Turning complex ideas into products that create value
I’m Paola Di Cretico, a product leader working across data products, product strategy and AI, with experience spanning digital transformation, financial services, healthcare data and genomics.
I’m interested in what happens when technology meets a real problem: how an idea becomes a product, how complex data becomes something people can actually use, and how organisations move beyond delivering technology to creating value.
Much of my work has involved making sense of complexity and turning it into clearer choices, useful products and practical outcomes, particularly in complex and regulated environments.
Earlier in my career, I worked on national NHS data collections, including the National Cost Collection, where I helped develop approaches to data validation and quality. In 2020, I presented the Data Validation Tool at the NHS England National Cost Collection launch.
My current focus sits at the intersection of product, data and AI. I write about building data products around genuine user needs, using emerging technologies responsibly, and making product and investment decisions based on evidence rather than assumptions.
From Creative Digital Ideas to Data with Purpose
I started Creative Digital Ideas in 2016 as a place to explore the methods and technologies shaping digital products.
Some of the earlier articles reflect the questions I was exploring at the time, including fintech, distributed ledgers, user experience, Agile and prototyping. I have kept many of them because they form part of the journey. They also show how quickly technology changes and how product thinking develops alongside it.
Data with Purpose is the next stage of that journey. It reflects my growing interest in the relationship between data, technology and value, and in what it takes to make data genuinely useful.
Today, I mainly write about:
Data and AI
How organisations can turn data and artificial intelligence into useful products and capabilities, including questions of governance, readiness and responsible use, starting with a real problem rather than a technology looking for one.
Data products
What it means to treat data as a product, with clear users, outcomes, ownership and measures of value, particularly in complex, regulated and research environments.
Product strategy
How teams decide where to focus, what to build and, just as importantly, what not to build.
Product discovery
How research, experimentation and customer evidence can reduce uncertainty before organisations make significant investments.
How I approach product
I believe good product decisions bring together customer value, organisational value and evidence.
Technology matters, but it rarely creates transformation on its own. Much of the real work lies in understanding the problem, the people affected by it and the assumptions behind a proposed solution.
That means asking practical questions:
- What problem are we trying to solve?
- Who experiences it?
- What evidence do we have?
- Which assumptions still need to be tested?
- How will we know whether what we build has created value?
These questions matter even more as AI makes it easier and faster to build new things. The challenge is no longer simply whether something can be built. It is about deciding whether it should be built, for whom, and for what purpose.
A record of continuous learning
Data with Purpose is also a record of what I am learning.
The articles were written at different points in my career and reflect the technologies, questions and experiences that shaped my thinking at the time. Newer pieces often revisit familiar ideas from a different perspective, informed by what I have learned since.
I do not see that as a contradiction. Ideas should evolve as experience and evidence change.
The purpose of this site is to explore that evolution, connect ideas across disciplines and share practical perspectives on building products in a world increasingly shaped by data and AI.
If you are interested in product strategy, data, and AI, I hope you find something here that helps you look at a familiar problem differently.
The most valuable digital ideas are not necessarily the most complex. They are the ones that turn complexity into meaningful value.