Advantages Of Incorporating Digitally Enhanced Technologies Into Organizational Business Model

Why this topic is important

The current study focuses on the advantages of incorporating digitally enhanced technologies into the organizational business model for improving a firm’s performance. The mentioned study also highlights the way the topic has become an integral part of today’s business. The Internet is recognized as the most noteworthy source of big data, therefore, firms are readdressing the procedure in a data-centric manner. The study delves deep into big data analytics and advanced machine-human interface for fraud detection, customer interaction as well as creating advanced algorithms for business process handling. The study sheds light on the usage of cloud computing while considering the phenomenon of big data.

Why you deem this topic to be of importance.

The incorporation of Information and Communication Technologies (ICT) is essential for the digital transformation of a business model. The firms are achieving business proficiency, therefore, ICT is recognized as one of the most important tools for increasing revenues as well as incorporating innovations into existing business scenarios. As per the deliberation of Bieser and Hilty (2018), along with the incorporation of big data analytics, organizations are more likely to boost employee morale in order to make an efficient manager plan. While considering strategic planning, analysis of previous data is essential. In addition, predictive analysis and deep learning are capable of enhancing the analytical ability of underlying employees. From data handling to monitoring consumers, generating potential customers into business leads every aspect comes under the mentioned topic (Puri 2018). A firm can analyse its financial performance, work to labour ratio, as well as forecasting market situations. Optimizing the trends, and understanding the market competition can be done by incorporating big data analysis. Along with this, advantages in leverage specificity can be provided by the embodiment of big data. Top performing organizations are using multiple data sources for establishing the Total Addressable Market (TAM).

 How this topic has developed

The topic has been developed by focusing on data analytics and business intelligence. Apart from that, the development of the underlying study is standing on three pillars such as value-added processes that use ICT, adoption of ICT, and ICT value development. As per the demonstration of Al-Rahmi et al. (2020), the introduction of digitized technologies implies noteworthy changes in the reformulation process of conventional commercial strategies. However, the development methods also concern the changing relationship between a business firm and the consumers. Along with the embodiment of the digitization process the consumers can directly access the communication with the organization, which helps the organization in the establishment of both ways of communication. Active responses from the consumers signify the better facilitation and exchange of data (Deepa et al. 2022). This phenomenon forces firms to focus more on their digital reputation. In addition, legal data mining with consumer concerns is an integral part of the data evaluation process while considering big data handling. Under this technology new data can be incorporated with the existing ones however, it primarily requires to be assimilated as well as internalized for enhanced strategic decisions.

How this topic has developed

A detailed description of the technology used in this topic and what it is used for?

While considering the digitization and influence of digital marketing, the use of big data facilitates an organization by improving the organizational culture irrespective of the nature of the service the organization is providing. According to the consideration of Ylijoki (2019), the mentioned technology is capable of offering information to the organization. The main advantages are depending on the motive of the usage. To be justified as big data, a data set needs to have three dimensions such as volume, variety, and velocity. Big data also addresses a blanket term used for the non-conventional technologies as well as technologies required in order to gather, process, organize and obtain insights from humongous data sets. The main facilitation of using big data resides in the computing power and the pervasiveness. Insights gathered from mining and optimizing the big data help in risk assessment by creating statistically correct risk matrices (Claudia 2019). Apart from that, it also helps in strategic decision-making by comparing the optimized forecast and the previously available results.

What technological platforms and software are used in the topic e. What type of business and organisation uses the technology?

Most prominent usage has been found in the banking sector, Media, communication and entertainment, Healthcare sectors, Manufacturing, Natural resources, Retail and wholesalers, and finance & trading sectors.

Big data technologies are divided into two major fields, operational and analytical technologies. In order to accomplish the motive organizations often use a variety of frameworks or software.

Artificial Intelligence (AI): AI incorporating helps in both analytical and operational fields by imitating human intelligence. Core concepts of machine learning and deep learning are the backbones of AI. Therefore, automatic choices can be created, auto-optimization of data, and imitating a neural network will be provided, and that can help an organization in performance monitoring or strategic decision making.

Hadoop Ecosystem: this platform constitutes a domain that helps in resolving issues regarding neighbouring big data. Ingesting, analyzing, storing as well as maintaining the data is the core fundament of the Hadoop ecosystem (Khan and Malviya 2020).

NoSQL Database: this basically stores unstructured datasets for delivering faster performance. While considering big size organization data assembling and transferring this framework is considered.

R Programming: R programming helps in analysing huge datasets as well as visually presenting those data for better understanding. Data analysis, data science, data optimization, and forecasting of every action that can help in organizational structure management can be performed here.

