Stacked Data Podcast (Cognify)

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The Semantic Layer, what cand it do?

The modern data stack has transformed the way businesses store, prepare, and consume data. Yet, amidst this data revolution, organisations are continuing to encounter challenges such as data quality, inconsistency, and escalating costs. One solution gaining traction is the semantic layer.

Excited to announce the latest episode of the Stacked Data Podcast, featuring David Jayatillake, the VP of AI at Cube! We explore the transformative impact of the modern data stack and delve into the intricacies of the semantic layer.

David reminisces about being a human semantic layer, translating business logic into usable data in his early career, to setting up an LLM NLP start-up, David has an incredible story and the best insights.

In this episode, we cover:

• David's      journey in the Data & AI space
• Understanding      the term "semantic layer" and its function within a data      infrastructure
• The      importance of the semantic layer in the modern data stack
• Specific      challenges like data inconsistency and scalability that the semantic layer      can effectively address
• Tangible      benefits of implementing a semantic layer, including enhanced security and      AI capabilities
• Cube’s      unique approach to the semantic layer and the value it brings to organisations
• Exciting      developments and future plans for Cube in the evolving data landscape

David's insights and expertise make this episode a must-listen for anyone interested in the modern data stack and the role of the semantic layer. Don't miss out on this deep dive into one of the most critical components of data infrastructure!

Is AI coming for data jobs?

Gaurav Tiwari, an Engineering Manager at Spotify, joining me on The Stacked Data Podcast. I first encountered Gaurav's insightful perspectives on AI at The London Analytics Engineering Meet-up, I had to get him on the show!

With a deep-seated passion for AI, Gaurav brings a critical eye to this rapidly evolving field. We dived deep into the implications of Generative AI on the data landscape and how it will impact your roles and responsibilities in data...

Gaurav, shares his thoughts on how AI will impact the life of a data professional…

In this episode, we cover:

✅ Understanding Generative AI

✅ GenAI at the Consumption Layer: Discussing how GenAI is reshaping the interaction between businesses and data through analytics.

✅ Benefits of GenAI in Analytics: Exploring the potential benefits GenAI could bring to self-serve analytics platforms.

✅ Challenges and Solutions: Identifying the biggest challenges when integrating GenAI into analytics processes and how to address them effectively.

✅ Strategic Investment and Pitfalls: Guidance for organisations on where to start their investments in GenAI and potential pitfalls to avoid.

✅ Data Engineering AI Impact:

✅ Challenges Specific to Data Engineering: Examining the unique challenges data engineers face when integrating GenAI and how to overcome them.

✅ Optimal Strategies for Implementation: Recommended strategies for adopting GenAI within data engineering teams and aligning methodologies with new tools.

✅ Tools and Technologies: Highlighting specific tools or technologies such as Infer, TurinTech AI and more

Gaurav's insights and expertise make this episode a must-listen for anyone interested in the evolving landscape of Generative AI and its impact on data analytics and engineering.

Is dbt lowering the bar for data modelling having a negative effect on data quality?

Data modeling is the cornerstone of data-driven decision-making. It's the art of translating a business's concepts, definitions, and activities into data structures. When done right, it empowers you to answer the crucial "why" questions by capturing the "who, when, how, and what" of a business. Moreover, it paves the way for future efficiency, reusability, and data consistency.

So, why do so many organizations still overlook the importance of a robust data modelling strategy?

This week, on The Stacked Data Podcast, I have the pleasure of hosting Rob, the Head of Data Product at Miro. Rob delves into the critical significance of data modelling, the common pitfalls to avoid, and shares invaluable insights on how to approach data modelling and effectively lead teams of data modellers.

🚀 Key Takeaways:

1. Know      Your Critical Concepts and Attributes: Define and design them upfront,      ensuring alignment across your organization. Regularly revisit and expand      your list of conceptual definitions.
2. Invest      in Ongoing Education: Constantly enhance the skills of your data      contributors. While not everyone needs to be a data expert, analysts      should grasp architectural principles, master their tools, and engage in      continuous learning. Rob, for instance, dedicates 5-7% of team time to      Learning and Development (L&D) activities.
3. Maintain      Your Models Like a Garden: Regularly dedicate time to review, refine,      refactor, clean, upgrade, and promote your data models. This should be a      shared responsibility and part of your sprint routine.

This week I’m joined by Leon Tang the VP of Data from @fresha. Leon shares his unique career, from frying fish and chips to building and leading data teams at some of the world's most well-known tech companies.

Leon stresses the importance of relationship building and business understanding as a key attributes to success in data.

We speak about how to make sure your data team is a driver of the business and not a support function.

I dive into how to excel in your data career and have impact on the business everyday!

