Data Collection: A Critical Analysis

Data collection and analysis has been named “the oil of the digital industry” (The economist, 2017). With the increased use of technology both business and consumers now generate large volumes of data every day with the ‘Big Data’ technology market growing at an annual growth rate of 23.1% from 2014-2019 (Herschel and Miori, 2017). Consumer data is proving to be of great value to business enabling a deeper understanding of consumers in order enhance their customer experience and inform marketing activities (Colombus, 2016). However alike to the oil industry, the big data industry does not come without it’s flaws. The volume and scope of data now being generated raises questions concerning morals, ethics and consumer privacy. This critical essay will investigate the moral implications of data collection and how data should inform marketing activities. Example cases will be examined to draw an informed conclusion.

The increase in technology and digitalisation of consumer culture has resulted in what’s known as ‘Big Data’ referring to the collection and analysis of extremely large sets of data (Deighton, 2018). Businesses, in particular marketers look at metrics now more than ever before (Olenski, 2018). Colombus (2016), believes the use of big data is “revolutionising” marketing and sales providing businesses with valuable information that allow managers to gain insights into future trends, improve their customer service, maximise their online reach and increase the efficiency of their supply chain. Boyd and Crawford (2012) support this in stating that the technology and techniques behind big data analysis provides a level of intelligence that can generate insights that were previously impossible. However, this named ‘revolutionary’ technological movement also brings uncertainties. The benefits of big data to businesses are unquestionable however it’s moral impact on society is less easily understood (Braschler et al,. 2019). There are concerns that the scope of big data is a major threat to individuals “freedom and privacy” (Christen et al., 2019).  That being said, the use of big data is now considered essential for businesses to effectively adapt to meet demands in order to survive amongst competitors (Cordon et al., 2019).

To understand the advance in data collection further, it is important to identify how technology is used to inform marketing activities. With the ever-increasing use of digital technology, the volume of data collected is continuously growing (Cordon et al., 2019). Consumer behaviour can now be tracked, searched and analysed through their use of social media, smart phone apps, web browsing, GPS tracking and online streaming which leave a digital record at every click (Deighton, 2018). Cordon et al (2019) explain in more detail, when an internet user visits a website, a company can determine both their geographical location from the IP address along with what web browser and device is being used. This enables the business to implement marketing “customisation” offering products or services to that consumer based on their estimated “purchasing power” and the habits of similar users (Corden et al., 2019). This portrays a clear example of the volume of information that can be collected and utilised from a consumer’s technological footprint.

Real life examples of big data initiatives provide a greater insight into the use data for business activities. Walt Disney theme parks provide a sophisticated example of the use of big data as each of their visitors are issued a ‘magic wristband’ containing radio- frequency identification (Alharthi et al., 2017). For the purposes of the visitors these provides a more personal experience with benefits such as queue jumping meanwhile for the company it provides data sets to analyse consumers purchasing behaviours, waiting time and preferences all in order to effectively enhance the customer experience (Alharthi et al., 2017). In contrast to this, Amazon displays how data collection has been used for business activities other than marketing. Amazon have similarly produced wristbands to collect data however for the purposes of tracking the productivity of their workers to the extreme that even toilet breaks are timed (Solon, 2018). Solon (2018) states the initiative is regarded as an unethical use of data collection as amazon workers are turned into low-paid “human robots”. These examples illustrate the extent to which data can be utilised for both good and bad. Businesses are producing vast quantities of data every day and have the potential to utilise this data in a moral manner to inform marketing activities and enhance the customer experience.

Understanding the accessibility and attainability of consumer data to businesses results in questioning regarding the ethics behind data collection and who else could potentially be accessing their data. Most of the excitement towards big data stems from the easiness to access such large amounts of it (Boyd and Crawford, 2011). Boyd and Crawford (2011) state however, simply because it is accessible does always not grant it to be ethical. In fact, it is stated that 50% of businesses ethical violations will be a result of the misuse of big data (Herschel and Miori, 2017). To give an illustration, a study carried out by a Harvard-Based research group collected information from anonymous profiles of 1700 college Facebook Users, unknown to them, to learn how their interests and friendships changed over a period of time (Lewis et al,. 2008). This information was passed on to other researchers for analysis who discovered that the data could remove the anonymity of the information therefore invading the privacy of the students (Zimmer, 2008). Martin (2015) highlights the issue this example encompasses, explaining that passing on consumer data can result in “secondary misuse” of the data. Individuals privacy may be compromised, or false conclusions may be drawn (Martin, 2015). There have further been cases where employees have simply copied and shared data they’ve had access to or glitches in the technology itself have resulted in leaks of consumers data (Alharthi et al,. 2017). With that being said, the benefits of the uses of big data cannot be fully disregarded due to unethical cases (Martin, 2015). Nevertheless, as data collection and analysis grows, individuals personal and sensitive information is at increased risk of exposure therefore the need to protect individuals privacy is essential (Alharthi et al,. 2017).

