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Workday Prism Analytics Training for Data Preparation and Reporting

Workday Prism Analytics Training provides knowledge about the methods through which one can integrate data from various sources into Workday, clean up and analyze data to make reports. Workday Prism Analytics Classes help learners understand data ingestion, data blending, and reporting through practical exercises.

4.0 (435 reviews)
812 Enrolled
160 Hrs of Content
Live Online Class with Recording
  • 23 modules · 193 topics · 160h of content
  • Industry-recognized certificate on completion
  • Live doubt-clearing sessions with expert mentors
  • Hands-on projects & real-world case studies
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Duration

160Hrs

Modules

23

Topics

193

Enrolled

812

Certificate

Included

Access

Lifetime

About Workday Prism Analytics Training

Workday Prism Analytics can help businesses integrate Workday data and other data sources for analysis. For instance, a company might have some employee information in Workday and some budgeting information in another platform. Workday Prism Analytics can help integrate this data for reporting purposes. Workday Prism Analytics Training is centered around the processes used in the preparation and transformation of the data.

The major topics covered in this training include the following:

Learn to feed the data into Workday Prism Analytics.
Learn about data transformations.
Explore the datasets and Data Catalog.
Merge Workday data with other data sources.
Learn about data pipelines and publishing.
Generate reports with data security considerations.
About Workday Prism Analytics Training

Workday Prism Analytics Classes

What Will You Learn in Workday Prism Analytics Training?

This course teaches the key activities related to preparing business data for analytics. In Workday Prism Analytics Training, you may study the following topics depending on the final curriculum of Corporates Academy.

Introduction to Workday Prism Analytics

Study what Workday Prism Analytics is and how businesses leverage it together with Workday reporting. Get familiar with reasons that may prompt companies to combine data from HR, Finance, and other business systems instead of looking at each source separately.

Data Ingestion and Data Catalog

Study the fundamentals of the data ingestion process into Prism Analytics. Get to know about the two sources from which the data may come – it might be a Workday custom report or an external source. Study the Data Catalog and its purpose, along with the concept of tables.

Data Transformation

Understand how raw data can be transformed before being incorporated into a report. Some of the tasks can be filtering of unwanted records, field modification, creation of calculated fields, aggregation of the information and verification whether the result meets business requirements.

Datasets and Data Blending

Understand how datasets are used for information preparation. Learn how a derived dataset combines the information from various tables/datasets. For example, employee information may be linked with information on departments.

Data Pipelines and Publishing

Understand how a data pipeline is built. After data collection and transformation, you should publish the data for reporting. Understand why dataset publishing results in the creation of a Prism data source, which can then be used in Workday reports.

Reporting, Dashboards, and Data Security

Understand how published Prism data sources can be used in Workday reports, scorecards, dashboards, and Discovery Boards. Understand why data access rules are important and how proper permissions can provide necessary security for business data.

Why Enroll in Corporates Academy for Workday Prism Analytics Training?

Before joining, please review the course syllabus, instructor details, class mode, practical sessions, and course fees at Corporates Academy. Workday Prism Analytics Training must ensure that you get to learn about the entire process of data flow and not just individual definitions of different topics.

  1. Topic Coverage: Please study the Workday Prism Analytics Certification curriculum to know how Prism Analytics will be covered and the time allocated for each topic.
  2. Step-Wise Training: Get trained on the entire process of data flow, starting from importing data through processing to reporting.
  3. Use Business Instances: Please use business instances from areas like HR, Finance, etc., to know why businesses integrate data from various sources. Workday Prism Analytics Classes cover datasets, data transformation, and dashboards to help learners understand business data.
  4. Practical Sessions: Please inquire whether the course contains practical sessions using datasets, data transformation, data blending, and publishing.
  5. Questioning Assistance: Please find out how you can raise your doubts if any topic or activity confuses you.
  6. Course Details: Before joining the workday course, please confirm all the information related to the batch, duration, fees, material, and certification.

Key Benefits of Learning the Workday Prism Analytics Course

Workday Prism Analytics is helpful if an organization requires analyzing data from multiple sources. Knowing the basic features of Prism will enable you to understand the data preparation process and the availability of data to users. Earning a Workday Prism Analytics Certification can help professionals build skills in data analysis, dashboards, and data management.

Understanding the Whole Process of the Data Life Cycle

You get to know about the whole process of moving data from the source to the catalog, the data transformation process, and finally getting access to data. Workday Prism Analytics Course Training teaches learners how to manage data pipelines, transform datasets, and create reports.

