Master the Leading Data Product Specification with GPT tool

In this lesson, we take a closer look at the data product details object—one of the core components of the Open Data Product Specification (ODPS).

Prompt we use in ODPS GPT:

Act as a data product owner. I’m building a data product called UrbanPulse. Its purpose is to detect and analyze public events in real time using open data (like road closures, noise complaints, social media, and sensor data). The product helps city departments, event organizers, and media outlets stay informed and react faster.

Help me define a clear, specific use case summary for ODPS. Emphasize users, value, and decisions supported. Generate a product.details block for UrbanPulse in ODPS 4.0 YAML format. Keep the language business-oriented and human-readable.

In this lesson, we explore the dataAccess object—a vital part of the Open Data Product Specification (ODPS) that defines how users and systems can interact with your data product.

Prompt we use in ODPS GPT:

Create a dataAccess block for UrbanPulse. Assume it provides data via a REST API with optional webhooks. Include access format (JSON), authentication type (API key). Add snapshot access as well. Oh and we have also AI agents, they need access point.

In this lesson, we focus on the dataQuality object—a key component of the Open Data Product Specification (ODPS) that allows you to define, communicate, and improve the quality of your data product.

We’ll walk through the structure and main attributes of the dataQuality object, demonstrating how to document data quality dimensions, metrics, and assessment processes. You’ll see how transparent quality information helps users trust and get more value from your data product.

Prompt we use in ODPS GPT:

Define a dataQuality block for UrbanPulse. Include dimensions like completeness (percent of known event types detected), timeliness (real-time latency in seconds), and accuracy (false positive/negative rate). Provide manual values as examples. Agents need higher quality. Others go with the default profile, make sure the define it

In this lesson, we explore the SLA (Service Level Agreement) object—an essential part of the Open Data Product Specification (ODPS) that sets clear expectations around the reliability, performance, and support of your data product.

We’ll unpack the structure and elements of the SLA object, showing you how to specify measurable commitments like uptime, response times, refresh rates, and incident management. You’ll learn how an explicit SLA builds trust with users and supports effective data product governance.

Prompt we use in ODPS GPT:

Generate an SLA block for UrbanPulse in ODPS YAML format. Include uptime commitment (99.5%), latency (under 2 minutes for push notifications), refresh rate (real-time with fallback to hourly), and public incident reporting link. Agents require higher SLA. Add the typical customer support emails etc as placeholders.

In this lesson, we explore the paymentGateways object—a crucial part of the Open Data Product Specification (ODPS) that enables you to define and manage how users pay for access to your data product.

We’ll break down the structure and key components of the paymentGateways object, demonstrating how to describe different payment methods, support multiple user segments, and link your data product to secure, automated payment solutions.

Prompt to use:

Generate a paymentGateways block for UrbanPulse in ODPS YAML format. Include two options: one for regular users and another for AI agents. The first is the default.

In this lesson, we explore the pricingPlans object—a powerful component of the Open Data Product Specification (ODPS) that lets you clearly communicate and manage the pricing structure for your data products.

We’ll break down the fields and options in the pricingPlans object, showing how you can create transparent, flexible pricing models that appeal to different user segments and use cases.

Prompt we use in ODPS GPT:

Write a pricingPlans block for UrbanPulse in ODPS 4.0 format. Include two plans: ‘Open Access’ (free, limited endpoints) and ‘Pro Access’ (paid, includes full feed and event forecasting). Reference the SLA, DQ, data access, and payment gateways from the pricing plans. Reminder that we have AI agents as customer type as well and they need a separate plan.

In this lesson, we explore the license object—a fundamental component of the Open Data Product Specification (ODPS) that clarifies the legal terms, usage rights, and restrictions for your data product.

We’ll break down the structure and options of the license object, showing how you can clearly communicate what users are permitted (or not permitted) to do with your data product, and how to select or define the most appropriate license for your needs.

Prompt we use in ODPS GPT:

Generate a license block in ODPS 4.0 for UrbanPulse. Use a modified Creative Commons license that allows reuse with attribution,  no resale, otherwise use as you see fit. Product is globally usable, no geographical limitations.

In this lesson, we explore the contract object—a key part of the Open Data Product Specification (ODPS) that captures the formal agreement governing the use, delivery, and terms of your data product.