Misuse of consumer data puts into questioning to what extent consumers are aware of how their data is used and how businesses can be more transparent in terms of data usage. Blackman (2018) states that a study by KPMG International showed that almost half of consumers are more anxious than they were in 2017 about giving businesses their data. However, despite the increased anxiousness 75% of those surveyed are still willing to do so, in particular millennials as they see the benefits in enhanced customer experience and personalisation (Blackman, 2018). Morey et al. (2015) support this in stating that consumers appreciate that sharing their data has the potential to save money and time leading them to more personalised experiences and discounts. On the other hand, Masters (2019), states that in many cases consumers do not understand the extent to which businesses investigate consumers lives. The essential key for both businesses and consumers regarding data collection and analysis is trust (The DMA UK Ltd, 2018). Consumers decide whether to share their information with a company primarily depending on their level of trust in the business, which can be built through business transparency (The DMA UK Ltd, 2018). Christen et al, (2019) explain that transparency means individuals have the right to know what data is being collected and how it is being used. Transparency can be advantageous for the business as well as their consumers as it enables them to gain customer loyalty (Blackman, 2018). That being said, businesses have a tendency to provide their terms and conditions, on their websites, in an incomprehensible, lengthy format that often deters customers from reading or understanding (Braschler et al,. 2019). Nonetheless, transparency of business data usage means consumers are more informed when deciding whether to trust a company with their data.

There are further advantages in technology that are resulting in new moral concerns surrounding privacy and algorithm profiling. Businesses are heavily investing in artificial intelligence which has resulted in new smart devices such as Amazon’s ‘Alexa’ and ‘Google home’ created for consumers amusement and convenience (Statt, 2019). Such devices seek to combine artificial intelligence and the real world allowing users to interact more naturally through speaking rather than using the likes of a mouse or keyboard (Deepika et al,. 2019). Devices like these, along with our smart phones, use powerful audio analysis algorithms that have the ability to gather personal information simply from the sound of someone’s voice (Mcloughlin, 2018). Smart devices carry out algorithm profiling utilising the information they listen to in order to build up individual ‘profiles’ containing knowledge of “habits and preferences” (Bendle and Dawar 2018). Bendle and Dawar, 2018 state that often devices such as these, are able to predict consumers needs and wants before they themselves can, providing extremely valuable information for businesses to inform marketing activities. However, this has caused concern regarding user’s privacy questioning whether these devices are listening to all private conversations (Cuthbertson, 2019). To illustrate, a Bloomberg, 2019 report shared that employees for Amazon’s Alexa and the Google Home have been found to share recordings amongst their colleagues when they find it to be amusing. In more extreme cases, employees have turned to the police when they have listened to recordings of what they determine to be sexual assault (Day et al,. 2019). However, Google and Amazon have both defended privacy accusations stating that only “small snippets” of recordings are analysed by their experts, simply to improve voice recognition (Cuthbertson, 2019; Statt, 2019). Amazon further stated that no audio is stored unless the “wake word” is used to activate it (Day et al,. 2019). Despite of these claims, a large proportion of society remain reluctant to bring smart devices into their homes over the concern that people are listening (Day et al,. 2019). New smart devices bring an increased level of data handling and therefore an increased level of consumer concerns. To be successful smart device companies must tackle consumer fears through establishing trust. 

Fortunately, as big data has grown rapidly in the last 5 years, law and regulation are also developing at speed (Herschel and Miori, 2017; Kemp, 2018). The year of 2018 saw significant legislation being passed consisting of new requirements replacing the out-dated 1998 Data Protection Act (Solon, 2018). The General Data Protection Regulation (GDRP) was implemented in May 2018, in all EU countries giving individuals greater protection of their privacy and forcing companies to make significant changes to their data collection techniques with large fines if failure to comply (Solon, 2018). The GDRP has also been applied in the UK along side the Data Protection Act 2018 (Burgess, 2019). The new legislation encompasses various rights for individuals including the “right to be forgotten” meaning businesses must delete data if an individual no longer wishes for it to be held (Solon, 2018). Further, as mentioned earlier instead of lengthy, incomprehensible terms and condition forms, businesses are required to replace these with easy to understand consent forms (Solon, 2018). Burgess (2019), believes that “Europe is now covered by the strongest data protection rules”. This legislation marks significant improvements for the moral implications of data use. However, Coos (2018), argues that there is a concern for overregulation as the GDRP could be a burden for customers affecting the enjoyment and convenience of customised marketing. This is a key factor for businesses customer retention (Coos, 2018). Further, enforcing the new legislation within a business can be extremely costly as many must hire employees simply for that purpose (TDS, Ltd. 2018). That being said, when it comes to laws and legislation enforcing good morals and ethics concerning data usage, the pros outweigh the cons, when an individual’s privacy is at stake.

