In today’s world of loosely coupled and distributed applications, communication and data centralization play a vital role. In large organizations, there are many applications and many teams that work on the data of customers, products, suppliers, and other business entities. Each application and team maintain a separate copy of the data, which means any application requiring this information needs to contact several other systems to obtain the most recent data for that entity. Therefore, this would increase the number of multiple lookups on various systems, resulting in an increase in the throughput of the application. To avoid this, the organization needs a centralized system called ”Master Data Management” that will maintain all the golden data across all the systems within the organization. Any system that is updated with new information will also be sent to MDM through events, and MDM will then be responsible for communicating with and updating the sub-systems within the organization’s Eco-System. Event-Driven Architecture will be used to communicate between distributed applications to achieve and implement Master Data Management. In recent years, there have been a lot of issues discovered in the MDM System between the data sync-up of all the sub-systems, resulting in a tedious process of identifying manually all the failures of the events, reprocessing the event  information, correcting the data,and re-synchronizing the other sub-systems with all the previous ones. To avoid this manual process and to address the issues mentioned above, we introduce the ”MDM Bot” in this article. Artificial Intelligence and Machine Learning capability are included in the proposed MDM Bot.It is an excellent value-added Bot for an Enterprise System that helps and saves a lot of time and money.

 In this world of quick delivery of quality products, DevOps test automation plays a vital role. This triggers the automation build to test whether the product quality is a good fit for the release or not?  When multiple test cases of a test suite got failed, it takes lots of time for the developer and analyst to analyze the error logs of each test case and do the root cause analysis (RCA). Second, Enterprises with complex distributed applications designed with multi-tier architecture systems and with a lot of third-party applications communicating with each other, cannot prevent failures from happening. As a result, finding the cause of failures becomes a real  challenge, as it requires cross-team cooperation, log analysis, and many more. As a result, a lot of investigation was required in order to perform a Root Cause Analysis and becomes part of the daily routine, hindering the organization’s productivity. To solve this time-consuming process, we are introducing the Root Cause Analysis Bot (RCA Bot) in this paper. This bot will analyze the failure logs, error messages, errorClasses, descriptions, and error codes and apply machine learning (ML) techniques to predict the root cause of the failure with a percentage of the prediction accuracy.

In today's fast-paced, technology-driven world, physical presence is no longer necessary, and since the covid-19 pandemic, most operations have been virtual, and with many people working from home, leading to social isolation, and there has been a steady decline in social interaction over the last few decades, which can adversely affect mental and physical health. The loss of a family member, the loss of a job, divorce, or financial hardship can all result in social isolation, discrimination, and a lack of social support. Due to these factors, many people suffer from depression, anxiety, and a variety of other diseases. To overcome this problem, we must be happier in our lives, engage in more social interactions, and be more culturally oriented. Encourage the patient to participate in the surrounding activities, events, functions and share with him the positive and happiest content that can be found on social media so that he can engage in those activities as well. In this chapter, we will discuss how robotic process automation and machine learning technology will improve the efficiency of this process. Robots will analyze social media videos, texts, and messages for sentiment analysis, user recommendations, and social network analysis, and share filtered happy context messages with emotionally depressed individuals. Virtual reality, online communities, and social media can facilitate mental health support, cultural engagement, and creative expression. As their mental health and happiness improve, they can make a greater contribution to society.