Block chain: Block chains are encrypted data that are connected with ciphered codes for maintaining internal connections. An organization's confidential data management is maintained by the usage of block chain technologies.

How is it used as a business tool and is this effective?

Organizations use big data as a business tool as it is capable of monitoring, optimizing, as well as analyzing a heavy amount of data at a single time. One of the major advantages of big data is that it provides data modulation facilitation by predictive analysis. Predictive analysis is a component of big data analytics that enables us to predict upcoming behaviour based on available data. Machine learning and modular analysis are considered for delivering future inference along with a captivating degree of accuracy. Therefore, the organizations can use the track record of the previous years for creating the expected target for the upcoming years. Apart from that, the degree of accuracy is intact while considering predictive analysis (Kannan et al. 2019). Therefore, forecasting market situations helps an organization to gain maximum. Big data analysis helps in optimizing the cost of an organization by monitoring the underlying usage of services. It also helps in hiring processes by analyzing the skills of the applicants. Big data also helps to compete with the competitors by optimizing the market analysis, special attention is noted in the finance sector. This way, big data analysis helps in maintaining the organizational culture by setting up a balance among the underlying services.

Technology used in this topic

Relation to this course content and discussion on the nature of the relationship between digital technologies and both old and newly emerging theories on business and management structures and practices?

Sectors that require enhanced data collection, resource management, and data-oriented consumer insight gathering are the most often users of these mentioned technologies are using big data affiliations. According to the contemplation of Yan. and Cheung (2019), while considering the emergence of business technologies the concept of an In-memory database (IMDB) needs special attention. This emerging technology helps in maintaining the authenticated data by storing it in the fundamental memory of a computer. The metadata of the big data that confronts the key to the access of databases is generally stored in the RAM of computers. The most important incorporation of IMDB is noted in the metadata analysis of the confidential information that is required for Big Data Analytics (BDA) (Santoro et al. 2019). This provides a superior advantage over a normal disk-based database system. In addition to that, the main data is stored in the clouds for virtual accession and remote access union purposes. While considering a virtual storage device the data transaction becomes entirely virtual therefore, the chances of data getting lost are also high. Therefore, organizations are using block chain technologies as well for maintaining data security. The cooperation of these new theories not only helps an organization in maintaining business fluency but also helps in maintaining the organizational culture. Block chains are virtually built distributed databases where a large amount of data can be stored. These block chain databases are essential for big data as the security measures are high and mutual connection among the interconnected parties is the only means of accessing the database. Apart from that, vertical cloud computing is considered one of the most significant frameworks or theories used by big data analysts (Zdravevsk et al. 2020). The phrase is used for optimizing the cloud computing functions considering specific organizations. Therefore, the internal analysis of the organizational structure is determined in the mentioned category. Apart from that, Application Programming Interface (API) is signed especially for performing a specific task regarding inter-organizational cultural valuation as well as employee credibility scoring or financial statement judging. BDA analysis indulged in vertical cloud computing can provide assurance related to the decision-making criteria. 

The most common business management theory using BDA is the usage of Predictive Analysis (PA). As per the deliberation of Yan and Cheung (2019), this framework is essential for setting up the degree of accuracy required for future analysis. The accomplishment of PA is determined in a few simple steps. The first stage is considered the gathering stage where the essential data are gathered. Once the data is gathered then predictive models such as decision tree or exploratory data analysis is created using the gathered data. Apart from that, regression models are formed for evaluating the prototype modelling (Jaiswal 2018). Thereafter the data is divided into two sets: train and test set using machine learning. While coinciding with audio-visual data the organizations considered the Convolution Neural Networks (CNN) for deep learning purposes. Finally, the accuracy curves are built depending on which the organization finalizes the decision. The decisions can be reading organizational structure as well as strategic management decisions or risk management. Irrespective of the nature of the analysis the mentioned process accomplishes the goal by providing superlative accuracy. A multinational organization that has thousands of active employees can evaluate the performance of the employees using the BDA. A definite and adequate determination of an employee's credibility is beneficial for an organization in terms of maintaining the organizational structure (Pappas et al. 2018). Apart from that, conceptualization can be used for determining market trends. Depending on the previous rise and downfalls, an organization can implement BDA into their services. The statistically correct data are required for forecasting the future model.

Technological platforms and software involved in the topic

BDA helps to manage the cost as the operations are entirely virtual and almost no physical storage is required except for the metadata storage. Therefore, BDA helps in the cost amendment of an organization. As stated by Loshkarev (2021), while considering the growth of an organization then any firm requires to focus on the strategies along with the technological enhancements. Therefore, the six-segment needs to assess with significant attention for developing practices for businesses.

