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CSI Communications | July 2014 | 37 This requires a good understanding o the domain and the knowledge o techniques as well. This need has given rise to a new breed o proessionals called data scientists [3] . An objective way o looking data and their relationships and unearthing patterns is an important requirement o a data scientist. Seeking data or tting a pre-conceived outcome is oten the trap many all into and thus damage the creative solution potential o data science. The conusion between correlation and causation could also become another trap. [4] And then there is the challenge o processing voluminous data, which is addressed by new technology innovations such as in-memory computing to of er higher perormance. The technical challenge o processing multi-structured data in a non-relational ormat in an optimal manner also needs to be addressed. Frameworks such as Hadoop address this problem. Output challenges: Today’s complex business depends on the indings o data scientists. This is substantially dierent rom the MIS outputs served by traditional business analysts, which were used or day-to-day operational decisions. The outputs o data scientist are used or business-critical decisions and or the same reason any error in these outputs could have serious consequences. Some o the issues that need to be addressed in reporting the indings: - Which is a black swan / which is a temporary aberration? - How soon / late should the business react? Does every problem require real-time resolution? - How do we resolve con icting signals? - Recency bias – does the most recent data represent customer sentiment more truly or otherwise? - How should weak signals be treated? Unlike normal statistical analysis, wherein isolated deviations are ignored, weak signals may lead to some critical decisions [5] - How do we report the ndings in a quick-to-assimilate ormat? Storage challenges: That the Big Data requires massive storage is obvious. More importantly, with the dynamic nature o owing massive data with a need or real- time resolution o issues as in customer sentiment monitoring and correction, the storage may pose an additional challenge. With no time available or extraction, transormation and loading o data, we need to think beyond data warehouse to provide real-time analytics through stream processing Conclusion Traditionally business analysis emphasized on structuring a problem to identiy a solution and on seeking data towards this ef ort. Analytics on the other hand starts with massive data as source, rom which it seeks to identiy a problem / opportunity and suggests a solution. This cultural shit has been a major challenge or both analysts and unctional managers. References [1] Thomas H Davenport, Paul Barth and Randy Bean, How Big Data is Dif erent, MIT Sloan Management Review, Fall 2012, Vol. 54, No.1. [2] Harvey Koeppel, Big Data is not Little Data Writ Large – It Changes Everything, http:// searchcio.techtarget.com [3] Judith Magyar, Data Scientist: Sexiest Job o the Century, www.orbes.com, SAPVoice, 21/01/2014 [4] Gil Press, Big Data News Round Up: Correlation Vs Causation, www.orbes. com, 19/04/2013. [5] Martin Harrysson, Estelle Metayer and Hugo Sarrazin, The Strength o Weak Signals, McKinsey Quarterly, February 2014. n S. Ramanathan is a management consultant and is a visiting aculty at IIM, Indore and Great Lakes Institute o Management. He is the immediate past secretary o Computer Society o India. He teaches Technology Management and IT Strategy related topics. He may be contacted at ram1951@hotmail.com About the Author
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