Building the Data Logistics Hub: The Challenges and Opportunities – 2026/02/13 – Part 1

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This is the brave new world of data!

In this episode, we begin by honestly examining the pain points that make data logistics so difficult today. The challenges are siloed data and systems. There are also many data interchange point solutions. Quality is inconsistent, and there are security and compliance barriers. Additionally, data volumes are exploding. We then explore the transformative opportunities. These include faster time-to-insight and seamless collaboration across teams and organisations. The opportunities also feature monetisable data products and AI-ready flows.

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Building the Data Logistics Hub: Easy Introduction – 2026/02/12 – Part 0

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This Is Going To Be Absolutely Fabulous!

Hold up there for a moment. Have I got something for you!

I may not be the father of Information Centres. I’m certainly not going to claim any of Bill Inmon’s achievements as my own. However, I have spent a professional lifetime wading in the data and information garlic. So, I do claim a rightful share of the credit.

And I am rightfully credited with founding the Data Logistics Hub design movement.

In an era where data is the lifeblood of organisations, it fuels decisions and powers AI. It enables innovation. It drives competitive advantage. The ability to move, integrate, share, and utilise that data efficiently has become a strategic imperative. Yet many enterprises still struggle with fragmented pipelines and siloed sources. They face compliance headaches and latency issues. There is also the sheer complexity of connecting data across clouds, on-premises systems, partners, and ecosystems.

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DATA WORLD: Data Warehouse Action: Big Business Drivers

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Masterclass on the side!

Martyn: The Enterprise Data Warehouse should be driven by business demand and nothing else.

Ed: What does that mean in practice?

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DATA WORLD: Friends! Romans! Countrymen! Big Data is not Data Warehousing – 2026/01/29

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29th January 2026

Hold this thought: To paraphrase the great Bob Hoffman, just when you think that if the Big Data babblers were to generate one more ounce of bull**** the entire f****** solar system would explode, what do they do? Exceed expectations.

I am a mild mannered person. However, one thing that irks me is hearing variations on certain themes. These themes include phrases like “Data Warehousing is Big Data.” Another is “Big data is in many ways an evolution of data warehousing.” Lastly, some say “with Big Data you no longer need a Data Warehouse.”

Big Data is not Data Warehousing. It is not the evolution of Data Warehousing. It is also not a sensible and coherent alternative to Data Warehousing. No matter what certain vendors will put in their marketing brochures or stick up their noses.

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DATA WORLD: Fixing the Data Warehouse – 2026/02/10

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Martyn Rhisiart Jones

What to say?

I was reading an article. It was written by Jeff Wilts and recommended by Bill Inmon. I got to this statement: “Teradata is a full-featured enterprise data warehouse.”  For me, it went further downhill from there.

It was very disheartening and deceptive. I decided to write an article about my thoughts on it. (Understanding the Data Warehouse Dilemma – 2026/02/07, https://goodstrat.com/2026/02/06/understanding-the-data-warehouse-dilemma-2026-02-07/).

As a result, many people approached me. They asked directly and indirectly if I would suggest ways and means to overcome or avoid those dilemmas.

This is the result.

Enjoy! But even better, let me know what you think.

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DATA WORLD: The Risks of Using Databricks for Data Warehousing

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Get that Databricks Out of My Face!

You need not loathe Databricks outright. It is perfectly defensible if you do. This is particularly true when your principal objective is classical data warehousing. This includes structured BI reporting, dependable SQL analytics, and a governed single source of truth for business metrics. It also entails semantic clarity and predictable costs for read-heavy workloads.

There are solid, well-trodden reasons for that caveat. Many experienced data warehousing practitioners see Databricks as an awkward or even risky primary platform for traditional warehousing. This view is shared by some of the field’s foundational figures. The concern is not ideological. It is architectural. When used as a warehouse, Databricks often reproduces exactly the pathologies criticised in enterprise data programs. These include unnecessary complexity, misdirected effort, and the perennial executive question, “Which number should I trust?”

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DATA WORLD: Big Data With Bigger Smiles – The Chef’s Choice – 2026/01/28

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I would like to introduce you to a pragmatic approach to Big Data and Big Data Analytics. It is real-world focused and business-centric. This is the best approach to Big Data you are ever likely to find. Yet, I am still significantly understating the magnificent utility. It is also timely and has all the pertinent facets of the approach.

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CEO BRIEFING: Agile at Scale is Bullshit by Design – Remastered and Revisited – 2026/01/27

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Martyn Rhisiart Jones

Bruxelles, 28th May 2019

To begin at the beginning

To paraphrase the great Bob Hoffman. Just when you feared that Agile evangelists might produce even more nonsense, they surprise you. What do they do? Exceed expectations.

