The Data Journey
A weekly podcast offering actionable insights on data architecture, education strategy, and leadership in under ten minutes per episode. Aimed at busy professionals, it provides practical frameworks and strategies to apply quickly. No fluff or filler, just concise advice for smarter decision-making. Also promotes a companion newsletter at The Data Journey website.
Episodes
Episode 98: The Operating Model of an AI-Ready Organisation
The conversation focuses on the concept of an AI-ready organization, emphasizing the importance of ownership, accountability, and the alignment of decision-making with governance. It highlights the need for a strong operational model that integrates AI into the organization's core processes and behaviors, enabling continuous adaptation and value creation.TakeawaysAI readiness requires ownershipOrg
Episode 97: From Use Cases to Value Streams
The conversation explores the shift from use case organization to value stream thinking, emphasizing the importance of integrating AI into larger business processes and workflows. It delves into the challenges of enterprise coordination and the transformational impact of value stream thinking on the operating model of an AI-ready organization.TakeawaysTransition from use case to value stream think
Episode 96: Incentives Drive Behaviour (And Kill Data Strategies)
The conversation delves into the impact of incentives on AI readiness and data strategy implementation. It highlights the conflict between organizational strategy and the reward system, emphasizing the importance of aligning incentives with desired behavior.TakeawaysIncentives drive behaviorAligning incentives with strategy is crucialChapters00:00 The Conflict of Incentives01:15 Incentives and Col
Episode 95: Organisational Readiness vs Technical Readiness
The conversation delves into the distinction between technology readiness and organizational readiness for AI implementation. It highlights the challenges and implications of organizational readiness, emphasizing the role of leadership, governance, and incentives in driving successful AI adoption.TakeawaysOrganizational readiness is a critical factor in the successful implementation of AI.Transfor
Episode 94: Why Most Organisations Can’t Scale AI Beyond Pilots
The conversation delves into the challenges of scaling AI pilots and the distinction between organizational readiness and technical readiness. It emphasizes the need for an operating model and organizational capability to turn AI pilots into repeatable value.TakeawaysAI pilots vs. AI scalingOrganizational readiness vs. technical readinessChapters00:00 The Challenge of Scaling AI Pilots03:03 The Op
Episode 93: The Missing Role: Product Managers for Data & AI
The missing role in many data and AI operating models is the product manager for data and AI. This role connects the problem to the capability, business to technology, strategy to execution, and investment to outcomes. Without product management, data teams become order takers, technology teams become capability factories, and executives become portfolio approvers without ever seeing the outcome o
Episode 92: Funding Models: Why Data & AI Initiatives Stall
The episode explores the impact of funding on data and AI initiatives, highlighting the mismatch between traditional funding models and the ongoing nature of data and AI capabilities. It emphasizes the need to fund strategic data and AI capabilities as persistent products or capabilities, rather than as temporary projects.TakeawaysFunding models for data and AI initiatives should align with the on
Episode 91: Why Governance Slows Teams Down (And How to Fix It)
The episode delves into the role of governance in organizations, highlighting the distinction between decision-making and approval. It explores the problems with traditional governance, challenges with decision-making and escalation, executive involvement, defining acceptable behavior and decision rights, layers of decision-making, characteristics of good governance, and the objective of governanc
Episode 90: The Role of the Data COE (What It Should Actually Do)
The conversation delves into the misunderstood role of the Data COE, highlighting the inherent flaws of undefined excellence and reframing the COE's role as a facilitator of good behavior. It emphasizes the actual definition of excellence, the empowerment of ownership through the COE, and the role of the COE in a federated structure. Additionally, it discusses the balance between control and enabl
Episode 89: Data Ownership Is Still Broken (And Why That Matters)
In this episode, Roland discusses the concept of ownership and its impact on behavior within an organization. He emphasizes the importance of real ownership, accountability, and value-driven ownership. The conversation delves into the challenges of ownership in federated models and the need for clear ownership to enable effective decision-making and reliable systems.TakeawaysOwnership is defined b
Episode 88: Centralised vs Federated: What Actually Works in Practice
