Episode web page: https://tinyurl.com/2b3dz2z8 ----------------------- Rate Insights Unlocked and write a review If you appreciate Insights Unlocked , please give it a rating and a review. Visit Apple Podcasts, pull up the Insights Unlocked show page and scroll to the bottom of the screen. Below the trailers, you'll find Ratings and Reviews. Click on a star rating. Scroll down past the highlighted review and click on "Write a Review." You'll make my day. ----------------------- In this episode of Insights Unlocked , we explore the evolving landscape of omnichannel strategies with Kate MacCabe, founder of Flywheel Strategy. With nearly two decades of experience in digital strategy and product management, Kate shares her insights on bridging internal silos, leveraging customer insights, and designing omnichannel experiences that truly resonate. From the early days of DTC growth to today’s complex, multi-touchpoint customer journeys, Kate explains why omnichannel is no longer optional—it’s essential. She highlights a standout example from Anthropologie, demonstrating how brands can create a unified customer experience across digital and physical spaces. Whether you’re a marketing leader, UX strategist, or product manager, this episode is packed with actionable advice on aligning teams, integrating user feedback, and building a future-proof omnichannel strategy. Key Takeaways: ✅ Omnichannel vs. Multichannel: Many brands think they’re omnichannel, but they’re really just multichannel. Kate breaks down the difference and how to shift toward true integration. ✅ Anthropologie’s Success Story: Learn how this brand seamlessly blended physical and digital experiences to create a memorable, data-driven customer journey. ✅ User Feedback is the Secret Weapon: Discover how continuous user testing—before, during, and after a launch—helps brands fine-tune their strategies and avoid costly mistakes. ✅ Aligning Teams for Success: Cross-functional collaboration is critical. Kate shares tips on breaking down silos between marketing, product, and development teams. ✅ Emerging Tech & Omnichannel: Instead of chasing the latest tech trends, Kate advises businesses to define their strategic goals first—then leverage AI, AR, and other innovations to enhance the customer experience. Quotes from the Episode: 💬 "Omnichannel isn’t just about being everywhere; it’s about creating seamless bridges between every touchpoint a customer interacts with." – Kate MacCabe 💬 "Companies that truly listen to their users—through qualitative and quantitative insights—are the ones that thrive in today’s competitive landscape." – Kate MacCabe Resources & Links: 🔗 Learn more about Flywheel Strategy 🔗 Connect with Kate MacCabe on LinkedIn 🔗 Explore UserTesting for customer insights for marketers…
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محتوای ارائه شده توسط Aaron Phethean. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Aaron Phethean یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
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S1E3 - Building trusted analytics at Potloc with Stéphane Burwash
Manage episode 440416438 series 3594170
محتوای ارائه شده توسط Aaron Phethean. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Aaron Phethean یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
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.
Takeaways
- Stéphane started his data engineering journey alone, relying on community support.
- Building a community is crucial for learning and growth in data engineering.
- Potloc evolved from a market insights company to a data-driven organization.
- Navigating data engineering challenges requires asking questions and seeking help.
- Stakeholder management is essential for successful data projects.
- Technologies like Meltano and DBT are integral to Potloc's data stack.
- AI is being leveraged to improve data quality and analytics processes.
- Self-service analytics can empower users but requires careful governance.
- Data quality issues often arise from a lack of awareness and communication.
- The role of a data practitioner is to maintain a big picture perspective.
Sound Bites
- "Ask questions, don't be afraid to learn."
- "Everybody has been in that position."
- "We shouldn't be trying to do the custom solution."
Chapters
00:00
Introduction and Background of Potloc
04:43
Role of a Data Engineer at Potluck
06:34
Data Sources and Technologies Used
09:58
Balancing Complexity and Impactful Work
15:30
Working with BI Analysts and Data Modeling
23:46
Focus on Data Quality and Maintenance
25:42
Challenges of Data Quality and Data Integrity
36:12
The Importance of Stakeholder Engagement
41:14
The Concept of Self-Serve Analytics
43:25
The Value of a Holistic Understanding of Data
47:14
The Role of Data Practitioners
48:15
Introduction
49:24
The Value of Online Communities and Asking Questions
50:22
Overcoming the Fear of Feeling Lost
50:48
The Generosity of the Data Community
52:10
Networking and Learning at Meetup Events
53:21
Building Connections and Getting Insights
11 قسمت
اشتراک گذاریManage episode 440416438 series 3594170
محتوای ارائه شده توسط Aaron Phethean. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Aaron Phethean یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
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.
