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Postgraduate DiplomaData, AI, Simulation & Analytics Applications open

Diploma in Roadmap, Data SLAs and Service Contracts

Online postgraduate diploma of 800 hours in data roadmaps, data SLAs and service contracts, with 8 modules covering data product management, data roadmaps, data service levels, data contracts, service and supplier contracts, observability and SLA compliance, and governance and communication, with an assigned technical tutor.

Duration6–8 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Manage data products applying data mesh principles.

02

Build data roadmaps prioritised by value and outcomes.

03

Define service level indicators and objectives for data.

04

Draft versioned data contracts that can be validated automatically.

05

Negotiate and review service level agreements with suppliers.

06

Implement data observability to measure SLO compliance.

07

Communicate service status to executives and consumers.

Profile

Who it's for

  • Data product managers.
  • Data product owners responsible for domain data products.
  • Data governance leads.
  • Data reliability engineers.
  • Data service managers handling internal and external SLAs.
  • Data mesh architects.
  • Engineering or science graduates aiming to enter banking and insurance.
Standards & tools

What you'll work with

  • ISO/IEC 20000-1:2018 (IT service management)
  • ISO 8000 (data quality)
  • Regulation (EU) 2023/2854 – Data Act (EU rules on fair access to and use of data)
  • Regulation (EU) 2016/679 – GDPR (General Data Protection Regulation)
  • ISO/IEC 25012 (data quality model)
  • ISO/IEC 38505-1 (governance of data)
  • ISO/IEC 19086-1 (cloud service level agreements)
  • Soda Core
  • Great Expectations
  • Confluent Schema Registry
  • Jira Product Discovery
  • DataHub
Syllabus

8 modules

  1. 1.1Data as a product: principles and life cycle
  2. 1.2Data mesh and ownership domains
  3. 1.3Roles: data product owner and steward
  4. 1.4Discovering consumer needs
  5. 1.5Value maps and prioritisation
  6. 1.6Adoption and usage indicators
  7. 1.7Federated computational governance
  8. 1.8Relationship with platform teams
  9. 1.9Documented success and failure cases
  10. 1.10Workshop: data product canvas
  1. 2.1Vision, objectives and key results (OKRs)
  2. 2.2Outcome-based versus feature-based roadmaps
  3. 2.3Prioritisation with RICE, WSJF and MoSCoW
  4. 2.4Managing data technical debt
  5. 2.5Dependencies between domains and platforms
  6. 2.6Quarterly planning and PI planning
  7. 2.7Communicating the roadmap to stakeholders
  8. 2.8Evidence-based review and rescheduling
  9. 2.9Tools: Jira Product Discovery and Productboard
  10. 2.10Practical: annual roadmap for a data platform
  1. 3.1SLI, SLO and SLA: differences and relationship
  2. 3.2Dimensions: freshness, completeness, accuracy and availability
  3. 3.3Measuring end-to-end latency
  4. 3.4Error budgets applied to data
  5. 3.5Setting thresholds with the business
  6. 3.6Calculating availability over rolling windows
  7. 3.7Penalties and service credits
  8. 3.8Escalation and responsibility matrix
  9. 3.9Periodic review of service level objectives
  10. 3.10Case study: freshness SLO for a dashboard
  1. 4.1Concept and anatomy of a data contract
  2. 4.2Open Data Contract Standard (ODCS)
  3. 4.3Schemas with JSON Schema, Avro and Protobuf
  4. 4.4Schema compatibility and evolution
  5. 4.5Schema registries and validation
  6. 4.6Semantics, owners and support channels
  7. 4.7Quality rules embedded in the contract
  8. 4.8Automatic enforcement in CI/CD
  9. 4.9Managing breaking changes
  10. 4.10Lab: ODCS contract validated in a pipeline
  1. 5.1Structure of a service level agreement
  2. 5.2ISO/IEC 20000-1 and service management
  3. 5.3ITIL 4 and service level management
  4. 5.4Contracts with cloud and data SaaS providers
  5. 5.5Data protection and processor clauses
  6. 5.6Portability and reversibility under the Data Act
  7. 5.7Audits and right of inspection
  8. 5.8Managing subcontractors
  9. 5.9Contractual performance indicators
  10. 5.10Case study: review of a cloud provider SLA
  1. 6.1Data observability: pillars and metrics
  2. 6.2Freshness and volume monitors
  3. 6.3Schema anomaly detection
  4. 6.4Lineage for impact analysis
  5. 6.5Automated compliance reports
  6. 6.6Data incident management
  7. 6.7Blameless post-mortems
  8. 6.8Tools: Monte Carlo, Soda and Great Expectations
  9. 6.9SLO dashboards for executives
  10. 6.10Workshop: SLO alerts with Soda Core
  1. 7.1Data governance committees
  2. 7.2Catalogue and business glossary
  3. 7.3Access and classification policies
  4. 7.4Managing stakeholder expectations
  5. 7.5Negotiating service levels
  6. 7.6Executive reports on quality and service
  7. 7.7Organisational change management
  8. 7.8Maturity with DAMA-DMBOK and DCAM
  9. 7.9Continuous improvement of data services
  10. 7.10Practical: simulated negotiation of an internal SLA
  1. 8.1Selecting the data product
  2. 8.2Analysis of consumers and needs
  3. 8.3Evolution roadmap
  4. 8.4Defining SLIs and SLOs
  5. 8.5Drafting the data contract
  6. 8.6Observability and alerting plan
  7. 8.7Governance and escalation model
  8. 8.8Validation with the product's consumers
  9. 8.9Report and presentation
  10. 8.10Final project: final review with the tutor
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 each intake, so it can be combined with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Soda Core, Great Expectations, Confluent Schema Registry, Jira Product Discovery and DataHub, in free, academic or evaluation versions. Real procedures are studied: defining freshness and completeness SLOs for data products, drafting and automatically validating data contracts, and reviewing service level agreements with cloud providers. Assessment is continuous by module plus the final integrative project.

Standards covered: ISO/IEC 20000-1:2018, ISO 8000, Regulation (EU) 2023/2854 (Data Act), Regulation (EU) 2016/679 (GDPR), ISO/IEC 25012, ISO/IEC 38505-1, ISO/IEC 19086-1.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO/IEC 20000-1, ISO 8000 and the Data Act applied to real cases, not as a theoretical annex.
Industry software:Soda Core, Great Expectations, Confluent Schema Registry, Jira Product Discovery and DataHub, in free, academic or evaluation versions.
Final project:a documented data service for a real data product, with its roadmap, SLIs and SLOs, data contract, observability plan and governance model, reviewed by your tutor.
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.

Product and consumers
Select the data product and analyse its consumers and needs.
Evolution roadmap
Build a prioritised roadmap for the product's evolution.
Service levels and contract
Define SLIs and SLOs and draft the data contract.
Observability and governance
Design the observability and alerting plan and the governance and escalation model.
Final project
Validate the service with its consumers and present the report for review by your tutor.
Career opportunities

Where this takes you

Data product managerData product ownerData governance leadData reliability engineerData service managerData mesh architect
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. No university degree is required.
  • 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.

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FAQ

Frequently asked questions

The programme totals 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Soda Core, Great Expectations, Confluent Schema Registry, Jira Product Discovery and DataHub, 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 final project. 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 are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.

Prior technical training or professional experience in the sector (no university degree required), a computer able to run the 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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