The conversation explores the debate between centralized and federated operating models, highlighting the impact of behavior on the success of these models. It emphasizes the need for a mature hybrid operating model that balances consistency and agility, with a focus on clarity and coordination across distributed ownership.TakeawaysCentralized vs. federated operating modelsBehavioral impact on ope
Episode 87: Architecture Is Not an Operating Model
In this episode, Roland Brown discusses the critical distinction between architecture and operating model, emphasizing the importance of aligning these two layers for successful execution of data and AI initiatives. The role of architecture in enterprise transformation, the significance of operating models in data and AI initiatives, and the impact of aligning architecture and operating models are
Episode 86: Why Data & AI Strategies Fail in Execution
The conversation delves into the journey of data products as intentional units of value, the gap between architecture and execution, the role of the operating model in execution, friction in the operating model, the danger of execution failure, and the importance of the operating model in creating value through consistent execution.TakeawaysData products as intentional units of valueExecution is w
Episode 85: From Experimentation to Production AI
The conversation delves into the challenges and considerations of transitioning AI systems to production, emphasising the organisational commitment, alignment, and maturity required for successful operation. It highlights the importance of trust, context, and intelligence in production AI, and the distinction between experimentation and real systems.TakeawaysAI in production is a commitmentProduct
Episode 84: AI Value vs AI Theatre
The conversation explores the concept of AI theater, where visibility masquerades as progress, and the value of AI is measured by sustainable impact rather than impressive demos. It emphasizes the importance of discipline in AI, focusing on trust, context, and intelligence as key factors in building real value.TakeawaysAI TheaterValue of AIDiscipline in AI🎧 Listen to The Data Journey wherever you
Episode 83: When Not to Use AI
The conversation explores the role of AI in architecture, emphasising the importance of architectural decision-making, complexity, clarity, data patterns, AI in policy-driven environments, risk and consequences of AI, ownership and governance of AI, and restraint in AI implementation.TakeawaysArchitectural decision-making is crucial in determining the necessity of AI implementation.Restraint in AI
Episode 82: Explainable AI Starts With Architecture
The episode introduces the concept of explainability and its importance in AI systems. It emphasizes that explainability is not an AI feature but an architectural outcome, and it's about being able to retrace intent. The conversation sets the stage for a deep dive into the topic of explainability and its practical implications in the context of customer 360.TakeawaysExplainability is not an AI fea
Episode 81: Governing AI Like a Product
The conversation explores the failure of AI governance, the need to move governance closer to where decisions are made, and the shift to product-centric governance. It also discusses the importance of specificity and context in governance, grounding governance in architecture, and enabling speed and scalability through product-based governance.TakeawaysAI governance fails due to a product problem,
Episode 80: Human-in-the-loop Design
The episode explores the concept of responsible AI and the role of humans in AI systems. It discusses the seduction of automation, the danger of full automation, effective human in the loop design, and common anti-patterns in AI systems. The importance of context, trust, and governance in AI systems is emphasized, highlighting the need for operational governance of AI as a product.TakeawaysHuman i
Episode 79: Observability for AI Systems
In this episode, Roland introduces the Data Journey and discusses the importance of observability in AI systems. He explains the significance of observability in detecting gradual failures in AI systems and emphasizes the need for observability in data, model, and decision behavior. Roland also highlights the importance of ownership and response in observability and its role in supporting the AI r
Episode 78: Data Quality for Machine Learning (Different Rules)
The podcast episode explores the critical importance of data quality in the context of AI and machine learning. It delves into the nuances of data quality for training and inference, the impact of contextual quality, and the need for continuous quality observation and observability in AI systems.TakeawaysData quality for machine learning follows different rules than traditional data quality framew
Episode 77: Metadata and Lineage for AI Explainability
In this episode, Roland Brown discusses the importance of explainability in AI systems, emphasizing that it begins in the architecture. He highlights the significance of metadata as the architecture of meaning and lineage as a key factor in establishing trust and responsibility in AI systems.TakeawaysExplainability begins in the architectureMetadata is the architecture of meaningLineage is about r
