Big Data

Introduction

Big data is one of the biggest and newest ideas in computing (Hopp 2013). Many chief technology officers, while agreeing that big data is an important concept in business, cannot give a formal definition of the concept. The most comprehensive definition has been advanced in a Gartner report in 2001 that encapsulates three Vs, namely: volume, velocity and variety. A fourth has been added to include veracity which deals with uncertainties. Other definitions seem to be centered on the need for businesses to adopt new ways of making decisions in light of streaming unstructured data (Johnson 2008). The major consensus in all available definitions is that big data is used to describe the presence of large data sets that make the foundation for comprehensive decision making (Brynjolfsson et al 2011). However, the difference between the definitions given by most companies does not agree on how big ‘big data’ is. Intel has capped big data as being a median of 300 terabytes a week that captures “business transactions stored in relational databases, followed by documents, e-mail, sensor data, blogs, and social media” (Wen 2012). All in all, it seems that the concept of big data can only be defined as far as individual organizations see fit in their special existential circumstances based on size (volume), complexity (variety), rate of change (velocity) and uncertainty (veracity or value).

Relevance of big data

            There are many challenges that arise from globalization including increased competition in markets amid shrinking resources (Bartlett et al 2008). Therefore, global managers are challenged to come up with more innovative ways to manage the wind of change that will ultimately spell survival or bankruptcy of their organizations. The changing global environment presents many opportunities as it does threats. One of the most exciting tools for the present and future is big data (Madden 2012). Big data combined with traditional data from enterprise to foster understanding of business environments which inevitably lead to increased productivity, enhanced competitiveness and innovativeness which eventually improve the business’ bottom line (Kiron 2010).

Cloudera is a top big data software company that builds data models for understanding and thereby treating different conditions (Leber 2013). The company is one of the many that is targeting reduced hospital visits and office hours where conditions can be anticipated from an avalanche of data meaning that patients can be attended to at home (Davis 2009). Big data is also being utilized by manufacturing companies like General Motors that fit cars with censors that return a telemetry stream (Brown & Caddick 2003). This model is applied by Renault’s R-link and is similar to GM’s OnStar. The systems primarily deliver communications, navigation and safety services to the companies such that individual performance of motor vehicles is tracked and mapped (Woody 2012). The data gathered is used in revealing how vehicles are used and their rates of failure which foster improvement in addition to lowering costs associated with assembly and development (Cavallo 2012). Burger King uses smartphones and GPS systems to track movement of customers and target them when they are in close proximity to their stores (Blank 2011). Other companies like LikedIn simply exist at the behest of big data. They customize experiences such that every client receives personalized attention.

The above examples are some of the companies that have realized the importance of big data. Key among the importance is allowance for target marketing as well as customization of client experiences. Big data ultimately improves profitability and competitiveness while lowering costs associated with product development.

Ethical issues of big data 

Ethical issues in big data have arisen as collection, retention and organization of data has become an important tool in today’s business environment. CNN, the news company, had a video catalogue that was deemed very private 25 years ago which warranted congress to pass laws to prevent sharing of its video rental history (Bohn & Short 2010). Today, many people with access to the internet constantly share this kind of information that was protected just over two decades ago. This represents a shift in the values of the society that have been occasioned by big data. Scientific theories point to the fact that it is difficult to observe a phenomenon without changing it. The evolution of big data is too fast and too erratic such that its observance has occasioned a shift in the values adhered to by traditional societies (Magoulas & Lorica 2009). Analyzing a large set of the population is important in identifying trends that are important in business, healthcare, government, education and other systems but this should not be at the expense of individual privacy (Farrell 2005). Experts argue that big data is in itself ethically neutral. However, its usage is not. This means that there is need for a universal code that attempts to streamline actions with values. The American administration realized this and released a blueprint for Consumer Privacy Bill of Rights that encapsulates values of security, transparency and accountability (Kord 2012). There are certain technologies that border on the infringement of privacy. For example, iPhone’s location logging, Foursquare and Facebook pinpoint a person’s location when they log into these services (Palmer 2013).

