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Master’s in Data Product Management for Engineering

Online master's programme of 1,900 hours in data product management for engineering. 12 modules covering the data product manager role, opportunity discovery, product strategy, requirements and data contracts, delivery, industrial and AI data products, metrics and adoption, platform architecture, governance and leadership, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Apply a product approach to data initiatives in engineering.

02

Discover value opportunities through user research.

03

Define strategies and roadmaps for data products.

04

Specify data products with contracts and measurable requirements.

05

Manage the agile delivery of data and AI products.

06

Measure the adoption and business value of products.

07

Govern data products in line with European regulations.

Profile

Who it's for

  • Data product managers working in engineering and industrial companies.
  • Data product owners responsible for backlogs, contracts and delivery.
  • Industrial analytics managers who need to turn pilots into adopted products.
  • Data platform managers balancing cost, quality and discoverability.
  • Data strategy consultants advising on portfolios and business cases.
  • AI product managers handling model metrics, risk and the AI Act.
  • Engineering or science graduates aiming to enter industry.
Standards & tools

What you'll work with

  • Regulation (EU) 2023/2854 (Data Act)
  • Regulation (EU) 2016/679 (GDPR)
  • Regulation (EU) 2024/1689 (AI Act)
  • ISO 8000-8 (data quality — information and data quality concepts and measuring)
  • ISO/IEC 25012 (data quality model)
  • ISO 9241-210 (human-centred design for interactive systems)
  • ISO/IEC 38505-1 (governance of data)
  • Jira
  • Productboard
  • Miro
  • Amplitude
  • Microsoft Power BI
Syllabus

