In this episode, we reveal how IRIS Software is borrowing principles from quantum computing to build data systems that are adaptable, explainable, and innovation-friendly—without compromising business-as-usual.

This conversation with David Draper offers a rare inside look at how a major enterprise balances legacy systems with forward-thinking experimentation.
You’ll learn how modular architecture, protected innovation time, and explainable AI are reshaping the way IRIS builds its data infrastructure. If you’re a data leader trying to scale without spiralling into chaos, this is the episode to watch.

David Draper is the Data Science Manager at IRIS Software Group, where he leads a high-performing team at the intersection of engineering, analytics, and emerging tech. With a background in education and a passion for quantum computing, David brings a unique lens to the challenges of modern data strategy.

Timestamps and Takeaways
✔️04:42 – From Classroom to Data Strategy
David’s journey from education to enterprise data science
→ Leverage curiosity and teaching mindset to lead technical teams with clarity and empathy

✔️09:15 – Why Quantum Thinking Isn’t Just for Physicists
How quantum logic helps IRIS reimagine compute and decision-making
→ Adopt non-linear thinking to structure smarter, more adaptable infrastructure

✔️14:08 – Designing Modular Systems for Innovation Without Risk
How IRIS builds infrastructure that allows safe experimentation
→ Create sandbox-style systems to test and deploy without affecting BAU

✔️19:22 – Ring-Fencing Innovation Time Inside a Busy Enterprise
Balancing research and delivery with “go wide, then narrow” phases
→ Allocate structured exploration time to prevent constant firefighting

✔️24:50 – The Real ROI of Explainable AI
Why clarity builds trust and momentum across the business
→ Choose tools your stakeholders understand to drive adoption and reduce resistance

✔️30:30 – Building Teams That Experiment Responsibly
How culture, structure and trust shape IRIS’s approach to innovation
→ Foster autonomy while staying aligned to business goals

Why Listen
This episode is for data leaders, architects, and CTOs who want to scale responsibly, embed innovation, and prepare for what’s next—without being distracted by hype. It’s a tactical, grounded conversation that offers immediately applicable ideas.

🔔 Subscribe to Data Matas for more real conversations with data leaders.
💬 What’s one lesson from David’s approach you’d apply to your own team? Drop your thoughts in the comments.
🎧 Available on YouTube, Spotify, Apple Podcasts, and all major platforms.

Links
🔗 David Draper on LinkedIn: https://www.linkedin.com/in/david-draper-b715aa46/
🔗 IRIS Software Group: https://www.linkedin.com/company/iris-software-group/
🌐 Listen to more episodes: https://pod.link/1763791020
📚 Matatika resources: https://www.matatika.com/library/
📺 Watch more episodes: https://www.matatika.com/podcasts/

Could the most innovative data management practices be hiding in the cryptocurrency sector?
In this episode, we uncover how crypto data teams have pioneered approaches to resource allocation, automation, and strategic focus that every data leader needs in their toolkit.

Discover how crypto data teams navigate unique challenges that force innovation and strategic thinking. Emily Loh, Director of Data at MoonPay, shares her framework for maximising team impact while balancing operations, innovation, and future-proofing in a rapidly evolving landscape.

Episode Summary
Crypto operates under pressure: irreversible transactions, rapidly shifting regulations, and unpredictable market conditions. These constraints have driven data teams to develop highly efficient, flexible, and impact-led ways of working.

In this conversation, Emily breaks down her team’s approach—from resisting reactivity to protecting time for research and using AI where it adds real value. It’s a roadmap for building resilient, focused data teams in any industry.

Emily Loh leads MoonPay’s 15-person data team across engineering, data science, and machine learning. With previous roles at Coinbase and a background in literature, she blends deep technical understanding with sharp communication and business alignment.

Key Insights and Practical Applications
1. The 20/40/40 Resource Allocation Framework
Emily’s team divides time intentionally—20% for operations, 40% for delivery, 40% for research—to avoid constant firefighting.
✔️ Audit how your team spends its time and protect research with tools like opportunity sizing and innovation showcases.

