Big Data Analytics Assignment Sample
INTRODUCTION
BUSINESS INTELLIGENCE: Business Intelligence makes analysis of data easier but it leaves decision making in the hands of human beings. The process of collecting, storing and analyzing data produced by companies activities through procedural and technical infrastructure is termed Business Intelligence (BI). it presents easily understandable reports, and trends that influence management decision making process. Some benefits companies experience from BI are faster and more accurate reporting and analyzing of data quality, better employee satisfaction, it also reduces costs and increases revenue, it makes the process of better business decisions by managers (Mikalef,2019).
ARTIFICIAL INTELLIGENCE (AI) : When a computer or a robot controlled by a computer has the ability to do tasks, those tasks that were done by intelligent beings i.e. human beings because these tasks required human intelligence is known as Artificial Intelligence (AI).
BIG DATA ANALYTICS: data processing cannot traditionally process data that are huge and complex. Big data is used to analyze, exact information and understand the data better. Big data analyses the patterns in the data which helps to understand the behavior of people and business easily, this process helps in efficient processing and customer satisfaction.
ARTIFICIAL INTELLIGENCE (AI) IN STRATEGY PLANNING AND DECISION MAKING
Corporate strategies theories have been debated traditionally. At present the strategy focuses on modernizing aspects that are traditionally left untouched. Artificial Intelligence (AI) techniques include machine learning that imports data from an abundance source, identifies patterns and strengths and thus enables supply to decision making (Osman,2019).
AI-enabled planning invalidates the traditional processes of decision making that depend on human bias. Intuition and experiences that influenced the decision making process are set to an end by current strategy decisions that are based on information based on multiple sources (Ranjan,2019).
AI offers a fact based foundation by analyzing the company’s strengths and weaknesses, and opportunities and threats, from which decision makers built a strategy framework. When the process of implementing such strategies starts, the AI monitors the scenarios and automatically alerts the management in case any shift is required. Thus, enabling senior managers to act promptly on the information to check on the current market or competitors.
AI solves issues and helps in the process of decision making. AI can free people from unmanageable and time consuming tasks. AI helps freeing important people from tasks that are carried out by AI in lesser time as compared to human beings (Zhang,2018).
AI HELP IN PLANNING LONG TERM STRATEGIES IN THE FOLLOWING WAYS:
- PROCESS OF HIRING ASSISTANCE: Usually the process of hiring and recruiting people is tiresome and repetitive. AI is helpful in this purpose, it leads as per requirement by the management and filters the candidates for review. AI can now understand resumes uploaded by human resources, they search and identify the best suited people from the database provided, they also connect and converse with the candidate for an initial level screening. It can now shortlist candidates for the interview process and provide complete details to human resources. The best aspect of such a resource is that the process saves valuable time of the management that spends hours in conducting interviews to test the credibility of candidates, thus AI does the process of bringing candidates to the organization in a softer skill and efficiently.
- HELPS IN IMPROVING EMPLOYEE PRODUCTIVITY: works like reporting, indexing and compiling data takes a lot of time of the employees. Machine learning through AI helps in simplifying this task and freeing up employees time and thus resulting in employees efficiency within the team. It lets the employees look beyond and utilize their time in more creative strategically focused tasks. It also enables the company to have an error-free directory of data that is created in real time.
- HELPS STREAMLINING PROCESSES: AI builds custom reports in seconds based on custom searches. They provide data by customizing across business groups and according to the functionality. In terms of processing data for purchase of indexes or invoices in which millions of data are needed to fulfill, processed automation tools simplify the entire operation with the highest level of accuracy. Thus enable processed data to be ready in time and with full accuracy (Choi,2018).
- PROVIDES CUSTOMER SATISFACTION: A smarter and more strategic approach has entered the era of automated customer service. Now the customer can effectively engage in activities using the medium of their choice, without interrupting their privacy. The process of interaction by customers with enterprises is simplified. Instead of having an informal conversation with customers, an AI assistant generates a more accurate consideration choice of actions for the customers. The AI process makes the interface not only cost effective but also provides more precise replies by learning about the customers needs and preferences from the previous chat history (Hamilton,2020).
- HELPS IN CROSS LEVERAGING INFORMATION: A large number of data are being churned by using several applications that carry out the process of collecting information. Thus the information that is obtained by one department can be utilized by others in the same organization. Data collected by the Resource and development team can be used by the marketing department as well as the sales department. This process helps in targeting the right audience by the concerned department in a better way. It helps understanding customers’ behavior and spending habits. The feedback that is received from customers chat can help the departments in improving their products. The data are stored which can be verified and analyzed as and when required this ultimately opens up the possibilities of business benefits are boundless.
