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

Cooked and synthetic data

Data is a critical component of an RPA project, as it provides the necessary input for the RPA robot to perform its tasks. The RPA robot uses data to process information, make decisions, and automate tasks. Without data, an RPA robot cannot perform its intended functions.

Cooking the data is a term used to describe the practice of manipulating data in order to produce a desired outcome or result. It typically involves altering the data in some way to make it appear more favorable or to hide unfavorable information.

In the context of RPA and automation projects, cooking the data can be a serious issue because it can lead to inaccurate results and decisions made based on faulty information. This can have serious consequences, particularly in industries such as finance or healthcare where decisions based on data are critical. To avoid cooking the data in RPA and automation projects, it is important to establish clear guidelines for data collection, processing...