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Tag Archives: data-logistics-hub

Podcast Episode: Building A Data Logistics Hub

25 Tuesday Aug 2026

Posted by Martyn Rhisiart Jones in Architecture, data architecture, Data governance, data hub, data management, data mesh, data science, Data Supply Framework, Data Warehouse, Data Warehousing

≈ 2 Comments

Tags

data architecture, data governance, data integration, data-logistics-hub, Enterprise Data Warehouse, Podcast

Futuristic data center labeled Central Logistics Hub, with glowing data routes, servers, and operators
Glowing data pathways weave through a multi-level logistics hub filled with servers and operators.

Pip: If you have ever looked at your organisation’s data landscape and thought “this resembles a motorway junction designed by someone who hates drivers,” then Martyn Jones has a blueprint with your name on it.

Mara: This episode follows a series of posts introducing the Data Logistics Hub — covering what the concept is, why the pain points are real, how strategy and architecture hold it together, and what the actual components look like in practice.

Pip: Let’s start with the foundations — what the hub is and why it exists.

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Building the Data Logistics Hub: Pieces and Parts – 2026/02/15 – Part 3

14 Saturday Feb 2026

Posted by Martyn Rhisiart Jones in data mesh, data science, Data Supply Framework, Data Warehouse, Data Warehousing, Inform, educate and entertain., information, Information Supply Framework, Knowledge, knowledge management, Martyn Jones, Masterclass

≈ 1 Comment

Tags

Apache Kafka, data architecture, Data Catalog, data governance, data integration, Data Streaming, data-engineering, data-logistics-hub, ETL

OLYMPUS DIGITAL CAMERA

This is the brave new world of data!

Martyn Rhisiart Jones

Bandoxa, 15th February 2026

Building the Data Logistics Hub: Pieces and Parts – 2026/02/15 – Part 3

Guide

This episode provides a comprehensive framework for the third installment in the series on the Data Logistics Hub (DLH). Martyn Jones conceptualised it as a technology-agnostic, centralised platform. Its purpose is efficiently moving, governing, and distributing data across organisations. This part expands on Part 1 (Challenges and Opportunities) and Part 2 (The Strategy). It focuses on the tangible “pieces and parts” of the DLH architecture. It outlines mandatory and optional elements. The episode also explores potential technologies. It examines key processes such as data pulling or pushing, translation from source to target, mapping, and data catalogues.

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Building the Data Logistics Hub: The Strategy – 2026/02/14 – Part 2

14 Saturday Feb 2026

Posted by Martyn Rhisiart Jones in data mesh, data science, Data Supply Framework, Data Warehouse, Data Warehousing, Inform, educate and entertain., information, Information Supply Framework, Knowledge, knowledge management, Martyn Jones, Masterclass

≈ 1 Comment

Tags

data architecture, data governance, data integration, data management, Data Warehouse, data-engineering, data-logistics-hub, data-strategy, Enterprise Architecture

OLYMPUS DIGITAL CAMERA

This is the brave new world of data!

Martyn Rhisiart Jones

Bandoxa, 14th February 2026


Building the Data Logistics Hub: The Strategy – 2026/02/14 – Part 2Before I begin, remember this: “All data roads lead to the Data Logistics Hub.” They also lead from it. It is the Rome of the age of data, information, knowledge, and wisdom. Be prepared!

Okay, we will now examine the Data Logistics Hub in terms of strategy, execution plans, and roadmaps.

A high-level blueprint for a successful Data Logistics Hub outlines several requirements. These include principles, guiding objectives, an imagined “better world” and organisational alignment. Key trade-offs must also be considered, such as centralised versus federated and batch versus streaming, among others.

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

13 Friday Feb 2026

Posted by Martyn Rhisiart Jones in data mesh, data science, Data Supply Framework, Data Warehouse, Data Warehousing, Inform, educate and entertain., information, Information Supply Framework, Knowledge, knowledge management, Martyn Jones, Masterclass

≈ 1 Comment

Tags

Artificial Intelligence, data architecture, data governance, data integration, Data Quality, Data Silos, data-logistics-hub, data-sharing, data-strategy

OLYMPUS DIGITAL CAMERA

This is the brave new world of data!

Martyn Rhisiart Jones

Bandoxa, 13th February 2026

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

12 Thursday Feb 2026

Posted by Martyn Rhisiart Jones in data mesh, data science, Data Supply Framework, Data Warehouse, Data Warehousing, Inform, educate and entertain., information, Information Supply Framework, Knowledge, knowledge management, Martyn Jones, Masterclass

≈ 1 Comment

Tags

Artificial Intelligence, data architecture, data governance, data integration, Data Quality, Data Silos, data-logistics-hub, data-sharing, data-strategy

  1. Home
  2. data-logistics-hub

This Is Going To Be Absolutely Fabulous!

Martyn Rhisiart Jones

Bandoxa, 12th February 2026

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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