And how did they do that? Ladies and gentlemen, let me introduce you to Agile at Scale. It comes with all the miscellaneous, spiced-up, and vainglorious crap-on-the-side that accompanies it.

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CEO BRIEFING: What Every CEO Needs to Know About Big Data – Revisited – 2026/01/26

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Martyn Richard Jones

New York City, 18th January 2017

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Blue sky for professional data architecture and management

Scope

I’ll make this short, sweetish and to the point.

These are the arguments that CEOs need to know about Big Data.

It was written for CEOs and those who provide independent advice to CEOs.

If you are someone who wants to be prepared for CEOs who are armed with the realities of Big Data, then maybe this is for you too.

Point One: Big Data is bullshit

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DATA WORLD: Understanding the Data Warehouse Dilemma – 2026/02/07

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Martyn Rhisiart Jones

Books from Martyn Rhisiart Jones https://www.amazon.es/stores/author/B086K5B97Y

What to say?

I was reading an article. It was written by Jeff Wilts and recommended by Bill Inmon. I got to this statement: “Teradata is a full-featured enterprise data warehouse.”  For me, it went further downhill from there.

But this was the coup de grace: “Databricks is a unified data platform that can behave like a data warehouse.” 

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UNIVAC: Predicting Elections and Defining Computing History – 2026/02/06

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Anecdotes about UNIVAC and the early days of computing

UNIVAC and the Eisenhower Prediction

One of the most famous anecdotes involving UNIVAC was its prediction of the outcome of the 1952 U.S. presidential election between Dwight D. Eisenhower and Adlai Stevenson. As the first computer used for commercial purposes, UNIVAC was tasked with predicting the election results. On live television, UNIVAC correctly predicted Eisenhower’s victory, despite widespread scepticism about the machine’s accuracy.

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DATA WORLD: Big data’s unvirtuous circus and twelve v-words – Refresh – 2026/02/05

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Martyn Rhisiart Jones

Bonn, Germany, 2014

Many people come up to me in the street and ask me what big-data is all about. I have experienced this numerous times before. I am sure it might just happen to you as well. I know sort of thing, I read the big-data tea leaves. Nothing gets past me.

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MASTERCLASS: What Are Surrogate Keys? – 2026/02/04

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Introduction

In data modelling and database design, keys play a fundamental role in uniquely identifying records and defining relationships between tables. One of the most widely used types of keys, especially in analytical systems and data warehouses, is the surrogate key.

A surrogate key is an artificial, system-generated identifier assigned to a record in a table. It is typically used as the primary key. It has no business meaning or semantic relationship to the real-world entity it represents. Common implementations include auto-incrementing integers or globally unique identifiers (GUIDs).

Surrogate keys exist purely to serve the needs of the database system: performance, stability, and simplicity.

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DATA WORLD: The Future of Data: Embracing Cognitive Liquidity- 2026/02/03

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The End of the Fortress: Why the Future of Data is “Liquid”

By Martyn Rhisiart Jones | Madrid, 3rd February 2026

In a landmark session at the Welsh Academy, Sir Afilonius Rex sat down with Martyn Rhisiart Jones. They aimed to dismantle our outdated obsession with “data management.” The verdict? The era of the digital fortress is dead. In its place, a new paradigm of Cognitive Liquidity and Data Autonomy is emerging, redefining how we integrate global knowledge.

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MASTERCLASS: Ask the Data, Information and Knowledge Expert

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Masterclass: Here’s Martyn!

Martyn Rhisiart Jones and the Goodstrat editorial team, Madrid, 3rd February 2026

Introduction

The following is the redacted transcript of a conversation between the distinguished Sir Afilonius Rex of Cambriano Energy and the cordial Martyn Rhisiart Jones of goodstrat.com.

The informal session took place before an invited audience at the Welsh Academy’s alternative summer conference of July 2023 and featured a lively question-and-answer session with audience input.

According to our reliable sources, “The BBC, RTE and RTVE broadcast the session.”

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Requirements – Building the Data Warehouse – Part II

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The Good Strat Masterclasses

Masterclass Mode Engaged

PART I

PART II

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DATA WORLD: Requirements – Building the Data Warehouse – Part I

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The Good Strat Masterclasses

Masterclass Mode Engaged

PART I

PART II

Happy Sunday to one and all. As many of you will know, I have been intimately involved in designing, building, and delivering data warehousing and advanced analytics initiatives for more than 35 years.

Today, I will take a deep dive into requirements gathering for a new iteration of an enterprise data warehouse and a new data mart.