Ability to Merge Data

Organizations might store their data in multiple systems. Prism Analytics helps to merge Workday and external data.

Getting Good at Data Preparation

It will be helpful for you to know that reports are valuable when the data used in the report is appropriate for that particular task.

Understanding Workday Reporting

You will learn about the usage of published Workday Prism data sources in Workday reporting.

Understanding Basic Concepts of Data Security

The organizational data contains sensitive information about employees and financial details.

Developing Skills Required for Workday Data Positions

Understanding Workday Prism Analytics will be helpful if you work with Workday reporting or data management projects.

Career Opportunities Related to Workday Prism Analytics

Following your training in Workday Prism Analytics, you will be able to look for positions that involve Workday data, reporting, and analytics. Your available career options will vary depending on your experience and technical background in Workday.

Job RoleWhat the Work May Involve
Workday Prism Analytics ConsultantHelping prepare, blend, and publish data for business reporting.
Workday Reporting AnalystWorking with business reports, data sources, and reporting needs.
Workday Data AnalystReviewing and preparing data to help teams understand business information.
Workday Functional ConsultantSupporting Workday business processes and helping teams meet their reporting needs.
Workday HRIS AnalystWorking with HR systems, employee data, reports, and system-related tasks.
Workday Data Integration SupportHelping monitor data movement, check errors, and support data-related processes.

Salary Expectations for Workday Prism Analytics Beginners

The amount of salary you would earn after taking Workday Prism Analytics training will depend on your current knowledge, your experience, where you are located, your company, and what you would do at your job. A Workday Prism Analytics Training program does not determine your salary.

FactorHow It Can Affect Salary
Previous Work ExperienceRelevant experience in HR, Finance, reporting, or data work may help when applying for suitable roles.
Workday KnowledgeKnowledge of Workday HCM, Finance, or reporting may be useful for certain jobs.
Prism Analytics SkillsUnderstanding data ingestion, transformation, blending, publishing, and security can match the needs of Prism-related roles.
Practical ExperienceExperience solving real data problems can help you explain your skills during interviews.
CertificationSome employers prefer or require relevant Workday certification, while others may consider practical experience and knowledge.
Job Location and EmployerPay can vary by company, city, role, and level of responsibility.

Recommended Workday Similar Courses
  1. Workday Payroll Training
  2. Workday Studio Training
  3. Workday Recruitment Training
  4. Workday HCM Functional Course
  5. Workday Integration Certification
  6. Workday HCM Technical Training
  7. Workday Finance Modules Course
  8. Workday Customer Accounts Training
  9. Workday HR Case Management Certification
  10. Workday HCM Techno Functional Course Training

Who Is This Course For?

Fresh Graduates

Fresh Graduates

Looking to break into tech with job-ready skills

Career Switchers

Career Switchers

Professionals pivoting from non-tech backgrounds

Curious Learners

Curious Learners

Anyone eager to explore this exciting field

What You'll Learn

Basic Concepts of Workday Prism Analytics: Understand the functions of Prism Analytics, the purposes for which companies utilize it, and how it allows them to bring together Workday and external data for reports.
Ingesting and Data Catalog: Understand how information may be ingested into Prism Analytics either through Workday reports or external data and how tables and the Data Catalog store information.
Data Transformation: Understand how filters, groups, joins, and calculated fields can transform raw data into information that is more beneficial for business purposes.
Datasets and Data Blending: Understand the concept of datasets and their purpose in blending information from various sources into a more comprehensive view of the business data.
Data Pipeline and Publishing: Understand the concept of a data pipeline and how a Prism data source becomes published.
Reports, Dashboards, and Security: Understand how the published data helps in creating Workday reports and dashboards and the reasons for granting access permissions.

Technologies Covered in This Course

Scipy

Career & Placement Support

Real professionals who accelerated their career growth and secured new opportunities with our Career & Placement Support.

Mock Session

Mock Interview Session

Practice Workday Prism Analytics Training for Data Preparation and Reporting interview questions, case studies, and technical scenarios with expert feedback.

LMS Learning

LMS Learning

Access Workday Prism Analytics Training for Data Preparation and Reporting recordings, Workday Prism Analytics Classes exercises, datasets, assignments, and study materials anytime.

Career Support

Career Support

Get resume preparation, LinkedIn optimization, interview guidance, and job-search assistance.

