Mastering ServiceNow Platform Analytics

In this session, we’ll lay the foundation for Platform Analytics by exploring Data Sources — the core building blocks for all your metrics and dashboards. You’ll learn how to set up:

1. Indicator Sources – Define the pool of records that form the basis of your metrics (for example, “Incidents Created Newly Daily”). We’ll walk through configuring validity (how often data is collected), source tables (like incident), filters (e.g. “Created on Today”), and list views (for drill-downs).

2. Breakdown Sources – Define how to segment or slice that data (e.g., by Priority). We’ll show how to use the sys_choice table to pull choice-list values (like “Critical”, “High”, “Medium”, “Low”) and how to configure mapping, security, and unmatched labels.

We’ll also cover advanced options like overriding record collection to improve performance, running diagnostics to validate your setup, and preparing for future indicator creation.

By the end of the session, you’ll understand how to build reusable, clean, and efficient Data Sources that will power your Platform Analytics indicators and breakdowns — setting you up to create meaningful, actionable dashboards in later sessions.

In this hands-on session, we transform raw data into a powerful, scheduled KPI using Platform Analytics. Starting with the Indicator and Breakdown sources you defined earlier (Newly Created Incidents Daily, and Priority Values), you’ll learn how to build an Automated Indicator that tracks the “Number of New Incidents Daily.” We’ll guide you through configuring each field:

• Setting up the metric’s time boundaries, frequency, and calendar

• Defining score meaning (direction, unit, precision)

• Connecting to your data source and choosing the correct aggregation

• Handling scenarios with no data (“value when nil”)

• Enabling record collection for drill-down capabilities

• Customizing how the metric is presented—chart types, default time series, and advanced options

• Linking your Breakdown Source (Priority) for segmented views

• Scheduling the data collection job to run daily

By the end of this session, you’ll have a fully configured, real-time KPI that empowers your team with actionable trend insights. Whether you want to monitor incident volume, identify priority spikes, or drive process improvements, this indicator becomes your daily performance compass.

In this session, we dive into creating the backbone of your Platform Analytics implementation — the Data Collection Jobs. Building on the indicator and breakdown logic we set up previously, you’ll learn how to configure two essential jobs:

1. Daily Data Collection Job — This scheduled job runs every day (typically just after midnight), collects yesterday’s data, and stores both numeric scores and text indices.

2. Historic Data Collection Job — This on-demand job backfills historical data (e.g., the last 60 days), giving your dashboards full trend context right from the start.

We walk through step-by-step guidance on navigation, configuring relative time ranges, defining job parameters (like run-as user, timezone, and frequency), and linking the correct indicator. You’ll also learn how to execute, test, and verify successful runs — and how to inspect job logs and the generated time-series scores.

By the end of this session, your “Number of New Incidents Daily” indicator will be powered by a fully automated engine: historical data backfilled and daily updates running in production.

In this session, we tackle two powerful but distinct types of indicators in Platform Analytics: Manual Indicators and Formula Indicators.

• Manual Indicators let you capture data that doesn’t exist in your ServiceNow tables — for example, external system metrics or manually tracked events. You’ll learn how to create a “Number of Zoom Outages” indicator, set its properties (unit, direction, frequency), and manually enter daily scores.

• Formula Indicators help you derive new KPIs by combining existing indicators through arithmetic. We’ll build an “Incident Backlog Growth” indicator that calculates daily backlog change as the difference between new and resolved incidents using a simple formula ([[New Incidents]] - [[Resolved Incidents]]). You’ll also see how formula indicators integrate with breakdowns, jobs, and score generation without needing a separate collection job.

By the end of the session, you’ll understand how to set up manual data collection and build dynamic, calculated indicators — unlocking more flexibility in your analytics framework.

In this session, we explore how to slice and segment your analytics using Breakdowns in Platform Analytics — giving you the power to break metrics down by dimensions like Priority, Category, or custom groupings.

