Significance Of Data Analytics To Businesses Assignment Sample

Significance Of Data Analytics To Businesses

Introduction - Significance Of Data Analytics To Businesses

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Part A: Literature review

Analysing the significance of data analytics in retail industry

The main motive of the retail industry is to achieve their targeted profitable goal. Proper data analytic on a business by the management helps the organisation to overcome their loopholes (Jin and Kim, 2018). The Importance of data analysis in a business is a crucial part, such as optimising the performance of the business for better work outputs. Growing the revenue of the business, an organisation should follow the current trends in data analytic for the development of the business.

Updating the process of data analysis in a business, the organisation has to update some steps in their management. The implication of Automation in the management of the business brings a fast analysis of data in the business. Updating the system and application update of software will help the organisation (Thalmann et al. 2018).

Management of the business organisation will set up Artificial Intelligence (AI) types experience review sections. These advanced AI reviews will provide the customers with a better user experience. Rectifying the customer's reviews and collecting data is the most important part of any business for the development of their business (Batra, 2017). Analytical tools such as SAS help for the interaction of the users and the management of the business organisation. Most important part of the data analysis of the retail organisation like TESCO is to better serve the customers by analysing and rectify there loopholes.

Determining current and future trends in data analytics for retail industry

Current trends

Running and successfully growing in the business, the organisation will follow up the current and predict their ideologies for the upcoming future trending in the data analysis. Up-gradation in the data analysis process in the organisation will help TESCO to maintain the managing operation in the organisation. Data analysis will help to optimize the business models and help the organisation to reach its goal for betterment. Analytic the core function of a business helps the management of the organisation to study and monitor the status of the business scenario (Marjani et al. 2017). This analytic core of the business also helps the manager of the business organisation to make strategic and vital decisions. 

Controlling the efficient work outputs in the retail industries as TESCO, the management of the organisation will increase the cloud environment. Increasing the storage in the cloud database will help the organisation to maintain a huge bulk of data. Maintaining the data, time-to-time help the management to proper analysis in there retail data. The cloud data environment and pre-data centre help in the reduction in the latency for any solutions (Sharda et al. 2014).

Descriptive data analysis is required for the proper data analysis in a retail industry like TESCO. These data analysis helps to identify the size of the recorded data these can also helps to allocated the centre of the data. The decrypted data analysis gives a proper constructive shape of data distribution (Atmowardoyo, 2018).

Predictive and prescriptive analytics both are the similar term in the form of data analysis. Predictive data analysis means to predict the allocated data to give a potential final result. Similarly the prescriptive data analysis the predictive data result a find the more way to give more options for consider. These two types of data analytical help the retail industries to analytic more perceptive data (Kaur and Mann, 2017). The data analytic helps the retail organisation to record the more data in linear paths of the development in management team.

SAS analysis helps in the business retailer industries critically in the development of data analytical part. According to Ajani and Habek, (2019), SAS analysis critically means that the intelligence tool provides analysis the statically part of the data. SAS also help the retail organisation for the data mining and also helps in the predictive the data modelling. These retail organisations like TESCO take the advantage of the intelligence SAS data analysis in the development of the organisation. Data mining is also a very critical part of the development of retail industries. This analysis helps the retail organisation to predict the common possible challenges. Similarly these intelligence analyses reduce the management cost. On the other hand customer review and the feedback are also important for retail industries. These data analysis helps the organisation TESCO to proper analyze the customers reviews and feedback. However after the analyze the data the retail industries quickly mitigate the problems and serve the quality service to the customers.

Customer’s satisfactions in the retail industries help in the increase in the profit revenue. According to the market segmentation the SAS analysis the best demands of the customers and give a collective way of data to the management of the organisation. Data retrieval is another important term in the SAS data analysis (Gregory et al. 2019). The data analysis helps in the identification and also helps in extracting the relevant data from a particular database. These retailer industries TESCO take the advantages from the data retrieval intelligence system to extract the identification of products from the data base

Online assessment of the data recording will help the management of the organisation to monitor the current working and the progress of the business. The main motive and the targeted goals of the business are to earn a good amount of profit in the turnover revenue. Data allows the real-time value to analyse the challenges to mitigate them (Kadre and Konasani, 2015).

Many advanced data analytics like SAS data analysis tools are introduced for the proper analysis of the data in the improvement of the business. The current scenario trend is that all the management of the organisation are used for ethically keeping their data. In the retail organisation like TESCO the management, setup an advanced data interactive system an interactive computer-based system helps in the proper analysis in the data. These interactive computer-based systems help the management to utilise the current data and help to solve the unstructured problems (Minelli et al. 2013). Decision-making processes receive support from the intellectual’s resource from the individuals; advanced updated computers have the capabilities to improve the quality of the decision.

Future trends

Data analytics systems in a business are growing and updating from time to time. Perspective analysis helps in identifying the different analysis actions. The retail industry like TESCO will critically analyse the modern and the advanced system. These will help in maintain the data analysis in a linear way in their future service. The SAS data analyses help to guide to the best possible outcome of the results. Betterment in the business organisation there are trends to stay in the competitive markets. Future Data Prediction Analysis is the most important for the management of business organisations (Elijah et al. 2018). According to the current market trends, advanced technologies are required for future prediction, which helps the management to bring a better improvement in the business.

