In this lecture, we explore the fundamental shift from the Cathedral of Data, where data was tightly controlled and managed by a select few, to the Bazaar of Data, where data is treated as a product—designed, packaged, and shared to create business value. You'll gain insights into why companies have moved to this model, why Data Product Stewardship is now a must-have skill, and why Data Product Owners are in high demand.

In this lecture, we break down why selling raw data is a bad idea and why data products are the key to creating real business value. You’ll learn the essential characteristics of a data product, how to explain them clearly to colleagues and stakeholders, and how to think like a Data Product Steward—connecting data to business impact. We’ll also prepare you with an elevator pitch to confidently communicate what a data product is.

In this lecture, we explore the shift from static data products to Data-as-a-Service (DaaS)—where data is continuously updated, on-demand, and embedded into decision-making.

You’ll learn why selling raw data is ineffective and how DaaS provides greater value, scalability, and monetization potential.

We’ll break down the key characteristics of a data product and how businesses are moving toward intelligent, AI-driven data solutions instead of static datasets.

You’ll see real-world examples of both pure data products and DaaS models, understand the challenges of static data, and discover why companies are willing to pay for convenience and automation.

By the end, you’ll be equipped to evaluate, design, and discuss modern data products with confidence, setting the stage for our next session on AI-powered data solutions.

You’ll learn how LLM-powered data products work, explore real-world examples, and see how predefined prompts, structured training data, and AI-powered automation streamline product development. We also introduce the Open Data Product Specification (ODPS) and Open Data Contract Standard (ODCS)—Linux Foundation-backed frameworks that ensure clarity and governance in AI-driven data products.

By the end of this lecture, you’ll understand how to leverage AI to build smarter, faster, and more scalable data products—and why even simple AI-powered solutions can drive massive business impact.

In this lecture, you will explore the concept of value in data products, breaking down its different aspects and how it evolves over time.

You will learn that value is not intrinsic—it is a journey, not a fixed destination, and it must be continuously assessed and refined. You will see how value varies based on the stakeholder’s perspective, with business leaders, data scientists, and AI applications each defining it differently.

You will examine the difference between perceived value and actual value, recognizing that assumptions about value can lead to disappointment if not validated.

Lastly, you will understand that value must be measured, not assumed, and that simply creating a data product is not enough—true value comes from adoption, integration, and sustained impact.

By the end of this session, you will have a practical understanding of how value works, why it is a moving target, and how organizations can ensure that data products drive measurable business impact.

In this lecture, you will learn how to define, measure, and communicate value in data products using the Data Product Value Measure Framework.

You will discover that value is not just about providing data—it must be aligned with business goals, customer needs, and measurable outcomes to drive real impact. You will explore the difference between data-focused and business-focused thinking, understanding why raw data alone is not enough.

Through a real-world case study, you will see how value measurement transforms a basic report into a strategic data product, ensuring that insights lead to action. You will also learn how to continuously refine value by tracking performance, gathering feedback, and adapting to stakeholder needs.

By the end of this session, you will have a practical framework to ensure that your data products are not just created, but adopted, integrated, and delivering measurable business value.

In this session, you will learn the fundamentals of data quality and data product quality, understanding how they differ and why both are essential for building valuable data products.

You will explore key dimensions of data quality, such as accuracy, completeness, and consistency, and see how these impact decision-making and business success. You'll also learn how data product quality goes beyond raw data, incorporating usability, governance, and business value to ensure data products are truly effective.

Through real-world examples, you will discover when high-quality data is critical, such as in financial risk management and real-time pricing, and when “good enough” quality is the smarter choice, like in AI recommendations and user-generated platforms.

By the end of this lecture, you will have a clear framework for choosing the right level of quality for your data products.

In this session, you will learn about key customer types for data products and how to create value for them. You’ll explore how business users, developers, analysts, and regulators interact with data and why understanding their needs is crucial.

You'll discover techniques like customer interviews, journey mapping, and analytics tracking to ensure data products align with real user needs. The lecture also covers internal vs. external customers, explaining how organizations transition from internal data use to external monetization.

By the end, you’ll have a clear framework for identifying and serving data product customers—just in time to explore the next topic: AI as a customer.

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Join the discussion in the Linkedin Group!  Look for "data product stewardship" in Linkedin and from the results pick the one under Group.

In this session, you will learn about the rise of AI agents as primary consumers of data products and how this shift impacts data product stewardship. AI is no longer just an enabler—it is becoming an autonomous customer that discovers, evaluates, and purchases data products without human intervention.

We explore the key characteristics of AI agents, including autonomy, goal-orientation, and continuous learning. You'll see how AI agents differ from AI copilots, as they operate with minimal human input and drive automation in data-driven decision-making.

Additionally, this lecture covers how AI-driven user experience (AI UX) plays a crucial role in optimizing data product monetization. AI agents require a seamless, intuitive, and frictionless data acquisition process. Failing to cater to AI-native customers can lead to revenue loss and competitive disadvantage.