In order to form a profitable business model, the first consideration needs to be the identification of business needs. As per the illustration of Chiang et al. (2018), while an organization is capable of monitoring data and analyzing them the chances of success increase.

For a detailed evaluation, a humongous amount of data is required. As stated by (), more fat provides more security related to future predictions.

Data is more acceptable when presented visually, therefore, organizations often use data visualization techniques to interpret the data. Data is often present in bar lot, displots, or pie charts for better understanding.

This stage is concerned with the scalability of the underlying data otherwise the organization will not have any specific idea reading the ongoing process.

As stated before, cloud computing is not only important for data storage but also helps in managing and transferring the data.

While working with large datasets governance is required, therefore, organizations often enable access control for maintaining the data integrity.

These management practices are required while considering big data analysis.

Conclusion:

The current study concludes on the role of digital technologies while considering the organizational culture. The mentioned study previously a brief idea about incorporating big data as a business tool and its advantages. The components of big data, the software, and the framework required for managing big data such as AI, Hadoop Ecospace, NoSQL, or Blockchain are mentioned in the study. The study also delivers a brief idea about the new emerging theories that are helping in strategic decision-making for an organization to maintain the organization's culture. The mentioned study also concludes the core implementation of business management techniques using big data.

References:

Al-Rahmi, W.M., Alzahrani, A.I., Yahaya, N., Alalwan, N. and Kamin, Y.B., 2020. Digital communication: Information and communication technology (ICT) usage for education sustainability. Sustainability, 12(12), p.5052.

Bieser, J.C. and Hilty, L.M., 2018. Assessing indirect environmental effects of information and communication technology (ICT): A systematic literature review. Sustainability, 10(8), p.2662.

Chiang, R.H., Grover, V., Liang, T.P. and Zhang, D., 2018. Strategic value of big data and business analytics. Journal of Management Information Systems, 35(2), pp.383-387.

Claudia, O.G.R.E.A.N., 2019. RELEVANCE OF BIG DATA FOR BUSINESS AND MANAGEMENT. EXPLORATORY INSIGHTS (PART II). Studies in Business & Economics, 14(1).

Deepa, N., Pham, Q.V., Nguyen, D.C., Bhattacharya, S., Prabadevi, B., Gadekallu, T.R., Maddikunta, P.K.R., Fang, F. and Pathirana, P.N., 2022. A survey on blockchain for big data: approaches, opportunities, and future directions. Future Generation Computer Systems.

Yan, C. and Cheung, A., 2019. Generating application-specific data layouts for in-memory databases. Proceedings of the VLDB Endowment, 12(11), pp.1513-1525.

Jaiswal, M., 2018. Big Data concept and imposts in business. Manishaben Jaiswal'Big Data Concept and Imposts in Business' International Journal of Advanced and Innovative Research (IJAIR) ISSN, pp.2278-7844.

Kannan, N., Sivasubramanian, S., Kaliappan, M., Vimal, S. and Suresh, A., 2019. Predictive big data analytic on demonetization data using support vector machine. Cluster Computing, 22(6), pp.14709-14720.

Khan, M. and Malviya, A., 2020, February. Big data approach for sentiment analysis of twitter data using Hadoop framework and deep learning. In 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) (pp. 1-5). IEEE.

Loshkarev, A.V., 2021. Applied pattern of artificial intelligence and big data in business. In Current Achievements, Challenges and Digital Chances of Knowledge Based Economy (pp. 383-388). Springer, Cham.

Pappas, I.O., Mikalef, P., Giannakos, M.N., Krogstie, J. and Lekakos, G., 2018. Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies. Information Systems and e-Business Management, 16(3), pp.479-491.

Puri, A., 2018, December. Application and Uses of Big Data Predictive Analysis in Public Sectors: A Systematic Review. In 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS) (pp. 539-543). IEEE.

Santoro, G., Fiano, F., Bertoldi, B. and Ciampi, F., 2018. Big data for business management in the retail industry. Management Decision.

Saritas, O., Bakhtin, P., Kuzminov, I. and Khabirova, E., 2021. Big data augmentated business trend identification: the case of mobile commerce. Scientometrics, 126(2), pp.1553-1579.

Ylijoki, O., 2019. Big data–towards data-driven business.

Zdravevski, E., Lameski, P., Apanowicz, C. and ?l?zak, D., 2020. From Big Data to business analytics: The case study of churn prediction. Applied Soft Computing, 90, p.106164.