Book Image

RPA Solution Architect's Handbook

By : Sachin Sahgal
Book Image

RPA Solution Architect's Handbook

By: Sachin Sahgal

Overview of this book

RPA solution architects play an important role in the automation journey and initiatives within the organization. However, the implementation process is quite complex and daunting at times. RPA Solution Architect’s Handbook is a playbook for solution architects looking to build well-designed and scalable RPA solutions. You’ll begin by understanding the different roles, responsibilities, and interactions between cross-functional teams. Then, you’ll learn about the pillars of a good design: stability, maintainability, scalability, and resilience, helping you develop a process design document, solution design document, SIT/UAT scripts, and wireframes. You’ll also learn how to design reusable components for faster, cheaper, and better RPA implementation, and design and develop best practices for module decoupling, handling garbage collection, and exception handling. At the end of the book, you’ll explore the concepts of privacy, security, reporting automated processes, analytics, and taking preventive action to keep the bots healthy. By the end of this book, you’ll be well equipped to undertake a complete RPA process from design to implementation efficiently.
Table of Contents (25 chapters)
1
Part 1:Role of a Solution Architect
5
Part 2:Being Techno/Functional
11
Part 3: Tool Agnostic Approach
17
Part 4:Best Practices
22
Epilogue

Advanced analytics

What is advanced analytics? Advanced analytics is the use of next-generation tools and techniques that are way more advanced than traditional business intelligence (BI) techniques. Advanced analytics helps in gaining deeper insights and finding patterns that can be used in making predictions or generating recommendations. Advanced analytics techniques include data/text mining, ML algorithms, pattern matching, semantic analysis, sentiment analysis, generative networks, cluster analysis, simulation, and neural networks.

As we can see from the preceding definition, advanced analytics is about going above and beyond the traditional ways of doing analytics. It uses ML, data/text mining, pattern matching, and semantic analysis, which all are somehow related to the data science field. Now, you might think that data science and advanced analytics are the same, but they are not. There is quite a difference between the two fields. Although they seem similar and share some...