1. What Is a Breakdown?

   • Learn how breakdowns let you segment indicator scores into meaningful categories (e.g. splitting “New Incidents” by Priority or Assignment Group) to gain deeper insights.

2. Creating an Automated Breakdown

   • We’ll walk through building a breakdown sourced from an existing field (like Priority on the Incident table).

   • You’ll set up the Breakdown record, choose the Breakdown Source, and link it to your facts table.

3. Breakdown Mappings

   • Understand how to connect a Breakdown to the underlying data via a mapping: choosing the facts table (e.g. Incident), selecting the field (e.g. Priority), and optionally scripting for more advanced scenarios.

   • This mapping is what enables dynamic grouping based on real data. ServiceNow+1

4. Manual Breakdowns

   • For cases where your categories aren’t based on a field (for example, fixed tiers or labels like “S/M/L”), you can define a Manual Breakdown and manually list its elements. Learn Now Lab

   • These don’t change automatically — giving you stable, administrator-controlled categories.

5. Breakdown Relations

   • Learn how to build hierarchical breakdowns (e.g. Category → Subcategory) so one breakdown filters another, enabling cascading filters and intuitive dashboard drilldowns. ServiceNow

6. Related Lists Overview

   • After creating a breakdown, you’ll interact with related lists like Breakdown Mappings, Elements, and Relations.

   • These define how data is segmented, secured, and displayed.

7. Putting Breakdowns to Work

   • We’ll attach an indicator (like “Number of New Incidents Daily”) to your breakdown so that when you run the Data Collection Jobs, scores are grouped by the breakdown values.

   • You’ll then explore how to verify the breakdown by executing the jobs and checking the indicator in the Analytics Hub or the score explorer.

In this session, we dive into advanced administrative features in Platform Analytics — adding context and actionability to your raw metrics. You’ll learn about:

1. Bucket Groups — How to segment continuous numeric data (like reassignment count) into meaningful categories (e.g., “No Reassignments,” “High”) using PA’s bucket logic. ServiceNow+1

2. Breakdown Sources for Bucket Groups — Creating a Breakdown Source that points to the pa_buckets table so your bucket definitions drive a breakdown. ServiceNow Developers+1

3. Automated Breakdown Creation — Building a Breakdown that uses the bucket-based source and linking it to an existing indicator, so data gets categorized every time jobs run.

4. Exploring Results — Once collected, you’ll see your indicator scores split by reassignment bucket in Analytics Hub or Breakdown Explorer.

5. (In Upcoming Session) Targets, Thresholds & KPI Signals — (Teaser) How to set performance goals, define thresholds for good / bad performance, and interpret KPI signals to act on your data.

By the end of this session, you’ll be able to transform raw continuous data into context-rich performance buckets — making it easier to interpret trends and identify improvement areas.

In this session, we’ll unlock the analytical intelligence layer in Platform Analytics — the trio of KPI Details, Targets & Thresholds, and KPI Signals — to help you turn raw data into actionable performance metrics.

1. KPI Details & Aggregation

   • Explore the KPI Details page, your control center for each indicator — where you can view score trends, statistics like average, median, standard deviation, and more.

   • Learn how Time Aggregation smooths out your data using reset, running (rolling), or cumulative logic. You’ll also discover how to choose your interval (e.g., 7-day running average) for better visualization.

   • Understand Period Aggregation (weekly, monthly, quarterly) for summarizing performance over larger time frames.

   • Dive into To-Date Aggregation (Week-to-Date, Month-to-Date, Year-to-Date) to see cumulative progress in real time.

2. Targets

   • Define what “good” looks like with Targets on your KPIs. You’ll learn how to create fixed (constant) targets or dynamic ones based on historical averages.

   • Set start and review dates to formalize goal timelines.

   • Control target visibility (global or personal) and assign responsibility to appropriate users. ServiceNow

3. Thresholds

   • Establish alert boundaries using Thresholds to catch performance issues early. Use fixed conditions (“Less than / More than”) or dynamic ones like “All-time High / Low.”