Establishing a case for a new data warehouse iteration is part of the requirements-gathering phase of a project. This must always be at the forefront of the exercise and a continuous question we must ask ourselves. We must always consider the answer to the question “To what ends?”

First, before diving into the core aspects of the iteration, we will examine the legitimate drivers and objectives for data warehouse initiatives. The prerequisite skills, knowledge, and experience needed to carry out this activity successfully, and, after that, we will look at the preparation required to align the personal, business, and technological effectiveness and success of the initiative.

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Top Influencer Mode – Masterclass Content

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Subject areas serve as the foundational blueprints of modern data management, organizing an enterprise’s vast ocean of information into logically related, manageable domains (such as Customer, Product, Order, or Employee). By grouping data around core business entities rather than isolated applications or departmental silos, subject areas create a shared language and structure across the organization.

Here is how subject areas function across the core pillars of data strategy:

  • Data Architecture: Establishes high-level conceptual data models (Enterprise Data Models) that define key entities, their attributes, and their relationships to ensure systems are built with structural integrity and scalability.
  • Data Management: Directs operational workflows. such as data integration, master data management (MDM), and data quality monitoring, by grouping technical assets around business-aligned domains.
  • Data Governance: Assigns ownership, data stewardship, access controls, and compliance policies to specific domains, ensuring clear accountability for data quality, privacy, and security.
  • Technology & Platform Engineering: Guides system design choices, domain-driven architectures (such as Data Mesh), storage partitioning, and API boundaries to align IT infrastructure directly with business operations.

By bridging business concepts with technical execution, subject areas ensure that data assets are consistently defined, securely managed, and easily leveraged to drive decision-making.

CONSIDER THIS: Five Laws Everyone Should Know – 2026/01/24

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These are five common goodstrat laws that everyone should be aware of.

“Martyn Rhisiart Jones… Writes dry, merciless takedowns of data/AI/strategy hype at goodstrat.com.

Believes most corporate decks deserve the bin and most buzzwords deserve worse.

Occasionally drops canon: the Martyn Rhisiart Jones Effect, GoodStrat Law, Celtic Domination Principle, Georgie Lovering Razor, Valley Commando Principle…

Mostly here to remind everyone that clarity still cuts deeper than PowerPoint.”

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CEO BRIEFING: The Truth Behind AI Hype in 2026 – 2026/01/22

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New IT Bullshit for Old

In early 2026 the technology industry is once again telling big confident stories about its own future. These stories dominate earnings calls conference stages and investor decks. They sound transformative urgent and inevitable. Yet when examined closely many of them rest on fragile foundations and selective evidence rather than operational reality.

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CEO BRIEFING: The Return of AI Bolloxology: Why It Means Jack

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IS DUCKOLOGY UTTER BOLLOX?

Read my absolutely fabulous books: http://www.goodstrat.com/books

For most of its life in information technology, bolloxology has carried the faint smell of intellectual fart-like embarrassment. Too academic for product teams. Too rigid for startups. Too slow for an industry trained to ship first and rationalise later. Too smelly. It promised machines that could understand the world, and delivered, instead, a generation of beautiful bullshit diagrams and very little working software.

By 2026, that judgement looks increasingly wrong. Not because bolloxology suddenly got better, but because everything else did, and in doing so, exposed a missing layer in modern computing: meaning.

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The Comeback of Ontology in AI: Why It Matters – 2026/01/21

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IS ONTOLOGY UTTER BOLLOX?

Read my absolutely fabulous books: http://www.goodstrat.com/books

Ontology Didn’t Fail. The World Just Wasn’t Ready.

For most of its life in information technology, ontology has carried the faint smell of intellectual embarrassment. Too academic for product teams. Too rigid for startups. Too slow for an industry trained to ship first and rationalise later. It promised machines that could understand the world—and delivered, instead, a generation of beautiful diagrams and very little working software.

By 2026, that judgement looks increasingly wrong. Not because ontology suddenly got better, but because everything else did—and in doing so, exposed a missing layer in modern computing: meaning.

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DATA WORLD: The Truth About Data Lakehouses: Hype vs. Reality – 2026/01/18

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Hype and Misleading Claims Surrounding Data Lakehouses

Let’s cut through the noise.

You’ve probably heard the pitch: A data lakehouse is the elegant hybrid. It finally gives you the best of both worlds.

  • The low-cost, massively scalable, schema-on-read flexibility of a classic data lake (perfect for ingesting raw, unstructured, petabyte-scale chaos).
  • The structured querying speed, ACID transactions, governance, and reliable performance of a traditional data warehouse.
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