🌐 What You'll Learn:

How to break from a “Senior” into Leadership

How to build your toolkit to succeed in data

How to foster a data driven culture to set your team up for success.

Top tips for interviewing and a successful career in data!

The Stacked Data Podcast isn't just about technology; it's about the stories, experiences, and lessons that drive innovation in the data landscape. Whether you're a seasoned data professional or simply curious about the future of data, this conversation offers a wealth of knowledge and inspiration.

Please give us a follow as we have lots more episodes coming!

We are always looking for feedback or topics you'd like to hear about. Please reach out!

In our latest episode of The Stacked Data podcast, I’m joined by Veronica Saha, Head of Analytics at Zoopla (part of Houseful), as she shares real-world examples and strategies on data monetization.

In 2024, data teams need to evolve beyond being mere support functions; they must become strategic drivers. Many data teams have struggled to show/prove their true value. The monetisation of data comes in many different forms, however, it's clear that raw data is not valuable on its own.

Data only hold value as insight driving decisions. It needs to be packed into a PRODUCT!

We discuss the need to treat data as a product in order to effectively implement a monetisation strategy.

🎙️ Episode Highlights:

1. Defining      Data Monetization: What exactly do we mean by 'data monetisation'?      When is the right time to think about it? How can data create business      value?
2. Identifying      Opportunities: How do you identify areas for data monetization? What's      the role of technical architecture in this process?
3. Building      a Revenue-Focused Team: Challenges and considerations in building a      team focused on generating revenue through data products.
4. Feedback      Loops and Continuous Improvement: Establishing effective feedback      loops and optimizing data products for better monetization outcomes.
5. Securing      Leadership Buy-In: Challenges in securing sign-off and budget from      leadership.
6. Common      Challenges and Lessons Learned: Insightful lessons from Zoopla's      journey in overcoming challenges in data monetization.

𝐓𝐡𝐞 𝐡𝐚𝐫𝐝𝐞𝐬𝐭 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐟𝐚𝐜𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐓𝐞𝐚𝐦𝐬 𝐢𝐬 𝐭𝐡𝐞 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐨𝐟 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬-𝐰𝐢𝐝𝐞 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐢𝐦𝐩𝐚𝐜𝐭!



🚀 This week on the Stacked Data Podcast, I had the pleasure of sitting down with McKinley Muir Hyden, the Director of Data Value & Strategy at the Financial Times. We dive into the critical topics of data adoption and culture change in today's data-driven world.



🌐 McKinley brings a wealth of experience, and we kick off the discussion by exploring her journey into the realm of data and her current unique role at the FT. McKinley’s role revolves around bridging the chasm between data and the business. It's about ensuring alignment and, most importantly, measuring the impact of the data team's efforts as well as building the FT’s Data Academy.



𝚆̲𝚎̲ ̲𝚌̲𝚘̲𝚟̲𝚎̲𝚛̲:̲

🔍 How McKinley navigates the challenge of bridging the gap between data and business.

🤔 Reasons for lack of data adoption and the strategies employed to combat them.

🌟 McKinley's role is unique, and we delve into the origins of this specialized position and the objectives she aims to achieve.

📊 What exactly is a data culture and why is it crucial for organizations?

📏 How McKinley measures and evaluates data culture.

🚀 The challenges encountered in creating a robust data culture.





If you are looking to build and foster a data culture and inspire trust to ensure analytics impact this is not one to miss. McKinley shares her strategies and approaches as well as the common pit falls and blockers such as; Awareness, Discoverability, and Understanding.



Tune in and give us a FOLLOW, like or share!



🙌 Stay tuned for more inspiring conversations, and don't forget to subscribe! hashtag

hashtag#DataCulture hashtag#DataAdoption hashtag#Podcast hashtag#DataInnovation hashtag#StackedDataPodcast

Uncovering a Data Mesh Self-Serve Data Platform.

This week I’m joined by Sandeep from Dojo. We discuss how Dojo is implementing a data mesh, first conceptualised by Zhamak Dehghni in her book “Delivering Data-driven Value at Scale”

Dojo’s self-serve platform empowers their distributed domains to own their data with the goal of increasing the velocity and quality of data products.

Dojos platform handles…

- £50 BILLION in transaction

- 100 million events

- 300k files of 15 different types

EVERY SINGLE DAY!

🔗 Listen now: [Link]

In this episode, I dive into why Dojo decided to follow that Data Mesh route, how they tackled the huge project, the challenges they ran into, the value it is driving and what they’d do differently now.

🌐 What You'll Learn:

How to start a data mesh project and what value it will drive.

Strategy to effectively reduce data debt.