   • Link thresholds to specific breakdown elements (e.g., only trigger alerts for P1 priority) and optionally automate comments when a threshold is breached.

4. KPI Signals

   • Activate KPI Signals to automatically detect anomalies in your data using baselines.

   • Understand different signal types:

     • Short Run — spikes / sudden deviations

     • Long Run — consistent out-of-baseline behavior

     • Trend Change — shifts in directionality

     • Outlier — one-off extreme values

   • Leverage these signals to proactively monitor and respond to unexpected behavior in your KPIs.

5. Chart & Visualization Options

   • Learn how to layer Targets, Thresholds, Forecasts, Trends, Comments, and Statistics in your KPI charts.

   • Choose from different time series (Score, Change, % Change) and chart types (Line, Spline, Area, Column) to best represent your data story.

In this session, we transition from the backend configuration of Platform Analytics to the user-facing experience — exploring how analytics is consumed and interacted with by end users. You’ll get familiar with the Platform Analytics Application, which is where users discover, explore, and act on insights.

Key topics covered:

1. Analytics Center

   • This is your central hub for dashboards and data visualizations — the “landing page” for analytics.

   • Users can search via natural language (“Ask a question about your data”), navigate to scorecards or the Analytics Hub, and even build new dashboards or visualizations in-context.

2. User Experience Analytics (UXA)

   • Focuses on tracking the user journey within ServiceNow, rather than process data.

   • Provides insights on active users, sessions, session durations, page load times, errors, and user behavior (e.g., where users drop off).

   • Offers filtering by date ranges, user type, application, country, and access channel (e.g., portal or mobile).

   • Includes metrics specific to Virtual Agent: conversations, topics, NLU model performance, and custom events.

3. Library

   • A catalog of certified analytics assets: dashboards, data visualizations, indicators, and filters.

   • Ensures consistency and governance by centralizing these components, so users can reuse trusted dashboards and avoid duplication.

   • Filters created in the Library can be used across dashboards to keep analyses consistent.

4. Scheduled Exports

   • Automate exports of dashboards or KPI data on a regular schedule (e.g., daily, weekly).

   • Export formats include PDF, CSV, or Excel, making it easy to share with stakeholders who do not use ServiceNow directly.

Why this matters:
This session shows how the analytics you built (indicators, breakdowns, KPIs) actually reach the people who need them. It’s about turning analytical configuration into actionable, consumable insights — empowering users to make data-driven decisions directly within ServiceNow.

In this session, we bring your analytics to life — building Dashboards and Data Visualizations in the Platform Analytics Application. You've already done the heavy lifting of creating indicators, breakdowns, and jobs — now, we're going to make them actionable and consumable.

Part 1 – Dashboards

• Learn how to create a new, configurable dashboard using the Inline Editor, and understand the difference between Inline and Technical editors.

• Add various elements like visualizations, filters, headings, lists, and rich text to build a meaningful layout. Dashboards serve as the canvas to tell your data story. ServiceNow+2ServiceNow+2

• Use governance features (like “certified” dashboards) to ensure trust and manage sharing. ServiceNow

Part 2 – Data Visualizations

• You can start a visualization either directly from a dashboard (as you build it) or by going to the Data Visualization module to create reusable charts. ServiceNow+1

• Configure your visualization: choose your data source (Indicator, Table, UX Analytics, etc.), set metrics, groupings, date range, and advanced options like trend, forecast, or targets. ServiceNow+1

• Customize the presentation: set chart type (line, bar, pivot, gauge, etc.), configure legend, labels, axes, and enable interactivity (so users can drill down into data). ServiceNow+1

• Add filters either via filter components or make visualizations themselves act as filters, enabling dynamic and context-sensitive dashboards. ServiceNow

• Once ready, share or certify your dashboard / visualizations to make them available broadly and establish them as trusted analytics assets. Royal Cyber+1

Why It Matters: By building dashboards and visualizations, you make your KPIs and breakdowns accessible to users in a consumable, interactive way — enabling data-driven decisions across teams.