EMERGING TREND WITH THE FIELD OF AI AND BI
Business organization has seen a rough phase the past two year due to COVID-19, despite this neither did businesses stop nor the data collection process stopped. As a result it became important for more intelligent processes to be adopted by enterprises for better data-informed decisions (Hariri,2019).
Development of natural language processing and Atom capabilities, planning tools and more money can be seen in the analytics market. Some of the emerging trends with the field of AI and BI are as follow:
- ROBOTIC AUTOMATION TREND CONTINUATION: Mike Leone (analyst, Enterprise Strategy group) stated “Together, AI and automation will revolutionize the way organizations utilize their analytics performance,”. Automation will make workers more efficient and it will make people use data in a more intelligent way to bring efficiency in their jobs. Analytics has been showing what has already happened i.e. it was more descriptive in nature. With time its next step was to become predictive i.e. demonstrating what is likely to happen next. Now analytics is becoming prescriptive, recommending what an organization should do next (Saggi,2018).
- ENABLES NATURAL LANGUAGE PROCESSING: with advancing capabilities organizations are trying to make their platform accessible to more users. With the lack of people having knowledge to study data or computer science, which result in barriers for working with data. Neither do they have training on data analysis or coding, augmented analytics tools reduce such barriers by providing no code capabilities that enable data analysis in natural language with written or spoken words.
- USE OF AutoML: AutoML tools enable self service data science. By using the process of no-code organizations will deploy data models for deep analysis and insight generation. AutoML use will bring challenges like -people not understanding at the beginning, and governance issues. This self-service data science needs to include protective measures and a team of scientists is required to overview.
- EMERGENCE OF NEW AREAS: With the emergence of AI more vendors can be seen adopting scenario planning. Tech helps organizations be more transparent and meet targets. It opens up a new era of development for vendors as they are now enabled to new technology and intelligence.
SOCIAL AND ETHICAL ASPECT OF AI
AI is essential across the world in the present era, it can be seen in industries including health, banking, retail and manufacturing. Most consumers became aware of it through Google, Facebook and businesses like Amazon. It promises to improve efficiency, reduce costs and accelerate research and development. Private companies use to determine health and medicine, and also used in other fields. Virtually AI systems are deployed as an integral system by companies in their strategy making process.AI include machine learning, robotics, sensor and industrial automation in the field of business development and business strategies. Sourcing of materials and supply products are managed by firms through AI. It plays an integral part in aiding information in strategic decision making. Because it uses data process so quickly it helps in minimizing time and in the process of trial and error of products.
- HEALTH CARE: Experts see uses of AI in health care systems. Billing and processing of paperwork is done easily. Medical professionals can analyze data, imaging and diagnose most immediate cases efficiently. It is said that AI will bring medical knowledge available on diseases to any given treatment decision.
- EMPLOYMENT: AI software are seen to be rosseing and analyzing job resumes which fastens the process of hiring and driving the growth of job. AI also takes important technical tasks of employees’ work which potentially frees workers to be more responsible towards more productive and valuable tasks of the organization, enabling the growth of the business as a whole.
AI is of ethical concern for the society in the following three major areas: Privacy and Surveillance, bias and discrimination in the role of human judgement. Conscious and unconscious prejudices of programme developers; is a debate about privacy safeguards which arise the question as to how to overcome bias in algorithmic decision making process and employment practices.
Ethical dimensions are taken into consideration by companies, citizens also have to educate themselves about technology and its social and ethical implications. Students need to learn enough about technology and implications of new technologies so that they are able to ensure that technology serves human purposes while running companies or when they are acting as democratic citizens. It is critical to prepare for a future civilization in which artificial intelligence will have an impact on every aspect of human life. It is vital to have a working knowledge of artificial intelligence and human intelligence. There is a fundamental difference between the two; while technological advancements are expected to improve the operation of artificial intelligence, AI will always be a function of human activity to some level.
AI, which uses computer science programming to replicate human activity, analyse data, solve issues, and adapt to a range of tasks, frees humans from a variety of repetitive jobs that they would otherwise have to perform. The technology just has to learn to operate once and then it can repeat the process as many times as humans programme it. AI allows robots to learn from their past experiences, adjust to new inputs, and execute activities that are similar to those performed by humans. One of the primary goals of artificial intelligence is to stimulate cognitive behaviour in order to enable computers to conduct intellectual activities such as decision making. Today, artificial intelligence (AI) is more than a technology; it is a way of life. New types of development may be observed in a variety of fields, from politics to economics. Many parts of life have been more easily accessible to humans as a result of artificial intelligence. It has ushered in a new era of technology across all industries and provided solutions to challenges that humanity have faced. Learning, thinking, and self-correction are all components of this process. AI is poised to develop optimal solutions in every industry that will lead to knowledge and wisdom as a result of its use.