Certified Expert

Trainer Experience & Expertise

A good Workday Prism Analytics instructor should give a clear explanation of each point and link it to tasks that may arise in a business environment. The instructor should not only demonstrate the functionality of each feature but also its rationale. The following are the subjects that the instructor should explain during the Workday Prism Analytics Course:

  • Workday Prism Knowledge: Explain the rationale of Prism Analytics and its role in business reporting.
  • Data Ingestion: Describe the process of data ingestion from Workday and third-party systems.
  • Data Transformation: Explain how datasets can be filtered, joined, and grouped.
  • Data Blending: Provide clear examples of data blending.
  • Reporting and Publishing: Explain how prepared data turns into reports and dashboards.
  • Security Basics: Explain the access policies and reasons why sensitive business data requires special security measures.
Your Learning Journey

Training Roadmap

A complete learning journey designed to build practical skills and prepare you for real-world opportunities.

01

Industry Expert Trainer

Learn Workday Prism Analytics Training for Data Preparation and Reporting from an experienced trainer through practical explanations and real-world examples. Build your skills through 160 hours of training.

02

Updated Learning Resources

Access updated presentation decks, training notes, practice files, and supporting resources used during live sessions, so you can revise every topic at your own pace and stay prepared for interviews.

03

Digital Study Material

Support your Workday Prism Analytics Training for Data Preparation and Reporting learning with structured digital materials, reference guides, and revision notes. Study materials are organized across 23 modules.

04

Practice & Assessments

Test your knowledge through module-wise assignments, practice exercises, and assessments. Reinforce your understanding of 193 topics covered in the curriculum.

05

Recorded Sessions

Revisit important concepts and catch up on missed classes with recorded sessions, where available. Choose from 3 batches scheduled each month.

06

Hands-On Projects

Apply your Workday Prism Analytics Training for Data Preparation and Reporting knowledge through practical projects and scenarios relevant to the course. 2 Live projects included.

07

Professional Resume Support

Create a professional resume that highlights your skills, practical work, and career goals with expert guidance with 200

08

Interview Preparation

Prepare for roles related to Workday Prism Analytics Training for Data Preparation and Reporting with mock interviews, relevant questions, practical scenarios, and trainer feedback.

Our Hiring Partners

Top companies actively hire from our alumni network.

tata-group
Cisco
Deloitte
Accenture
Wipro
Ganpact
TCS
Infosys
sap
Motherson
Tech Mahindra
Samsung
120+
Hiring Partners
85%
Placement Rate
₹12 LPA
Avg. Salary Offered