Episode 76: Training Data vs Inference Data
The podcast episode explores the distinction between training data and inference data, highlighting the architectural discipline required for each type of data. It emphasises the challenges and root causes of architectural issues, and introduces the three-layer model for trust and the importance of metadata and lineage for AI systems.TakeawaysTraining data and inference data require different arch
Episode 75: The Three Layers of AI-Ready Architecture: Trust, Context, Intelligence
The podcast episode explores the concept of AI-readiness and the architecture required for successful AI implementation. It emphasises the importance of trust, context, and intelligence in building a robust AI-ready architecture.TakeawaysAI-readiness is about building a disciplined architecture from the ground up, with trust, context, and intelligence as the foundational layers.Trust, context, and
Episode 74: AI doesn’t fail - data does
When AI initiatives fail, the model is usually blamed.But that explanation is structurally wrong.In this episode, Roland reframes AI failure as a data architecture accountability problem, not a mathematical one. AI doesn’t invent new issues, it faithfully exposes the decisions, trade-offs, and ambiguities that already exist upstream.This conversation moves the focus from model performance to:meani
Episode 73: Why AI Exposes Weak Data Foundations
Most organisations believe they’re starting their AI journey by choosing tools, models, or use cases.In reality, they’re starting a foundation test they’ve been postponing for years.In this series transition episode, Roland Brown connects everything explored from Episode 1 through Episode 70 to a single, uncomfortable truth: AI does not fix data it exposes it.AI removes the human buffer that allo
Episode 72: From Project Delivery to Product Thinking
Most data initiatives don’t fail because they were badly executed. They fail because success was defined as delivery instead of value.In this episode, Roland Brown brings the entire data products series together by tackling the foundational shift that determines whether everything discussed in Episodes 64 through 71 actually sticks: moving from project delivery to product thinking.Roland explains
Episode 71: Customer 360 as a Data Product: An End-to-End Example
Almost every organisation claims to have a Customer 360.Very few trust it. Even fewer use it consistently to make better decisions.In this episode, Roland Brown takes one of the most familiar and most misunderstood concepts in data and walks through it end-to-end as a true data product. Building on the principles established in Episodes 64 through 70, he shows why Customer 360 initiatives so often
Episode 70: Data Marketplaces and Discovery: Finding what actually matters
Most organisations don’t struggle to find data.They struggle to find data they can trust.In this episode, Roland Brown reframes one of the most hyped topics in modern data architecture, data marketplaces and discovery and explains why discovery is never a tooling problem on its own. Building on the foundations laid in Episodes 64 through 69, he shows why effective discovery is the last mile of tru
Episode 69: Killing Bad Data Products: Sunsetting Properly
Most organisations are very good at building data products.They are far less good at stopping them.In this episode, Roland Brown tackles one of the most uncomfortable yet essential capabilities of mature data organisations: sunsetting data products properly. Building directly on the failure modes discussed in Episode 68, he explains why keeping bad or outdated data products alive quietly damages t
Episode 68: Why most data products fail
Most data products don’t fail because the data is wrong.They fail because the conditions required for trust, accountability, and value were never designed in.In this episode, Roland Brown confronts an uncomfortable reality: despite modern platforms, sophisticated pipelines, and well-intentioned teams, most data products still fail to deliver lasting value. Building directly on Episodes 64 through
Episode 67: Measuring Data Product Success: Reuse, Adoption, and Trust
Most organisations measure their data success by how much they build.Pipelines delivered. Tables published. Dashboards created.And yet, trust still erodes, duplication spreads, and decisions remain slow.In this episode, Roland Brown challenges one of the most entrenched habits in modern data teams: measuring activity instead of value. Building on the foundations laid in Episodes 64, 65, and 66, he
Episode 66:Data contracts in practice (not theory)
Most organisations don’t lose trust in data because of bad intentions or poor tooling.They lose trust because expectations are implicit, undocumented, and constantly shifting.In this episode, Roland Brown takes one of the most talked about and least understood concepts in modern data architecture and brings it firmly down to earth: data contracts. Building on the ownership and accountability found
Episode 65: Ownership Models: Who Is Accountable for Value?