Social media in particular is one frontier where people might one day regret about how much information about themselves they have voluntarily surrendered under their frictionless interactions with others (Goldfarb & Tucker 2012). In analyzing the impact that big data has had on our lives, it is important to consider the actions of a company like Google that now offers an avalanche of free software in order to display advertisements on the web (Choi & Varian 2012). The same is true for Facebook where software that was expensive in the not too distant past is now freely available, increasing the sheer size, scale and speed of information dissemination (Ayes 2007). In considering how much big data has influenced our values, one should consider the Arab spring and the Occupy Movement that have been a direct result of coordination and communication of activities through the social media (Hilbert & López 2011).

Social responsibility for organizations using big data

Much of the hype from big data today is based on targeting and tracking consumers of different products. The proliferation of many different datasets that include cell phone histories to point-of-sale records that are public have allowed for more granularity in drawing correlations that are insightful (Akella 2008). There has been a lot of literature on how big data has helped increase competition, allowed for multiple occurrences to be measured in real time, created reliable customer profiles and helped craft the most effective marketing techniques that have ultimately impacted the profit margins of most companies. However, not all researchers and entrepreneurs are interested in profit margins as some have employed big data to improve the social status of others (Thomke 2003). While companies like Google have endeavored to use big data to reach out to a majority of people as part of its corporate social responsibility, some activists and social entrepreneurs have taken it upon themselves to improve the world by attempting to alleviate hunger, disease, poverty, corruption, war, environmental degradation among other pressing problems that form the bulk of our everyday deluge (Brynjolfsson & Saunders 2009). Most practitioners in health and humanitarian sectors view big data as a means of empirical problem solving.

Public-private partnerships and non-profit companies have coalesced to form innovations that have been fundamental in improving the livelihoods of many global citizens. For example, in 2007 after the post election violence in Kenya, Ushahidi was formed as a way of tracking individual’s accounts of the violence (Gangadharan 2013). This largely helped in spurring donations and goodwill in the country which helped alleviate the situation. Ushahidi, through text messages, eyewitness reports, emails and social media has gone on to highlight the plight of residents in Macedonia as well as victims of the earthquake in Haiti. Other initiatives have endeavored to pair practitioners with civil society groups in a non-for-profit arrangement. An example is DataKind which pairs data scientists and the civil society in identifying patterns that are eventually used by organizations such as the World Bank in its desire to promote good governance, fighting corruption and fostering transparency (LaValle et al 2010).

Orange, which has its operations in 20 countries in Africa, uses big data as part of its corporate social responsibility to deal with developmental challenges. The company “recently aggregated data from its networks in the Ivory Coast, stripped it of personal information and then issued a global challenge to see how the data could be used to help development efforts in the country” (Saldinger 2013). Teams, ranging from WHO to NASA, used this information and found a new way of mapping traffic in towns in addition to a way of informing people about epidemics. This demonstrates that the applications of big data are limitless and can be used to improve the economic wellbeing as well as the social status of many global citizens.

Responsibility of consumers pertaining big data

There has been increased consciousness as to who bears the greatest brunt in case of any negatives like emission footprints: between producers and customers. On the internet, the level to which a producer understands the customer is decided mostly by how much information the customer provides about him/herself (Lenzen et al 2007). Naturally, the producer of the commodity usually has the prerogative to decide what information is relevant to reach the targeted customer. The bottom line here is that unless a customer volunteers information, then the producer cannot make the necessary contact. Today, upon visiting the internet, one leaves a digital footprint that is the subsistence for data analysts keen on utilizing big data for the sake of their companies’ profitability. Uploading videos on YouTube, sharing pictures on Instagram, tweeting and sharing content on Facebook are among the most common actions of consumers on the internet (Chui et al 2010; Howe 2006). The large amount of data that is left behind as history from using these companies, by millions of potential customers constitutes ‘big data’. If for example one posts that he/she is planning to visit New York on Facebook, there will be numerous banner advertisements from companies that sell flight tickets to New York the next time he/she accesses his/her account. This is not a coincidence but rather the practical use of big data in analyzing information that potential customers freely share on the web. Facebook is one company that has successfully used big data to get ahead in advertising (Gantz & Reinsel 2010).