Conclusion

To conclude, data collection and analysis, otherwise known as big data, is a digital phenomenon that has grown rapidly in recent years becoming an essential tool for businesses to compete with others. The collection of data allows businesses to understand their consumer’s needs and wants, building profiles in order to inform marketing activities more efficiently. Additionally, evidence shows consumers enjoy the benefits that the convenience elements data collection provides such as customised marketing. However, it is imperative that businesses carry out their data use ethically and in line with regulations in order to protect the privacy of individuals. Evidence illustrates numerous cases where individuals privacy have been breached, data has been used for unethical purposes or secondary misuse of data has compromised data anonymity. Ethical use of data demonstrated through business transparency is beneficial for both businesses and consumers as it forms trust between the two and results in customer loyalty. Data collection and analysis is a valuable tool for informing marketing activities provided moral implications are considered.

References

• ALHARTHI, A. KROTOV, V. BOWMAN, M. 2017. Addressing barriers to big data. Business Horizons (Online). 60(3). pp. 285-292. (Viewed 04 January 2020). Available from: https://www-sciencedirect-com.gcu.idm.oclc.org/science/article/pii/S0007681317300022
• BENDLE, N. DAWAR, N. 2018. Marketing in the age of Alexa. Harvard Business Review. 96 (3). pp. 80-86. (Viewed 08 January 2020). Available from: http://web.a.ebscohost.com.gcu.idm.oclc.org/ehost/detail/detail?vid=1&sid=a8492773-285c-492e-b0c8
• BOYD, D. CRAWFORD, K. 2012. Critical questions for big data. Information, communication & Society (Online). 15(5). pp. 662-679. (viewed 02 January 2020). Available from: https://www-tandfonline-com.gcu.idm.oclc.org/doi/full/10.1080/1369118X.2012.678878
• BLACKMAN, P. 2018. Big data needs a big re-think: consumers are more anxious, but businesses can restore trust with greater transparency. KPMG International. (Viewed 05 January 2020). Available from: https://home.kpmg/xx/en/home/media/press-releases/2018/11/consumers-more-anxious-about-big-data-kpmg-study.html
• BRASCHLER, M. STADELMANN, T. STOCKINGER, K. 2019. Applied Data Science: Lessons Learned for the Data-Driven Business (Online). New York: Springer Publishing. (Viewed 03 January 2020). Available from: https://link-springer-com.gcu.idm.oclc.org/book/10.1007%2F978-3-030-11821-1
• BURGESS, M. 2019. What is GDPR? The summary guide to GDPR compliance in the UK. WIRED (Online). 21 January. (Viewed 09 January 2020). Available from: https://www.wired.co.uk/article/what-is-gdpr-uk-eu-legislation-compliance-summary-fines-2018
• CHRISTEN, M. BLUMER, H. HAUSER, C. HUPPENBAUER, M. 2019. The Ethics of Big Data Applications in the Consumer Sector. In: BRASHLER, M., ed. Applied Data Science. New York: Springer Publishing, pp. 161-180
• COLOMBUS, L. 2016. Ten ways big data is revolutionising marketing and sales. Forbes (Online). 19 May. (Viewed 03 January 2020). Available from: https://www.forbes.com/sites/steveolenski/2018/01/24/5-ways-tech-will-change-marketing-in-2018/#617e0b543db5
• CORDON, C. GARCIA-MILÀ, P. FERREIRO VILARINO, T. CABALLERO, P. 2016. Strategy is Digital: How Companies Can Use Big Data in the Value Chain (Online). New York: Springer. (Viewed 03 January 2020). Available from: https://link-springer-com.gcu.idm.oclc.org/book/10.1007%2F978-3-319-31132-6#about
• COOS, A. 2018. GDRP: The Pros and Cons. Endpoint Protector (Online). 01 February. (Viewed 08 January 2020). Available from: https://www.endpointprotector.com/blog/gdpr-the-pros-and-the-cons/
• CUTHBERTSON, A. 2029. Google defends listening to private conversations on google home – but what intimate moments are recorded? The Independent (Online). 12 July. (Viewed 08 January 2020). Available from: https://www.independent.co.uk/life-style/gadgets-and-tech/news/google-home-recordings-listen-privacy-amazon-alexa-hack-a9002096.html
