AI and Big Data New Trends in Business Data Analytics

Introduction

To thrive and remain competitive in today’s business landscape, making data-driven decisions is mandatory. Big data and Artificial Intelligence (AI) are used majorly in marketing to establish mountains and mountains of customer data, through various digital channels that are analysed by marketers. Customer data is one of the vital assets that a business should have through the Big Data to process, customer associations, trends and patterns to be able to reveal different human interactions and behaviour. AI ns Big Data in business data analytics are trends that are positively impacting the business world today. According to research conducted by Henry Friday on the Big Data and AI business analytics, he stipulated that the generation of data in business is growing exponentially. Additionally, he held that the data includes unstructured and structured data that floods business organizations and institutions. Therefore, the essay focuses on Big Data and AI new trends in business data analytics.

AI and Big Data new trends transforming business

The business surrounding is experiencing a digital trend formation space and buzz through cloud computing, augmented reality, internet of things and virtual reality. Through these various digital spaces advancement in business it is easy to lose track of the amazing advancements happening in data management and analytics that help businesses grow. Therefore, the following are new big data and AI trends that are transforming business in the contemporary world (Babu & Devi, 2019, p. 2341).

Augmented business analytics

Augmented analytics is one of the new AI and Big data approaches that is used by businesses to transform data workflow through use of machines automation, AI and natural language generation. According to research conducted by Garner, this new approach of business analytics aims at removing bias from the modelling, preparation, interpretation and analysis of business data (Vada & Chen, 2018, p. 31).  Additionally, the approach enhances impartial contextual awareness by acting on insights and helping data scientists to deploy better automated algorithms to explore many ways to improve businesses. In addition augmented business analytics compliments other analytical approaches rather than replacing (Henry, 2019, p. 74).

The benefits augmented analytics provides to the business by 2010 are expected to dominate driver of new purchases of business intelligence, platforms and toolsets that will be integrated and embedded in business enterprise applications. The crucial difference of augmented analytics from the rests is the use of advanced machine learning automation to identify hidden patters in business data, without the problem or risk of unintentional human bias being inserted in the systems (Sun & Huo, 2019, p. 43). Augmented analytics operates through application of a range of models and assembling learning data for actionable findings to help businesses and companies to notice and discover any overlooked correlations in their data. For instance, in the United States health insurance businesses used this data analysis to discover and track costs metrics based on the sickness of patients (Khan, 2019, p. 27). The business fed their augmented analytics algorithms and models that were running parallel, which explored their data in all combinations and tracked all driving costs in the business. The final results demonstrated that children under the age of twelve years were the largest cost drivers (Bekker, 2019, p. 12). Therefore, augmented analytics allowed the insurance business to look at their business data from a completely new perspective that they would not have been discovered through use of manual exploration.

Increasing amounts of data for business analysts’ to analyse, prepare and organise is becoming an impossible task to accomplish through manual processes and techniques alone. Therefore, using and implementing augmented business analytics ensures that businesses do not miss any crucial insights that will affect their businesses and also help in making crucial decisions (Sandra, 2019, p. 27).

Edge computing in business analytics

Edge computing AI and Big Data new trends in business data analytics is an AI computing paradigm that businesses use to place information processing at the device level to generate data at the edge of the business network, rather than within a centralised or cloud data processing system. Edge computing system helps business to improve data transfer and comprehension at the connectivity part of the technology stack and also in conjunction with internet of things helps the business make more possible applications.

Edge computing data analysis has existed for a long period but has had several modifications and adoptions to enable it continue existing. Various businesses and corporations have made edge computing a core part of their business data analysis. McKinsey a business expert stipulates that in two years use of edge computing data analysis will grow by more than five hundred billion dollars. Furthermore, he held that much focus and development will focus on edge computing, it will be tied on the internet of things, because of the B2B applications.

There is no standard formula that businesses can implement the edge computing data analysis. However, there are various cases in which the system has demonstrated value in analysing data in organizations and businesses. For instance, Block chain is one of the edge computing data analyses that are beneficial to businesses. Through a distributed ledger technology, businesses decentralise computing models to function and process data locally. Additionally, connected office is also another edge computing data analysis that is used by many businesses across the world. In the United Kingdom, for example, Google, Amazon, and Alexa use edge computing to increase its business operations. Predictive maintenance also adds value to the business because of the edge computing systems by closely linking industrial and business sectors through the use of the internet. The computing through the use of internet of things helps to detect when business equipments and machines are in danger of breaking and therefore requests for repairs before they break down. Furthermore, international business chains such as the Nordstrom use the edge computing technology system to attract customers.

The edge computing system is also beneficial because it helps businesses to perform real-time data analysis faster than traditional capture and relay processes. The use of this data analysis has established extensive new value around the use of internet of things, therefore, with the use of AI and data analysis business services continue to mature every tear, hence reducing and closing the gap between the way data is generated and used by companies to act in a more insightful and useful well to take a competitive advantage in business.

Enterprise data catalogues analysis

Businesses in the United Kingdom and across the world use enterprise data catalogue which involves information governance model and metadata management that help businesses find and manage all data stored in their systems e-commerce. Catalogues help businesses by improving accessibility, security, and transparency. The data catalogue is a crucial part of modern business organisations information systems acting as a central place to organise, contain, and perform discovery of business data assets. Data inventory allows all data catalogues to update the curation of all assets and liabilities, giving valuable contexts that help decision-makers and experts to find their categorised data efficiently and faster for the purpose and objective of extracting better business insights.

