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Big Data - Online Marketing
Transcript of Big Data - Online Marketing
Objectivity / Apophenia
Is Big Data the "better data"?
Privacy Issues & Ethical Problems
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
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
Why companies should use Big Data
mobile devices in use
sent every day
> 1 billion
Google searches every day
of user-generated data
stored, accessed and analyzed
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
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
Big Data exploration
Enhanced 360° view of customer
Assessing mixed data from multiple sources
Security Intelligence Extension
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.
Thank you for your undivided attention!
Technology & Analytical Methods
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