ITSM

How to get started with ThoughtSpot for ServiceNow Analytics

Since the start of the pandemic, business demands on your IT team have skyrocketed. You need granular, actionable insights to keep up with the speed and volume of digital transformation projects and IT incidents occurring across your organization. Canned reports from SaaS-based systems like ServiceNow aren’t fundamentally built for analytics.

ThoughtSpot, ServiceNow, and Snowflake for IT Workload Management

As the developer of the leading data cloud, Snowflake generates a wealth of IT Service Management data with ServiceNow. But uncovering actionable, granular insights has been a challenge. Now, ThoughtSpot and Snowflake are empowering IT executives to answer all their questions about support ticket backlog and effort with a single pane of interactive insights in ThoughtSpot, powered by Snowflake.

ThoughtSpot, ServiceNow, and Snowflake for Business Application Management

As the developer of the leading data cloud, Snowflake relies on a number of business applications. But creating a holistic view of these applications has been a challenge, as the data is sourced from a variety of systems. By combining application data from multiple sources in the Snowflake data cloud, ThoughtSpot and Snowflake are empowering internal organizations to answer all their questions about enterprise application quality with a single pane of interactive insights in ThoughtSpot, powered by Snowflake.

ThoughtSpot, ServiceNow, and Snowflake for Operational Metrics

ThoughtSpot for ServiceNow at Snowflake - As the developer of the leading data cloud, Snowflake generates a wealth of operational helpdesk data with ServiceNow. ThoughtSpot and Snowflake are enabling helpdesk and operations executives to answer all their questions about operational metrics with a single pane of interactive insights in ThoughtSpot, powered by Snowflake.

Customer Health Metrics Help CSM Teams Reduce Churn and Accelerate Upsell in API Platform Companies

One of our API-first brethren in San Francisco recently shared with us how they built their synthetic testing system to monitor uptime and latency. It was a large undertaking involving a huge Redshift warehouse, Datadog and many man months of engineering effort. At the end of it, they could measure latency’s completeness, whether it was functionally correct and, when it moved out of bounds, alert CSM/engineering teams.