AI is used in a variety of industries, including health care, finance, logistics, tourism, and education, to give a superior experience. Artificial intelligence systems have gained knowledge of material such as natural language, social media, and social networks.
CURRENT THEORY AND PRACTICES IN AI
- The application of analytics in the financial services industry is beneficial in the areas of risk mitigation and regulatory control. It allows for the creation of new consumer engagement channels, as well as the more efficient processing of no code data translation. Machine learning techniques are used by executives, financial analysts, and data scientists to collaborate on data visualisation and streaming in real time. This is accomplished through the usage of reliable and accurate data.
- Automization in the reconciliation process is a key component of the process. Automated workflows maximise the use of technology while minimising the need of humans in bank and business reconciliation procedures. Thousands of reports and spreadsheets are combined, and reporting formats are standardised in order to increase forecasting accuracy. The use of robotic processing automation in the financial services industry eliminates inefficiencies in operation tasks and lowers operating costs (RPA).
- The use of artificial intelligence can detect fraudulent activities. The use of artificial intelligence can automate the extraction and transformation of data from data formats, as well as the application of advanced fraud detection techniques to trigger against probable fraudulent activities. Clients may report double payment, cash or billing schemes, and other types of corporate fraud. It is effective in modelling complicated relationships and in detecting false patterns between inputs and outputs of vast volumes of data since it is fast and efficient.
- FINANCIAL MARKETING ANALYTICS: Customer response to campaigns are often difficult and challenging to be predicted by marketing teams. To enable successful marketing campaigns distinctive and different data sets are tailored on product cross-sell and up-sell and on new customer acquisition. Developing marketing analytics used in machine learning models shows the probability of success. Changes in control and uncontrolled variables enables marketing campaigns towards deploying strategy that reaches the right audience by the right channel with the right message.
- GOVERNMENT: AI have necessary accreditation to operate on government networks with the help of no code, self service analytic solutions. It enables different levels of people with different skills in government agencies to use data more efficiently and to solve security issues, logistic issues and policy based issues.
CONCLUSION
Artificial Intelligence enables organizations to operationalise data analytics with secure governed and scalable strategies. It is essential that teams in an organization must collaboratively generate and share data across the organization, which enables a better understanding of the organization process and products by customers. AI solutions are designed by many skilled data scientists and engineers who specialize in business analysis. The cloud ready interface delivers organizations powerful capabilities to use the full power of data analytics throughout the complete data processing process. Combined with the increased robust stimulation, testing and field data sets made engineering data science a modern product development life cycle component. AI offers manufactures viability to explore solutions to complex design problems through physical AI driven workflow, it enables in achieving greater product and innovation through design convergence. AI offers the easiest and the fastest data extraction from difficult pdfs spreadsheets and ta files and from other structured sources, freeing up time by automated preparation tasks, enabling avoidance of repetitive and error prone tasks.
Checkboxes driven by artificial intelligence are transforming the world, bringing innovation to customer services and redefining the process of providing consumers by cutting operating costs and using the full potential of artificial intelligence. Unlock long-term strategic benefits for companies, health-care organization’s, financial services organizations, and the government as a whole
References
Choi, T.M., Wallace, S.W. and Wang, Y., 2018. Big data analytics in operations management. Production and Operations Management, 27(10), pp.1868-1883.
Hamilton, R.H. and Sodeman, W.A., 2020. The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources. Business Horizons, 63(1), pp.85-95.
Hariri, R.H., Fredericks, E.M. and Bowers, K.M., 2019. Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data, 6(1), pp.1-16.
Mehta, N. and Pandit, A., 2018. Concurrence of big data analytics and healthcare: A systematic review. International journal of medical informatics, 114, pp.57-65.
Mikalef, P., Boura, M., Lekakos, G. and Krogstie, J., 2019. Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, pp.261-276.
Osman, A.M.S., 2019. A novel big data analytics framework for smart cities. Future Generation Computer Systems, 91, pp.620-633.
Ranjan, J. and Foropon, C., 2021. Big data analytics in building the competitive intelligence of organizations. International Journal of Information Management, 56, p.102231.
Ristevski, B. and Chen, M., 2018. Big data analytics in medicine and healthcare. Journal of integrative bioinformatics, 15(3).
Saggi, M.K. and Jain, S., 2018. A survey towards an integration of big data analytics to big insights for value-creation. Information Processing & Management, 54(5), pp.758-790.
Tiwari, S., Wee, H.M. and Daryanto, Y., 2018. Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers & Industrial Engineering, 115, pp.319-330.
Zhang, Y., Huang, T. and Bompard, E.F., 2018. Big data analytics in smart grids: a review. Energy informatics, 1(1), pp.1-24.
Zhu, L., Yu, F.R., Wang, Y., Ning, B. and Tang, T., 2018. Big data analytics in intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 20(1), pp.383-398.
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