How to align your strategy with the business to get buy-in

Top tips for interviewing and a successful career in data!

The Stacked Data Podcast isn't just about technology; it's about the stories, experiences, and lessons that drive innovation in the data landscape. Whether you're a seasoned data professional or simply curious about the future of data, this conversation offers a wealth of knowledge and inspiration.

Please give us a follow as we have lots more episodes coming!

I'm joined by Sami Rahman as we dive deep into the fascinating world of Modern Data, covering topics around maximising your stack and the power of a semantic layer.

In this episode, I had the privilege of sitting down with Sami, who has a truly unique path into data. Together, we explore the intricacies of the semantic layer and modern data stack, unearthing its transformative potential and its vital role in modern data architecture.

🌐 What You'll Learn:

An honest insight into Sami unique career journey (including a stint in ANTI-TERRORISM)

Insights on harnessing the power of the modern data stack and a semantic layer to unlock data's true potential.

Real-world applications and success stories that shed light on its impact.

Practical tips and strategies to navigate the challenges of implementing a robust semantic layer.

Top tips for interviewing and a successful career in data!

This podcast isn't just about technology; it's about the stories, experiences, and lessons that drive innovation in the data landscape. Whether you're a seasoned data professional or simply curious about the future of data, this conversation offers a wealth of knowledge and inspiration.

Please give us a follow as we have lots more episodes coming!

We are always looking for feedback or topics you'd like to hear about. Please reach out!

The Rise of the Data Product Manager…

How often have you joined a data team where data is poorly understood by the business, siloed, and churning out tickets with little impact? Or found that the Analytics team doesn’t communicate with Data Engineering, leading to project delays due to missing data?

These common challenges underscore the need for a new role that’s gaining traction: the Data Product Manager. This week on The Stacked Data, I'm joined by Karen Francis, a Data Product Manager at B&Q, and Rianna Kelly a Head of Data at Zeps.

Karen and Rianna have firsthand experience with these issues and many more. They’ve seen how powerful and transformative the role of a Data Product Manager can be. In this episode, they share insightful stories and discuss why this role is crucial for high-performing modern data teams.

We discuss:

• The responsibilities of a Data Product Manager
• The typical challenges they overcome
• How Data Product Managers drive value and efficiency
• Case  Studies and Success Stories
• Why more data teams need to  adopt this role

Tune in to gain valuable insights and understand why the Data Product Manager is essential for any data-driven organization.

🎙️ In this week's episode of @the stacked data podcast, we dive deep into the world of data modelling and uncover strategies for success with our special guest Nieves Gorriti the Head of Data and Insights The Dot Collective

Here's a sneak peek into the insightful discussion:

🔍 Introduction to Data Modelling:

What exactly is data modelling, and why is it crucial for effective data management? Nives provides expert insights into this fundamental aspect of the data lifecycle.

🛑 Common Pitfalls and Challenges:

Discover the common pitfalls organisations face in the data modelling process and learn how to avoid them to ensure smooth data operations.

🔑 Importance of Data Modelling:

Uncover the pivotal role of data modelling in driving successful data-driven initiatives within organisations, along with real-life examples demonstrating its impact.

🔧 Guiding Through the Process:

Nives shares invaluable advice and practical tips for enhancing your data modelling skills and provides a step-by-step guide to approaching data modelling successfully.

🔄 Balancing Flexibility and Standardization:

Learn how to strike the right balance between flexibility and standardisation in data modelling to meet business demands while maintaining data integrity.

🤝 Adapting to Evolving Data Environments:

Explore strategies for adapting data modelling strategies to accommodate the ever-evolving data landscape, including new technologies and data sources.

Don't miss out on this conversation! Tune in now to Stacked Data Podcast to gain valuable insights into mastering data modelling for organisational success. 🎧

🌱 Our planet is getting warmer, and the undeniable impact of climate change is a stark reminder that we all need to take action. But as individuals, it's easy to feel powerless in the face of such a monumental challenge. That's where modern data teams come in, with the potential to drive positive change and make businesses greener and more efficient.

🎙 In our latest episode of the The Stacked Data Podcast, we dive headfirst into the world of Carbon Analytics, a space filled with untapped potential. Join me as I sit down with the passionate advocate for sustainability, Nina Anderson from Infinite Lambda. Nina is on a mission to empower data professionals and shed light on the transformative power of Carbon Analytics.

Why Carbon Analytics? The Essential Business Imperative

You might be wondering, "Why does Carbon Analytics matter for my business?" Nina breaks it down for us in this insightful episode, outlining key reasons why it's no longer a luxury but an essential:

0️⃣ NET ZERO Targets: As the world races towards carbon neutrality, understanding your environmental impact is crucial to achieving NET ZERO targets.