12 modules · 1900 h

  1. 1.1The role of the data product manager
  2. 1.2Differences from software product management
  3. 1.3Internal and external data products
  4. 1.4Data as an engineering asset
  5. 1.5Product thinking in technical teams
  6. 1.6Operating models and data mesh
  7. 1.7Competencies of the data PM
  8. 1.8Relationship with engineering, data science and the business
  9. 1.9Product maturity in industrial organisations
  10. 1.10Case study: data portfolio of an engineering firm
  1. 2.1Interviews with engineers and operators
  2. 2.2Jobs to be done in technical contexts
  3. 2.3Engineering process maps
  4. 2.4Identifying decisions that data can improve
  5. 2.5Opportunity solution tree
  6. 2.6Sizing the value
  7. 2.7Assessing data feasibility
  8. 2.8Prioritisation with RICE and cost of delay
  9. 2.9Hypothesis validation
  10. 2.10Workshop: opportunity tree for an industrial plant
  1. 3.1Vision and value proposition
  2. 3.2Objectives and key results
  3. 3.3Platform versus solution strategy
  4. 3.4Build, buy or partner
  5. 3.5Competitive analysis of solutions
  6. 3.6Outcome-oriented roadmap
  7. 3.7Alignment with engineering strategy
  8. 3.8Stakeholder management
  9. 3.9Communicating the strategy
  10. 3.10Practical exercise: asset analytics roadmap
  1. 4.1User stories for data products
  2. 4.2Acceptance criteria with quality metrics
  3. 4.3Data contracts and SLAs
  4. 4.4Latency and freshness requirements
  5. 4.5Model accuracy requirements
  6. 4.6Interface and API specification
  7. 4.7Security and privacy requirements
  8. 4.8Minimum viable data product
  9. 4.9Managing technical debt
  10. 4.10Workshop: specification of a minimum viable data product
  1. 5.1Agile frameworks in data teams
  2. 5.2Adapted Scrum and Kanban
  3. 5.3Uncertainty in data science
  4. 5.4Experiments and proofs of concept
  5. 5.5From prototype to production
  6. 5.6Collaboration with data engineering and MLOps
  7. 5.7Definition of done for models
  8. 5.8Dependency management
  9. 5.9Launches and communication
  10. 5.10Practical exercise: planning a product quarter
  1. 6.1Asset monitoring platforms
  2. 6.2Predictive maintenance products
  3. 6.3Quality and traceability products
  4. 6.4Energy efficiency products
  5. 6.5Digital twins as a product
  6. 6.6Supply chain products
  7. 6.7Products for design engineering
  8. 6.8Products based on customer data
  9. 6.9Servitisation and pay-per-use models
  10. 6.10Case study: equipment fleet monitoring product
  1. 7.1Product management with machine learning models
  2. 7.2Offline and online metrics
  3. 7.3Managing accuracy expectations
  4. 7.4Human-AI interaction design
  5. 7.5Generative AI products
  6. 7.6Risks and mitigations
  7. 7.7Feedback and continuous improvement
  8. 7.8Inference costs
  9. 7.9AI Act compliance
  10. 7.10Workshop: AI product canvas for engineering
  1. 8.1North star product metrics
  2. 8.2Adoption and retention metrics
  3. 8.3Usage telemetry
  4. 8.4A/B experiments in internal products
  5. 8.5Business value and savings
  6. 8.6Surveys and satisfaction
  7. 8.7Product analytics
  8. 8.8Managing adoption on the plant floor
  9. 8.9Value reporting to management
  10. 8.10Practical exercise: metrics plan for a data product
  1. 9.1Data architectures explained for PMs
  2. 9.2Lakehouse and data mesh for non-specialists
  3. 9.3Catalogues and discoverability
  4. 9.4Data quality and observability
  5. 9.5Platform cost and FinOps
  6. 9.6Integration with OT systems
  7. 9.7Security and access
  8. 9.8Technical decisions with product impact
  9. 9.9Dialogue with architects
  10. 9.10Lab: cost and architecture assessment of a product
  1. 10.1Data ownership and accountability
  2. 10.2Federated data governance
  3. 10.3Quality according to ISO 8000
  4. 10.4The GDPR in data products
  5. 10.5The Data Act and connected product data
  6. 10.6The AI Act
  7. 10.7Industrial cybersecurity in connected products
  8. 10.8Data licences and contracts
  9. 10.9Legal and contractual risks
  10. 10.10Case study: the Data Act for a manufacturer
  1. 11.1Data product teams
  2. 11.2Roles and responsibilities
  3. 11.3Leading without authority
  4. 11.4Technical and executive communication
  5. 11.5Negotiating priorities
  6. 11.6Change management
  7. 11.7Data-driven culture
  8. 11.8Career development of the data PM
  9. 11.9Scaling the organisation
  10. 11.10Practical exercise: presenting a business case to management
  1. 12.1Problem discovery
  2. 12.2User research
  3. 12.3Strategy and value proposition
  4. 12.4Product definition
  5. 12.5Delivery plan and metrics
  6. 12.6Governance model
  7. 12.7Product business case
  8. 12.8Conclusions and next steps
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so you can combine it with your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Jira, Productboard, Miro, Amplitude and Microsoft Power BI, in free, academic or evaluation versions. Practical work is based on real procedures: discovery of data opportunities on the plant floor, definition of a minimum viable data product and the business case for an industrial analytics product. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: Regulation (EU) 2023/2854, Regulation (EU) 2016/679, Regulation (EU) 2024/1689, ISO 8000-8, ISO/IEC 25012, ISO 9241-210, ISO/IEC 38505-1.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of the Data Act (Regulation (EU) 2023/2854), the GDPR (Regulation (EU) 2016/679) and the AI Act (Regulation (EU) 2024/1689) applied to real cases, not as a theoretical annex.
Industry software:Jira, Productboard, Miro, Amplitude and Microsoft Power BI, in free, academic or evaluation versions.
Master's Thesis with defence:A complete data product proposal, from discovery and user research to strategy, delivery plan, governance model and business case, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Discover the opportunity
Interview engineers and operators and build an opportunity solution tree.
Set the strategy
Define vision, OKRs and an outcome-oriented roadmap.
Specify the MVP
Write a minimum viable data product with data contracts and SLAs.
Plan metrics and governance
Design the metrics plan and governance model for the product.
Master's Thesis
A full data product proposal with business case, defended before a panel.
Career opportunities

Where this takes you

Data product managerData product ownerIndustrial analytics managerData platform managerData strategy consultantAI product manager
Admissions & scholarships

Requirements

  • A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • An internet connection and a computer capable of running the programme's software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

Apply now Scholarships
FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Jira, Productboard, Miro, Amplitude and Microsoft Power BI, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the programme's software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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