Data products require ongoing refinement and are designed for mass-scale consumption.
The future of data products lies in internal marketplaces, where customizable data products are accessible to the business.
Deep dives
Data as a Product and the Role of a Data Product Manager
The podcast episode discusses the concept of data as a product and the role of a data product manager. The guest, Bethany Lyons, Chief Product Officer at Cowah Analytics, highlights the importance of enabling distributed ownership of data products and aligning product managers to specific business functions. She emphasizes the need to focus on creating value-driven data projects before scaling to data products. Lyons suggests that businesses can learn from data providers in the financial services industry who have already mastered the art of data as a product. Furthermore, she envisions the future of data products being delivered through internal marketplaces, where customizable data products are accessible to the business.
Differentiating Data Projects and Data Products
Lyons explains the distinction between data projects and data products. She states that data projects are focused on delivering a predefined value proposition, while data products continuously deliver value and evolve over time. Data products require ongoing refinement and are designed for mass-scale consumption. Lyons also notes that data products can take various forms, such as dashboards, code assets, or queries, as long as they meet the criteria of delivering continuous value and enabling mass-scale consumption.
The Benefits and Challenges of Implementing Data Products
The podcast delves into the benefits and challenges of implementing data products within an organization. The benefits include the ability to measure value and deliver it at scale, as well as the opportunity to shift data ownership to separate business units for cross-domain use. The main challenge lies in creating incentives for individuals to build high-leverage products designed for use in departments outside their own. Lyons raises the importance of aligning incentives to foster collaboration and engagement among various teams and stakeholders working on data products.
The Future of Data Products and Internal Marketplaces
Lyons highlights the emergence of internal marketplaces as the future delivery mechanism for data products within organizations. She mentions the partnership between Cowah Analytics and Harvard, a data marketplace platform, envisioning a scenario where internal private marketplaces provide accessible and customizable data products to businesses. This would allow users to find and evaluate data assets through the marketplace, leveraging tools like Cowah Analytics to explore and create customized data products that can be shared back into the marketplace. The integration of marketplaces and exploratory interfaces like Cowah Analytics aims to enable seamless access to data products within organizations.
In Episode 30 of Season 3, of Driven by Data: The Podcast, Kyle Winterbottom is joined by Bethany Lyons, Chief Product Officer at Kawa Analytics, where they discuss the current data product landscape and what actually constitutes a data product, which includes;
The fascinating story of Kawa and the problems it solves
Delivering Data Products versus Data Projects
The differences between product management principles applied to data and data products
Why most data teams operate like an IT Service Desk and why product thinking can change that
Why you should start by applying product management to data before you try to build data products
Why a product management approach forces value-driven thinking
How we have ‘Data Product Managers’ who aren’t doing Data Product management
Why there’s a better success measure for your data initiative than usage/adoption
Where you should hire your first Data Product Manager
Platform versus Feature Product Managers and why they’re rarely interchangeable
How data products differ from traditional products
Why marketplaces will be the future of data product
The relationship between self-service analytics and data as a product
What actually constitutes a data product
The biggest unspoken challenge in the data-mesh discussion
The impact of org structure and operating model on data product success
Why too many businesses try to scale their data product before establishing product market fit
What data product people should do to position themselves for the wave of incoming opportunity
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