According to research conducted by Gartner, he demonstrated that businesses that full implemented the enterprise data catalogue analysis through useful metadata management capabilities realised the benefits twice than those businesses that failed to implement the new trend of data analysis. Furthermore, he holds that there is resurgence in demand for the past two years by analytic business leaders struggling to conform to the new requirements, compliance systems and regulatory standards such as the European Data Protection Regulation.

Enterprise data analysis catalogues are increasingly being used to document and provide context to the value and meaning of data loaded into lakes. Businesses that store data in lakes use it to analyse the unstructured forms of the business to be able to improve usability and reliability. Without use of metadata management systems in the business, a vast amount of information and data becomes unusable and unreliable hence the need to make use of enterprise data analysis to offer a solution to the increasing amounts of Big Data that is collected and stored.

Predictive business data analytics

Predictive analytics is also another form of Big Data analytics that is advanced. It offers organisations and businesses an opportunity to explore their historical through data a suite of techniques such as data modeling, mining machine learning, statistics, and AI to make predictions on the future of the business. Furthermore, predictive analytics also helps businesses and companies to get around in case of a lack of data through the analysis of historical datasets and reliably forecast the business trends due to the automated nature.

The trend is heavily automated, enabling business users to analyse well what might occur in the future that may affect the business and therefore control the outcome. Instead of using manual time-consuming analytics, business organisations can act on identified meaningful trends and patterns more reliably and faster, more than just predicting the future.

Natural language processing (NLP) business data analytics    

Natural language is also another form of artificial intelligence used in business analytics. NLP is fast becoming a significant feature of the most popular data analysis, big data, and artificial intelligence tool on the market. Additionally, it is a data analysis form that businesses and companies across the United Kingdom are using to create reports and visualise dashboards on top of their data warehouse to be able to improve the company’s overall data analytics. According to Gartner, an e-commerce business expert, he holds that approximately more than fifty percent of companies across the world, in their business intelligence software systems, will incorporate the NLP by the beginning of 2020. Furthermore, he expects natural language processing business analytics and AI to feature in modern businesses, including those that are powered by Azure and Microsoft. The systems will have an inbuilt question and answer feature that will permit businesses to respond to questions using national language. For instance, a company may establish a natural language such as ‘what were the total units sold in the last financial year,’ and the system offers an automatic answer, which will be fast and useful to the operations of the business. Therefore, the availability of NLP and its implementation by a business is the most powerful analysis tool that will help in achieving higher efficiency and decision making through easier data exploration.

Impact of AI and Big Data trends on business

Improved the sales platform

Advancement in technology through the use of Big Data and AI has encouraged and introduced the concept of electronic commerce business models. Several business institutions have switched from the traditional methods of analysis to the electronic models to be able to increase sales of their services and products.  AI and Big Data automation business analysis strengthens the business by providing a better buy. It sells the experience to both the sellers and buyers via the sale prediction, warehouse automation, recommendation engines, and the innovative e-commerce platform. Some of the companies and businesses that have significantly improved and transformed the market through Big Data and AI trends include eBay, Amazon, and Netflix. The trends guide customers towards the purchasing of products. For instance, through personalised digital advertisements on the websites, distinctive emails or messages and customised coupons.  Therefore, the data analysis makes use of the customers past behaviour such as ratings, selections, purchases, and current keywords search, to recommend different products to the buyer (Kapoor & Sharma, 2017, p. 58).

Enhances customer interactions

The use of new trends of AI and Big Data to analyse business data enhances customer interactions. Customer interaction between the company and its customers is the most basic form of communication. Through the data analysis, the company gets an opportunity to satisfy a customer and also be able to retrain them. In the conventional process of using AI and Big Data to analyse the business, employees are able to interact with customers, through sales persons, retail shop executives, cashiers and customers (Rocha, 2019, p. 260). Therefore, through artificial intelligence companies and businesses have transformed from the human to human business interaction to human to machine communication. Furthermore, businesses through agents’ intelligence conversations are able to communicate and interact with customers and various stakeholders through auditory and textual methods. The AI and Big Data business agents through their analysis can eliminate human errors, eliminate delays and provide immediate personalised responses to the buyers and customers (Pacrk, 2017, p. 437).

Shapes business context

The adoption of AI and Big Data trends by business organisations in the United Kingdom has led to the shaping of business contexts. These several factors influence the performance of business after the analysis of data. According to research conducted by Neha et al., the factors are known as the third dimension. The study collected several data from business companies’ annual reports, press releases, research reports and innovation reports from intelligence departments. After going through the data collected the researchers concluded that there are several categories of business contexts such as customer interaction, employee skill set and sales platform.

Enhances human skills

Businesses across the United Kingdom and the world have been forced to lay off some employees or displace them due to technology.  Therefore, this has been a major concern in the process of innovation. Currently, every business in the country wants to grab an opportunity to make their business digital through artificial intelligence and Big Data analysis. Therefore, to be able to fulfil this promise and demand there will be rise of new labour force that will be required to build and develop a productive AI and Big data that will analyse business data to enable the owners and several partners to foresee the future and also have insights. According to the study, business reports demonstrated that through the identification of technological skills employed persons to help in the improving of the company’s digital space. Additionally, the research demonstrated that business organisations trained its employees on AI and Big Data new trends business analysis to be able to enhance the skills and enable the business to take a competitive advantage.

Conclusion

The paper focuses on the Artificial Intelligence (AI) and Big Data new trends in business data analytics. The essay discusses various new trends of analysing business data such as augmented data analysis, edge computing system data analysis, predictive data analytics, and natural language processing. Furthermore, the essay discusses the impact of Big Data and AI business analytics on companies. Some of the effects discussed include the improved customer sales platform, enhancement of human skills, and customer or buyer interaction.

References

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