Most organisations don’t struggle with data because they lack platforms, pipelines, or tooling.They struggle because no one is truly accountable for the value their data is supposed to create.In this episode, Roland Brown tackles one of the most misunderstood and quietly destructive aspects of modern data product thinking: ownership. Building directly on Episodes 60 through 64, he explores why so
Episode 64: Designing Data Products for Consumers, Not Producers
Most organisations don’t fail at data because of poor engineering or weak platforms.They fail because they design data products for the people who build them not the people who depend on them.In this episode, Roland Brown tackles one of the quietest but most damaging design mistakes in modern data teams: producer-first data product design. Building on the foundations laid in Episodes 61, 62, and 6
Episode 63: Data Products vs Reports vs Datasets
Most organisations don’t struggle with data because of tooling or platforms.They struggle because they mix up fundamentally different things and then expect them to behave the same.In this episode, Roland Brown tackles one of the most common and damaging category errors in modern data teams: confusing datasets, reports, and data products. Building on the foundations laid in Episodes 61 and 62, he
Episode 62: The Anatomy of a Good Data Product
Most organisations don’t fail at data because of technology.They fail because what they build isn’t designed to be used.In this episode, Roland Brown breaks down the anatomy of a good data product and explains why simply adopting the language of “data products” doesn’t automatically lead to trust, adoption, or value. Building on the foundation set in Episode 61, he moves from definition to design,
Episode 61: What a Data Product Really Is (and What It Isn’t)
Most organisations don’t struggle with collecting or moving data.They struggle with turning data into something people can actually use.In this episode, Roland Brown explores what a data product really is and why so many organisations believe they are building data products when they are actually just producing data assets. He explains how pipelines, tables, and dashboards are often mistaken for p
Episode 60: Why Pipelines Don’t Deliver Value on Their Own
Most organisations don’t struggle with moving data.They struggle with turning data movement into meaningful outcomes.In this episode, Roland Brown explores why pipelines on their own rarely deliver business value — and why so many data teams confuse activity with impact. He explains how pipelines are often treated as the end goal, rather than as an enabling layer in a much larger value chain.Rolan
Episode 59: Data Culture — Turning Curiosity into Capability
Most organisations don’t fail because of a lack of data they fail because data is not trusted, understood, or acted upon.In this episode, Roland Brown explores how data culture is the missing link between modern platforms and real business value. He explains why technology alone cannot drive better decisions, and how curiosity, when paired with capability, transforms data from a passive asset int
Episode 58: Data is the Product: Building AI-Ready Data Pipelines
Most organisations don’t have a data problem — they have a value problem. In this episode, Roland Brown explains how shifting from pipelines to data products turns raw information into trusted, reusable assets that power AI.He explores why data without design creates noise, how AI exposes weak foundations, and why the path to explainable AI begins with architecture, not algorithms. From the data-t
Episode 57: Observability & SLAs — SLOs, Metrics and Reliability Engineering for Data
In this episode of The Data Journey, Roland Brown explores how observability and reliability engineering turn data quality into a measurable contract. He explains how SLIs, SLOs, and SLAs translate dependability into metrics and how error budgets balance innovation with stability. Listeners learn a five-step implementation pattern — instrument, alert, visualize, review, and improve — and hear a re
Episode 56: DataOps & Automation — Continuous Data Delivery
In this episode of The Data Journey, Roland Brown builds on Episode 8 (*Logical vs Physical Models*) and Episode 16 (*Data Stewardship — Who Owns Your Data?*) to explore DataOps — the bridge between data architecture, automation, and accountability.He explains how applying DevOps principles to data pipelines transforms them from fragile, manual workflows into reliable, continuously delivering syst
Episode 55: Data Contracts & Semantic Layers — From Agreement to Accountability
In this episode of The Data Journey, Roland Brown revisits the foundation of data contracts and evolves it into the next stage of architectural maturity — semantic accountability.He explains how data contracts establish trust through structure, while semantic layers extend that trust through shared understanding. Together, they define what data is, what it means, and who is responsible for keeping
