Abstract

The emerging technologies have shaped the way users communicate online, where LinkedIn drives users to expand their social network to the greater community to gain significant and objective outcomes. When users expose their profiles and opportunities on LinkedIn, the exposure users gather can be utilised, then users can exploit and store data for future promotion. However, data consent must take in place prior to importing data into a database. Data should be segmented and organised for a potential data analysis report. This has a huge impact on the way users utilise significant data, especially when there are regulations and powerful laws interfering in the way of the users’ ability and views when exporting data from a social network.

Keywords: Networking, Social network, data, organisations, data collectors, privacy

According to Borgatti and Everett (Cited in Abraham, Hassanien & Panda, 2018), the internet is accessed by millions of social media users, but nonetheless important, the connection and data access are little understood due to the mandatory specification being asked online (Abraham, Hassanien & Panda 2018). LinkedIn is the most used social media platform for businesses and networks to connect and gain online communication (Young Entrepreneur Council, 2018). LinkedIn as a social network platform exposes self-identity to LinkedIn users who have created a LinkedIn account online or through their online application. Though LinkedIn is used to connect and expand self-identity, the extreme ability LinkedIn offers is noted in several aspects of networking digitally and seems it has been understood by LinkedIn account owners. Academic authors such as Abraham, Hassanien & Panda (2018) questions the power of data and the value of LinkedIn which seeks, collects and store data from what LinkedIn offers in order to create noise for their organisation or project’s objective (Kohler, 2019). Despite LinkedIn affords users with significant data, it is being methodically exterminated because users are uncertain on the ability of the power LinkedIn promotes.

LinkedIn is a social media platform that allows identity expression (Gündüz, 2017), exploration of different online communities (Papacharissi, 2009) and experimenting with data visible online (Gündüz, 2017). LinkedIn is based on a niche audience (Papacharissi, 2009), where profiles are targeted using the information provided by users’ professionalism within and outside of their network (Papacharissi, 2009). LinkedIn specialises to connect with different organisations and allowing associations to establish a networking environment.  Furthermore, LinkedIn allows users to “connect, communicate, and develop valuable relationships that are crucial to a business’ success” (Sean L, August 2018). The social network prior to LinkedIn had a different mechanism, where personal blogs were created and advertised digitally that began to burst into popularity (Hendricks, 2013). This established another networking environment where users can “reach the maximum number of people without sacrificing the intimacy of person-to-person communication” (Hendricks, 2013). The observation of this LinkedIn platform only portrays a view of how one’s organisation can reach success just by connecting with other networks (Hendricks, 2013). Exploring data using LinkedIn can provide users with endless options to seek people with similar interests, hierarchy levels and influencers to expand professional online community and knowledge (LinkedIn, 2019). Examples of searching for people using LinkedIn is through keywords such as company or job title or search for titles associated with “director” or searches can expand to more than one person by using the search bar (LinkedIn, 2019). LinkedIn has the power of enabling users to explore and experiment with endless information to suit their company’s objective.

A user of LinkedIn can connect with other users, where users will find personal information prior to accepting the connection. When users are, they are given the other users’ identity which means data is given. The information displayed on LinkedIn can be collected and measured to identify the type of data, the performance of users’ interactions and content sharing (Pang et al. 2008, cited in Panda et al., 2018). This entirely means that data can be found anywhere on the world wide web, however, the utilisation and measurement are significant to businesses (Abraham, Hassanien & Panda, 2018). It is vital to understand the background and the mechanism of LinkedIn; however, businesses and professionals are in no doubt connected with other professionals to gain knowledge and support (Abraham, Hassanien & Panda, 2018). Other users will argue their objective differs where they need to grow their database instead. LinkedIn provides data such as job titles, industry, geographical and contact information. Once data is collected, and prior data is stored, data integrity enhances the health and maintenance of any digital information which there are several types of data integrity procedures (Brook, 2019). In this case, Entity Integrity identifies each record from each other. Users of LinkedIn does not provide a numeric number except a number is not provided on LinkedIn, however, an email address can be a unique identifier if data is sourced from LinkedIn. Other ways in storing data may include contacts should not have any spelling mistakes or errors when importing data.

Once data integrity is applied, data should be measured through various methods of data analysis (Brook, 2019). For example, data should be analysed using a tool that is stronger enough to create reports that compare each data that was transferred into a lead or not. Data integrity and data measurements are vital, though there are other ways to do this digitally (Sridhar, 2018). LinkedIn allows administrators to access analytics that can identify the movement across each business’ profile (Sridhar, 2018). Through this type of measurement using Linkedin, users have the potential to analyse the noise made on their page (Sridhar, 2018). For example, the analytics within LinkedIn allows them to explore who clicked and interacted that provides an overall engagement. This will allow boosting inbound marketing procedures which feedback into the organisation’s database. However, this cannot happen unless data collected from LinkedIn’s analytics is only accepted if data integrity is applied (Sridhar, 2018). Subsequently, data analysis is used to compare the inflation between page visits, an analysis of themes and other information an organisation requires. Data analysis is applied through its methodical statistics or rational technique to highlight, summarise and evaluate data within a system (Raulf, 2016). LinkedIn provides this analysis through its own portal. Google analytics is also used to compare and analyse each movement made on a social network (Raulf, 2016). In relation to LinkedIn and an internal database, Power Business Intelligence allows owners to connect a segmented database, social media channels and other systems to analyse movements across the digital platform (Raulf, 2016).