Takeaways
- Stéphane started his data engineering journey alone, relying on community support.
- Building a community is crucial for learning and growth in data engineering.
- Potloc evolved from a market insights company to a data-driven organization.
- Navigating data engineering challenges requires asking questions and seeking help.
- Stakeholder management is essential for successful data projects.
- Technologies like Meltano and DBT are integral to Potloc's data stack.
- AI is being leveraged to improve data quality and analytics processes.
- Self-service analytics can empower users but requires careful governance.
- Data quality issues often arise from a lack of awareness and communication.
- The role of a data practitioner is to maintain a big picture perspective.
Sound Bites
- "Ask questions, don't be afraid to learn."
- "Everybody has been in that position."
- "We shouldn't be trying to do the custom solution."
Chapters
00:00
Introduction and Background of Potloc
04:43
Role of a Data Engineer at Potluck
06:34
Data Sources and Technologies Used
09:58
Balancing Complexity and Impactful Work
15:30
Working with BI Analysts and Data Modeling
23:46
Focus on Data Quality and Maintenance
25:42
Challenges of Data Quality and Data Integrity
36:12
The Importance of Stakeholder Engagement
41:14
The Concept of Self-Serve Analytics
43:25
The Value of a Holistic Understanding of Data
47:14
The Role of Data Practitioners
48:15
Introduction
49:24
The Value of Online Communities and Asking Questions
50:22
Overcoming the Fear of Feeling Lost
50:48
The Generosity of the Data Community
52:10
Networking and Learning at Meetup Events
53:21
Building Connections and Getting Insights
11 قسمت
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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/…
What Crypto Data Teams Do Differently with Emily Loh Episode Snapshot In this illuminating conversation, Emily Loh reveals how leading a data team in the volatile crypto space has forced innovations in resource allocation, strategic prioritisation, and AI implementation that data leaders across all industries can apply for greater impact. Guest Introduction Emily Loh serves as Director of Data at MoonPay, where she leads a 15-person team spanning data engineering, data science, and machine learning in a fast-evolving crypto environment. With previous experience at Coinbase and a surprising background in literature rather than computer science, Emily brings a unique storytelling perspective to data leadership that emphasises business outcomes over technical outputs. Conversation Highlights The discussion takes an unexpected turn when Emily reveals her humanities background, noting that her literature studies prepared her surprisingly well for data leadership: "I never thought I'd be in data, but specifically I studied literature. And in my career right now, I'm just like, 'oh, this is just really just storytelling.'" This insight evolves into a thoughtful exchange about how effective data work requires understanding human needs first, with technical implementation second. A particularly candid moment occurs when Emily confesses, "Full disclosure, I am a formed people pleaser," sparking an authentic discussion about the struggle many data leaders face in saying "no" to low-value requests. Aaron and Emily explore how this seemingly simple act requires both courage and strategic frameworks to implement effectively. Actionable Insights 1. Implement the 20/40/40 resource allocation framework - Emily's team divides their time into 20% BAU (business as usual), 40% building, and 40% research, creating space for innovation even during challenging market periods. Practical Application : Start by auditing your team's current time allocation, then gradually shift toward this balanced model using clear opportunity sizing frameworks to evaluate potential projects. 2.Break free from the "service trap" by transforming request handling - Instead of immediately building requested dashboards, train your team to ask "What decisions are you trying to make?" to focus on outcomes rather than outputs. Practical Application : Develop a structured intake process that guides stakeholders toward better request formulation and establishes clear value criteria for accepting work. 3. Use AI strategically to eliminate team drudgery - At MoonPay, tools like Cursor help automate tedious tasks such as YAML file management, freeing analyst time for strategic work. Practical Application : Survey team members about their most tedious regular activities, then select appropriate AI tools that can automate these specific tasks while measuring success through time savings. 