This application of big data enables companies to create advertisements that are more relevant to individual clients rather than focus on a large client pool (Brown 2011). It is prudent to realize that customers are the ones who are responsible for triggering big data according to the examples above. Caution must however be taken by marketers not to be so overwhelmed with the capabilities of big data (Bollier 2010). When potential customers update their profiles on the net, marketers must first recognize that they are human beings who behave in very unpredictable ways and thus may give information that is false in order to advance a different motive (Lyster 2013). They may also be using ad blockers meaning that their targeted marketing is not received as envisaged (Goldfarb & Tucker 2010). Businesses must also realize that consumers are opinionated and may as a matter of fact be tired by constant bombardment with advertisements every time they are on the net. The fact that a mere post, download, upload or tweet can spark an avalanche of relevant advertisements can make consumers skeptical of the tactics of marketers to the point that they feel like they are being stalked (Einav 2013). Therefore, consumers have a big responsibility in the way that big data is used by businesses as they are ultimately the targeted party (Goel et al 2010). With this in mind, it is prudent to point out that there is going to be a very advanced way of marketing that will spark from the combination of big data and social media (Einav 2013). Targeted marketing will ultimately create business opportunities for companies. However, it should be used in a responsible way.

Globalization has presented new challenges, not only for managers and businesses, but also for individuals keen on socializing more with different cultures (Stonehouse et al 2004). Big data has had an unprecedented impact on businesses such that they can no longer exist without it.

Conclusion        

Big data is a concept that will exist for the foreseeable future. Many businesses have realized its importance as it provides information that is relevant to their improvement. Many multinational have found big data as an invaluable tool for steadying the tide of global competitiveness as well as ensuring that customer needs are anticipated and met (Daft 2009). There is need for massive investment if the raw data sourced from big data is to be sorted, organized and utilized for the benefit of an organization. Moreover, caution should be taken to ensure that individual rights to privacy are not infringed.

 

 

 

References

Akella, J. et al, 2008. Data-driven management: Bringing more science into management, McKinsey Technology Initiative Perspective

Ayes, I., 2007. Super crunchers: Why thinking-by-numbers is the new way to be smart. New York: Bantam Books

Bartlett, C. Et al, 2008. Transnational Management. 5^th edn. McGraw-Hill.

Blank, C., 2011. Mobile Makeover:Mobile marketing use increases as quick serves try to attract on-the-go customers. QSR. Retrieved January 7, 2014 from http://www.qsrmagazine.com/exclusives/mobile-makeover

Bohn, R. & Short, J., 2010. How much information? 2009: Report on American consumers, University of California, San Diego, Global Information Industry Center.

Bollier, D., 2010 The promise and peril of big data. Aspen Institute.

Brown, B. et al, 2011. Are you ready for the era of ‘big data’? Mckinsey Quarterly, 4, 24-35.

Brown, R.G. & Caddick, J.M., 2003. The factory is virtual…the savings are real, paper presented at RTO AVT symposium.

Brynjolfsson, E. & Saunders, A., 2009. Wired for innovation: How information technology is reshaping the economy. Cambridge, MA: MIT Press.

Brynjolfsson, E. et al, 2011. Strength in numbers: How does data-driven decision making affect firm performance? SSRN. Retrieved January 7, 2014 from http://www.ssrn.com/abstract=1819486

Cavallo, A., 2012. Scraped Data and Sticky Prices. MIT Sloan Working Paper.

Choi, H. & Varian, H., 2012. Predicting the Present with Google Trends. Economic Record, 88, 2–9.

Chui, M. et al, 2010. The Internet of things. McKinsey Quarterly.

Daft, R.L., 2009. New Era of Management. London: Cengage

Davis, J. et al, 2009. The microeconomics of personalized medicine: Today’s challenge and tomorrow’s promise. Nature Reviews Drug Discovery, 8, 279–286.