2. Strategic AI Implementation That Delivers Real Value
MoonPay uses tools like Cursor to eliminate tedious engineering tasks (like YAML updates), freeing time for high-impact work.
✔️ Start with the repetitive tasks your team dislikes most—then measure the time you save.

3. Building Data Systems for Uncertain Futures
Crypto’s speed forces teams to prepare for unknowns. Emily shares how they build modular, flexible systems ready for whatever comes next.
✔️ Invest in solid data foundations and scenario planning, not just speed.

Why Listen
If you’re leading a data team in an environment shaped by complexity, growth, or change, this episode offers practical, field-tested strategies to help your team stay ahead—without burning out.

🔔 Subscribe to Data Matas for more real conversations with data leaders.
💬 What’s one change you’d make to your team’s structure after hearing this? Share it in the comments.
🎧 Available on YouTube, Spotify, Apple Podcasts, and all major platforms.

Links
🔗 Emily Loh on LinkedIn: https://www.linkedin.com/in/emilyloh/
🌐 Listen to more episodes: https://pod.link/1763791020
📚 Matatika resources: https://www.matatika.com/library/
📺 Watch more episodes: https://www.matatika.com/podcasts/

How can businesses scale their data infrastructure without falling into the trap of hidden cloud costs?

In this episode of Data Matas, AWS expert Jon Hammant reveals the biggest cost pitfalls businesses face in cloud data management, how AI-driven analytics is changing the game, and practical steps to cut costs while scaling efficiently.

Jon Hammant is the UK & Ireland Lead for AWS Specialist Team, overseeing technical and sales strategies across AI, Compute, Data Modernisation, and Cloud Infrastructure. With a background in high-performance computing, networking, and cloud cost optimisation, Jon has worked with some of the world’s largest enterprises to reduce cloud expenses while enabling AI-powered scalability. His unique blend of technical and commercial expertise gives him a front-row seat to how businesses can optimise cloud spend without sacrificing performance.

💡 AI adoption is moving faster than controls – Jon compares today’s AI explosion to the early days of mobile adoption, where organisations rushed to integrate new technology before fully understanding its risks and costs.

💡 Cloud spending can feel out-of-control – Where businesses are paying for cloud resources they never use, just like people forgetting to cancel streaming services they no longer watch.

💡 Real-time data is the new normal, but it comes at a price – We discuss why companies moving from batch processing to real-time analytics often experience unexpected cost surges and how they can optimise without overpaying.

💡 The best metric isn’t more dashboards, it’s better decisions – Businesses are drowning in data but struggling to turn insights into action.  Data infrastructure should be built for decision-making, not just reporting.

With the explosion of AI-driven analytics and real-time data processing, cloud costs are rising faster than many businesses can control. Companies that fail to optimise their cloud infrastructure risk being priced out of their own innovation. Cloud cost optimisation is no longer just an IT issue—it is a strategic business imperative.

This episode is a must-listen for CTOs, data engineers, and business leaders who are struggling with rising cloud costs, inefficient data pipelines, or scaling AI-driven analytics. If you are questioning whether you are overpaying for your cloud services, this conversation will help you identify hidden inefficiencies and unlock smarter, cost-effective growth strategies.

👉 Subscribe to Data Matas for more expert insights on cloud cost optimisation and AI-driven analytics.

Want to optimise your cloud data spend?

Get our free cost comparison tool and Data Efficiency Blueprint here.

What separates high-performing data teams from the rest?

In this Season 1 Highlights episode, we break down seven proven strategies that help teams cut costs, improve data quality, and scale smarter. We’ve spoken with data leaders, engineers, and BI experts who have tackled real-world challenges—from legacy systems and broken workflows to AI risks and siloed teams. This episode is your actionable playbook to making your data work better.

✅ Fix data chaos and create a single source of truth – Jessica Franks (Not On The High Street) shares how she aligned business and tech using Wardley Maps.

✅ Rebuild trust in business intelligence – Joe Wright (CitySprint) explains how they solved reporting inconsistencies by consolidating systems.