By the end of this lecture, you’ll understand how to design data products that serve AI consumers, including automated value assessment, real-time compliance enforcement, and adaptive pricing models.

In this session, you will learn how use cases bridge data and business value by transforming vague customer needs into structured, actionable solutions.

You’ll explore common pitfalls, where stakeholders focus on what they need rather than the outcomes they want. The lecture covers key components of a use case—business challenge, stakeholders, data requirements, expected outcomes, and impact metrics—to ensure data products deliver measurable value.

Additionally, you'll understand use case pipeline management, treating use cases like data products to prioritize and scale effectively. This is critical for crossing the first data product chasm, ensuring that data initiatives align with business impact rather than just technical execution.

In this session, you will gain a comprehensive understanding of how Market Pull and Data Push strategies shape the data economy. You’ll learn how companies either lead with data availability (push) or respond to market demand (pull) to create and monetize data products.

We will explore the Data Push Strategy, where organizations proactively release data to drive innovation, ecosystem growth, and regulatory compliance. This approach is commonly seen in government open data initiatives, IoT ecosystems, and large-scale data-sharing platforms.

Next, we’ll examine the Market Pull Strategy, where companies refine and commercialize high-value datasets based on clear business needs. This model ensures profitability and is used in financial markets, location intelligence, and enterprise data services.

We will also cover the Hybrid Strategy, which blends both models—offering some data freely to foster adoption while monetizing premium datasets through APIs, tiered access, and exclusive licensing. Companies like Google Maps API, AWS Data Exchange, and Snowflake Marketplace exemplify this balanced approach.

In this session, you will gain a deep understanding of how data product management connects data to real business objectives. You’ll learn why traditional data stewardship, which focuses on data governance, quality, and compliance, is not enough to unlock true value—and why a shift toward data product stewardship is essential.

We will explore the transition from data-in-a-catalog to data-products-in-use, emphasizing how data must evolve from being stored and governed to being actively consumed, monetized, and embedded in business workflows. This includes key principles such as product design, lifecycle management, value creation, and user-centric governance.

Next, we’ll examine the differences between data stewards and data product stewards. While data stewards focus on protection and policy enforcement, data product stewards enable business impact by designing and managing data products that are reusable and aligned with strategic objectives.

Finally, we will discuss the two data product chasms. The first chasm, which we covered earlier, is crossed by defining clear use cases. The second chasm, which we will address in the next and final section, is crossed through data agreements, ensuring structured governance and usability.

In this session, you will learn how to shift from Project Thinking to Product Thinking, ensuring data products create continuous value rather than being one-time efforts.

We start by exploring how projects focus on outputs and efficiency, while products prioritize outcomes, impact, and ongoing improvement. Next, we cover structuring and packaging data products—ensuring they are usable, accessible, and aligned with real business needs.

Lifecycle management is key to sustainability. A good data product evolves through iterations, responding to user needs while keeping previous versions stable for existing users. Without this, data products risk becoming short-lived projects.

Finally, we discuss the two data product chasms—the first is crossed by defining use cases and using data contracts, while the second requires legal agreements for external monetization.

By the end of this lecture, you’ll understand how to package, sustain, and scale data products for long-term success.

In this short session, you'll receive an overview of the Agile Mindset as it relates specifically to delivering value through data products. You’ll understand the fundamental concepts of Agile methodologies, exploring how they apply effectively—and when alternative methods might better serve data product development.

You’ll gain insights into the importance of rapid learning cycles and fast value delivery, understanding how to make data products accessible, usable, and valuable to their target audience. The session will clarify common misconceptions about Agile and highlight conditions under which Agile methodologies are most effective, along with scenarios where alternative approaches could provide greater benefits.

Additionally, you'll explore modern interpretations of Agile principles tailored for data products, learning how to avoid common pitfalls and how to adapt Agile thinking to suit the unique challenges of data-driven projects. By the end of this session, you'll have a clearer understanding of how to strategically apply an Agile mindset to maximize the value and impact of your data products.

Data Product Steward Toolkit acts as the bridge between strategy and execution, turning the Product Mindset into structured productization and the Agile Mindset into a scalable, iterative delivery model. It ensures that data products are not just well-designed but also measurable, deployable, and continuously improving.

You'll become familiar with the toolkit's components that help identify critical business problems, capture essential stakeholder requirements, and ensure alignment with your organization's strategic objectives.

The session introduces key tools to systematically track and prioritize your data product initiatives, facilitating efficient and effective project execution. You'll get acquainted with techniques for measuring the success and value of your data products, monitoring progress, recognizing trends, and making informed, data-driven decisions.

Finally, the overview covers how the toolkit integrates fundamental aspects such as design, governance, and lifecycle management, enabling you to sustainably scale your data products and ensure consistent compliance and value creation within your organization's data ecosystem.

As you conclude this course, continue your journey by exploring additional resources, joining weekly Q&A group sessions, and engaging with peers in LinkedIn discussions.

Dive deeper into concepts by reviewing materials such as the book "Deliver Value in the Data Economy."

Congratulations on completing the course, and keep scaling value through effective data stewardship!