📜 Regulation Change: Regulations around carbon emissions are evolving rapidly. Being prepared is not just advisable; it's necessary.

💸 Cost Reduction: Carbon Analytics can uncover hidden efficiencies and cost-saving opportunities in your operations.

💰 Competitive Advantage: Sustainability is a competitive differentiator. Embrace Carbon Analytics to stay ahead of the curve.

😇 Customer Satisfaction: Modern consumers favor businesses that prioritize sustainability. Happy customers mean better business.

We also explore practical examples of how you can leverage your own modern data stack to start generating actionable insights.

Did you know that the National Grid provides detailed data on the carbon intensity of electricity across regions? When combined in your Data Warehouse with your electricity consumption, you can quickly assess your carbon impact. Surface this in your BI tool per sale and you might be able to start shifting a culture! ⚡

A Wealth of Insight for Sustainability Advocates

This episode is full of knowledge about an emerging and important space. If you're passionate about sustainability and committed to making a positive impact, this is an episode you can't afford to miss.

𝐈𝐬 𝐢𝐧𝐟𝐥𝐮𝐞𝐧𝐜𝐢𝐧𝐠 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐬𝐤𝐢𝐥𝐥 𝐢𝐧 𝐝𝐚𝐭𝐚?


The ability to influence is pivotal for success in data. After all, the role of a data function is to inform business decision-making. One of the most effective ways to achieve this is through Storytelling.

Excited to share the latest episode of The Stacked Data Podcast, where I had the privilege of chatting with Phil Thirlwell, the Director of Analytics and Data Strategy at FIS. Today, we unravel the transformative power of storytelling in driving business impact and value creation.

As a leader, the ability to manage and influence C-Suite is pivotal for success.

Join us for some insightful takeaways:

🛣️ 𝐏𝐡𝐢𝐥'𝐬 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 𝐢𝐧 𝐃𝐚𝐭𝐚

🌐 𝐆𝐥𝐨𝐛𝐚𝐥 𝐈𝐦𝐩𝐚𝐜𝐭 & 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧:
As the Head of Analytics at FIS, Phil plays a pivotal role in influencing global business strategy. We dive into the dynamics of working closely with executive teams and explore how it differs from collaborating with other stakeholders.

📈 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 𝐌𝐚𝐭𝐭𝐞𝐫:
The importance of setting the right metrics and measures cannot be overstated. Phil shares his insights on the critical role metrics play in ensuring impactful data-driven decisions.

🔍 𝐌𝐞𝐞𝐭𝐢𝐧𝐠 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞 𝐍𝐞𝐞𝐝𝐬:
One common challenge faced by data leaders is aligning with executive requirements. Phil shares strategies on ensuring executives get what they need, not just what they want.

📢 𝐓𝐡𝐞 𝐀𝐫𝐭 𝐨𝐟 𝐒𝐭𝐨𝐫𝐲𝐭𝐞𝐥𝐥𝐢𝐧𝐠:
Discover how Phil leverages storytelling to influence stakeholders and drive business decisions. We delve into the nuances of storytelling with data products.

📈 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐄𝐱𝐜𝐞𝐥𝐥𝐞𝐧𝐜𝐞:
Phil reflects on the qualities that distinguish a good analyst from a great one and what it takes to rise to leadership within the realm of data.

🌐 𝐂𝐮𝐥𝐭𝐮𝐫𝐚𝐥 𝐒𝐡𝐢𝐟𝐭 𝐭𝐨 𝐃𝐚𝐭𝐚-𝐃𝐫𝐢𝐯𝐞𝐧:
With organizations striving to become "data-driven," Phil shares insights on changing the mindset of more traditional businesses stuck in their ways.



🎙️ Tune in for More 🎙️
Be sure to listen to the full episode for in-depth insights from Phil. Subscribe for more thought-provoking discussions on The Stacked Data Podcast!

This week on The Stacked Data Podcast, I had the pleasure of hosting Simone Spinalbelli from Phoenix Group.

Simone, the Head of Analytics Engineering, shared invaluable insights into implementing DataOps, taking us beyond the standard definitions. He delved into a real narrative on how Phoenix Group successfully implemented a DataOps strategy, unblocking vast and diverse data, effectively processing, analyzing, and leveraging it for business insights and decisions to drive them forward.

Simone’s key integrations in their data environment include:

🚀 Agile Working: Going beyond the familiar Agile methodology, Simone emphasized the significance of focusing on people and interactions over rigid processes. Continuous collaboration with end-users remains a priority, fostering an adaptive and responsive work culture.

🔧 DevOps Integration: By embracing DevOps tooling and software engineering practices, Phoenix Group unlocked critical functionalities such as CI/CD pipelines, version control, and robust testing mechanisms.