Episode 54: Data Modelling 2.0 — From Entities to Ontologies
In this episode of The Data Journey, Roland Brown builds on Episode 8 (*Logical vs Physical Models*) and introduces Data Modelling 2.0 — a modern approach that adds a semantic layer of meaning and context. He explains how physical, logical, and semantic layers together describe where data lives, how it’s structured, and what it means.Through real-world scenarios and practical steps, Roland shows h
Episode 53: Data Catalogues & Knowledge Graphs — Discovery for Reuse at Scale
In this episode of The Data Journey, Roland Brown explores how data catalogues and knowledge graphs power discovery that leads to reuse, not rework. Building on Ep 5 (quality) and Ep 49 (openness), he explains why discovery is more than search: it’s the ability to find the right asset, understand it quickly, and use it safely.A modern catalogue surfaces assets with owners, definitions, and quality
Episode 52: End-to-End Lineage — The X-Ray of Data
In this episode of The Data Journey, Roland Brown unveils how end-to-end lineage acts as the x-ray of modern data architecture—revealing data’s complete story from origin to outcome.Building on Episodes 6 and 31, he connects lineage, observability, and metadata into a single control plane that turns governance from reactive to predictive.Roland introduces the Rule of 30 / 60 / 90, a derived heuris
Episode 51: The Metadata Mindset — From Documentation to Automation
In this episode of The Data Journey, Roland Brown explores the transformation of metadata from forgotten documentation to active intelligence.Building on Episodes 5 (*Data Quality – The Foundation of Trust*) and 16 (*Data Stewardship – Who Owns Your Data*), he reveals how active metadata becomes the invisible architecture enabling discovery, governance, and automation.You’ll learn how organisation
Episode 50: Data Architecture Patterns — The Hidden Frameworks
In this episode of The Data Journey, Roland Brown unveils the unseen blueprints that shape every modern data platform — the architectural patterns that quietly define how data flows, scales, and evolves.Building on earlier discussions like Architecture = Strategy (Episode 33) and Data Stewardship (Episode 16), this episode decodes the five foundational patterns — Hub-and-Spoke, Layered, Mesh, Fabr
Episode 49: Open Data — Sharing to Strengthen Insight
In this episode of The Data Journey, Roland Brown explores how open data — information made freely available for anyone to use, modify, and share — is redefining the future of innovation, AI transparency, and collaboration.Building on Episode 48 (Alternative Data — Expanding the Edges of Insight), this discussion looks at how open ecosystems can break down silos, accelerate discovery, and make dat
Episode 48: Alternative Data — Expanding the Edges of Insight
In this episode of The Data Journey, Roland Brown explores the next frontier beyond Customer 360 — Alternative Data— and how it’s redefining trust, context, and competitive advantage.Building on Episode 47 (Customer 360 & Master Data — The Architecture of Trust), this discussion unpacks how organisations can augment internal data with external signals to build richer, more predictive insights
Episode 47: Customer 360 & Master Data — The Architecture of Trust
If data quality is about the health of your data, MDM is about its identity. Without it, you’re left with multiple versions of customers, products, or employees across your systems. Today, in Episode 47, we’re going deeper. We’re asking: how does MDM connect directly to Customer 360, and why is trust the architecture that makes it possible?Sign Up for Newsletter: www.thedatajourney.com
Episode 46: From Data Engineer to Architect: Skills & Mindset Shift
How do I make the leap to becoming a data architect?The leap isn’t technical — it’s mental. Engineers build pipelines. Architects design ecosystems. And making that leap requires not just new skills, but a completely different mindset. In this episode I explain ..Sign Up for Newsletter: www.thedatajourney.com
Episode 45: Architecting for Value Streams, Not Pipelines
Pipelines deliver data — but value streams deliver impact. If we architect around pipelines alone, we optimise for movement. If we architect around value streams, we optimise for outcomes. And that’s where true business value emerges. Lets dig deeper ..Sign Up for Newsletter: www.thedatajourney.com
Episode 44: Data Architecting for Multi-Cloud & Hybrid Futures
The future of data isn’t about one cloud or one platform — it’s about ecosystems. Organisations are realising that no single provider can meet all needs, so multi-cloud and hybrid architectures are becoming the norm. The challenge is designing for agility without drowning in complexity. Lets Unpack...Sign Up for Newsletter: www.thedatajourney.com
