Creating New Types of Business and Economic Indicators Using Big Data Technologies
Science Journal of Business and Management
Volume 3, Issue 1-1, February 2015, Pages: 18-24
Received: Nov. 7, 2014; Accepted: Nov. 29, 2014; Published: Dec. 27, 2014
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Author
Péter Szármes, Multidisciplinary Doctoral School of Engineering Sciences, Széchenyi István University, Györ, Hungary
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Abstract
Today, every business is a data business. Data is available from internal and external sources about transactions, processes, customers, competitors, trends, technological changes, etc. The challenge is to create actionable information and useful knowledge for the company. If companies are not leveraging their data assets, then competitors will outperform them. Big data technologies can provide a very efficient tool for the discovery of knowledge hidden in the company and its environment. Creating company specific indicators by analyzing large datasets can lead to valuable insights and better decisions. Big data technologies can also provide new and faster methods to calculate economic indicators (GDP figures, tax revenue forecasts, etc.). It can help the work of economic policy makers by reducing the latency of data that allows for timely intervention if necessary. It can also create new, not yet available information.
Keywords
Indicators, Big Data, Business Analytics
To cite this article
Péter Szármes, Creating New Types of Business and Economic Indicators Using Big Data Technologies, Science Journal of Business and Management. Special Issue: The Role of Knowledge and Management’s Tasks in the Companies. Vol. 3, No. 1-1, 2015, pp. 18-24. doi: 10.11648/j.sjbm.s.2015030101.14
References
[1]
"Ch. Arthur, “Tech giants may be huge, but nothing matches big data”, The Guardian, 23 August 2013, URL: http://www.theguardian.com/technology/2013/aug/23/tech-giants-data (May 5, 2014)
[2]
R. S. Kaplan and D. P. Norton, “The balanced scorecard - Measures that drive performance”, Harvard Business Review, January-February 1992, pp. 71-79
[3]
E. Enkel, M. Rumyantseva and G. Gurgul, “Integrated Performance Measurement System for Knowledge Networks for Growth” in Knowledge Networks for Business Growth (eds. A. Back, E. Enkel, G. von Krogh), Springer, 2007, pp. 165-190
[4]
J. Haji, “The reality of Big Data”, conference lecture, CNMEOnline.com - Big Data Symposium, 20 May, 2013, URL: http://cnmeonline.com/bigdatasymposium/docs/Jassim-Haji.pdf (April 30, 2014)
[5]
S. Mika, “Telematics Sensor-Equipped Trucks Help UPS Control Costs”, Automotive Fleet, July 2010, URL: http://www.automotive-fleet.com/article/story/2010/07/green-fleet-telematics-sensor-equipped-trucks-help-ups-control-costs.aspx (May 30, 2014)
[6]
Z. Karabell, “(Mis)leading indicators”, Foreign Affairs, March/April 2014, URL: http://www.foreignaffairs.com/articles/140749/zachary-karabell/misleading-indicators(September 24, 2014)
[7]
D. Laney, “3D Data Management: Controlling Data Volume, Velocity, and Variety”, META Group, 2001, URL: http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf (November 29, 2013)
[8]
S. Lucas, “Beyond the Balance Sheet: Run Your Business on New Signals in the Age of Big Data”, SAP Hana blog post, August 21, 2012, URL: http://www.saphana.com/community/blogs/blog/2012/08/21/beyond-the-balance-sheet-run-your-business-on-new-signals-in-the-age-of-big-data (October 25, 2014)
[9]
L. Burgelman, “Attention, Big Data Enthusiasts: Here's What You Shouldn't Ignore”, Wired, Innovation Insights, February 8, 2013, URL: http://insights.wired.com/profiles/ blogs/attention-big-data-enthusiasts-here-s-what-you-shouldn-t-ignore (October 21, 2014)
[10]
J. Kelly, “Big Data: Hadoop, Business Analytics and Beyond”, Wikibon blog post, February 5, 2014, URL: http://wikibon.org/wiki/v/Big_Data:_Hadoop,_Business_Analytics_and_Beyond (October 29, 2014)
[11]
IBM Business Analytics, “Business Analytics for Big Data: Unlock Value to Fuel Performance”, IBM Corporation, Software Group, June 2013, URL: http://www-01.ibm.com/software/analytics/solutions/big-data/ (September 30, 2014)
[12]
B. Marr, “Big Data Is Nothing Without Its Little Brother”, Smart Data Collective Blog, March 18, 2014, URL: http://smartdatacollective.com/bernardmarr/191631/big-data-nothing-without-it-s-little-brother (September 19, 2014)
[13]
IBM, “Global CFO Study 2010”, IBM Corporation, 2010, URL: http://www.ibm.com/services/us/cfo/cfostudy2010/ (May 5, 2014)
[14]
Google, “Clickthrough rate (CTR)”, AdWords Help, Google, URL: https://support.google.com/adwords/answer/2615875?hl=en (October 14, 2014)
[15]
R.E. Bucklin, C. Sismeiro, “Click Here for Internet Insight: Advances in Clickstream Data Analysis in Marketing”, Journal of Interactive Marketing 23 (2009), pp. 35–48
[16]
Q. Su, L. Chen, “A method for discovering clusters of e-commerce interest patterns using click-stream data”, Electronic Commerce Res. Appl. (2014), http://dx.doi.org/10.1016/j.elerap.2014.10.002
[17]
J. Ginsberg, M. H. Mohebbi, R. S. Patel, L. Brammer, M. S. Smolinski and L. Brilliant, “Detecting influenza epidemics using search engine query data”, Nature 457, 19 February, 2009, pp. 1012-1014
[18]
V. O. Sust, E. G. Illera, A. S. Berengué, R. G. García, M. V. P. Alonso, M. J. T. Torres, G. R. Verard, O. L. Albert, X. C. Ramos and P. R. Rodríguez, “Big Data And Tourism: New Indicators For Tourism Management”, RocaSalvatella and Telefónica, Barcelona, May 2014, URL: http://www.rocasalvatella.com/en/big-data-and-tourism-new-indicators-tourism-management-0 (September 19, 2014)
[19]
H. Choi and H. R. Varian, “Predicting the Present with Google Trends”, Technical Report, Google, December 18, 2011, URL: http://people.ischool.berkeley.edu/~hal/Papers/2011/ptp.pdf (October 10, 2014)
[20]
S. L. Scott and H. R. Varian, “Bayesian Variable Selection for Nowcasting Economic Time Series”, Technical Report, Google, July 2012, URL: http://people.ischool.berkeley.edu/~hal/Papers/2012/fat.pdf
[21]
M. Banbura, D. Giannone and L. Reichlin, “Nowcasting”, Working Paper Series No. 1275, December 2010, European Central Bank, URL: http://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1275.pdf (October 5, 2014)
[22]
D. Antenucci, M. Cafarella, M. Levenstein, Ch. Ré, M. D. Shapiro, “Using Social Media to Measure Labor Market Flows”, Working Paper No. 20010, National Bureau of Economic Research, March 2014, URL: http://www.nber.org/papers/w20010.pdf (October 24, 2014)
[23]
This work was supported by the European Union and the European Social Fund through FuturICT.hu project (grant no.: TAMOP-4.2.2.C-11/1/KONV-2012-0013). "
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