Course Curriculum

23 modules · 193 topics · 160h total

1 1. Introduction to Workday Prism Analytics 7 topics
Overview and business purpose of Workday Prism Analytics
Prism Analytics within the Workday analytics ecosystem
Typical use cases and business value
Core concepts: datasets, data sources, transformations, and published data
Relationship with Workday reporting, dashboards, and analytics
Prism Analytics lifecycle and high-level architecture
Roles of analysts, data stewards, administrators, and consumers
2 2. Architecture, Terminology, and Navigation 7 topics
Prism Analytics architecture and key components
Workspaces and available authoring areas
Navigation, menus, object search, and task access
Dataset lifecycle and object relationships
Development, validation, and publishing concepts
Tenant considerations and feature availability
Data ownership, stewardship, and operational responsibilities
3 3. Data Sources and Data Ingestion 11 topics
Overview of supported data source patterns
Workday data and external data concepts
File-based data ingestion and structured data files
Source data preparation and required formats
Delimited text files and common file considerations
Column headers, data types, encodings, and date formats
Source selection and ingestion configuration
Import validation and data quality checks
Handling missing, malformed, and inconsistent source data
Refresh and re-ingestion considerations
Data source documentation and lineage
4 4. Dataset Design and Management 9 topics
Dataset concepts and design principles
Create, name, organize, and maintain datasets
Dataset fields, metadata, and data types
Primary keys and business identifiers
Grain, row-level meaning, and dataset purpose
Dataset versioning and change considerations
Preview and validate dataset contents
Dataset ownership, documentation, and reuse
Dataset lifecycle: draft, validate, publish, maintain
5 5. Data Profiling and Quality Management 9 topics
Data profiling objectives and techniques
Completeness, accuracy, consistency, and uniqueness
Identify nulls, blanks, duplicates, and outliers
Standardize values and resolve inconsistent formats
Validate dates, numbers, currencies, and identifiers
Business-rule checks and exception identification
Data quality thresholds and acceptance criteria
Record rejected or excluded data
Document remediation and data quality outcomes
6 6. Data Transformation Fundamentals 11 topics
Transformation concepts and workflow
Select, rename, reorder, and remove fields
Filter rows and define inclusion criteria
Sort and organize records for analysis
Change and standardize data types
Create derived and calculated fields
Conditional logic and value mapping
Text cleaning, trimming, and normalization
Date and numeric transformation concepts
Handle nulls, defaults, and invalid values
Validate transformation outputs
7 7. Combining Data: Joins and Unions 10 topics
Why and when to combine datasets
Join concepts and matching keys
Inner, left, and other supported join patterns
Choose appropriate join keys and data grain
Identify unmatched records and join fallout
Prevent duplicate row multiplication
Union and append compatible datasets
Align schemas and data types before combining
Validate row counts and reconciliation totals
Document join logic and assumptions
8 8. Aggregation and Analytical Preparation 8 topics
Aggregation concepts and business questions
Group data by business dimensions
Count, sum, minimum, maximum, and average concepts where supported
Detail-level versus summary-level datasets
Build reusable analytical datasets
Reconcile aggregate totals against source data
Choose appropriate level of detail for reporting
Prepare datasets for dashboards and downstream analytics
9 9. Calculated Fields and Business Logic 9 topics
Calculated field purpose and design
Arithmetic and conditional calculations
Business classifications and category mapping
Derived measures and ratios
Date-based calculations and period logic
Null handling and calculation edge cases
Reusable calculation patterns
Validate calculations with sample records
Document definitions and business assumptions
10 10. Data Modeling and Analytical Design 8 topics
Translate business questions into data requirements
Fact-like and dimension-like data concepts
Define data grain and relationship cardinality
Design datasets for performance and usability
Reusable data models and naming conventions
Avoid ambiguous field names and conflicting definitions
Model documentation and stakeholder sign-off
Maintainable transformation pipeline design
11 11. Workday Data and External Data Blending 8 topics
Use cases for combining Workday and external data
Identify relevant Workday business data
External operational, financial, and benchmark data examples
Align identifiers across source systems
Resolve differences in dates, currencies, and categories
Data mapping and reference-data alignment
Validate blended results with business owners
Data provenance and source attribution
12 12. Publishing and Making Data Available 8 topics
Publish-ready dataset validation
Publishing workflow and lifecycle concepts
Published data and downstream reporting use
Refresh and update considerations
Publication dependencies and consumer impact
Validation after publication
Manage changes to published datasets
Communicate dataset availability and definitions
13 13. Reporting, Dashboards, and Consumption 8 topics
How Prism-prepared data supports Workday reporting
Report and dashboard consumption concepts
Choose measures, dimensions, and filters
Build analytical views for business audiences
Drill-down and summary-to-detail analysis concepts
Validate report totals and filter behavior
Data freshness and reporting expectations
Explain dataset limitations to report consumers
14 14. Security, Privacy, and Governance 9 topics
Workday security concepts relevant to Prism
Role-based access and least-privilege principles
Data access and sharing considerations
Sensitive and personally identifiable information
Data minimization and purpose limitation
Governance of external and blended data
Auditability, lineage, and ownership
Retention, privacy, and compliance considerations
Security testing and access review
15 15. Data Lineage, Audit, and Change Control 7 topics
Trace source-to-output data flow
Document source objects and transformations
Record business definitions and assumptions
Audit trail and operational evidence concepts
Version and change management
Assess downstream impact before changing datasets
Review and approval practices
16 16. Performance and Optimization 8 topics
Design efficient transformation pipelines
Reduce unnecessary columns and records early
Use suitable data grain and join keys
Identify expensive or redundant transformations
Manage dataset size and processing complexity
Refresh scheduling and operational considerations
Performance validation and troubleshooting
Balance refresh frequency with business needs
17 17. Refresh, Scheduling, and Operational Monitoring 7 topics
Data refresh concepts and dependencies
Refresh frequency and business requirements
Monitor ingestion and transformation outcomes
Identify failed or incomplete refreshes
Recovery, retry, and reprocessing considerations
Operational runbooks and support ownership
Communicate data delays and service impact
18 18. Error Handling and Troubleshooting 9 topics
Common ingestion and file-format issues
Data type conversion and malformed-value errors
Join mismatch and unexpected row-count changes
Duplicate records and aggregation discrepancies
Null and missing-field investigation
Publishing and access issues
Trace errors to source or transformation steps
Root-cause analysis and corrective actions
Defect documentation and regression checks
19 19. Integration, Migration, and Release Management 7 topics
Source-system and data-provider coordination
Data migration and cutover planning
Promote changes through appropriate environments where supported
Configuration and dataset documentation
Release impact analysis
Regression testing after updates
Deployment checklist and rollback considerations
20 20. Testing Strategy and Validation 9 topics
Define acceptance criteria and expected results
Unit testing individual transformations
End-to-end pipeline testing
Source-to-target reconciliation
Boundary, null, duplicate, and exception test cases
Join and aggregation validation
Security and access testing
User acceptance testing and sign-off
Regression test suite and evidence retention
21 21. Practical Labs and Capstone Project 10 topics
Lab 1: Analyze a business requirement and map data needs
Lab 2: Prepare and ingest an external sample data file
Lab 3: Profile data and correct quality issues
Lab 4: Create a dataset and define field metadata
Lab 5: Build transformations, filters, and calculated fields
Lab 6: Join two datasets and validate row counts
Lab 7: Create an aggregated analytical dataset
Lab 8: Publish and validate a dataset for reporting
Lab 9: Investigate a refresh or data-quality failure
Capstone: Blend external data with a business dataset, document lineage, validate outputs, and present analytical findings
22 22. Advanced Use Cases and Best Practices 7 topics
Cross-functional analytics use cases
Workforce, finance, operational, and external benchmark scenarios
Reusable data preparation patterns
Data product ownership and stakeholder collaboration
Governance and sustainable maintenance practices
Design for explainability and auditability
Limitations, tenant-specific features, and roadmap awareness
23 23. Interview Preparation and Project Readiness 7 topics
Prism Analytics terminology and core concepts
Explain a dataset pipeline end to end
Scenario questions on joins, transformations, and data quality
Troubleshooting and reconciliation case studies
Security, governance, and publishing questions
Describe a Prism Analytics project with measurable outcomes
Functional consultant and analyst interview practice