✅ Scale smarter without overcomplicating – Stéphane Burwash (Potloc) shows how open-source tools and a data champions programme transformed their approach.

✅ Why ‘good enough’ beats perfection – Bethany Lyons explains how streamlining data pipelines saves time without sacrificing quality.

✅ Make data quality everyone’s job – Adam Dathi (MVF) reveals how cross-team collaboration fixes unreliable reporting.

✅ Using real-time data for better decision-making – Nick Bromley shares how transport data integration is driving smarter city planning.

✅ AI without the risk – Murtaza Kanchwala (Amplify Capital) details how his team successfully implemented AI while staying compliant.

🚀 Whether you’re a Head of Data, CTO, BI Manager, or Data Engineer, these practical insights will help you fix inefficiencies, scale with confidence, and build a high-impact data team.

In this conversation, Murtaza Kanchwala from Amplifi Capital discusses the integration of Generative AI (Gen.AI) in financial services, sharing insights on early applications, challenges faced, and the importance of regulatory compliance. He emphasizes the need for effective team organization and the selection of appropriate AI models to enhance productivity and efficiency. The discussion also touches on the future of AI in finance, predicting the emergence of personalized AI assistants and the ongoing evolution of AI technologies.

In this conversation, Aaron Phethean and Nick Bromley discuss the evolution and importance of transport data, particularly focusing on the integration of real-time data and mobile phone data into transport planning. They explore the challenges of data collection, the role of AI and big data in optimizing transport systems, and the future of transport data with an emphasis on privacy and security concerns.

This episode with Adam Dathi is a must-listen for anyone looking to turn data into a strategic powerhouse within their business.  Adam shares practical insights into how data teams can work seamlessly with other departments for maximum business-wide impact and gives his take on the future of AI-driven data analysis.

Aaron and Adam discuss the critical role of reliable sources and governance, but also observe that this is not an isolated issue for a specific team, but a company-wide responsibility if one is looking to harness the true power of data for their business.

This is an unmissable conversation packed with innovative perspectives where Aaron Phethean and Bethany Lyons dive into the intricate world of what “full stack” genuinely means in the data realm. Armed with facts and humor, they explore the vital role of trust, and the complexities of data reconciliation.

Their discussion also ventures into the dynamic startup landscape, stressing the urgent need for creative solutions to the persistent challenges in data management and analytics that so many face. With their fresh insights, this conversation is a must for anyone looking to understand the future of the industry.

In this episode of Data Matas, host Aaron Phethean and his guest Stéphane Burwash dive deep into what it takes to build a true data-driven culture. Recently promoted to Data Engineering Lead at Potloc, Stéphane shares his thoughts on building trusted analytics, where quality data is at the foundation.

The conversation digs into the hot topics of AI and self-service analytics – and questioning their relevance – as well as the application of modern technologies such as Meltano and BigQuery and “the separation of church and state” in the data space. Not only that but the two touch on the importance of the people element and emphasise the need for open and honest stakeholder management in an organisations journey to data excellence.

CitySprint is one of the largest same-day courier providers in the UK, with a strong presence in London. They operate a UK-wide network and offer same-day logistics services. The company relies on a fleet of couriers who use various modes of transport, including bikes, to quickly deliver parcels. CitySprint’s goal is to move away from investigating data challenges and focus on building trust in the accuracy of their data. They are working on modernizing their infrastructure and implementing a new data management system to improve data quality and reporting.

The BI team at CitySprint plays a crucial role in analyzing data and providing performance stats to different teams within the company. The team is also responsible for bridging gaps in the existing systems and ensuring the data remains current and relevant. The project aims to streamline the BI stack, create a single version of the truth, and enable faster reporting in smaller time windows. The challenge lies in managing the people side of the project and helping the team adapt to the new ways of working.

In this conversation, Aaron and Joe discuss the legacy technology stack at CitySprint, including BI visualization tools, ETL tools, and the transition to Snowflake and Power BI. They also touch on the potential of AI in the business and the importance of embracing change. Joe emphasises the need for data managers to straddle the technical and business perspectives and build strong stakeholder relationships.