The data leadership series part four
DemystifyingData Products
- Approaches to data products
- Understanding the core concepts
- Foundations for success
The data leadership series part four
Each month, with the help of data experts on the panel, we will guide you and your teams through a relevant data leadership topic – with practical advice that you can follow.
A Data Product is a way to make a data asset reusable and consumable. Data assets can be datasets, dashboards, machine-learning models and much more!
It is an idea that combines data management with product thinking (a practice already well established in software development) but there is a lot of confusion about what it is or what it should be.
We hope to provide some context to this debate and share some useful insights for Data Leaders who are reflecting on this topic.
Part 1 looks at data products in the context of an organisation, with a multidisciplinary approach that includes best practices from product design, product management and enterprise data management. We’ll apply product thinking and provide pointers for successful outcomes when both producing and consuming data products.
Part 2 describes the underlying concepts and building blocks of data products, and the role of metadata. We’ll discuss what data products are and what they are not, how they fit with modern data architecture such as data mesh and how they’re different from data sets and other data assets.
Part 3 considers some of the practicalities: how working with data products can impact a data strategy and the way companies organise themselves; what data products can offer, and how to provide and exploit them successfully.
Principal Consultant, Ortecha
CTO & Founder, Dataception
Director of Data Advisory, BigID
Data Leaders debate managing data as a product. Embracing this shift can fuel product development, with engagement, assessment, and robust frameworks being key.
Organisations navigate complex data environments, with Data Leaders leveraging data products for utility, quality & convenience in the Data Product lifecycle.
A data product makes a data asset reusable and consumable. But what are the pitfalls you should consider when building data products?