4. Design data systems for uncertain futures - In crypto's rapidly changing landscape, Emily's team builds flexible architectures that can adapt to regulatory shifts and market changes. Practical Application : Implement modular data models that can evolve without complete rebuilds while maintaining strong data quality foundations that support agility regardless of specific technologies. Industry Context This conversation arrives at a critical inflection point for data leaders across industries, as generative AI promises transformation while many teams still struggle with the fundamental challenge of delivering strategic value rather than reactive reporting. Emily's experience navigating crypto's extreme volatility provides a stress-tested framework applicable to any data team facing resource constraints and rapid change. Why Listen This episode is essential for mid to senior-level data leaders who feel trapped in reactive work cycles and are seeking practical frameworks to increase their strategic impact. Whether you're in a traditional enterprise struggling with legacy approaches or a high-growth startup trying to balance immediate demands with future needs, Emily's battle-tested insights provide immediately applicable strategies for transformation. Episode Details Length: 37 minutes Release Date: March 2025 Episode Number: #127 This conversation with Emily Loh offers rare insight into how crypto's extreme conditions have forced innovation in data team management—innovations that can give any data leader a competitive advantage in today's rapidly evolving landscape.…
Are you unknowingly overspending on cloud data infrastructure? Many businesses migrate to the cloud expecting cost savings and efficiency, but hidden costs, vendor lock-in, and inefficient ETL processes often result in ballooning expenses. Without a strategic approach, organisations risk wasting budget on unnecessary compute, storage, and manual data management. What You’ll Learn in This Episode In this episode of Data Matas, host Aaron Phethean speaks with Jon Hammant, Head of Compute for UK & Ireland at AWS, about the true cost of scaling data infrastructure and how businesses can optimise cloud spend. Jon shares his insights on avoiding pricing traps, reducing data migration costs, and leveraging AI-driven automation to improve efficiency. Key Insights & Timestamps 1. - Reducing unnecessary data syncing to cut costs Real-time syncing is often overused, leading to excessive compute costs. Discover how batch processing can reduce ETL expenses by up to 50%. 2. - Conducting cloud audits to eliminate waste Many organisations pay for idle compute and unused storage without realising it. Learn how to audit cloud usage and remove unnecessary expenses. 3. - Avoiding vendor lock-in and costly renewal contracts Row-based ETL pricing can trap businesses into increasing costs as data volumes grow. Find out how switching to usage-based pricing can provide more control over cloud spend. 4. - Automating data management with AI Manual ETL processes drain resources and increase operational costs. Learn how AI-driven automation can streamline workflows, reduce errors, and improve efficiency. About Jon Hammant Jon Hammant is the Head of Compute for UK & Ireland at AWS, where he helps businesses optimise cloud infrastructure, modernise data strategies, and reduce operational costs. With extensive experience in AI, cloud computing, and high-performance networking, Jon has worked with some of the world’s largest enterprises to scale data infrastructure efficiently without unnecessary spend. Subscribe & Join the Conversation If you're looking to optimise cloud spend, reduce migration costs, or navigate vendor pricing models, this episode is for you. 🔔 Subscribe to Data Matas to get the latest insights on cloud cost optimisation, data infrastructure, and AI-driven efficiency. Resources & Links 📌 Jon Hammant on LinkedIn: https://www.linkedin.com/in/jhammant/ 📌 Matatika Podcast & Resources: https://www.matatika.com/podcasts/s2e1-scaling-your-data-infrastructure-with-jon-hammant…
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. Key Takeaways: ✅ 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. 🎧 Listen now and take your data strategy to the next level and 📩 Subscribe for more insights…
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. Takeaways Murtaza has been involved in financial services since 2014. Gen.AI applications started with content generation in 2021. Feedback from users was gathered through personal discussions. AI hallucinations posed significant challenges in early implementations. Internal AI solutions were prioritized before customer-facing applications. Testing AI requires a different approach than traditional software. The maturity of Gen.AI use cases is improving over time. Amplifi Capital is building an AI Matrix platform for Gen.AI use cases. Choosing the right LLM is crucial for specific use cases. Regulatory compliance is essential in financial services AI applications. Sound Bites "AI is delivering something artificially." "The landscape is moving faster than we think." Chapters 00:00 Introduction to Gen.AI in Financial Services 06:27 Early Applications of Gen.AI in Finance 11:07 Maturity of Gen.AI Use Cases 16:51 Building the AI Matrix Platform 22:48 Regulatory Landscape for AI in Finance 29:37 Building Effective Squads in AI Projects 35:18 Future of AI in Financial Services…
