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Quest Training

Government Data Monetization, Pricing & Licensing Training Course

Shows data owners how to design, value, price and license data products built on government data within regulatory limits and measure the value they create.

DOPS · Data & AI Operating ModelsAll Levels#3030ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Data, Analytics & Digital TransformationCategories:Data & AI Operating Models

Course Overview

This advanced practical course focuses on the commercial and regulatory side of data products: how organizations that hold government or public-interest data design, own, value, price, and license data products, and how they measure the value those products create. Rather than treating data products as an architecture concept, the program addresses the decisions a data product owner must take before any data leaves the organization. Participants will examine direct and indirect monetization models, data product design and ownership, consumer segmentation, valuation methods, and pricing approaches ranging from cost recovery to subscription, tiered, and usage-based models. The course also addresses license terms, permitted and prohibited uses, privacy-preserving techniques, and the regulatory limits on monetizing government data, including national policies that prohibit the sale of raw government data and allow only approved, licensed data products, such as the framework issued by the Saudi Data and AI Authority. Through practical scenarios, participants will build a data product proposal, a pricing model, a license schedule, and a value measurement framework for a selected data asset.

Learning Objectives

  • Analyze the economic and public-value case for monetizing government data.
  • Distinguish direct, indirect, and ecosystem monetization models.
  • Screen data assets for productization potential and legal, privacy, and reputational risk.
  • Design data products around defined consumer needs and delivery channels.
  • Define data product ownership, accountability, and lifecycle responsibilities.
  • Apply cost, market, and income methods to value data products.
  • Develop pricing models that balance cost recovery, fair access, and return.
  • Structure licenses covering permitted uses, restrictions, derived data, and audit.
  • Apply anonymization and aggregation to protect personal and sensitive data.
  • Interpret regulatory limits on monetizing government data, including the ban on selling raw data.
  • Establish indicators for the financial, operational, and public value of data products.
  • Develop a governance model and roadmap for a data product portfolio.

Who Should Attend

This course is designed for data product owners and managers, Chief Data Officers, heads of data offices, data governance and data strategy leaders, commercial and business development managers, and legal, privacy, and compliance professionals responsible for sharing or commercializing data assets. It is also suitable for government data offices, statistical authorities, ministries, telecommunications operators, banks and financial institutions, utilities, and other organizations that hold large data assets and are assessing data sharing or monetization opportunities. The program is particularly relevant to teams preparing their first licensed data products or seeking to bring existing data-sharing arrangements under consistent pricing, licensing, and value measurement.

Learning Outcomes

  • Evaluate data assets for monetization potential, legal basis, and reputational risk.
  • Select a monetization model suited to the data asset and policy context.
  • Produce a data product specification with consumers, service levels, and delivery channels.
  • Assign data product ownership, decision rights, and lifecycle responsibilities.
  • Value a data product using cost, market, and income methods.
  • Design pricing tiers that respect fair-access principles.
  • Draft license terms covering permitted uses, derived data, onward sharing, and audit.
  • Apply anonymization and aggregation controls suited to data sensitivity.
  • Confirm that proposed data products respect regulatory limits on government data.
  • Track adoption, cost to serve, revenue, and public value.
  • Establish monetization governance with approvals, pricing reviews, and a data product register.
  • Prepare a phased roadmap for a data product portfolio.

Course Outline

Day 1

Data Monetization Strategy and Opportunity Assessment

  • Data as an economic asset in public and private organizations.
  • Direct, indirect, and ecosystem monetization models.
  • Balancing revenue with public value, fair access, and trust.
  • Open data, data sharing, and commercial data products compared.
  • Inventory of data assets with external value potential.
  • Screening opportunities for demand, legal basis, and reputational risk.
  • Use cases from government, telecom, banking, and utility data.
  • Readiness, mandate, and approval routes for monetization.
  • Practical Application: Screen a data asset portfolio and select priority data product candidates.
Day 2

Data Product Design, Ownership and Lifecycle

  • Defining content, granularity, refresh frequency, and format.
  • Consumer segmentation and value propositions.
  • Data product canvas: purpose, users, inputs, and outputs.
  • Data product owner role, decision rights, and accountability.
  • Data contracts and service levels for quality and availability.
  • Delivery channels: APIs, feeds, dashboards, and secure data environments.
  • Documentation, metadata, and onboarding for external consumers.
  • Lifecycle management from pilot to versioning and retirement.
  • Product roadmap and backlog driven by consumer feedback.
  • Practical Application: Write a data product specification with owner, consumers, service levels, and delivery channel.
Day 3