🛠️ Pipelines and Control Gates: Developing pipelines with controlled gates and integrated tests ensures the validation of business logic and a smooth flow of data throughout the process.

DataOps, as Simone highlighted, is not just a methodology but a proactive approach that enables a shift from reactive practices. It provides a secure and structured framework for the entire data team, allowing accelerated and more efficient work processes.

🌟 New episode on The Stacked Podcast featuring an incredible guest, Joao, the Head of Data at Plum. We dive deep into the Challenges of Building a Data Team in a Start-up. 🎙️✨

🌐 Introduction: In this episode, we take a journey with Joao, exploring key milestones in his career that led him to leadership in the dynamic world of data.

🚀 Building from the Ground Up: When stepping into a scale-up as the Head of Data. Where do you begin? Joao shares his insights into the crucial first steps and priorities on the to-do list.

💻 Tech Talk: Tech is the backbone of any data team. Joao discusses the overwhelming choices data leaders face when building their tech stack and how technology influences the hiring process.

🤝 Hiring Hacks:Hiring isn't just about filling roles; it's about building a successful team. Joao sheds light on how to decide which roles to hire, the crucial skill sets for an early data team, and how roles evolve as the function matures.

🌐 Cultivating a Data Culture: Tech and team are essential, but how is it all perceived by the business? Joao shares his insights on growing a data culture in a start-up, what defines a good data culture, and common pitfalls to avoid.

📈 Measuring Impact:How do you know if your team is making a real impact? Joao discusses the metrics of success, showcasing value to secure more resources and bigger opportunities.

💡 Facing Challenges:Joao opens up about the common challenge of being under-resourced as a Data Leader in a rapidly growing organisation and how to tackle it head-on.

🚀 Beyond the Horizon:Explore with Joao the additional challenges Data Leaders in start-ups often face, gaining valuable insights into navigating the dynamic landscape.

Here is the link Joao spoke about - https://towardsdatascience.com/how-i-won-singapores-gpt-4-prompt-engineering-competition-34c195a93d41

Tune in now 🎧

And give us a follow and a rating, your feedback means the world to me!

✨ Don't miss out – it's a knowledge-packed episode you won't want to skip! 🚀🌟

#DataLeadership #Startups #PodcastLaunch #TechTalks

The data industry is evolving at an exponential rate; what is possible today was a dream just 10 years ago. As data teams become more complex and roles increasingly specialised, the risk of losing a holistic view of the systems and elements that make up a successful data team grows.

This week, I’m joined by data strategist Dylan Anderson from Rekite to discuss the importance of understanding the entire data ecosystem, not just your own "fragment."

Data professionals are becoming more specialised in their respective areas, adopting an inch-wide, mile-deep approach as data roles demand ever-increasing expertise. While specialisation is necessary, are we overlooking the benefits of a broader perspective?

Dylan puts it well: "It’s like appreciating a single piece of a puzzle without acknowledging the entire picture."

In This Episode, We Discuss:

• How      to Navigate the Data Ecosystem: Gain insights into understanding the      interconnected elements of data roles.
• Avoiding      Siloes in Teams and Skill Sets: Learn why it's crucial not to isolate      your team or your personal skill set.
• Consequences      of Poor Communication: Discover the impact of inadequate communication      within data teams.
• The      Importance of a Broad Understanding: Understand why having a      wide-ranging knowledge of the data ecosystem is beneficial.

Dylan is a globally recognised data strategist who regularly shares his insights, thoughts, and lessons on LinkedIn and through his new newsletter. As a long-time follower of Dylan and his content, it was a privilege to have him on the podcast.

If you don’t already follow Dylan or subscribe to his newsletter, I highly recommend doing so!

Dylan newsletter: Issue #2 - The Data Ecosystem: Where do you even start? (substack.com)

Self-serve BI was one of the trailblazers for the modern data stack. The ability to allow business stakeholders to not only see reports but also start exploring their own data was truly a seismic shift in data.

What is next for BI?

Join me in the latest episode of the Stacked Data podcast as I sit down with Collin Zima, the Co-founder and CEO of Omni, for an insightful discussion on the future of Business Intelligence.

🚀 About Colin:

Collin Zima's journey from an early Google employee to leading data teams at Hoteltonight before eventually becoming the Chief Analytics Officer and VP of Product at Looker gives him a unique perspective on the evolution of BI. Now, as the driving force behind Omni, he's shaping the next generation of BI solutions.

