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Postgraduate DiplomaData, AI, Simulation & Analytics Applications open

Diploma in Value Metrics and Data Monetization

Online postgraduate diploma of 800 hours in data value metrics and monetisation, with 8 modules covering data economics, data value metrics, causal impact measurement, monetisation models, data pricing, the legal and ethical framework, and strategy and organisation, 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

Analyse the economic characteristics of data as an asset.

02

Build metric trees that link data to business value.

03

Measure the causal impact of analytics initiatives with experimental and quasi-experimental methods.

04

Design internal and external data monetisation models.

05

Set pricing strategies for data products and services.

06

Assess the legal and ethical requirements for sharing and selling data.

07

Prepare data business cases for management committees.

Profile

Who it's for

  • Data product managers.
  • Data monetisation leads.
  • Business value analysts.
  • Chief data officers.
  • Data strategy consultants.
  • Pricing analysts working with data products.
  • Engineering or science graduates aiming to enter banking and insurance.
Standards & tools

What you'll work with

  • Regulation (EU) 2016/679 – GDPR (General Data Protection Regulation)
  • Regulation (EU) 2023/2854 – Data Act (EU rules on fair access to and use of data)
  • Regulation (EU) 2022/868 – Data Governance Act (data sharing and intermediaries)
  • Regulation (EU) 2022/1925 – Digital Markets Act (contestable digital markets)
  • Directive 96/9/EC (legal protection of databases)
  • ISO/IEC 38505-1 (governance of data)
  • ISO 55001 (asset management)
  • ISO/IEC 20889:2018 (de-identification techniques)
  • Snowflake Marketplace
  • AWS Data Exchange
  • GrowthBook
  • Power BI
  • Python with statsmodels
Syllabus

8 modules

  1. 1.1Economic characteristics of data as an asset
  2. 1.2Rivalry, excludability and network effects
  3. 1.3Data value chains
  4. 1.4European data markets and data spaces
  5. 1.5Accounting valuation of data intangibles
  6. 1.6Acquisition, storage and quality costs
  7. 1.7Data as an enabler versus a saleable product
  8. 1.8Monetisation cases across industries
  9. 1.9Ethical and reputational risks
  10. 1.10Workshop: data asset map of a company
  1. 2.1Valuation models: cost, market and income
  2. 2.2Usage and adoption metrics for data products
  3. 2.3Metric trees and the North Star Metric
  4. 2.4Attributing value to analytics initiatives
  5. 2.5ROI and payback period of data projects
  6. 2.6Data value with Laney's metrics (infonomics)
  7. 2.7Quality indicators linked to value
  8. 2.8Value dashboards for management
  9. 2.9Sector benchmarks for data value
  10. 2.10Practical: metric tree for an analytics product
  1. 3.1A/B experiments and statistical design
  2. 3.2Sample size and power
  3. 3.3Difference-in-differences
  4. 3.4Synthetic control method
  5. 3.5Regression discontinuity designs
  6. 3.6Uplift modelling to segment impact
  7. 3.7Common errors: peeking and multiple comparisons
  8. 3.8Tools: GrowthBook and Statsig
  9. 3.9Communicating causal results
  10. 3.10Lab: impact evaluation with difference-in-differences
  1. 4.1Internal versus external monetisation
  2. 4.2Data as a Service and data APIs
  3. 4.3Insight and benchmark products
  4. 4.4Marketplaces: AWS Data Exchange and Snowflake Marketplace
  5. 4.5Subscription, usage-based and freemium models
  6. 4.6Monetisation through AI features
  7. 4.7Partnerships and data sharing between companies
  8. 4.8Data clean rooms
  9. 4.9Cannibalisation risks
  10. 4.10Case study: business model for a data API
  1. 5.1Perceived value-based pricing
  2. 5.2Usage-based and tiered pricing
  3. 5.3Elasticity and willingness to pay
  4. 5.4Van Westendorp surveys and conjoint analysis
  5. 5.5Bundling and licensing
  6. 5.6Marginal costs and scalability
  7. 5.7SaaS metrics: ARR, churn and LTV/CAC
  8. 5.8Dynamic price review
  9. 5.9Data licences and terms of use
  10. 5.10Workshop: pricing table for a data product
  1. 6.1GDPR legal bases for processing
  2. 6.2Anonymisation and pseudonymisation
  3. 6.3Differential privacy and synthetic data
  4. 6.4The Data Act and access to connected product data
  5. 6.5The Data Governance Act and data intermediaries
  6. 6.6The Digital Markets Act
  7. 6.7Intellectual property and the sui generis database right
  8. 6.8Data protection impact assessments
  9. 6.9Transparency and customer trust
  10. 6.10Practical: risk assessment of a monetisation case
  1. 7.1Data strategy aligned with the business
  2. 7.2Business case for data initiatives
  3. 7.3Governance of the data product portfolio
  4. 7.4Operating model and commercial roles
  5. 7.5Go-to-market for data products
  6. 7.6Consultative selling and technical pre-sales
  7. 7.7Account management and customer success
  8. 7.8International scaling of the offering
  9. 7.9Lessons from data-driven companies
  10. 7.10Case study: business case for a management committee
  1. 8.1Inventory and selection of assets
  2. 8.2Value hypotheses and metrics
  3. 8.3Product or service design
  4. 8.4Pricing model
  5. 8.5Legal and ethical assessment
  6. 8.6Impact measurement plan
  7. 8.7Launch plan
  8. 8.8Risks and mitigation plan
  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 Snowflake Marketplace, AWS Data Exchange, GrowthBook, Power BI and Python with statsmodels, in free, academic or evaluation versions. Real procedures are studied: valuing an organisation's data assets, designing experiments to measure the impact of analytics products and setting prices for a data API. Assessment is continuous by module plus the final integrative project.

Standards covered: Regulation (EU) 2016/679 (GDPR), Regulation (EU) 2023/2854 (Data Act), Regulation (EU) 2022/868 (Data Governance Act), Regulation (EU) 2022/1925 (Digital Markets Act), Directive 96/9/EC, ISO/IEC 38505-1, ISO 55001, ISO/IEC 20889:2018.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of the GDPR, the Data Act and the Data Governance Act applied to real cases, not as a theoretical annex.
Industry software:Snowflake Marketplace, AWS Data Exchange, GrowthBook, Power BI and Python with statsmodels, in free, academic or evaluation versions.
Final project:a documented data monetisation plan for real assets, covering value metrics, product design, pricing, legal assessment and launch, 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.

Assets and value hypotheses
Inventory and select the data assets and set value hypotheses and metrics.
Product design
Design the data product or service to be monetised.
Pricing and legal review
Define the pricing model and carry out the legal and ethical assessment.
Impact and launch
Plan impact measurement, the launch and risk mitigation.
Final project
Present the monetisation plan and report for review by your tutor.
Career opportunities

Where this takes you

Data product managerData monetisation leadBusiness value analystChief data officerData strategy consultantPricing analyst
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 Snowflake Marketplace, AWS Data Exchange, GrowthBook, Power BI and Python with statsmodels, 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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