About the Course
Organizations do not just want more data products, they want data products they can defend in planning meetings, audit reviews, and customer-facing decisions. To do that, you need to show capability in product discovery, roadmap prioritization, data governance, stakeholder alignment, and metric design, with practical reference points from Scrum, OKRs, and the Jobs-to-be-Done framework. In data product management, credibility depends on whether you can turn a messy request into a scoped backlog, a clear acceptance criterion, and a release plan that reflects real delivery constraints.
This course turns scattered product experience into a repeatable operating system for data products. You will practice customer interview synthesis, metric-tree design, feature prioritization with MoSCoW and Kano Model, PRD drafting, and backlog refinement for analytics or data platform work. You will also be introduced to semantic layer concepts, data catalog workflows, and AI-assisted product analytics so you can frame modern delivery decisions without overpromising implementation depth. What you will learn: how to define a data product, prioritize data product features, and build a roadmap that connects user needs, governance requirements, and measurable adoption. You will practice the core tools hands-on and be introduced to advanced operational patterns at a working level.
The reality for most teams is constrained: limited engineering capacity, inconsistent data definitions, slow approvals, competing stakeholder agendas, and pressure to show value quickly. This course is built for professionals who must make disciplined product decisions under those conditions and still keep the data product lifecycle moving.
Target Audience
This course is aimed at professionals who manage, shape, or support data products across discovery, delivery, governance, and adoption. It is especially useful when you need to balance user needs, delivery capacity, and data quality expectations.
- Data Product Manager shaping discovery, roadmap priorities, and release decisions
- Product Manager responsible for analytics or platform features
- Data Product Owner managing backlog, acceptance criteria, and stakeholder trade-offs
- Business Analyst translating user needs into data product requirements
- Analytics Manager overseeing dashboard, metric, or semantic model delivery
- Data Governance Lead aligning product decisions with metadata and access rules
- BI Product Owner prioritizing reporting features and metric definitions
- Data Platform Manager coordinating engineering capacity for data products
- Customer Insights Manager defining self-service analytics requirements
- Digital Transformation Lead linking data product investments to business outcomes
Course Objectives
This course equips you to plan, execute, and measure data product initiatives that improve user adoption, strengthen governance, and support better product decisions.
- Assess the current state of a data product using Jobs-to-be-Done, KPI trees, and a product canvas.
- Apply MoSCoW and Kano Model prioritization to data product requests and roadmap trade-offs.
- Design a data product roadmap that aligns semantic layer changes, user needs, and release sequencing.
- Build a product requirements document and backlog with clear acceptance criteria for analytics delivery.
- Evaluate data product quality against data governance controls, metadata standards, and definition consistency.
- Navigate stakeholder and governance reviews using RACI, decision logs, and release approval checkpoints.
- Implement measurable targets with OKRs, adoption metrics, and dashboard usage indicators.
- Synthesize discovery findings into a roadmap presentation, product brief, and executive status report.
Requirements & Prerequisites
You should have working familiarity with product management, business analysis, or data and analytics delivery. Prior exposure to user stories, backlog grooming, or KPI reporting will help, but you do not need coding experience to complete the course. A laptop is recommended for workshop exercises involving roadmaps, product briefs, and analytics templates.
Participants who come with a current data product, analytics feature, dashboard, or platform issue will get the most value because exercises can be mapped directly to real work. Familiarity with SQL, data warehousing concepts, or data governance vocabulary is helpful but not mandatory.
Local Application and Business Return in Singapore
How participants can apply the training in local operating conditions, and the return their organisation can plan for.
How participants apply this
Expected ROI
Training Methodology
This is a practical, outcome-driven course designed to turn data product management aspiration into measurable action and credible reporting.
Methodology includes:
- Hands-on KPI tree calculation using sample product analytics and adoption data.
- Scenario simulation for a conflicting roadmap request from sales, analytics, and engineering.
- Assessment using a product canvas, backlog checklist, and data governance review template.
- Stakeholder mapping across product, data engineering, governance, legal, and customer success.
- Case study analysis from fintech, healthcare, SaaS, and retail data products.
- Workshop to create a prioritized roadmap and PRD under tight delivery constraints.
- Reflection exercise using OKRs, dashboard evidence, and adoption benchmarks.
Upcoming Sessions
Next available dates worldwide
No international sessions scheduled
Certification
Recognized credentials that advance your career
Participants who complete the Data Product Management Training Program earn a Trainingcred Certificate of Achievement, demonstrating professional competence and alignment with global standards in learning and development.
NITA Accredited
Accredited by the National Industrial Training Authority, ensuring programs meet nationally recognized standards of quality and relevance.
CPD Certified
Recognized by the CPD Certification Service, ensuring every program meets internationally benchmarked standards of professional excellence.
Why this course earns its place on your CV
Accredited training, practitioner trainers, and peers on the same career track — the three things real expertise is built on.
Effective Learning & Skill Development
- Build expertise with structured, outcome-driven learning.
- Equip individuals and teams with skills that grow with industry needs.
- Reinforce learning through real-world scenarios, case studies and practical exercises.
Career Growth & Professional Advancement
- Apply what you learn with a proven methodology that ensures lasting impact.
- Develop immediately usable skills that translate directly into workplace success.
- Gain the expertise needed for career advancement and leadership roles.
Training Optimization & Learning Excellence
- Tailor training to industry-specific challenges and organizational goals.
- Use data-driven insights and automation to enhance training effectiveness.
- Evaluate progress and ensure long-term learning success.
Tools and platforms relevant to this field
Examples Singapore teams may encounter, and that may be featured in training where they support the confirmed course scope.
These are field-relevant examples, not a promise that every tool will be covered. Exact coverage depends on the confirmed course scope, participant needs, and delivery format.
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Power BI MicrosoftUsed to publish governed dashboards and metrics layers that need broad business adoption with controlled access.
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Tableau SalesforceUsed for analytical products and executive reporting where data product teams need repeatable, user-facing visualisations.
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Snowflake Snowflake Inc.Used as a data platform for sharing curated datasets and building reusable analytics products with access controls.
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Databricks DatabricksUsed for collaborative data engineering and analytics workflows that support packaged data products and experimentation.
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Collibra CollibraUsed to support data governance, cataloguing, stewardship, and policy alignment around data products.
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Alation AlationUsed to improve dataset discoverability and business-facing metadata so data products are easier to find and trust.