Einav, L., 2013. What Happened to Online Auctions? Mimeo, Stanford University.

Einav, L., 2013. Learning from Seller Experiments in Online Markets. NBER Working Paper No. 17385.

Farrell, D., 2005. Sizing the emerging global labor market. McKinsey Quarterly

Gangadharan, S.P., 2013. How can big data be used for social good? The Guardian 30 May

Gantz, J.F. & Reinsel, D., 2010. The digital universe decade—Are you ready? IDC iView, sponsored by EMC

Goel, S. et al., 2010. Predicting Consumer  Behavior with  Web Search. Proceedings of the National Academy of Sciences, 107(41), 17486–‐17490.

Goldfarb, A. & Tucker, C., 2010. Advertising bans and the substitutability of online and offline advertising. SSRN retrieved January 7, 2014 from http://www.ssrn.com/abstract=1600221

Goldfarb, A. & Tucker, C., 2012. Privacy and Innovation. Innovation Policy and The Economy, 12, 65–‐90.

Hilbert, M. & López, P., 2011. The world’s technological capacity to store, communicate, and compute information. Science.

Hopp, M., 2013. Big Data – Nothing New But Still A Game-Changer. London: Law Research Business Ltd

Howe, J., 2006. The rise of crowdsourcing. Wired, 14

Johnson, M.W. et al, 2008. Reinventing Your Business Model. Harvard Business Review, 86(12) p: 50

Kiron, D., 2010.Analytics: The widening divide: How companies are achieving competitive advantage through analytics. IBM Institute for Business Value in collaboration with MIT Sloan Management Review.

Kord, D., 2012. Ethics of Big Data: Balancing Risk and Innovation. O’Reilly Media

LaValle, S. et al, 2010. Analytics: The new path to value: How the smartest organizations are embedding analytics to transform insights into action. IBM Institute for Business Value in collaboration with MIT Sloan Management Review

Leber, J., 2013. In The Hospital Of The Future, Big Data Is One Of Your Doctors. Fat Company. Retrieved January 6, 2014 from http://www.fastcoexist.com/3022050/futurist-forum/in-the-hospital-of-the-future-big-data-is-one-of-your-doctors

Lenzen, M. et al, (2007). Shared producer and consumer responsibility — Theory and practice. Ecological Economics, 61(1), P. 27–42

Lyster, F., 2013. When Social Media Meet Big Data. Mindjumpers. Retrieved January 6, 2014 from http://www.mindjumpers.com/blog/2013/11/social-big-data/

Madden, S., 2012. From databases to big data. Internet Computing, IEEE,16 (3), 4-6.

Magoulas, R. & Lorica, B., 2009. Big data: Technologies and techniques for large scale data. Release 2.0, 11.

Palmer, D., 2013. Splunk 2013: Mobile matters for WorldPay in big data boom. Computing. Retrieved January 6, 2014 from http://www.computing.co.uk/ctg/analysis/2297722/splunk-2013-mobile-matters-for-worldpay-in-big-data-boom

Saldinger, A., 2013. Big data good for development — and business. Devex Impact. Retrieved January 6, 2014 from https://www.devex.com/en/news/big-data-good-for-development-and-business/82545

Stonehouse, G. et al, 2004. Global and Transnational Business. 2^nd edn. Wiley

Thomke, S.H., 2003. Experimentation matters: Unlocking the potential of new technologies for innovation. Cambridge, MA: Harvard Business Press.

Wen, H., 2012. Big ethics for big data. Oreilly. Retrieved January 6, 2014 from http://strata.oreilly.com/2012/06/ethics-big-data-business-decisions.html

Woody, T., 2012. Automakers, Tech Companies Mining Electric Car Big Data to Plot Industry’s Future. Forbes. http://www.forbes.com/sites/toddwoody/2012/06/18/automakers-tech-companies-mining-electric-car-big-data-to-plot-industrys-future/

 

 

 

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