Episode 43: Big Data and the V’s — From Web1.0 to Web2.0 and Beyond
Big data didn’t appear out of nowhere. It was born when the internet shifted from static consumption to dynamic participation. And at the heart of it are the famous V’s of Big Data — volume, velocity, variety, and more. Lets Explore ....Sign Up for Newsletter: www.thedatajourney.com
Episode 42: Data Sovereignty & Localisation — How Regulations Shape How Data Flows Across Borders
Data doesn’t just live in databases — it lives under laws. Where your data sits physically and how it flows across borders can mean the difference between innovation and regulatory breach. Lets Unpack.Sign Up for Newsletter: www.thedatajourney.com
Episode 41: Data Ethics and Responsible AI — Building Trust Before Innovation
Technology can scale faster than trust* AI has incredible potential, but without solid data practices, it risks reinforcing bias, breaching compliance, or undermining customer confidence. That’s why responsible AI must be built on responsible data. Lets Explore...Sign Up for Newsletter: www.thedatajourney.com
Episode 40: Data Monetisation — Turning Ecosystems into Revenue
Data is no longer just a by-product of business — it is the business. Organisations are now packaging, sharing, and selling data as products. The question is: how do you move from raw data to actual revenue streams? Lets Explore.Sign Up for Newsletter: www.thedatajourney.com
Episode 39: Platform Interoperability Connecting Data Across Multi-Cloud & Hybrid Environments
The future of data isn’t one platform, it’s many. Most enterprises now live in multi-cloud and hybrid environments. That means your AWS data lake, Azure machine learning services, and on-premise warehouse all need to connect seamlessly. Without interoperability, silos return — just in a new form. So today, we’ll unpack how APIs, contracts, and standards allow platforms to talk, ensuring your ecosy
Episode 38: API-First Strategy – Unlocking Data Consumption & Enabling Strategy
APIs aren’t just technical plumbing — they are the front door to your data. If your APIs are poorly designed, hard to discover, or inconsistent, your data strategy collapses at the point of consumption. API-first is about flipping the mindset — you design for consumption first, not as an afterthought. Let's Discuss.Sign Up for Newsletter: www.thedatajourney.com
Episode 37: PaaS vs. SaaS vs. IaaS – Choosing the Right Model for Your Data Strategy
Picking the wrong model isn’t just a technology decision, it’s a business strategy risk. Get it right, and you unlock agility, cost efficiency, and focus. Get it wrong, and you end up with lock-in, spiralling bills, and systems nobody loves using.Sign Up for Newsletter: www.thedatajourney.com
Episode 36: Metadata in Action: From Catalogues to Context
Metadata is the oxygen of data ecosystems. Without it, nothing breathes. It’s more than tags. It’s context, lineage, quality, and meaning, the very things that power governance, trust, and self-service analytics. Let’s dive in. Sign Up for Newsletter: www.thedatajourney.com
Episode 35: The Data Operating Model – Organising for Scale
Without a clear operating model, even the best data platforms collapse under scale. Technology alone won’t deliver value — it’s the way you organise teams, roles, and responsibilities that determines success. Lets Unpack.Sign Up for Newsletter: www.thedatajourney.com
Episode 34: Beyond ETL vs. ELT: Orchestration Patterns for Modern Workflows
Modern data platforms don’t just move data, they choreograph it. Whether it’s batch jobs, streaming triggers, or event-driven workflows, orchestration ensures that data pipelines run reliably, at scale, and in harmony with business needs. Lets explore..Sign Up for Newsletter: www. thedatajourney.com
Episode 33: Data Architecture as Strategy – Bridging Business & Tech
Data architecture is not just plumbing — it is business strategy. The best organisations don’t treat architecture as background IT. They embed it into their business model, aligning data flows with revenue, customer experience, and product innovation.So this episode is about bridging the gap between business and tech — showing how data architecture drives strategy, not just supports it.Sign Up for
Episode 32: Cost Management in Data Platforms – Avoiding the Cloud Bill Shock
Cloud platforms don’t fail because of technology — they fail because of cost mismanagement. The promise of agility and scalability quickly turns into runaway bills if left unchecked. So today, we’ll talk about practical strategies to keep costs under control without slowing innovation.Sign Up for Newsletter: www.thedatajourney.com
Episode 31: The Next Storage Wars — Delta, Iceberg, and OneLake Compared