We’ll break down the structure and essential fields of the contract object, showing you how to reference standardized data contracts, specify contract versions, and include links to full contract documents. You’ll see how contracts underpin trust, compliance, and accountability between data providers and consumers.

Prompt we use is ODPS GPT:

Create a data contract block for UrbanPulse in ODPS 4.0 YAML.

In this session, we’ll recap the full ODPS-compliant data product specification built in previous lessons and take a detailed look at the ODPS 4.0 standard. You’ll learn how each core component fits together—including product details, data access, data quality profiles, SLA definitions, payment gateways, pricing plans, licensing, and data contracts. We’ll also explore how to use the ODPS GPT tool to review your spec in YAML format, ensuring every aspect meets industry best practices. By the end, you’ll be able to structure, reference, and validate complete data product specifications using the latest ODPS 4.0 guidelines.

What you’ll learn:

• Recap of the UrbanPulse ODPS-compliant spec

• In-depth review of all ODPS 4.0 specification blocks

• How to apply multilingual and multi-profile (user/agent) details

• Referencing SLA, data quality, access, and payment components

• Practical tips for building, documenting, and sharing robust data product specs

In this session, we’ll review the full YAML specification of the UrbanPulse data product as a single, unified file. This hands-on walkthrough lets you see exactly how all ODPS 4.0 components come together in practice—from product details and data access to data quality, SLA, payment gateways, pricing, licensing, and contract blocks.

You’ll learn what a fully ODPS-compliant spec looks like, understand best practices for structuring your own, and gain the confidence to build robust data product specs using the ODPS standard.

What you’ll learn:

• How an ODPS-compliant data product YAML looks in its entirety

• The role and placement of each ODPS 4.0 component within the spec

• How UrbanPulse’s details, quality, SLA, payment, and licensing are modeled

BONUS: Creating the product with one prompt and lets make it bilingual:

Act as a data product owner creating a machine-readable ODPS 4.0 YAML for a data product called UrbanPulse.

UrbanPulse detects and analyzes public events in real time using open data sources such as road closures, noise complaints, social media feeds, and urban sensors. The main users are smart city operations teams, event organizers, media outlets, and mobility planners. The goal is to provide real-time situational awareness and alerting to help them respond quickly, deploy resources efficiently, and maintain transparency. I need the product both in English and French.

Please generate a complete ODPS 4.0 YAML with the following blocks :

1. product.details: Include name, summary, keywords, productOwner, domain, and maturity. Semantic versioning starting at 0.1.0, with release notes covering event detection types and capabilities.

2. dataAccess: Describe access via REST API and webhooks, in JSON format, with API key authentication and both real-time and hourly snapshot access. Make sure default option is generated, as it is the fallback

3. pricingPlans: Include three plans — “Open Access” (free, limited), “Pro Access” (paid, full feed + forecasting), and "Agents" (low latency API access, with programmable paymentgateway. Use declarative format and reference dataAccess, SLA, dataQuality, and payment gateways.

4. license: Use a modified Creative Commons license that allows reuse with attribution, restricts resale, and links to full terms.

5. dataQuality: Include completeness, timeliness (latency), and accuracy metrics with example manual values. Make sure default option is generated, as it is the fallback

6. SLA: Uptime 99.5%, latency under 2 minutes, real-time + hourly fallback refresh, public incident link. Make sure default option is generated, as it is the fallback. Add support and include contact email, documentation link, 24h response time (standard) and 4h (Pro), and office hours in Gulf Standard Time.

7. dataHolder: add basic contact details.

Format the output strictly according to the ODPS 4.0 YAML structure.

Wrap up: In this session, we…

• Reflected on the key concepts and skills you’ve gained throughout the course, including the business value of data products and the structure of ODPS 4.0.

• Reviewed how to build a complete, machine-readable ODPS YAML file from start to finish.

• Discussed ways to reuse parts of the ODPS spec across multiple data products for efficiency and consistency.

• Explored practical steps for applying what you’ve learned—starting small, sharing your knowledge, and experimenting with ODPS and GPT tools.

• Highlighted resources and communities to support your ongoing journey, including the Data Maestro Academy and ODPS networks.

As you move forward, remember: mastery comes from practice, sharing, and continuous learning. Stay connected, keep experimenting, and be part of the growing ODPS community!