History Of Data Analytics And Current Scenarios Assignment Sample

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Introduction of History Of Data Analytics And Current Scenarios Assignment

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Data analytics is referred to as the method of scrutinising the set of data to draw a conclusion statement regarding the data they contain. "History of data analytics" states that it is based on the statistical facts which were initiated in ancient times during the building of pyramids. Data analytics plays a significant role in aiding assurance, audit and decision-making of the business. The digital community assists the professionals of finance to improve the skills of visualisation and advanced analytics of data through “data analytics” that are required to succeed in the driven era. The aim of the study focuses on analysing the significance of data analytics and its contribution to the current globalised era. This topic has been selected in the study because it can provide different insights regarding the digital world and the use of digital methods to fasten the future world.

The technique of data analytics enables the individual to uncover the patterns and take the raw information to extract the important insights. In this digital world, the analytics of data is mentioned as the procedure of evaluating digital information from distinct sources such as mobile applications and websites. Grounded theory is the utilised theory for the data analysis process which is generally more centric on facts and approach. Data analytics in recent scenarios give a precise and clear overview to the companies on how the customers or users are behaving. In the study, the overview of data analytics and its contribution to the growth of business in the recent era of digitisation are critically discussed. The study has focused more on the problems in the implementation of data analytics and its future scope in the digitalisation era.

Research Questions

  1. What is the significance of data analytics in the recent digitized era?
  2. How does data analytics usage contribute toward business transformation?
  3. What are the challenges of the implementation of data analytics in the current digitized era?
  4. What is the future scope of data analytics in the current digitized era?

Literature review

Data analytics and current scenarios

Data analytics is enormously essential because it assists the business organisation in optimising performance. The demand for "Data analytics in the industry" in this recent stage is extremely strong and this has resulted in the huge demand for salaries by the data analyst. In the current scenario of big data analytics, Artificial Intelligence (AI) is capturing the concentration of the whole world and becoming a game-changer for distinct organisations. As opined by Fan et al. (2021), it is making good progress in the data analytics field which assists in augmenting the capabilities of humans and derives better value for a business. The organisations that foresee the trend of obtaining data analytics get significant competitive benefits over the others that failed to offer different functionality to the other customers.

 Data analytics

(Source: Fan et al. 2021)

The market of data analytics is booming in this recent era of digitalisation where the demand for professionals is exploding. From the Forbes news, it has been gathered that IDC analysts have conducted research which showed that the increase in demand has risen to 10 per cent in 2021 from 2020 (Forbes, 2022). Recently, everything is based on analytics which proves immensely beneficial to the potential industrial market. Analytics do not assist only the organisation but also the customers in obtaining the personalised services that are powered by data analytics. Currently, most industries are extremely aware of choosing data analytics in the organisation as it provides flexibility in the operational process within the organisation. In this era of digitalisation, people are changing themselves digitally for obtaining personalised and customised products.

Data analytics and its contribution to the business world

The emergence of data analytics in the digital world is contributing hugely to the business world. The usage of data analytics in the operational process of the organisation has transfigured the organisational procedures and processes for fleecing the operational process and continuing the operation. As stated by Ranjan and Foropon (2021), data analytics in the business assists in comprehending the present state of the business and gives the solid foundation to assume the outcomes of the future. The technique of data analytics enables the business to comprehend the recent scenario of the market and trigger the requirement for a new development of a product or transform the procedures that assist in matching the needs of the market.

The targeted content of the business is well, comprehended by distinct organisations through the use of big data analytics. The business organisation with the help of data analytics focuses on the targeted content knowing the requirements of customers and making the campaign's marketing that is customer-centric or customer-oriented. Data analytics in the business organisation assists them to customise the advertisements and determine which segment of the customer base responds better to the campaign. However, it saves time and money for the business organisation on the convincing cost to create the purchases and develop the overall effectiveness of the marketing efforts. Data analytics contribute highly to recognising the potential chances for increasing the profits or streamlining the operations (Solanki et al. 2019). The business organisation with help of data analytics removes the procedure of waiting for them to happen and takes necessary actions on the same.

Data analytics and its application

Data analytics is widely utilised in the business which is demonstrated as the act of interpreting and evaluating the data. Apart from the business, data analytics is applied in distinct fields such as "Transportation", "delivery and logistics", "healthcare", "manufacturing", "security", and "education and military". As mentioned by Karnouskos (2020, in the banking and educational sites, the possibility of risk and fraud are immensely high. As a result, the banking organisation has learned to conquer and divide the information via profiling and past experiences of the customers.

