Software

The main framework used for data management is currently Hadoop. This is an

Apache software infrastructure used by Facebook, Amazon, Netflix and many other

digital businesses. The key advantage of this tool is that many of its components are

open source. Key Hadoop users such as Facebook (Hive) and Netflix (Cassandra)

have developed add-ons to Hadoop framework, which adds further value to Hadoop ecosystem.

Key to the evaluation of how digital businesses should use big data is under-

standing how its customers utilize the data and then creating solutions, which fit

these specific requirements. In the case studies we see how hardware and software

vary though the data are seemingly similar. This is because key to the big data

agenda is flexibility in how digital businesses organize and utilize their data for

maximum business gain. This gain is only possible when companies understand their data and implement strategies to effectively monetize it without alienating customers. In conclusion data can be used to create information and information to create knowledge, and knowledge for a digital business is money.

6.7

Summary

This chapter discussed how big data and analytics can be used to evaluate business performance. It described six steps that summarize this process: Goals, Selection of Data, Processing Data, Data Mining, Evaluation and Visualization & Feedback. It then presented an analysis of the advantages and opportunities of using big data and analytics, identifying customer value proposition, customer segmentation, channel diversity, and better customer relationship as the most important ones. On the other hand, it also analyzed the challenges that organizations are facing when they want to adopt these technologies and create organizational advantage and highlights the importance of having skilled people in this area that is relatively new and thus the talent supply is scarce. Privacy and Security and the relatively high costs were also

identified as challenges. Finally, this chapter closes with the analysis of two case

studies: Facebook and Netflix, to demonstrate how these organizations have used

big data and analytics to evaluate and shape their business models.

References

Apple Store.http://store.apple.com. Accessed 16 Nov 2014

Barrenchea, M.: Big data: big hype?http://www.forbes.com/sites/ciocentral/2013/02/04/big-data- big-hype/. Accessed 16 Nov 2014

Cukier, K.: A special report on managing information: data, data everywhere. http://www. economist.com/node/15557443(2010). Accessed 16 Nov 2014

Davenport, T.H., Patil, D.J.: Data scientist: the sexiest job of the 21st century. Harvard Bus. Rev.

Experian Hitwise. Getting to grips with social media—An Experian Insight Report, Experian Limited (2010)

Google Play.http://play.google.com/store. Accessed 16 Nov 2014

Gopalkrishnan, V., Steier, D.: Big data, big business: bridging the gap. In: BigMine ’12: Proceedings of the 1st International Workshop on Big Data, Streams and heterogeneous Source mining, pp. 7–11 (2012)

Katal, A., Wazid, M., Goudar, R.H.: Big data: issues, challenges, tools and good practices. In: 2013 6th International Conference on Contemporary Computing (IC3 2013), pp. 404–409 (2013)

Kelion, L.: eBay makes users change their passwords after hack. http://www.bbc.co.uk/news/ technology-27503290. Accessed 16 Nov 2014

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big data: the next frontier for innovation, competition, and productivity. McKinsey Global Institute, San Francisco (2011)

Mayer, C.: Hortonworks show YARN future Hadoop with HDP 2.0 preview.http://jaxenter.com/ hortonworks-show-yarn-future-hadoop-with-hdp-2-0-preview-106254.html (2013). Accessed 16 Nov 2014

Mithas, S., Lucas, H.C.: What is your digital business strategy? IEEE IT Prof.12, 4–6 (2010) Morabito, V.: Trends and challenges in digital business innovation. Springer International

Publishing, New York (2014)

Muhtaroglu, F.C.P., Demir, S., Obali, M., Girgin, C.: Business model canvas perspective on big data applications. In: Proceedings—2013 IEEE International Conference on Big Data, Big Data 2013, pp. 32–37 (2013)

Osterwalder, A., Pigneur, Y., Smith, A., Movement, T.: Business model generation: a handbook for visionaries, game changers, and challengers. Wiley, New York (2010)

Rajpurohit, A.: Big data for business managers—bridging the gap between potential and value. In: Proceedings—2013 IEEE International Conference on Big Data, Big Data 2013, pp. 29–31 (2013)

Rogers, D.: The network is your customer: 5 strategies do thrive in a digital age. Yale University Press, UK (2011)

Sagiroglu, S., Sinanc, D.: Big data: a review. In: International Conference on Collaboration Technologies and Systems (CTS), pp. 42–47. IEEE, San Diego, CA (2013)

Vanderbilt, T.: The science behind the Netflix algorithms that decide what you’ll watch next. http://www.wired.com/2013/08/qq_netflix-algorithm/(2013). Accessed 16 Nov 2014 Wikipedia: Facebook. http://en.wikipedia.org/wiki/Facebook?uselang=ja?iframe=true&width=

90%&height=90%. Accessed 16 Nov 2014

Wikipedia: Netflix.http://en.wikipedia.org/wiki/Netflix. Accessed 16 Nov 2014 Wikipedia: Twitter.http://en.wikipedia.org/wiki/Twitter. Accessed 16 Nov 2014

Zhang, D.: Inconsistencies in big data. In: Proceedings of the 12th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC), pp. 61–67 (2013)

Zheng, Z., Zhu, J., Lyu, M.R.: Service-generated big data and big data-as-a-service: an overview. In: IEEE (ed.) IEEE International Congress on Big Data, pp. 403–410. IEEE, Santa Clara, CA (2013)

7

Innovation

Abstract

Big data is now becoming a key organizational asset, which represents a strategic basis for business competition. This development is making organizations to consider new innovative techniques on maximizing the potentials of big data as well as the challenges it creates. Yet, the success of many organizations demands new skills as well as new perspectives on how the epoch of big data could advance the speed of business processes. With the growth of big data, new analytics tools have evolved together with new progressive business models. In this chapter, we explore the innovative capabilities of the growing big data phenomenon by discussing issues concerning its methodologies, its impact on organizations business models, novel tools for analytics including challenges encountered by many business organizations. Ourfindings are substantiated by describing the real-life cases of Adobe and Hewlett Packard organizations.