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Big Data - Online Marketing

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Janika Schmid

on 29 October 2014

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Transcript of Big Data - Online Marketing

Current State of Technologies & Trends
Big Data
Practical Examples
Big Data
Objectivity / Apophenia
Is Big Data the "better data"?
Privacy Issues & Ethical Problems
Critical Issues
Social Semantics

2011: Walmart acquired big data experts for 300$ million
Created own semantic search algorithm (Polaris)
200 million users -> 10-15% sales increase
Uses Twitter, Facebook messages/postings, Youtube videos/likes and more to understand intent of purchase
Movie Success
Data: Twitter (average number of tweets per hour)
Sentiment for better predictions after release
Outperformed HSX (Hollywood Stock Exchange)
Only 23M Twitter users in 2010 – today: 230M+
Similar system predicting IMDB User Rating (MAE of 0.35)
Predicting Box Office Revenues
Janika Schmid, Erich Eberl, Andreas Toth,
Christina Eberharter, Verena Schlenck

What is Big Data?
The Scale of Data
Different Forms of Data
Speed of Data
Uncertainty of Data
4 Vs
Why companies should use Big Data
10 billion
mobile devices in use
7.7 billion
world population
294 billion
sent every day
> 1 billion
Google searches every day
30+ petabytes
of user-generated data
stored, accessed and analyzed
Computing infrastructure
230+ million
Tweets each day
don't trust the information they use to make decisions
1 in 3 business leaders
Time for Big Data
Near real-time collection, analysis & reporting
Unpredictable content
struggling to manage and extract value from the growing volume of data
deeper understand of customer sentiment appreciate
wish to increase customer loyalty and satisfaction
want to have real-time visibility of business operations
Data resides from all sorts of different sources
in all kinds of different formats
social networks
search engines
Big Data exploration
Enhanced 360° view of customer
Assessing mixed data from multiple sources
Security Intelligence Extension
Operation Analysis
Data Warehouse modernization
Objectivity / Apophenia
Agrawal, D., Bernstein, P., Bertino, E., Davidson, S., Dayal, U., & Franklin, M. et al. (2012). Challenges and Opportunities with Big Data.
Asur, S., & Huberman, B. A. (2010, August). Predicting the future with social media. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on (Vol. 1, pp. 492-499). IEEE.
Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662-679.
Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of ‘big data’. McKinsey Quarterly, 4, 24-35.
Chen, H., Chiang, R., & Storey, V. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188.
Davenport, T. H., Barth, P., & Bean, R. (2013). How ‘big data’is different. MIT Sloan Management Review, 54(1).
Deutscher Bundestag,. (2013). Aktueller Begriff Big Data. Berlin: Deutscher Bundestag.
IBM,. (2014). IBM big data use cases. The 5 game changing big data use cases. Retrieved 26 October 2014, from http://www-01.ibm.com/software/data/bigdata/use-cases.html
Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons, 54(3), 241-251.
Lohr, S. (2012). The Age of Big Data. The New York Times. Retrieved from http://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html?pagewanted=all&_r=0
McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big Data. The management revolution. Harvard Bus Rev, 90(10), 61-67.
Newman, D. (2014). Big Data Isn't Just For Big Businesses Anymore. Entrepreneur. Retrieved 26 October 2014, from http://www.entrepreneur.com/article/232947
Ward, J., & Barker, A. (2013). Undefined By Data: A Survey of Big Data Definitions. Arxiv Preprint Arxiv:1309.5821.
Big Data
Thank you for your undivided attention!
Technology & Analytical Methods
Google File System
Text Analytics
Web Analytics
Mobile Analytics
Opinion Mining
Sentiment Analysis
Full transcript