Risks and frauds detention through the help of data analytics has become feasible and push the products of banking based on the purchasing power of customers. In the healthcare system, the application of data analytics is highly beneficial as it analyses the medical image, genomics and genetics, and the development of drugs. Virtual assistance for customer support and patients is also evaluated based on data analytics (Boulianne and Theocharis, 2020). Internet search becomes easier and feasible with the assistance of data analytics. Data analytics helps the business organisation in targeted advertising initiating from the display banners on distinct websites to the "digital billboards". Speech recognition and advanced image recognition become feasible in comparison to the conventional method with the help of "big data analytics."

Challenges of implementation of data analytics

"Gathering meaningful information", "choosing the appropriate tool", "Consolidating information from multiple sources", "Data accumulation quality ", "creating a data culture among employees", "security of data" are regarded as some of the problems in the implementation of data analytics (Sranalytics, 2022). The data plethora from distinct channels creates it enormously difficult for the organisation to drill down and demonstrate the crucial insight. Another issue with the choice of appropriate tools emerges with the wide number of tools that are available in the market. Evaluating and consolidating the information from distinct structures in one place is the most common issue in the analytics of data. It becomes more complicated if it is done by humans.

 Challenges

(Source: Sranalytics, 2022)

Chances of error occur by creating the information highly unreliable for all. The data output may not be reliable all the time if the data input is erroneous and flawed. As stated by Nguyen et al. (2020), one of the major reasons behind the data that are inaccurate is errors created during the entry of data which is the manual errors. The cause for the poor quality data is because of data disparity. One of the major issues in becoming a "data-driven” organisation lies in the culture of the organisation and not only in the technologies. Security of the data of an organisation is tremendously important and one of the biggest issues in the analytics of data.

Data analytics and future scope

The industry of Data analytics is projected to make over 11 million jobs by around 2026. The organisations utilising big data analytics in the UK are going to invest more in machine learning and AI by approximately 33.49 per cent in 2022 (Forbes, 2022). The major advantages that data analytics provides to a business organisation are:

  • It maximised the effectiveness of business operations.
  • It develops the allocation of resources.
  • It helps in evaluating the aggregation of data.
  • It assists in discovering the potentiality of “untapped markets”.

The sectors that are going to focus more on in the future are the IT sector, Banking, insurance and financial service, E-commerce and retailing due to their immense benefits and transparency (Vassakis et al. 2018). The development has aided the industry of data analytics with the remarkable maximisation in the data collection that is potentially utilised to tap into several markets.

Methods

Data analytics is a booming industry in context to the current situation. The main purpose, of Data analytics is to raw data from different available sources and utilize this source of information for analysing th trends and insights about the market. On the contrary, it can be used in the growth of the organisation. As per the view of Lindgren (2021), in the context of the study of Data analytics, the case study that has been conducted is priory dependent upon the qualitative study which further includes journals, articles, and books. A qualitative study has been chosen as the method since it covers a deeper understanding in obtaining vital concepts about the significance of data analytics in corporate organisation.

 Methods

(Source: Lindgren, 2021)

Qualitative study and analysis of data include the recognition, scrutinisation and interpretation of themes and patterns in the textual data. The qualitative analysis of data demonstrates how the themes and patterns assist in responding to the research questions. As opined by Awwad et al. (2018), qualitative data can be analysed into several categories which include "content analysis”, “narrative analysis”, “discourse analysis”, “framework analysis” and “grounded theory”. Qualitative analysis of data is selected over quantitative data as it adds details and provides detailed information on the topic. A unique in-depth understanding of the context of the study is obtained through qualitative analysis of data.

Case study

The current case study primarily concentrates on disinformation in the world of Data analytics. Disinformation can be described as misleading information or false spreading of news that is devoid of the actual incident. However, it is mostly applied by some organisations that have the greed to cross or overtake any fellow market from its line and promote the faster growth of the company. As opined by Wyatt (2021), Due to this rising issue, the organisations are utilizing special precautions that allow the protection of the information and data collections of the company and the customers of the organisation.

 Disinformation in data analytics

(Sources: Lindgren, 2021)

As suggested by Lindgren (2021), some of the strange and misinformation related to the Disinformation of data analytics are the harassment through which the organisation of the company can be trapped in the clutch of the hacker or the fellow organisation in the rivalry. As opined by Ng (2021), another aspect of the misinformation like misleading content that is in reality doesn't even exist and this kind of information and event has the capability to drive away from the potential customer of the firm resulting in the discrete the annual revenue generation. According to the view of Pereira et al. (2019), the case study also concentrates on the problem of leakage in the case of disinformation. It is crucial for every organisation to have certain plans and strategies for future development however; there is always a lingering fear of losing the vital information in context to disinformation in the data analytics.