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. Takeaways Good transport planning requires both short-term and long-term data analysis. Buses provide the most flexible capacity in public transport systems. Real-time data is crucial for understanding current transport demands. Historical data often fails to reflect current population movements. Mobile phone data can significantly enhance transport planning accuracy. Data collection methods must evolve to include modern technology. AI and big data can process vast amounts of transport data effectively. Privacy concerns must be addressed when using personal data for transport planning. Transparency in data usage is essential for public trust. The future of transport data relies on secure and anonymized data sharing. Sound Bites "What does good look like for transport data?" "It's ludicrous to use 1920s data." Chapters 00:00 Introduction to Innovation in Transport Data 03:28 The Importance of Buses in Urban Mobility 04:06 Understanding Transport Data Needs 06:54 The Role of Mobile Phone Data in Transport Planning 09:35 Challenges and Innovations in Data Collection 12:29 The Future of Data Privacy and Public Good 15:21 AI and Big Data in Transport Decision Making 18:15 Conclusions and Future Directions for Transport Data…
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. Takeaways Data quality is a company problem, not just a data problem. The sophistication of data teams depends on company size. Data governance is a hidden long-term investment. AI can't generate ideas without human direction. Data maturity impacts decision-making effectiveness. Data teams need to interface better with the rest of the company. The value of data is tied to decision-making outcomes. Standardizing terms can improve data governance. Data quality issues often stem from company culture. Investing in data governance can yield hidden benefits. Titles AI and the Future of Data Analysis Unlocking the Power of Data in Business Sound Bites "Data quality is a company problem, not just a data problem." "The sophistication of data teams depends on company size." "Data governance is a hidden long-term investment." Chapters 00:00 Introduction to Data in Marketing 04:43 The Role of Data in Strategic Decision-Making 07:34 MVF: An Integrated Media and Marketing Company 10:45 Challenges in Data Technology and Team Dynamics 13:52 Data Quality vs. Company Culture 16:49 The Importance of Data Governance 19:55 AI's Role in Data Analysis and Decision-Making 22:39 The Future of Data and AI in Business 25:51 Conclusion and Reflections on Data's Impact 49:59 Wrapup 49:59 Final Thoughts and Reflections…
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. Takeaways Be a full stack data person to have a bigger impact. Data is a digital twin of real-world processes. Understanding data representation is crucial for business insights. 99% of data work involves plumbing, not just visualization. Trust in data is essential for effective decision-making. Reconciliation of data is a complex and painful process. Startups must solve specific problems for individual users. Data analytics is not an assembly line; it's iterative. The future of data solutions lies in addressing unsolved problems. Navigating the startup landscape requires understanding customer needs. Titles Data: An Unsolved Problem Innovating in the Data Space Sound Bites "Be a full stack data person." "Data is just a digital twin of the process." "How do we enrich the data?" Chapters 00:00 Introduction 00:34 Introduction and Background 03:31 Sales Dynamics in Startups 06:25 The Importance of Trust in Data 09:39 Challenges in Reconciliation 12:32 Navigating Startup Challenges 14:44 Understanding Data Challenges in Organizations 17:46 The Importance of Data Representation 20:37 Navigating Data Complexity in Business 23:39 The Role of Data Teams in Organizations 26:46 Shadow IT and Data Solutions 30:04 Broadening the Data Skillset 31:03 The Concept of Full Stack Data Professionals…
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. Takeaways Stéphane started his data engineering journey alone, relying on community support. Building a community is crucial for learning and growth in data engineering. Potloc evolved from a market insights company to a data-driven organization. Navigating data engineering challenges requires asking questions and seeking help. Stakeholder management is essential for successful data projects. Technologies like Meltano and DBT are integral to Potloc's data stack. AI is being leveraged to improve data quality and analytics processes. Self-service analytics can empower users but requires careful governance. Data quality issues often arise from a lack of awareness and communication. The role of a data practitioner is to maintain a big picture perspective. Sound Bites "Ask questions, don't be afraid to learn." "Everybody has been in that position." "We shouldn't be trying to do the custom solution." Chapters 00:00 Introduction and Background of Potloc 04:43 Role of a Data Engineer at Potluck 06:34 Data Sources and Technologies Used 09:58 Balancing Complexity and Impactful Work 15:30 Working with BI Analysts and Data Modeling 23:46 Focus on Data Quality and Maintenance 25:42 Challenges of Data Quality and Data Integrity 36:12 The Importance of Stakeholder Engagement 41:14 The Concept of Self-Serve Analytics 43:25 The Value of a Holistic Understanding of Data 47:14 The Role of Data Practitioners 48:15 Introduction 49:24 The Value of Online Communities and Asking Questions 50:22 Overcoming the Fear of Feeling Lost 50:48 The Generosity of the Data Community 52:10 Networking and Learning at Meetup Events 53:21 Building Connections and Getting Insights…