• DEIGHTON, J. 2018. Big Data. Consumption Markets & Culture (Online). 22(1). pp. 68-73. (viewed 02 January 2020). Available from: https://www-tandfonline-com.gcu.idm.oclc.org/doi/full/10.1080/10253866.2017.1422902
• DAY, M. TURNER, G. DROZDIAK, N. 2019. Amazon Workers Are Listening to What You Tell Alexa. Bloomberg (Online). 10 April. (Viewed 09 January 2020). Available from: https://www.bloomberg.com/news/articles/2019-04-10/is-anyone-listening-to-you-on-alexa-a-global-team-reviews-audio
• DEEPIKA, M. VIJAY, K.C. SRIVASTAVA, A. SRINIVAS, M. 2019. AI & ML - Powering the Agents of Automation. INDIA: BPB Publications.
• DMA. 2018. Data Privacy: What the Consumer really thinks – UK- February 2018 (Online). London: The DMA UK Ltd. (Viewed 03 January 2020). Available from: https://dma.org.uk/uploads/misc/5a857c4fdf846-data-privacy---what-the-consumer-really-thinks-final_5a857c4fdf799.pdf
• THE ECONOMIST. 2017. The world’s most valuable resource is no longer oil, but data. The Economist. (Viewed 07 January 2020). Available from: https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data
• HERSCHEL, R. MIORI, V.M. 2017. Ethics and Big Data. Technology in Society (Online). 49 (1). pp. 31-36. (Viewed 08 January 2020). Available from: https://www-sciencedirect-com.gcu.idm.oclc.org/science/article/pii/S0160791X16301373
• KEMP, R. 2018. Big data and data protection (GDPR and DPA 2018). Thomson Reuters: Practical Law (Online). (Viewed 09 January 2020). Available from: https://uk.practicallaw.thomsonreuters.com/w-017-1623?transitionType=Default&contextData=(sc.Default)&firstPage=true&bhcp=1
• LEWIS, K. KAUFMAN, J. GONZALEZ, M. WIMMER, A. CHRISTAKIS, N. 2008. Tastes, ties and time: a new social network dataset using Facebook. Social Networks. 30(4). pp. 330-342
• MARTIN, K. 2015. Ethical Issues in the Big Data Industry. MIS Quarterly Executive. 13(2). Pp. 67-85
• MASTERS, T. 2019. Ethical Considerations of Marketing Research (Online). Chron. (Viewed 07 January 2019). Available from: https://smallbusiness.chron.com/ethical-considerations-marketing-research-43621.html
• MCLOUGHLIN, I. 2018. Are phones listening to us? What they can learn from the sound of your voice. The Conversation (Online). 27 November. (Viewed 08 January 2020). Available from: https://theconversation.com/are-phones-listening-to-us-what-they-can-learn-from-the-sound-of-your-voice-105753
• MOREY, T. FORBATH, T. SCHOOP, A. 2015. Customer Data: Designing for Transparency and Trust (Online). Harvard Business Review. (Viewed 06 January 2020). Available from: https://hbr.org/2015/05/customer-data-designing-for-transparency-and-trust
• OLENSKI, S. 2018. 5 Ways Tech Will Change Marketing In 2018. Forbes (Online). 24 January. (Viewed 02 January 2020). Available from: https://www.forbes.com/sites/steveolenski/2018/01/24/5-ways-tech-will-change-marketing-in-2018/#617e0b543db5
• SOLON, O. 2018. Amazon patents wristband that tracks warehouse workers' movements. The Guardian (Online). 01 February. (Viewed 04 January 2020). Available from: https://www.theguardian.com/technology/2018/jan/31/amazon-warehouse-wristband-tracking
• SOLON, O. 2018. How Europe's 'breakthrough' privacy law takes on Facebook and Google. The Guardian (Online). 19 April. (Viewed 09 January 2020). Available from: https://www.theguardian.com/technology/2018/apr/19/gdpr-facebook-google-amazon-data-privacy-regulation
• STATT, N. 2019. Amazon’s Alexa isn’t just AI — thousands of humans are listening. The Verge (Online).10 April. (Viewed 08 January 2020). Available from: https://www.theverge.com/2019/4/10/18305378/amazon-alexa-ai-voice-assistant-annotation-listen-private-recordings
• TDS. Ltd. 2018. The positive and negative implications of GDRP – Ireland – September 2018 (Online). Dublin: TDS. Ltd. (Viewed 08 January 2020). Available from: https://www.timedatasecurity.com/blogs/the-positive-and-negative-implications-of-gdpr
• ZIMMER, M. 2008. More on the “Anonymity” of the Facebook dataset- it’s Harvard College (Online). Michael Zimmer Blog. 03 January. (Viewed 04 January 2020). Available from: http://www.michaelzimmer.org/2008/01/03/more-on-the-anonymity-of-the-facebook-dataset-its-harvard-college/