🔍 Dive into  Next GenBI with Us:

In this episode, we explore:

The primary purpose and value of BI

Challenges facing the BI sector and strategies to overcome them

Omni's innovative approach and key focus points

The importance of a customer-centric approach in building BI solutions

Future trends and advancements shaping the BI landscape

Tune in as Collin shares his experiences, insights, and advice for organisations navigating the complex world of BI in today's competitive landscape.

Colin's message was clear to me, he and Omni doesn't seek to rewrite the BI script but to refine it!

Navigating Data Transformation in Traditional Organisations: Insights from Matt Webb, CIO


As the modern data stack continues to evolve, traditional organisations are embarking on journeys to unlock the full potential of their data assets. Join us in our latest podcast episode as we delve into this fascinating topic with Matt Webb, the CIO at UK Power Networks.


🔍 Here's What You'll Learn in This Episode:
• 𝐌𝐞𝐞𝐭 𝐌𝐚𝐭𝐭: Get to know Matt, his background, and his pivotal role in leading UK Power Networks' data transformation journey.

• 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐔𝐊 𝐏𝐨𝐰𝐞𝐫 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬: Discover the mission and significance of UK Power Networks and their ambitious goals in decarbonising the UK and achieving NET ZERO targets.

• 𝐓𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧: Explore the motivations behind UK Power Networks' data transformation project and the technologies being leveraged, including cloud migration and Databricks.

• 𝐃𝐫𝐢𝐯𝐢𝐧𝐠 𝐃𝐞𝐜𝐚𝐫𝐛𝐨𝐧𝐢𝐬𝐚𝐭𝐢𝐨𝐧: Learn how cloud migration and Databricks play a crucial role in UK Power Networks' mission to decarbonise the UK and reach NET ZERO targets.

• 𝐍𝐚𝐯𝐢𝐠𝐚𝐭𝐢𝐧𝐠 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: Hear firsthand about the biggest challenges faced within data management and the strategies employed to overcome them.

• 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐃𝐚𝐭𝐚: Gain insights into Matt's vision for the future of data at UK Power Networks and the anticipated advancements in data management and analytics.


Tune in to gain exclusive insights from Matt Webb and learn how traditional organisations like UK Power Networks are navigating the complexities of data transformation to drive innovation, efficiency, and sustainability.

In this episode on Stacked Data Podcast, we explored how the team at Carbon Underwriting use  the modern data stack to innovate and revolutionized the way they operate in a the traditional industry of Insurance, allowing them to swiftly deliver top-tier data products that can exponentially improve work in the insurance space.

We dive into there leading data product “Graphene”, taking an old school Excel reporting, consolidating this all in a common data model and surfacing an analytics layer on top! This allows there underwriters to deliver work at a much faster rate.

This episode is all about how to build teams, infrastructure and processes to create data products that are truly impactful.

Key Takeaways:

Start with your customer: Work closely with your stakeholders to understand their pain points and mission, from here you can start to design a data product that can enable them.

The power of leveraging the modern data stack: The use of cutting-edge technology has transformed there data infrastructure, enabling faster, more agile operations.

Accelerating product delivery: How this modern approach has supercharged our ability to bring data products to the market at an unprecedented pace.

Overcoming challenges with efficiency: We discussed the hurdles faced and the strategies employed by the team to harness the potential of the modern data stack effectively.

In Richard's own words, a data platform serves as a horizontal service—a team and platform that supports various vertical pillars across a business, enabling them to operate as efficiently as possible.

Large enterprises are often notorious for lacking speed, agility, and efficiency. Richard tackled this challenge head-on at VMO2 by combining cutting-edge technology with impeccable processes to construct a data platform that fuels exponential growth.

Richard's data platform team has achieved a remarkable 10x impact across an organization, with over 200+ engineers and analysts using the platform daily to drive business value.

Tune in now to hear more, and you'll discover:

• How to structure a team to successfully deliver such a wide-ranging project.
• The      Return on Investment (ROI) of a data platform.
• The most significant challenges in building an enterprise data platform.
• The essential skills required for successful execution.
• Striking the right balance between technical expertise and soft skills as a data leader.

SEE THE EXACT QUESTIONS AT THESE TIMES

2.00 Richards's background and experience

5.50 What is a data platform

10.40 What is the end goal of a data platform how does it facilitate ROI

13.30 How do you structure a team to deliver a data platform at scale

19.00 What have been your biggest challenges and lessons on this journey

25.00 if you could do it all again what would you do differently

27.22 How do you have the agility to navigate the ever-evolving landscape

24.00 How do you use your tools and platform to “level up “ the whole organization

41.20 Quick fire questions

Data Contracts have gained a huge amount of traction over recent years as Data teams strive to increase the quality of their offering.