Analysis

Data analytics are the heart and soul of the better functionality of an organisation. It facilities the company’s to promote ad, campaigns and advertisements effortlessly. The rise of Data analytics had a booming growth in the digital era. As stated by Shen et al. (2019), due to the rise in globalization people all across the world have come untidily and supported each other in the building of the business. Social media and digital media acts as a vital part of today's scenario giving an opportunity to the big and small business to showcase their products and service to the world through the help of Data analytics and digitalization.

The most crucial aspect of Data analytics is to pivot to campaigning, publicizing the products and building interaction with the customers of the organisation. As per the view of Boulianne and Theocharis (2020), it further helps in the assistance development of a better communication and interaction level with the consumer of the firm. Data analytics allows the harnessing of the data and utilizing it for the building up of new opportunities within the enterprise. As opined by Plantin and Punathambekar (2019), there is a clear depiction of using and receiving advantages from Data analytics in an organisation. However, Data analytics is used in almost both the sectors public and governmental sectors of the world. Providing a piece detailed information on the usage of Data analytics in sectors like

  • Retail industries
  • Finance and banking
  • Medicinal sectors
  • Transportation
  • Construction
  • Educational platform
  • Entertainment, communication and media
  • Natural and manufacturing resources
  • Outsourcing industries
  • Government
  • Utilities and energy-oriented industries

Each of the industries in the above-mentioned category is different it has different roles to play in the functionality of the Data analytics in their respective organisation. As opined by Kumar and Singh (2018), the medical industry is one of the large and most sensitive sectors of the business industry that uses Data analytics. As per the view of Qin and Chiang (2019), as there are a large number of patients being admitted to the hospital for medical treatment and medical purposes there are a variety of data collected from this and it further contributes to the massive usage of Data analytics in this field. For instance, the utility of a wearable tracker helps in the tracking of the patient's recent status along with taking better care of the patient through taking supplements, proper care and oyster factors for the development of the patient. Data analytics is very useful in this area and assists in the growth of the medical sector. According to the view of Plantin and Punathambekar (2019), data analytics could also be used in providing better and quick facilities to the members the patients through a reduction in the waiting time at the hospital.

In the finance sector, Data analytics has the most usage in context to the advancement of the working structure of the banking industries. In banking sectors, the techniques used to provide better and quick service to the customer and allow the development of quick decision making. As stated by Javed et al. (2019), the best way to learn about the usability of Data analytics in banking can be done through the usage of predictive analytics and ”natural language processing”. This format of Data analytics permits the mode of virtual assistance which eventually provides growth in the company’s pay structure.

The utmost common usage of Data analytics is that it is used in the discovery, communication and interpretation of sensible sentences or work allocated. As per the view of Agbehadji et al. (2020), organisations are investing in the recruitment of employee that has a keen knowledge of the study and promotes the benefits of the firm. As per the view of Wyatt (2021), while working in an organisation there is a detail with several customers on a regular basis so as to keep a record of the existing customers and their needs and demands Data analytics plays a pivotal role in the accumulation of data and helps both on an organisational level or even fo ran individual.

 “Exploratory data analysis”

(Sources: Ng, 2021)

It is critical to choose a theory or strategy that works best for the organization’s growth and development. In the case of Data analytics “Exploratory data analysis” (EDA) is beneficial and it can be utilized for getting the desired outcome from Data analytics. As stated by Boulianne and Theocharis (2020), EDA is considered a vital analysing EST of approach that concentrates on one of the most prior charterers of the Data Analytics and is often described in the format of illustration and colours. As suggested by Ng (2021), the best part about the theory is that it levitates on receiving a preconceived notion before even initiating the model task of the organisation. In the application of this method, strategies and theories can levitate in the transformation of the business structure.

According to the view of Pereira et al. (2019), it is evident that a plan comes with booth some pros and cons that permits the other member to select the best available platform of work. Some of the extreme challenges witnessed by the organisation regarding

  • Data analytics is like the accumulation of data in an authentic manner.
  • Providing data security
  • Choosing the right tool for measuring Data analytics
  • Consolidation of data through different sources

As per the view of Qin and Chiang (2019), it is vital responsibility to hold back and take proper care of this task allowing the growth of the company by embracing Data analytics in the current scenario.