Challenges and opportunities during the data analytical process in retail business

Managers of the business organisation are the responsible persons to analyse the challenges during monitoring the data. Process challenge is encountered while integrating data. Privacy is the key issue in the data analytic process. Many business organisations have no advanced system to protect their system (Krishnamoorthi and Mathew, 2018). There are two effective tools for the collection of information about any individual: cookies and another one is spyware. SAS data analysis helps in the extract the analysis part of the critical data anaysis part. To mitigate the problems in the cookies the management of the organisation has applied the single sign up process by which it protects privacy.

There are other issues and challenges for the business organisation to protect the individual data, which depicts data challenge in the online search browsers. Many of the employees in the business organisation are using Google to allocate the data. Protecting the data and the information of any individuals are the most challenging patriot of the management of the organisation (Sivarajah et al. 2017). Applying high antivirus and high-security data helps to protect personal and the relevant data of the organisation. There is the main responsibility of the management of the business organisation for giving better knowledge and training during the collection of the data.

Electronic surveillance will help the business organisation for recording and properly collect authenticated data. An advanced and updated electronic surveillance helps to collect the current and real-time data. There are also other analysis challenges as management challenges (Vidgen et al. 2017). TESCO as the retail enterprise also faces many challenges consist of privacy, security and the governance ethical aspects (Tesco UK, 2021). Security challenges are the main threat in the management of the business organisation.

Map reducing programming will help the organisation to gain valuable insight from big data. Scalability helps in the expansion in the storage of the data for the organisation (Appelbaum et al. 2017). Flexibility is used to access the multiple access of any data storage. Speed and the simple way of accessibility help the users to work fast and collect data

Reference list

Ajani, T. and Habek, G., 2019. A Machine Learning Approach to Optimizing Diabetes Healthcare Management Using SAS Analytic Suite. Journal of Information Systems Applied Research, 12(2), p.4. viewed from http://jisar.org/2019-12/n2/JISARv12n2p4.pdf [accessedon 03.01.2022].

Appelbaum, D., Kogan, A. and Vasarhelyi, M.A., 2017. Big Data and analytics in the modern audit engagement: Research needs. Auditing: A Journal of Practice & Theory, 36(4), pp.1-27.

Atmowardoyo, H., 2018. Research methods in TEFL studies: Descriptive research, case study, error analysis, and R & D. Journal of Language Teaching and Research, 9(1), pp.197-204.

Batra, M.M., 2017. Customer experience-an emerging frontier in customer service excellence. In Competition Forum (Vol. 15, No. 1, pp. 198-207). American Society for Competitiveness.

Elijah, O., Rahman, T.A., Orikumhi, I., Leow, C.Y. and Hindia, M.N., 2018. An overview of Internet of Things (IoT) and data analytics in agriculture: Benefits and challenges. IEEE Internet of Things Journal, 5(5), pp.3758-3773.

Gregory, K., Groth, P., Cousijn, H., Scharnhorst, A. and Wyatt, S., 2019. Searching data: a review of observational data retrieval practices in selected disciplines. Journal of the Association for Information Science and Technology, 70(5), pp.419-432

Jin, D.H. and Kim, H.J., 2018. Integrated understanding of big data, big data analysis, and business intelligence: a case study of logistics. Sustainability, 10(10), p.3778.

Kadre, S. and Konasani, V.R., 2015. Practical Business Analytics Using SAS: A Hands-on Guide. Apress

Kaur, J. and Mann, K.S., 2017, December. AI based healthcare platform for real time, predictive and prescriptive analytics using reactive programming. In Journal of Physics: Conference Series (Vol. 933, No. 1, p. 012010). IOP Publishing

Krishnamoorthi, S. and Mathew, S.K., 2018. Business analytics and business value: A comparative case study. Information & Management, 55(5), pp.643-666.

Marjani, M., Nasaruddin, F., Gani, A., Karim, A., Hashem, I.A.T., Siddiqa, A. and Yaqoob, I., 2017. Big IoT data analytics: architecture, opportunities, and open research challenges. ieee access, 5, pp.5247-5261.

Minelli, M., Chambers, M. and Dhiraj, A., 2013. Big data, big analytics: emerging business intelligence and analytic trends for today's businesses (Vol. 578). John Wiley & Sons.

Sharda, R., Delen, D., Turban, E., Aronson, J.E. and Liang, T.P., 2014. Business intelligence: a managerial perspective on analytics

Sivarajah, U., Kamal, M.M., Irani, Z. and Weerakkody, V., 2017. Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, pp.263-286.

Tesco UK., 2021. Home. Available at: https://www.tesco.com/

Thalmann, S., Mangler, J., Schreck, T., Huemer, C., Streit, M., Pauker, F., Weichhart, G., Schulte, S., Kittl, C., Pollak, C. and Vukovic, M., 2018, July. Data analytics for industrial process improvement a vision paper. In 2018 IEEE 20th Conference on Business Informatics (CBI) (Vol. 2, pp. 92-96). IEEE.

Vidgen, R., Shaw, S. and Grant, D.B., 2017. Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), pp.626-639.