This week on Stacked Data podcast I had the pleasure of taking a deep dive into the world of data contracts with none other than Andrew Jones, the Principal Engineer at GoCardless and the pioneer behind this transformative concept.

If you haven’t heard of Data Contracts they bring several significant benefits to a data team and their data management practices, including:

Improved Data Quality

Standardisation and Consistency

Scalability

Data Governance

Facilitates Data Monetization

🔍 Episode Highlights:

• Introduction      to Data Contracts: Andrew kicks off our episode with a basic      understanding of what data contracts are and how they revolutionise GoCardless      approach to data management.
• Why      Data Contracts?: Discover the crucial role data contracts play in      today's complex data landscape and why Andrew felt compelled to develop      them.
• Benefits      Unveiled: Learn about the substantial advantages data contracts bring      to data governance, integrity, and management practices.
• Real-World      Impact: Andrew shares compelling examples from his experiences at      GoCardless, illustrating the significant improvements data contracts have      facilitated.
• Addressing      Common Data Challenges: Find out how data contracts can solve      prevalent issues like data inconsistency and quality concerns.
• Implementation      Insights: Gain practical strategies and tips for integrating data      contracts into your data management processes, alongside the best tools      and cultural shifts required for success.
• Overcoming      Obstacles: Hear about the common challenges organizations face when      adopting data contracts and the solutions to overcome them.
• Future      Perspectives: What does the future hold for data contracts? Andrew      shares his vision for adapting to upcoming data management trends and      opportunities.
• Final      Thoughts: Before we wrap up, Andrew imparts some invaluable advice for      anyone considering data contracts as part of their data strategy.

Andrew Book: Driving Data Quality with Data Contracts: A comprehensive guide to building reliable, trusted, and effective data platforms: Amazon.co.uk: Jones, Andrew: 9781837635009: Books

AI and LLM’s have soared into the mainstream, but did you know @Cleo has been pioneering in this space for years?

Join me on @stacked as I dive into conversation with Andrea, the Lead Analytics Engineer at Cleo. He unveils his fascinating journey from Data Science to Analytics Engineering, a transition many can resonate with. Discover how Analytics Engineering is transforming data reliability, accuracy, and velocity to drive invaluable insights for Celo.

🎙️ Highlights Include:

1) The DS to AE Journey: Spending 70% of his time transforming data, Andrea noticed a lack of clear consistency in data quality—a common post-DS hype challenge.

2) Rise of AE and Data Modeling: In a world with more complex data, more analysts, and a growing push to be "data-driven," Analytics Engineers are recognized for their crucial role in creating high-quality, trustworthy modeled data.

3) Justifying AE Team Costs: Cleo treats Analytics Engineering like a standard Product quad, aligning OKRs with stakeholders' needs and measuring success metrics.

4) Automated PR Reviews: Cleo's team ensures efficiency and data quality by implementing an automated PR review system in dbt, scoring queries out of 100% and providing valuable recommendations.

Andrea shares even more insights in the episode! If you're curious about what Analytics Engineering truly entails, the challenges it faces, and where the sector is heading, this episode is a must-listen.

In our latest episode of The Stacked Data podcast, we're thrilled to have Thomas, CEO and co-founder of Tasman Analytics, sharing insights on "Maximizing ROI in Data & Analytics."

🔍 Episode Highlights:

• Uncover      the common reasons why data teams are often perceived as cost centers.
• Learn      strategies to transform your data team into a value generator.
• Dive      into the keys to driving ROI for your analytics team.
• Discover      where teams typically go wrong in maximizing the value of their data.
• Gain      expert advice on crafting a clear strategy for a high ROI.

🎙️ Introduction: Get to know Thomas and Tasman, exploring their background and journey in the dynamic world of data.

💼 Data Team Transformation: Explore the challenges of data teams being seen as cost centers and learn how to assess if your team is a cost centre or value adder.

📈 Driving ROI:Thomas shares valuable insights on evaluating, strategizing, and ensuring your team drives a positive ROI.

🚀 Workload Management:Learn effective ways to manage the immense workload on data teams and optimize team efficiency.

🌟 Avoiding Common Pitfalls: Discover the biggest mistakes data teams make and get expert advice on resolving them.

In the digital era, where data is the new gold, understanding how to unlock its full potential is crucial.

For data to be truly valuable it needs to be actionable, across the industry we are seeing a change in mindset to see “Data as a Product”

That's precisely what we dive into in our latest podcast episode featuring the brilliant @Mohit, Director of Data Platforms at @Collison. With a vast experience in leading transformative data projects, Mohit shares his invaluable insights on why and how businesses need to shift their mindset to view "data as a product."

