Data Matas

Matatika
Data Matas

A show to explore all data matters. From small to big, every company on the market, irrespective of their industry, is a data merchant. How they choose to keep, interrogate and understand their data is now mission critical. 30 years of spaghetti-tech, data tech debt, or rapid growth challenges are the reality in most companies. Join Aaron Phethean, veteran intrapreneur-come-entrepreneur with hundreds of lived examples of wins and losses in the data space, as he embarques on a journey of discovering what matters most in data nowadays by speaking to other technologists and business leaders who tackle their own data challenges every day. Learn from their mistakes and be inspired by their stories of how they've made their data make sense and work for them. This podcast brought to you by Matatika - "Unlock the Insights in your Data"

에피소드

  1. 2월 13일

    Season 1 Highlights: 7 Data Strategies That Work - What the Best Data Teams Do Differently

    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

    28분

  2. 2024. 12. 06.

    S1E7 - Unlocking Gen.AI Potential in Financial Services With Murtaza Kanchwala

    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

    41분

  3. 2024. 11. 01.

    S1E6 - Navigating the Future of Transport with Real-Time Data With Nick Bromley

    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

    21분

  4. 2024. 10. 18.

    S1E5 - Data quality is a company problem, not just a data problem with Adam Dathi

    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 AnalysisUnlocking the Power of Data in BusinessSound 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 Marketing04:43 The Role of Data in Strategic Decision-Making07:34 MVF: An Integrated Media and Marketing Company10:45 Challenges in Data Technology and Team Dynamics13:52 Data Quality vs. Company Culture16:49 The Importance of Data Governance19:55 AI's Role in Data Analysis and Decision-Making22:39 The Future of Data and AI in Business25:51 Conclusion and Reflections on Data's Impact49:59 Wrapup49:59 Final Thoughts and Reflections

    51분

  5. 2024. 10. 01.

    S1E4 -Full stack data people with Bethany Lyons

    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

    32분

  6. 2024. 09. 17.

    S1E3 - Building trusted analytics at Potloc with Stéphane Burwash

    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 00:00Introduction and Background of Potloc 04:43Role of a Data Engineer at Potluck 06:34Data Sources and Technologies Used 09:58Balancing Complexity and Impactful Work 15:30Working with BI Analysts and Data Modeling 23:46Focus on Data Quality and Maintenance 25:42Challenges of Data Quality and Data Integrity 36:12The Importance of Stakeholder Engagement 41:14The Concept of Self-Serve Analytics 43:25The Value of a Holistic Understanding of Data 47:14The Role of Data Practitioners 48:15Introduction 49:24The Value of Online Communities and Asking Questions 50:22Overcoming the Fear of Feeling Lost 50:48The Generosity of the Data Community 52:10Networking and Learning at Meetup Events 53:21Building Connections and Getting Insights

    55분

  7. 2024. 09. 03.

    S1E2 - How CitySprint Deliver, Not Only Parcels, but Data and BI - A conversation with Joe Wright

    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 CitySprintImproving Data Quality and Reporting at CitySprint Exploring the Potential of AI in BusinessThe Importance of Adaptability in the Data FieldSound 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 00:00Introduction to CitySprint 03:14Data and Analytics at CitySprint 05:03Modernizing Management Systems 16:29Exploring AI at CitySprint 32:36The Importance of Data Quality and Trust 34:11Innovative Reporting and Test-Driven Development for Data Quality 35:36Shifting Mindset and Processes for Data Quality 38:56Building Relationships with Stakeholders in Data Management 43:16The Role of People Management in Data Management 44:07Designing KPIs: Balancing Behavior and Culture

    42분

  8. 2024. 08. 20.

    S1E1 - Data engineering and Not on the High Street with Jessica Franks

    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 CountryPrioritizing Infrastructure and Data QualitySound 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 00:00Introduction and Setting the Scene 02:13Navigating a New Role and Team 11:06Visualizing Data Architecture with Wardley Maps 19:53Prioritizing Infrastructure and Data Quality 28:11Challenges of AI and ML in Data Initiatives 32:05Conclusion and Key Takeaways

    35분
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소개

A show to explore all data matters. From small to big, every company on the market, irrespective of their industry, is a data merchant. How they choose to keep, interrogate and understand their data is now mission critical. 30 years of spaghetti-tech, data tech debt, or rapid growth challenges are the reality in most companies. Join Aaron Phethean, veteran intrapreneur-come-entrepreneur with hundreds of lived examples of wins and losses in the data space, as he embarques on a journey of discovering what matters most in data nowadays by speaking to other technologists and business leaders who tackle their own data challenges every day. Learn from their mistakes and be inspired by their stories of how they've made their data make sense and work for them. This podcast brought to you by Matatika - "Unlock the Insights in your Data"

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  • 제작진
    Matatika
  • 방송 연도
    2024년 - 2025년
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    8
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    © 2025 Matatika

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