WHAT IS MACHINE LEARNING?
What do interactive speakers and self-driving cars have in common? They both make use of “machine learning.” When we give systems access to data, they can “learn”—or find patterns— and develop themselves without the need for human programming. Artificial intelligence (AI), which is the imitation of human behaviour by robots and software, is sometimes confused with machine learning. Although they frequently collaborate, machine learning goes beyond human-like behaviour by allowing computers to become more intelligent as they learn more. Because of this, your Alexa speaker, for instance, can offer suggestions based on what you’ve previously said. Computer systems must have access to sufficient digital data to analyse, organise, and store data before making predictions. The internet is useful in this situation. Even though British AI pioneer Arthur Samuel first used the phrase “machine learning” in 1959, the technology wasn’t feasible until the internet was developed enough to allow access to rich data. However, machine learning today makes it possible for us to communicate with technology as if it were a human, monitor our health, receive personalised advice, and even increase our online security. Consider the claim made by Google that it has been utilising machine learning to lessen security concerns in its Play store. The business claimed that in 2017, machine learning features built into Google Play Protect were used to identify about 60.3% of potentially hazardous apps. Additionally, some security developers are employing the technology in conjunction with AI and game theory to identify possible flaws and fix them before hackers take advantage of them. Researchers are also exploring ways to improve the efficiency of power grids themselves while detecting and analysing potential cyberattacks using machine learning and sensors. However, these sophisticated technological developments could potentially backfire. Cybercriminals will soon be looking for new access points and attack methods utilising the same approaches, according to security researchers. This is problematic because as the globe becomes more linked every day, the bad guys will have more methods than ever to access our devices and important data.
HOW THE MACHINE LEARNING CHANGES THE ENTREPRENEUR IN CREATING NEW BUSINESS FOR THE NEW MARKET?
The benefits of applying AI in the field of Data Science and Business Analytics are discussed by the present experience with introducing AI, the implementation of conceptual development, and certain pilot solutions that have just been accepted in the market. According to Gartner, these two fields will soon converge. The removal of repetitive chores, automation of work processes, optimization of business processes, improved decision-making, and the production of intriguing new directions and ideas are among the key advantages of utilising AI in data analysis, according to a study of business leaders. Let’s examine each of these advantages in more detail.
ELIMINATE THE REPETITIVE TASK
One of the key advantages of integrating machine learning and other artificial intelligence technology into company operations is that analysts may now concentrate on carrying out more imaginative jobs. This refers to the automation of data-related tasks when machine intelligence handles the majority of the search, creation, and presentation tasks, freeing up more time for employees. So that financial records are kept as accurate as possible, AI, for instance, helps the financial industry streamline accounting operations and consistently carry out predictable tasks like data input, payments, invoicing, etc. Process automation makes technical tasks for employees an object of monitoring and control rather than present production, which reduces the likelihood of common human errors when working with data. The repetitious processes of gathering and evaluating data from numerous sources can be replaced by working with software models and algorithms for marketers and business analysts. These models and algorithms complete these tasks quicker and more effectively than people. This enables big businesses to scale back on the technical manpower needed for information gathering and organisation as well as automated transactions. Employees of startups and small businesses can also do their tasks effectively. Additionally, Forrester’s research demonstrates that when normal and non-routine jobs are automated, employee productivity rises dramatically.
BETTER DECISION-MAKING
This is yet another important advantage of applying AI to data science. According to 84% of respondents to a Forbes Insights poll conducted for Microsoft, AI can help brain workers be more creative and concentrate on intellectual work by eliminating repetitive activities and enhancing decision-making. Decision-making primarily impacts management and strategic planning, both of which are crucial for top management and shareholders. Analysts and supervisors are typically responsible for working with the System of Records, which houses the data needed to make decisions. Today, however, AI algorithms are being used to develop intelligent systems, which “may offer all the capabilities of SOR while giving the data and insights needed to make smarter decisions across the business.” The majority of these processes still need data handlers and digital analysts to refine and validate models and graphs to be maintained, although AI processes data at a far more intensive level. This has an impact on the management of the supply chain and the workforce, business forecasting, cost reduction, and working with customers and partner organisations. A more effective decision-making circuit increases the accuracy and speed of dealing with information by lowering the chance of being swayed by erroneous facts and making rash conclusions.
PROMISING IDEA GENERATION
This is another important advantage of using AI in business analytics. The ability of AI to recognise “invisible” thoughts and predict the context required to process data correctly is vital, and 45% of respondents think it is extremely significant, according to a survey already highlighted by Forbes Insights. In other words, AI enables the alternative organisation of information. Such technology identifies patterns and abnormalities where humans might not look, going beyond human vision. The interaction of the AI’s numerous functions with different storages and databases, as well as the application of heuristic data analysis algorithms, enable the development of concepts with great promise. This optimization of predictive models makes it possible to predict changes in demand and needs for new products or services, as well as to open and develop fundamentally new markets, as is the case with app stores and AirBnB. An important feature of using AI for analysis is 24/7 access to its results. This enables business leaders to determine important business performance indicators, make necessary adjustments as they arise, negotiate sales, make hiring and fundraising decisions and finalize partnership agreements – all this quickly and in real-time. To enable such solutions, new AI tools must fully shift to the creation of promising and unbroken data transfer chains (Future-Proof and Anti-Fragile Data Supply Chains).The analysis of data from contemporary multimedia devices can be quite successful in gaining insight into a variety of manufacturing processes and consumer behaviour.
OTHER BENEFITS OF AI TECHNOLOGY
Other significant corporate advantages of AI systems include improved systems and lower costs, according to a RELX survey. High levels of automation, fewer errors, and improved resource use all increase process efficiency. Building efficient supply networks, efficient staff management models, and optimal production methods are all made possible by such advanced data-processing algorithms. For businesses in the marketing, sales, and manufacturing sectors, these solutions are highly effective at lowering costs and raising profits, according to McKinsey. In general, advancements were made in all crucial areas. A customer-centric approach, better customer retention strategies through mechanisms to research their requests, and providing appropriate answers at the level of intelligent data processing algorithms are the final major benefits of employing AI for Business Analytics. As a part of contextual advertising algorithms, bot advisors, and personalised suggestions on websites and in the mail, these services are already partially deployed. Working with customers’ data facilitates the development of their direct and suppressed demand models and fosters continuous, one-on-one communication between businesses and customers. Of course, AI technology cannot address every client issue. According to a poll by Accenture, the majority of customers still like speaking with real people to get opinions or guidance. However, it should be kept in mind that more than half of customers will choose to locate a new supplier if the customer service is weak, for instance, because of a shortage of professionals.
CONCLUSION
In e-commerce, artificial intelligence is a major force behind creative solutions and individualised customer experiences. Personalized shopping, product recommendations, chatbots, and online shopping security are the commercial tactics used by AI in the e-commerce sector the most. Artificial intelligence is a helpful tool to include in your e-commerce firm since it can analyse personal data like buying patterns and behaviour while giving customers a tailored shopping experience. By continuously organising and analysing data, AI can effectively predict client behaviour, enabling businesses to boost revenues and enhance the overall customer experience. Therefore, the main benefits of using AI for Data Science and Business Analytics are as follows: • Eliminate Repetitive Tasks • Better Decision Making • Promising Idea Generation In theory, this advantage contributes significantly to the promotion of AI technology to the market for business services, analytics and IT outsourcing services. However, the current trend is also determined by the success and failure of certain cases of the introduction of such technology, which we will discuss in the third part of this article.
