improving mine-to-mill by data warehousing and data mining. Int J Min Reclam Environ:1–16. https://doi.org/10.1080/17480930.2018.1496885

  14. Fekete JA (2015) “Big Data in mining operations”, Master’s Thesis, Copenhagen Business School, Copenhagen, Denmark

  15. Janseen R, de Poot H (2006) “Information overload: why some people seem to suffer more than others,” NordiCHI '06

  16. Kahraman M (2015) “Holistic mine management by identification of real-time and historical production bottlenecks”, Ph.D Thesis, University of Arizona, Tucson, AZ

  17. Kaplan RS, Norton DP (2004) Measuring the strategic readiness of intangible assets. Harv Bus Rev 82(2):52–63

    Google Scholar 

  18. Kearns DT (2017) “Machine learning in the mining industry — a case study”, Retrieved 8 29, 2018, from https://medium.com/sustainable-data/machine-learning-in-the-mining-industry-a-case-study-33b771729eb2. Accessed 12/2017

  19. Komac M (2006) A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74(1–4):17–28

    Article  Google Scholar 

  20. LaValle S, Lesser E, Shockley R, Hopkins MS, Kruschwitz N (2013) “Big data, analytics and the path from insights to value,” MIT Sloan Management Review. Retrieved 7 2014, from http://sloanreview.mit.edu/article/big-data-analytics-and-the-path-from-insights-to-value/. Accessed 12/2017

  21. MacDonald J (2013) Managing a mining operation requires actionable production intelligence. Min Eng 65(3):55–56

    Google Scholar 

  22. Mulligan G (2018) “How tech is revolutionising the mining industry.” BBC News, BBC, Retrieved 10 July 2018, from www.bbc.com/news/business-44728346. Accessed 12/2017

  23. Nusca A (2015) “The mining industry in 2016: sensors, robots, and drones (oh my!)” Fortune. Retrieved 8 25, 2015, from http://fortune.com/2015/08/25/internet-things-mining-industry/. Accessed 12/2017

  24. Rogers WP (2015) “Formal assessment and measurement of data utilization and value for mines”, Ph.D. Thesis, University of Arizona, Tucson AZ

  25. Rogers WP, Kahraman MM, Dessureault S (2017) Exploring the value of using data: a case study of continuous improvement through data warehousing. Int J Min Reclam Environ, pp 1 - 11. https://doi.org/10.1080/17480930.2017.1405473

  26. Saaty TL (1990) Decision making for leaders: the analytical hierarchy process for decisions in a complex world. RWS Publications, Pittsburgh

    Google Scholar 

  27. Strharsky J (2018) The future of mining: more digging through data than strata. AusIMM Bulletin, Retrieved 18 July 2018, from www.ausimmbulletin.com/opinion/future-mining-digging-data-strata/. Accessed 12/2017

  28. Tilton JE, Landsburg HH (1999) “Innovation productivity growth, and the survival of the U.S. copper industry”, Productivity in Natural Resource Industries; Improvement through Innovation, 109-139

  29. Venkatesh V, Morris M, Davis G, Davis F (2003) User acceptance of information technology: toward a unified view. MIS Q 27:425–478

    Article  Google Scholar 

  30. Williams S, Williams N (2010) The profit impact of business intelligence. Morgan Kaufmann, Burlington

    Google Scholar 

  31. Witchalls C (2013), “The internet of things business index,” The Economist. Retrieved from http://www.mcrockcapital.com/uploads/1/0/9/6/10961847/the_economist_iot_2013.pdf.. Accessed 12/2017

  32. Yavuz M (2015) Equipment selection based on the AHP and Yager’s method. J South Afr Inst Min Metall 115(5):425–433

    Article  Google Scholar 

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Authors and Affiliations

  1. Mining Engineering, University of Utah, Salt Lake City, USA

    W. Pratt Rogers

  2. Department of Mining Engineering, Gumushane University, Gumushane, Turkey

    M. Mustafa Kahraman

  3. Mining and Geological Engineering, University of Arizona, Tucson, USA

    Sean Dessureault

Authors
  1. W. Pratt Rogers
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  2. M. Mustafa Kahraman
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  3. Sean Dessureault
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Correspondence to W. Pratt Rogers.

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Rogers, W.P., Kahraman, M.M. & Dessureault, S. Formal Assessment and Measurement of Data Utilization and Value for Mines. Mining, Metallurgy & Exploration 36, 257–268 (2019). https://doi.org/10.1007/s42461-018-0044-4

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