LinkedIn is used on a daily basis, and many organisations are utilising this platform, however, the uncertainty of the importance and use of power can be seen obvious. In all organisation, data is the foundation of success and without quality data, data will not be a significant matter to one’s use. Therefore, it is important to “understand the dynamics and mechanisms” (Grindrod et al. 2011 cited in pg. 5, Panda, 2018 behind the formation of the network (Grindrod et al. 2011 cited in pg. 5, Panda, 2018) in order to generate information from LinkedIn. Once data is collected from LinkedIn, the cleaned and aggregated data will then be imported into a data analysis tool. The tools will define the data, capture sequential pattern and provides what-if question, as it ensures the data is gathered in the application of statistical methods (Sridhar, 2018).

LinkedIn has the ability to provide data which can be collected and stored in a database, however, precaution discussions on the data protection, for both individual and users must be considered at all times. Mrutyunjaya Panda thoroughly explains that “the social network is observed to be an inevitable part of human life” (Panda et, al., 2018). Consumers and businesses must be aware of the risks in place when playing with data, especially when personal information is involved. There are concerns in today’s world where data is magically taken away from an individual’s privacy, who can access their data, and the movement from the time data is collected to data applied elsewhere. It is understandable organisations including governments are struggling with modern trends of data protection and ensuring right practices are in place (Stenons, 2018). The Australia government may protect interruptions, only if one gives permission to the other (Eyers, 2018). In some respects, individuals can control the way other users can access their profiles, however, if data is provided by users and displayed online, then data may be used for contact (Constine, 2018). A huge part of the social network is based on providing and consuming data which “are rich in content and linkage data” (Panda et, al., 2018) within the social network of LinkedIn. LinkedIn provides all users maximum capability to access other users’ information such as email addresses (Constine, 2018). Users must accept the conditions prior to allowing to provide LinkedIn with their personal information as users can visit profiles where email addresses are provided. Though this procedure may be the easiest yet, valuable way to communicate the objective of a project, data privacy and protection must be in place (Stenons, 2018). The power of LinkedIn allows users access to collect data; however, it is vital that data collectors demonstrate consent prior to processing any personal information (OAIC, 2019).

According to the Office of the Australia Information Commissioner, “businesses also need to make the withdrawal of consent as easy as giving consent, and, before individuals give consent, must inform individuals about this right to withdraw consent” (OAIC, 2019). When consent is made, a formal written confirmation must be crystallised and uniquely identified on the purpose of the data collection (OAIC, 2019). There is a risk of losing a potential client, however, it is better than interfering with law. Technology and its platforms provide maximum usability to its users. There are cases where LinkedIn is falling short with data protection where some areas do not meet the best practice of compliance (ICE, 2019). Organisations must be vigilant on how they access their data, and whether they are meeting data privacy practices, even though LinkedIn provides vital information to its users. An example of carrying this procedure ethically, data should always be varied by the person who they connect with – it can either be through cold calling, verifying and asking permission or private message with their enquiry (ICE, 2019). Data protection is not only for the organisation, but it is also for all parties involved. Risks for not having a practice or making a decision only based on business’ selfishness objectives, can interfere with data law (ICE, 2019). Therefore, questions should be raised on the importance of data – it is basically a decision based upon significant elements of a company’s objective.

LinkedIn has the power of promoting data to users who seek to grow their central database. Therefore, data maintenance is a significant task in ensuring data is segmented according to common themes and codes (Papacharissi, 2009). Segmenting finds the relevance within a database which assists in promoting marketing activities to customers listed in a database (Linthorst, 2013). The exploration and the efficiency of LinkedIn allow users to capture information such as demographic and firmographic answers. Users can go against the power of LinkedIn and segment data into common themes and codes which ensures efficiency, maintenance and accordance. the significant element is the recognition of overall themes within a database, and “coded according to thematic significance” (Papacharissi, 2009. Pg. 206). Once information is gathered, segmenting data according to preference search will identify the meaning of each data imported into the central database (Papacharissi, 2009).

An example of segmenting data could be coded according to the type of industry and ensuring an email address is a unique identity (Papacharissi, 2009) which this method can identify the purpose of each contact sustained (cited in Boyd and Ellison, 2007. Papacharissi, 2009). Furthermore, when data is extracted from LinkedIn, whether it is personal information or the overall page visits, data should be segmented into common themes such as demographics, interests, users’ industry or position titles (Papacharissi, 2009), depending on the objective of the project.  It will not only allow future communication mail-outs to be successful, but it will also provide users of the central databases to search smoother (Papacharissi, 2009). Nevertheless, if data is stored and used for distribution to active LinkedIn users that are being used unethically, users of LinkedIn may report content as spam for purposes such as inappropriate content and profile, fake accounts or phishing or suspicious messaged on LinkedIn (LinkedIn, 2018). Therefore, LinkedIn offers to flag content and profiles directly online (LinkedIn, 2018). LinkedIn will take action and remove content if necessary, as it is against LinkedIn’s privacy policy to continue to share accounts if being used (LinkedIn, 2018).

LinkedIn and its data provided is used for endless reasons. The collection and dissemination of raw data are significant in today’s world, LinkedIn collects sand expose user’s demographic and firmographic information to existing and potential connections for reasons that can be unknown or beneficial. Organisations may abuse data, and other users seek to source vital information to grow success, though data should be segmented in case of any data loss or confusion when data is stored, above all data should be used with respect by seeking consent prior to sorting and storing the data. LinkedIn a powerful platform that drives and promotes data through providing numerous demographics, geographic firmographic information however, LinkedIn drives exploitation because users have been gathering information and storing data into a centralised database. LinkedIn and data marry together well, LinkedIn provides users with data and LinkedIn’s users provide data. Abraham, Hassanien & Panda (2018) believe big data is taken over the world, therefore the appreciation of data is unfolding thanking the power of social network such as LinkedIn providing data with significant success.

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