Book Image

ServiceNow for Architects and Project Leaders

By : Roy Justus, David Zhao
Book Image

ServiceNow for Architects and Project Leaders

By: Roy Justus, David Zhao

Overview of this book

ServiceNow is the leading enterprise service management platform that enables the effective management of services in a modern enterprise. In this book, you’ll learn how to avoid pitfalls that can challenge value realization, where to focus, how to balance tradeoffs, and how to get buy-in for complex decisions. You’ll understand the key drivers of value in ServiceNow implementation and how to structure your program for successful delivery. Moving ahead, you’ll get practical guidance on the methods and considerations in securely using ServiceNow. You’ll also learn how to set up a multi-instance environment including best practices, patterns and alternatives in the use and maintenance of a multi-instance pipeline. Later chapters cover methods and approaches to design processes that deliver optimal ROI. Further, you’ll receive tips for designing technical standards, designing for scale, ensuring maintainability, and building a supportable instance. Finally, you’ll focus on the innovative possibilities that can be unlocked in a ServiceNow journey which will help you to manage uncertainty and claim the value of being an early adopter. By the end of this book, you’ll be prepared to lead or support a ServiceNow implementation with confidence that you’re bringing not only a solution but also making an impact in your organization.
Table of Contents (17 chapters)
Part 1 – Pursuit of Value
Part 2 – The Checklist
Part 3 – From Success to Innovation

Clustering framework

Clustering is a tool for exploring data and detecting patterns that are not apparent to people looking at individual records. Like similarity solutions, a clustering solution quantifies the degree to which records are like one another. The difference is that while similarity search applies to one record at a time, the clustering framework applies to your entire dataset, creating groups of records that mean similar things.

These results are then grouped in a visualization called a treemap, where clusters represent blocks of different records and are sized according to the number of records in each cluster.

Once your clustering solution has been trained, it can be used as an exploratory tool to identify large groups of records that adhere to common patterns. Clustering is very useful for identifying automation opportunities or reoccurring issues.

Creating a clustering solution

Setting up a clustering solution is somewhat more complex than other models...