The future of your data platform may be defined less by where you store data, and more by how you manage it. And the choice between Delta, Iceberg, and OneLake could determine your platform’s scalability, interoperability, and cost for the next decade. Lets Unpack.Sign Up for Newsletter: www.thedatajourney.com
Episode 30: Star vs. Snowflake vs. Galaxy Schema – Choosing the Right Data Warehouse Model
The way you model your warehouse determines performance, usability, and trust. Should you go with the simplicity of a star schema, the structure of a snowflake schema, or even the complexity of a galaxy schema? That’s exactly what we’ll unpack today.Sign Up for Newletter: www.thedatajourney.com
Episode 29: The Data Product Marketplace – Fueling the Data Mesh
If you want people to consume data like a product, you need to make it discoverable, trustworthy, and easy to use, just like Amazon makes it easy to shop. The marketplace is the place where supply meets demand. Lets Unpack.Sign Up for Newsletter: www.thedatajourney.com
Episode 28: Data Architecture According to TOGAF
TOGAF isn’t just about IT architecture, it’s a method for aligning data, applications, technology, and business strategy into one coherent enterprise model. In this episode, we unpack where data architecture fits within TOGAF, how the ADM cycle guides it, and what it means for designing sustainable data strategies.Sign Up for Newsletter: www.thedatajourney.com
Episode 27: Data Architecture According to DAMA-DMBOK
If you think of data management as a city, then architecture is the city plan*. Without it, you just get roads built randomly, buildings popping up everywhere, and eventually, traffic jams and chaos. Let’s explore how DMBOK frames data architecture as both a discipline and a foundation.Sign Up for Newsletter: www.thedatajourney.com
Episode 26: Inmon vs. Kimball – Choosing the Right Data Warehouse Design
In earlier episodes, we’ve compared Data Lake vs. Data Lakehouse vs. Data Warehouse in Episode 9, and explored the Medallion Architecture in Episode 13. Those gave us the big-picture landscape of where data can live and how it flows.Today, we’re going to narrow the focus and talk about a choice that shaped enterprise data for decades and still influences decisions today: Inmon vs. Kimball. Let's u
Episode 25: Data Observability – Building Trust Through Transparency
Observability is like having a health monitor for your data ecosystem. It doesn’t just tell you when something breaks, it helps you understand why, and how to fix it. Let’s dive in.Sign Up for Newsletter: www.thedatajourney.com
Episode 24: Data Virtualisation – Simplifying Access Without Moving Data
What if you could access and join data from multiple systems without physically moving it? That’s the promise of data virtualisation. Let’s explore what it is, how it works, where it shines, and where you still need to be cautious.Sign Up for Newletter: www.thedatajourney.com
Episode 23: Streaming vs. Batch vs. Event-Driven – Choosing the Right Processing Model
Not every problem needs real-time data. Sometimes batch is cheaper and more reliable. Other times, streaming or event-driven architectures are essential to compete. The trick is knowing when to use which and how to combine them. Lets Unpack.Sign Up for Newletter: www.thedatajourney.com
Episode 22: ETL vs. ELT – Which One Works Where?
How you move and transform your data is just as important as where you store it. Get this choice wrong, and you can slow your pipelines, inflate costs, or compromise trust. So let’s break down the differences, when to use each, and how to decide.Sign Up to Newletter: www.thedatajourney.com
Episode 21: Archiving & Disposal – Knowing When to Let Data Go
Not all data deserves to live forever. Holding onto everything leads to spiralling storage costs, compliance risks, and a cluttered ecosystem. The real discipline is knowing when to archive for reference and when to dispose responsibly. Lets unpack.Sign Up for Newsletter: www.thedatajourney.com
Episode 20: Data Usage & Sharing – Making Data Work Responsibly
Data only creates value when it’s used — but the way we share it determines whether it drives trust or chaos. If data is used without context, governance, or responsibility, it creates risk. If it’s shared with discipline and transparency, it becomes a true asset. Let’s unpack what that means.Sign Up for Newsletter: www.thedatajourney.com
Episode 19: Data Storage & Protection - Beyond Warehouses, Lakes and Lakehouses
The way you store data determines not just cost and performance, but also trust, governance, and the ability to innovate. And it’s not just about data warehouses, lakes, and lakehouses. We need to zoom out and look at the full spectrum of storage options, from classic relational databases to NoSQL, time-series, and beyond. Let’s explore.Sign Up for Newletter: www.thedatajourney.com
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