1 S1E2 - How CitySprint Deliver, Not Only Parcels, but Data and BI - A conversation with Joe Wright 41:51
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 emphasizes the need for data managers to straddle the technical and business perspectives and build strong stakeholder relationships. Takeaways CitySprint is a leading same-day courier provider in the UK, with a strong presence in London. The company is focused on improving data quality and reporting by modernizing their infrastructure and implementing a new data management system. The BI team at CitySprint plays a crucial role in analyzing data and providing performance stats to different teams within the company. Managing the people side of the project and helping the team adapt to the new ways of working is a key challenge. CitySprint had a legacy technology stack that included BI visualization tools and multiple ETL tools before transitioning to Snowflake and Power BI. AI is a buzzword in the business world, and CitySprint is exploring its potential in areas such as customer sentiment analysis. Embracing change is crucial for success in the data field, and building strong stakeholder relationships is essential for effective communication and collaboration. Data managers need to straddle the technical and business perspectives to bridge the gap between technical experts and business managers. The ability to adapt and embrace change is key in a rapidly evolving technological landscape. Titles The Role of the BI Team at CitySprint Improving Data Quality and Reporting at CitySprint Exploring the Potential of AI in Business The Importance of Adaptability in the Data Field Sound Bites "CitySprint has a fleet of couriers who use everything from large vans to bikes to quickly deliver parcels all across the UK." "The measure of success is moving away from investigating challenges and issues within the data." "CitySprint has a fleet of couriers who use bikes to quickly deliver parcels in London." "What are the kinds of technologies that CitySprint had as legacy?" "We had to rewrite the whole process for getting the data out of the business systems into Snowflake" "AI is buzzword everywhere, isn't it?" Chapters 00:00 Introduction to CitySprint 03:14 Data and Analytics at CitySprint 05:03 Modernizing Management Systems 16:29 Exploring AI at CitySprint 32:36 The Importance of Data Quality and Trust 34:11 Innovative Reporting and Test-Driven Development for Data Quality 35:36 Shifting Mindset and Processes for Data Quality 38:56 Building Relationships with Stakeholders in Data Management 43:16 The Role of People Management in Data Management 44:07 Designing KPIs: Balancing Behavior and Culture…
Jessica Franks shares her experience of joining Not on the High Street as an engineering manager for the data team. She discusses the challenges of starting a new role in a new country and managing a team with diverse skill sets. Jessica explains how she tackled the lack of data strategy and created a visual representation of the data architecture using a Wardley map. She emphasizes the importance of simplifying infrastructure and improving data quality before diving into AI and ML projects. Jessica also highlights the need for clear communication and collaboration with stakeholders to ensure successful data initiatives. Takeaways Starting a new role in a new country can be overwhelming, but with the right experience and skills, it can be managed effectively. Creating a visual representation of the data architecture, such as a Wardley map, can help communicate the complexity and prioritize projects. Simplifying infrastructure and improving data quality are crucial before diving into AI and ML projects. Clear communication and collaboration with stakeholders are essential for successful data initiatives. Titles Navigating a New Role in a New Country Prioritizing Infrastructure and Data Quality Sound Bites "Everything new sounds like you set out on an adventure and you got what you asked for. Was it scary though? Was that like, oh my god, what am I going to do?" "Having a picture that everyone agrees this is what it is. You know, there's all too often like 10 little things in the corner that nobody really understands that that's crucial to the way things operate." "Using a Wardley map was new to me and I instantly loved it. The way it laid it out, the way it communicated to everyone what was visible and important, but then not visible and important was sort of also, you know, that I think as a person running an engineering team is often really hard to explain that this crucial piece that no one can see, we need to do something about." Chapters 00:00 Introduction and Setting the Scene 02:13 Navigating a New Role and Team 11:06 Visualizing Data Architecture with Wardley Maps 19:53 Prioritizing Infrastructure and Data Quality 28:11 Challenges of AI and ML in Data Initiatives 32:05 Conclusion and Key Takeaways…
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