Economics of User Data


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User data has been the buzzword for quite sometime. This is due to the enormous growth in internet connectivity and accessibility to smart services. With the number of users growing due to the strides made in technology and cheap devices on other, the amount of data generated by user activity is also growing at an exponential rate. Data is giving rise to a new economy and every company wants to be a part of the bandwagon. Organizations such as Google, Facebook, Amazon, LinkedIn and others have identified data as the driver for growth and change. These silicon valley giants have adopted the data-driven business model in one form or the other and a significant chunk of their revenue is generated by leveraging the data from various user activities for various purposes.

According to a research published by IDC [idc17], the data generated is doubled at least every two years and it is estimated to be 44 zettabytes by 2020 and reach 180 zettabytes by 2025.

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Data generated from users is not limited to just data from user activity in mobile devices and laptops but it also includes data from smart home devices like Alexa and Google home. There have been various reports of smart home devices listening to user conversations and uploading them to dedicated servers. Recently there have been news regarding Echo, Amazon’s signature smart home device which was used to solve a murder investigation in United States [Sau17]. IoT enabled devices have given options to all major stakeholders to identify user behaviour patterns based on their voice commands and it is seen as a viable alternative for user pro ling instead of observing online user activities. Siri, Alexa, Cortana and now Google’s voice assistant are making forays into the smart home segment and the market is expected to see an upward trend in terms of the user growth over the next few years. Also IoT is seen as an alternate source of revenue and data generation by these companies. Based on an IDC research journal [Kan16] the number of smart devices connected to the internet is expected to triple every three years and reach 80 billion by 2025.

Data analytics has been widely adopted and competitors are investing heavily on big data analytics due to the immeasurable opportunities. Big data is an emerging trend and companies for prediction and forecasting analysis and companies are queuing up to capitalize on the monetization potential of the data. It is estimated that the big data market will rise from $130 billion in 2016 to more than $203 billion in 2020 [Wil18] and companies are scrambling to benefit from this. Ad based revenue is the major revenue driver for tech giants like Google and Facebook which contributes to almost 80% of their revenue. Both these tech behemoths are churning out new suite of products to stay ahead of other competitors by adopting a proactive mover advantage in order to increase the user base, thereby collectively enriching the existing user database and hence increase the current market share. With each user signing up to be a part of the online community by creating accounts in either Google or Facebook, they unknowingly give their data to these companies. These companies then re ne, process and extract the necessary information from the raw data collected and they use artificial intelligence, machine learning , user pro ling and other techniques to categorize users according to their demographic interests and activities. Based on the categorization different ads are displayed to the users by these companies and these companies are payed a premium by vendors to display their ads.

Data driven business model(DDBM) has been altered according to the needs and strategy of each company. This is evident from the competitive acquiring of startups like Whatsapp and Instagram by Facebook, Linkedin by Microsoft. These startups were acquired for huge sums due to the rich data generated from users through text messaging, audio and video sharing and online pro le creation. Companies have moved from the traditional data collection techniques such as web forms and surveys to advanced cognitive services which are used to streamline the steady flow of unstructured data that is generated by users. Data generated from users is used to create new services which in turn attracts even more users causing a ripple effect and this is termed as the data-network effect as mentioned in a recent journal [Bub17].

Apart from computer generated techniques to collaborate data, organizations are investing a lot in smart home devices and other IoT devices. This effect is due to the rapid development of new state of the art products from companies to gain a sign cant share in the market. Competitiveness among organizations has seen them invest a lot in RD of data analytics of new methods to extract data from users. Knowingly or unknowingly device users give their data to companies either through their online activity or through their smart devices. Based on a recent study on android platform fragmentation [FLG17] there are lot of security loopholes when an android platform is customized according to various device vendors. Companies are willing to exchange data with these device vendors for mutual benefit between the companies i.e the company selling the data stands to be incentivized with new users while exchanging data.

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