🔍 In This Episode, You'll Discover:

• The      Evolution of Data Management: How companies are revolutionizing their approach to data through cloud infrastructure and modern tools.
• Mohit's      Journey: An overview of Mohit's impressive career and his pivotal role at Collinson, driving significant data transformation.
• The      Core of Innovation: Understanding Mohit's mission for data at      Collinson and how it aligns with the broader vision of treating data as a cornerstone for value creation.
• Decoding      'Data as a Product': A deep dive into what this concept means and why it's becoming increasingly important in today's data-centric world.
• The      Catalyst for Change: Insights into the mindset shift towards seeing data as a product and the intrinsic value this perception brings.
• Strategies for Implementation: Practical examples and strategies from Mohit's playbook on fostering this mindset change within organizations.
• Fostering      Collaboration: Tactics for encouraging cross-functional collaboration      to maximize data's value across the business spectrum. 
• Overcoming      Challenges: An honest look at the hurdles in treating data as a product and strategies to navigate these challenges effectively.
• Measuring      Success: How to quantify this transformative change and the key performance indicators (KPIs) that signal progress and success.

This episode is a must-listen for anyone keen on unlocking the true value of data in their organization and pioneering a culture that treats data not just as a resource, but as a product with its own lifecycle, value proposition, and customer base.

🔍 𝐖𝐢𝐭𝐡 𝐭𝐡𝐞 𝐫𝐢𝐬𝐞 𝐨𝐟 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐢𝐧 𝐭𝐡𝐞 𝐥𝐚𝐬𝐭 𝐲𝐞𝐚𝐫, 𝐀𝐈/𝐌𝐋 𝐡𝐚𝐬 𝐛𝐞𝐞𝐧 𝐩𝐮𝐭 𝐨𝐧 𝐚 𝐩𝐞𝐝𝐞𝐬𝐭𝐚𝐥 𝐚𝐬 𝐭𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐦𝐮𝐬𝐭-𝐡𝐚𝐯𝐞 𝐟𝐨𝐫 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬, 𝐛𝐮𝐭 𝐜𝐨𝐮𝐥𝐝 𝐭𝐡𝐢𝐬 𝐚𝐬𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧 𝐛𝐞 𝐝𝐨𝐢𝐧𝐠 𝐦𝐨𝐫𝐞 𝐡𝐚𝐫𝐦 𝐭𝐡𝐚𝐧 𝐠𝐨𝐨𝐝?

🎧 In this episode of The Stacked Data Podcast, we dive deep into understanding the strategies for implementing ML/DS effectively and avoiding common pitfalls that lead to a lack of ROI and missed opportunities.

👥 Join me as I chat with Irina Ioana Brudaru, the Head of Data from Element Insurance AG. Irina has had an incredible career, including a stint at Google; her passion for this subject is infectious. Tune in to uncover insights into:

➡️ What is ML & AI, and how are they linked/differentiated?

➡️ How can a business determine the right time to incorporate machine learning and data science into its operations?

➡️ Signs indicating a company is ready to bring in ML expertise.

➡️ Strategies for building a team and infrastructure ready to utilise ML/DS projects.

➡️ Common pitfalls when launching DS/ML capabilities and their consequences.

➡️ We touch on some of the latest tools that are breaking down the barriers to advanced ML like Infer

➡️ Balancing immediate gains with a long-term vision in developing ML products.

🎙️ Tune in and I’d love you to give us a FOLLOW to hear all the great conversation we have coming up!

We’re back, and we’re kicking off with a BANG! 💥

Monzo is home to one of the most respected data teams in the UK—true trailblazers in Analytics Engineering, regularly sharing insights into how they scale data. This episode is no different!

I had the pleasure of sitting down with John Azzopardi, Senior Analytics Engineering Manager at Monzo, who’s been with the company for over 6 years. We dive deep into how Monzo’s data team scaled from 20 to over 170 people and how Analytics Engineering plays a crucial role in supporting their hyper-growth.

In this episode, we cover:
✅The evolution of the Analytics Engineering function at Monzo
✅How the team is structured for success and their key responsibilities
✅Challenges of scaling Monzo’s data warehouse and infrastructure
✅Why incremental modeling is a cornerstone of their data strategy
✅The future of Analytics Engineering in fintech and what’s next for Monzo

If you’re looking to learn from one of the UK’s top data teams, this episode is a must-listen! 🎧

✨ Hear firsthand how Monzo is mastering data at scale and driving innovation in their infrastructure.

We’re dropping new episodes every other week, so make sure to FOLLOW & SHARE to stay in the loop!

#Podcast #Fintech #AnalyticsEngineering #DataEngineering #Monzo #Data #Innovation #DataInfrastructure #Scaling #IncrementalModeling #TechLeadership #ModernData