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- Design and Implementation of Big data utilities for Business\u00a0Analytics<\/li>\n
- Data mining and Explorative\u00a0analytics\u00a0for various Industry Benchmark Reports<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
2. Competency Required\u00a0:- contains terms of technology understanding , logical thinking and ability to solve unstructured problems<\/span><\/p>\n
3. Desirable Skill Sets\u00a0:-<\/p>\n
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- Good understanding of Business Metrics and parameters.<\/li>\n
- Ability to apply right statistical tools to business case<\/li>\n
- Technical – Apache\u00a0Hadoop, Pig, hive, big data<\/li>\n
- Domain Exp MANDATORY<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
4. Pay \u2013 Best in the industry \u2013 often 1.5 times that in pure play analytics or Hadoop<\/span><\/p>\n
5. Experience \u2013 4+ years\u2019<\/p>\n
6. Employer – Top MNC<\/p>\n
Terms that crop up include<\/p>\n
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- Identify pilot opportunities<\/li>\n
- Drive early development<\/li>\n
- Build reference architecture and blue prints<\/li>\n
- Network extensively with the ecosystem, lead and participate in conferences and workshops, and establish thought leadership<\/li>\n<\/ul>\n
So what is big data analytics? As a domain, it is very new and encompasses ETL, data visualisation and Statistical analytics on \u2018big data\u2019. Big data is data which is larger than \u2018normal\u2019, contains structured and unstructured elements (tables, videos, images, text) and is coming in at a very high rate (internet, sensors, ERP data on transactions etc.).<\/p>\n
What would a company need in a Big Data Analytics expert? A confident, tech friendly, \u2018early adopter\u2019 who is willing to look at amalgamating understanding on big data with skills from analytics. This person will have the opportunity to emerge as a SME (subject matter expert) who will guide a team of Hadoop engineers and Data Analysts to create value from the data that is available to the company.<\/p>\n
For such a candidate, remuneration is no bar and the industry is willing to pay handsomely.<\/p>\n
Some data types are particularly ripe for analysis, such as:<\/p>\n
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- Surveillance footage.\u00a0Typically, generic metadata (date, time, location, etc.) is automatically attached to a video file. However, as IP cameras continue to proliferate, there is greater opportunity to embed more intelligence into the camera (on the edge) so that footage can be captured, analyzed, and tagged in real time. This type of tagging can expedite crime investigations, enhance retail analytics for consumer traffic patterns, and, of course, improve military intelligence as videos from drones across multiple geographies are compared for pattern correlations, crowd emergence and response, or measuring the effectiveness of counterinsurgency.<\/li>\n
- Embedded and medical devices.\u00a0In the future, sensors of all types (including those that may be implanted into the body) will capture vital and non-vital biometrics, track medicine effectiveness, correlate bodily activity with health, monitor potential outbreaks of viruses, etc. \u2014 all in real time.<\/li>\n
- Entertainment and social media.\u00a0Trends based on crowds or massive groups of individuals can be a great source of Big Data to help bring to market the \u201cnext big thing,\u201d help pick winners and losers in the stock market, and yes, even predict the outcome of elections \u2014 all based on information users freely publish through social outlets.<\/li>\n
- Consumer images.\u00a0We say a lot about ourselves when we post pictures of ourselves or our families or friends. A picture used to be worth a thousand words, but the advent of Big Data has introduced a significant multiplier. The key will be the introduction of sophisticated tagging algorithms that can analyze images either in real time when pictures are taken or uploaded or en masse after they are aggregated from various Web sites.<\/li>\n<\/ul>\n
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![\"\"](\"https:\/\/farm9.staticflickr.com\/8343\/8263368169_f19559acdf_z.jpg\")
<\/p>\n![\"\"](\"https:\/\/www.emc.com\/leadership\/digital-universe\/iview\/images\/investment-managing-du-by-region-lg.jpg\")
<\/p>\n![\"\"](\"https:\/\/ibsindia.org\/blog\/wp-content\/uploads\/2012\/12\/Shubhashini.jpg\")
From 2002 onwards, Subhashini has a decade of experience across roles in Analytics in Retail Finance and Banking. These roles have been across Risk Management , Collections strategy , Fraud Control and Marketing in\u00a0 GE Money, Standard Chartered Bank, Tata Motors Finance and Citi GDM . Her area of interest is the integration of results \/ outputs of Analytics with Business Decisions \u2013 Tactics and strategy.<\/p>\n