Conclusion

The study focuses on the core concept of data analytics. Data analytics can be termed as the systematic “Computational analysis” that prior concentrates on the statistics or the data. The most basic function of Data analytics in context to the current scenario of the world, it has gained a huge demand and popularity in the global market and it ensures the security and quality purpose of the services. The main priority of the study is the pivot on drawing a fine line between the history and recent condition of using Data analytics in the global market.

The unavailability of time made the purpose of the study a little difficult and it also culminated in the form of limitations on the organization’s performance. Another impactful drawback of the study was that due to the unavailability of finance, better survey conduction could not be performed based on which the case study would have been more successful. In order to gain more fine research work there can be an incorporation of less paperwork, collaborating with a selective researcher, taking note of the background of the research work, and learning of the management regarding the project or case study.

References

Book

Lindgren, S., 2021. Digital media and society. U.S: Sage

Journals

Agbehadji, I.E., Awuzie, B.O., Ngowi, A.B. and Millham, R.C., 2020. Review of big data analytics, artificial intelligence and nature-inspired computing models towards accurate detection of COVID-19 pandemic cases and contact tracing. International journal of environmental research and public health17(15), p.5330.

Awwad, M., Kulkarni, P., Bapna, R. and Marathe, A., 2018, September. Big data analytics in supply chain: a literature review. In Proceedings of the international conference on industrial engineering and operations management (Vol. 2018, pp. 418-25).

Boulianne, S. and Theocharis, Y., 2020. Young people, digital media, and engagement: A meta-analysis of research. Social Science Computer Review38(2), pp.111-127.

Boulianne, S. and Theocharis, Y., 2020. Young people, digital media, and engagement: A meta-analysis of research. Social Science Computer Review, 38(2), pp.111-127.

Fan, C., Yan, D., Xiao, F., Li, A., An, J. and Kang, X., 2021, February. Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches. In Building Simulation (Vol. 14, No. 1, pp. 3-24). Tsinghua University Press.

Javed, M.A., Zeadally, S. and Hamida, E.B., 2019. Data analytics for cooperative intelligent transport systems. Vehicular communications15, pp.63-72.

Karnouskos, S., 2020. Artificial intelligence in digital media: The era of deepfakes. IEEE Transactions on Technology and Society, 1(3), pp.138-147.

Kumar, S. and Singh, M., 2018. Big data analytics for healthcare industry: impact, applications, and tools. Big data mining and analytics2(1), pp.48-57.

Ng, E., 2020. No grand pronouncements here...: Reflections on cancel culture and digital media participation. Television & New Media21(6), pp.621-627.

Nguyen, T., Gosine, R.G. and Warrian, P., 2020. A systematic review of big data analytics for oil and gas industry 4.0. IEEE access, 8, pp.61183-61201.

Pereira, S., Fillol, J. and Moura, P., 2019. Young people learning from digital media outside of school: The informal meets the formal. Comunicar. Media Education Research Journal27(1).

Plantin, J.C. and Punathambekar, A., 2019. Digital media infrastructures: pipes, platforms, and politics. Media, culture & society41(2), pp.163-174.

Qin, S.J. and Chiang, L.H., 2019. Advances and opportunities in machine learning for process data analytics. Computers & Chemical Engineering126, pp.465-473.

Ranjan, J. and Foropon, C., 2021. Big data analytics in building the competitive intelligence of organizations. International Journal of Information Management, 56, p.102231.

Shen, C., Kasra, M., Pan, W., Bassett, G.A., Malloch, Y. and O’Brien, J.F., 2019. Fake images: The effects of source, intermediary, and digital media literacy on contextual assessment of image credibility online. New media & society21(2), pp.438-463.

Solanki, V.K., Makkar, S., Kumar, R. and Chatterjee, J.M., 2019. Theoretical analysis of big data for smart scenarios. In Internet of things and big data analytics for smart generation (pp. 1-12). Springer, Cham.

Vassakis, K., Petrakis, E. and Kopanakis, I., 2018. Big data analytics: applications, prospects and challenges. In Mobile big data (pp. 3-20). Springer, Cham.

Wyatt, S., 2021. Metaphors in critical Internet and digital media studies. New Media & Society23(2), pp.406-416.

Websites

Forbes, 2022, About Eight Trends Predicted To Define Data Analytics In 2022, Available from: https://www.forbes.com/sites/forbestechcouncil/2022/02/25/eight-trends-predicted-to-define-data-analytics-in-2022/?sh=43e31d88ffd7 [Accessed 21st April 2022]

Sranalytics, 2022, About 7 most frustrating data analytics challenges faced by businesses, Available from: https://sranalytics.io/blog/data-analytics-challenges/ [Accessed 21st April 2022]

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