Data Valuation and Pricing Models

  • Why data is hard to value: non-rivalry, context, and quality.
  • Cost-based valuation: collection, curation, and cost to serve.
  • Market-based valuation: comparable products and willingness to pay.
  • Income-based valuation and the value of better decisions.
  • Public-sector pricing principles: cost recovery, marginal cost, and non-discrimination.
  • Subscription, tiered, usage-based, freemium, and revenue-sharing models.
  • Differentiated pricing for government, research, and commercial users.
  • Business case and data product profit and loss view.
  • Practical Application: Value a selected data product and build a tiered pricing model.
Day 4

Licensing, Permitted Uses and Regulatory Limits

  • License structure: grant, scope, term, fees, liability, audit, and termination.
  • Permitted and prohibited uses, onward sharing, and sublicensing.
  • Rights in derived data, insights, and trained models.
  • Regulatory limits on monetizing government data, including no sale of raw data.
  • Personal data protection laws, such as the Saudi Personal Data Protection Law.
  • Anonymization, pseudonymization, aggregation, and synthetic data as release controls.
  • Data classification, cross-border transfer, and sector regulator requirements.
  • Competition, exclusivity, and fair-access considerations.
  • Practical Application: Draft a license schedule and compliance checklist for a proposed data product.
Day 5

Measuring Data Product Value and Monetization Governance

  • Success measures for financial, operational, and public value.
  • Adoption, retention, usage, quality, and cost-to-serve indicators.
  • Return on investment and contribution to strategic objectives.
  • Monetization governance: approval committees, pricing reviews, and a data product register.
  • Monitoring license compliance, misuse, and re-identification risk.
  • Portfolio decisions on prioritization, investment, and retirement.
  • Reporting performance to executives and oversight bodies.
  • Implementation planning: pilots, partnerships, capabilities, and scaling.
  • Final Workshop: Present a data product proposal covering design, ownership, valuation, pricing, licensing, regulatory controls, value indicators, and roadmap.

Upcoming Dates

Selected date

Erbil · 10 – 14 Jan 2027 · Classroom

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CityDatesPrice per ParticipantEarly Bird
AccraScheduled
2 – 6 Nov 2026€5,100—
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AbujaScheduled
2 – 6 Nov 2026€5,100—
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AmmanScheduled
8 – 12 Nov 2026€3,100—
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BangkokScheduled
16 – 20 Nov 2026€6,100—
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AmsterdamScheduled
16 – 20 Nov 2026€5,400—
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BarcelonaScheduled
23 – 27 Nov 2026€5,400€4,860until 2 Nov
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BeirutScheduled
29 Nov – 3 Dec 2026€4,000€3,600until 9 Nov
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CairoScheduled
6 – 10 Dec 2026€3,300€2,970until 16 Nov
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BerlinScheduled
7 – 11 Dec 2026€5,800€5,220until 16 Nov
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Cape TownScheduled
14 – 18 Dec 2026€5,100€4,590until 23 Nov
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CasablancaScheduled
21 – 25 Dec 2026€4,300€3,870until 30 Nov
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DammamScheduled
27 – 31 Dec 2026€4,200€3,780until 7 Dec
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DohaScheduled
27 – 31 Dec 2026€4,400€3,960until 7 Dec
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DubaiScheduled
3 – 7 Jan 2027€4,400€3,960until 14 Dec
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ErbilScheduled
10 – 14 Jan 2027€3,100€2,790until 21 Dec
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IstanbulScheduled
11 – 15 Jan 2027€4,700€4,230until 21 Dec
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JeddahScheduled
17 – 21 Jan 2027€4,200€3,780until 28 Dec
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Kuala LumpurScheduled
25 – 29 Jan 2027€4,400€3,960until 4 Jan
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KigaliScheduled
25 – 29 Jan 2027€5,100€4,590until 4 Jan
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LisbonScheduled
1 – 5 Feb 2027€5,400€4,860until 11 Jan
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MadinahScheduled
8 – 12 Feb 2027€4,200€3,780until 18 Jan
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LondonScheduled
8 – 12 Feb 2027€5,600€5,040until 18 Jan
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MadridScheduled
15 – 19 Feb 2027€5,400€4,860until 25 Jan
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MakkahScheduled
22 – 26 Feb 2027€4,200€3,780until 1 Feb
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ManamaScheduled
22 – 26 Feb 2027€4,700€4,230until 1 Feb
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MarbellaScheduled
1 – 5 Mar 2027€5,400€4,860until 8 Feb
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MuscatScheduled
8 – 12 Mar 2027€5,300€4,770until 15 Feb
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OnlineScheduled
14 – 18 Mar 2027€1,700€1,530until 22 Feb
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ParisScheduled
15 – 19 Mar 2027€5,700€5,130until 22 Feb
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SalalahScheduled
22 – 26 Mar 2027€5,300€4,770until 1 Mar
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Available in: Istanbul · Dubai · London · Doha · Amsterdam

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