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Performance Analytics (PA) Essentials

Learn to access key performance indicators (KPIs) and metrics for proactive business service improvement.

Performance Analytics provides simple access to key performance indicators (KPIs) and metrics for proactive business service improvement. In this 2-day interactive technical training, attendees master the setup, configuration, and usage of the Performance Analytics application.

Extensive hands-on exercises are included with each course module to reinforce the lecture concepts and provide practical experience. Exercises are performed in a personal dedicated training instance.

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Who Should Attend?

This course is designed for Performance Analytics Administrators and Power Users who administer and create content for the Performance Analytics application. Service owners designing KPIs and Dashboard requirements for deeper service insight will also benefit from this course.

What You'll Learn
  • Identify Performance Analytics use cases, architecture, and deployment process
  • Navigate the Analytics Hub (formerly the Indicator Scorecard)
  • Configure and monitor Data Collection jobs
  • Build Automated Indicators to track KPIs
  • Enhance Indicators with Breakdowns for detailed data navigation
  • Create Manual Indicators and manage manual data population
  • Build Formula Indicators for added process and service inferences
  • Configure Targets and Thresholds to make process visualization actionable
  • Build various Performance Analytics Widgets and Reporting Filters
  • Design and populate Dashboards

Course Outline

Module 1: Performance Analytics Introduction

Objectives:

Identify Performance Analytics key functionality, differences from operational reporting, roles, deployment, and key use cases.

Lab work:

Lab 1.1: Essential Concepts

Module 2: Data Collection

Objectives: Learn the basics of data collection, job configuration, and best practices. Distinguish between daily and historic collection. Discuss data management and retention practices.

Lab work:

Module 3: Analytics Hub

Objectives: Learn about performing a detailed analysis and comparison of Indicator data using the Analytics Hub. View indicator data and statistical summaries, use Confidence and Prediction bands, understand Time series aggregations and Breakdowns, create and view Targets, Thresholds, and Comments.

Lab work:

Lab 3.1: Analytics Hub Navigation

Module 4: Automated Indicators

Objectives: Configure Automated Indicators and Indicator Sources. Practice form-based and assisted Indicator creation using the built-in Indicator creation workflow.

Lab work:

  • Lab 4.1: Automated Indicators

Module 5: Automated Breakdowns

Objectives: Configure Breakdowns, Breakdown Sources, and Breakdown Mappings. Manage Breakdowns using the Breakdown matrix and Breakdown Matrix exclusions.

Lab work:

  • Lab 5.1: Automated Breakdowns
  • Lab 5.2: Managing Breakdowns

Module 6: Formula and Manual Indicators

Objectives: Learn about Manual indicators and their use cases. Discuss manual breakdowns in the context of manual indicators. Learn about formula indicators and the basics of formula syntax.

Lab work:

  • Lab 6.1: Formula Indicators
  • Lab 6.2: Manual Indicators

Module 7: Displaying Actionable Data

Objectives: Discover how to refine Performance Analytics data to make it more meaningful and useful using Targets, Thresholds, Time Series, Trends, Breakdowns, and Elements Filters.

Lab work:

  • Lab 7.1: Targets
  • Lab 7.2: Thresholds
  • Lab 7.3: Element Filters

Module 8: Widgets and Dashboards

Objectives: Build dashboards and add widgets to create a role-oriented dashboard. Explore Dashboard Administration, Responsive Canvas, adding Reports to dashboards and using Interactive filters, and In-Form (Context-Sensitive) Analytics.

Lab work:

  • Lab 8.1: Dashboards and Widgets
  • Lab 8.2: Data Filtering
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Prerequisites

Related Certifications

Micro-certification in Performance Analytics.

Follow-On Courses

The Performance Analytics Advanced course is recommended for a more in-depth understanding and advanced Performance Analytics use case implementation.