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3 Faktoren helfen den Leser zum Verweilen zu animieren.
Deshalb 3 Dinge die Business Blogger interessieren:
1. What data do we really need answers for?
2. Why is a sound methodology critical?
3. Do metrics that focus on small but useful improvements make sense?
With business analytics, the toughest challenge is colleting data needed for answering questions one must get answers for. The emphasis is here on:
– must get answers for, not on
– would like to have answers for!
New technqiues will not do
Often, we focus on predicting or forecasting the future. However, for managing it is more important to understand the analytic “hows” and “whys”. These matter more than the promise of prediction. In the past we did not call things predictive analytics but forecasts instead. We used.
– time series as economists still often do and tried our luck with
– multivariate analysis (both part of what is called parametric statistics).
r instance, analysts as well as investors and the public must get significant news simultaneously under its Regulation Fair Disclosure. But this regulation does not dictate what CEOs can do, when it comes to the timing of non-discretionary releases.
If these non-discretionary releases are used to raise stock prices to reap additional rewards when vested stocks become sellable, one must wonder about the CEO’s code of conduct. Now it is on boards of directors to scrutinize chief executives in years or months that they have a lot of equity vesting coming their way. Regulation may not be able to reduce this risk much but boards should be able to to protect the shareholders’ interests.
The critical issue is to learn as a borad but also regulator from the above findings in order to minimize these undesirable effects that occur due to timing non-discretionary releases by the CEO. But learning from data makes sense only, if these were collected and analysis was done in a methological way so others can repeat the study.
What is your opinion?
What kind of data have helped you gain insights in your work? What kind of big data sets does your employer use? What #bigfail involving bigdata do you know about?
Thanks again for sharing your insights – I always appreciate your very helpful feedback.
Ariely, Dan (2009). Predictably irrational. The hidden forces that shape our decisions. New York: Harper-Collins. Edmans, Alex; Goncalves-Pinto, Luis; Wang, Yanbo; Xu, Moqi (August 29, 2014). Strategic news releases in equity vesting months. London Business School: Working paper. Retrieved, September 8, 2014 from http://ssrn.com/abstract=2489152
Gattiker, Urs E. (August 17, 2014). Secrets of analytics 1: UPS or Apple? Retrieved, September u, 2014 from http://blog.drkpi.com/big-data-2
Gattiker, Urs E. (September 15, 2014). Big data: A false sense of precision? Retrieved, September 16, 2014 from http://blog.drkpi.com/big-data-3/
Fairchild, Geoffrey; Fries, Jason (January 26, 2012). Lecture notes. Social networks: Models, algorithms, and applications. Retrieved, September 8, 2014 from http://homepage.cs.uiowa.edu/~sriram/196/spring12/lectureNotes/Lecture4.pdf
No author. Class material – random graphs (July 3, 2009). Cornell University, Computer Science. Retrieved, September 8, 2014 from http://www.cs.cornell.edu/courses/cs4850/2010sp/Course Notes/Random-graphs-from-jeh-Feb-06-2010.pdf
Pham, D.T.; Dimov, S. S.; Nguyen, C.C. (2005). Selection of K in K-means clustering. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 219. DOI: 10.1243/095440605X82982014 Retrieved, August 31, 2014 from http://www.ee.columbia.edu/~dpwe/papers/PhamDN05-kmeans.pdf
Schrage Michael (September 3, 2014). Learn from your analytics failures. Harvard Business Review – Blog Network. Retrieved, September 4, 2014 from http://blogs.hbr.org/2014/09/learn-from-your-analytics-failures
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