Salary Range & Job Opportunities

Roles our alumni land after this course

Workday Consultant

₹ 8–18 LPA

Deloitte Accenture IBM

HRIS Analyst

₹ 6–14 LPA

Capgemini TCS Infosys

Workday Architect

₹ 20–40 LPA

PwC EY Cognizant

87%

Placement Rate

₹ 3.5L

Avg. Salary Hike

45 days

Avg. Time to Hire

Student Reviews

4.0

Course Rating

5
62%
4
25%
3
9%
2
3%
1
1%
435

Total reviews

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Certificate of Completion

Workday Prism Analytics Training for Data Preparation and Reporting

Corporate Academy

Awarded upon successful completion

Industry-Recognised Certificate

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Finish every module and project to earn the Corporate Academy course completion certificate. Share the Corporate Academy certificate on LinkedIn attach it to your resume and display this Corporate Academy achievement to recruiters, across India and worldwide.

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FAQs for Workday Prism Analytics Classes

How can I use Workday Prism Analytics?

Prism Analytics is a tool to combine data from both Workday and external sources, prepare it for analysis, and report it in Workday. The Workday Prism Analytics Certification helps learners understand data ingestion, data transformation, and reporting in Workday.

Is any coding knowledge necessary to study the Workday Prism Analytics Course?

It all depends on what exactly you want to learn, which Workday Prism Analytics Course you are going to take, and what positions you are willing to occupy in the future. Some knowledge of data and the basics of Workday reporting is useful.

What is the difference between a dataset and Data Catalog?

Data Catalog is a repository of accessible Prism data objects. A dataset describes the data transformation and manipulation process. These tools work together in data preparation for analysis.

Does this course guarantee Workday Pro Prism Analytics Certification?

Please note that taking a training course does not automatically give you a certification of Workday Pro Prism Analytics. You should check Workday's information on certification and learning opportunities.

Which job positions will be available for me after Workday Prism Analytics training?

Depending on your previous experience and additional skills, you may become a Workday Prism Analytics consultant, Workday Reporting analyst, Workday Data Analyst, or HRIS Analyst. Workday Prism Analytics Course Training helps learners understand data security, data blending, and reporting for better business insights.

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Format

Live Online Class with Recording

Duration

160h of content

Access

Lifetime

Certificate

Included on completion

Language

English · Hindi subtitles

Enrolled

812

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