16 Mon Feb 2026
Posted in Inform, educate and entertain.
Tags
conceit, crazy, death-march, deathwish, elon-musk, hubris, News, Politics, social media, suicide, technology, x
http://www.goodstrat.com/books
X Is Dying In Europe: Here’s Why
I asked X about its massive losses in Europe.
“@grok, can you explain the massive drop in interactions in Europe with X?”
This is what Grok had to say and updated for today.
Continue reading15 Sun Feb 2026
Posted in Inform, educate and entertain.
Tags
AI, app-development, Artificial Intelligence, Business, digital-marketing, diy, end-user-computing, euc, self-sufficiency, shadow-apps, technology
Martyn Rhisiart Jones
Oza-Cesuras, Sunday 15th February 2026
In the quiet underbelly of corporate life, sanctioned software often lags behind real needs. Shadow apps, which are unsanctioned tools employees adopt on their own, continue to flourish. Nowhere is their value more pronounced than in data analytics. Teams quietly sidestep lengthy procurement and rigid platforms. They harness spreadsheets, personal BI instances, open-source scripts, and cloud sandboxes. Far from mere rebellion, these shadow practices reveal institutional shortcomings while delivering tangible gains. Here are seven compelling advantages, viewed through a lens that values both ingenuity and measured reflection.
We asked seven highly engaged professionals for their top advantages of allowing Shadow Apps to thrive. We then requested comments from industry leaders. And here is what they told us.
Continue reading14 Sat Feb 2026
Tags
agility, AI, Artificial Intelligence, Business, chatgpt, cloud, data architecture, Data governance, data hub, data management, Data Warehouse, data-logictics-hub, data-logistics-hub, data-marketing, data-on-demand, data-sharing, devops, digital-marketing, dlh, dw, protection, security, technology
Martyn Rhisiart Jones
Bandoxa, 15th February 2026
Building the Data Logistics Hub: Pieces and Parts – 2026/02/15 – Part 3
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.
Continue reading14 Sat Feb 2026
Tags
agility, AI, Artificial Intelligence, Business, chatgpt, data architecture, Data governance, data hub, data management, Data Warehouse, data-logictics-hub, data-logistics-hub, data-marketing, data-on-demand, data-sharing, digital-marketing, dlh, dw, protection, security, technology
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.
Continue reading13 Fri Feb 2026
Posted in Inform, educate and entertain.
Tags
AI, Artificial Intelligence, aws, azure, Business, chatgpt, Data Warehouse, Data Warehousing, databricks, ia, inteligencia-artificial, oracle, snowflake, technology, tecnologia
Madrid, sábado 7 de febrero de 2026
Libros de Martyn Rhisiart Jones: https://www.amazon.es/stores/author/B086K5B97Y
Estaba leyendo un artículo escrito por Jeff Wilts y recomendado por Bill Inmon. Llegué a esta afirmación: «Teradata es un almacén de datos empresarial con todas las funciones». Para mí, la cosa fue aún más cuesta abajo a partir de ahí.
Pero esto fue el golpe de gracia: «Databricks es una plataforma de datos unificada que puede comportarse como un almacén de datos».
Continue reading13 Fri Feb 2026
Tags
agility, AI, Artificial Intelligence, Business, data hub, Data Warehouse, data-logictics-hub, data-logistics-hub, data-on-demand, data-sharing, digital-marketing, dlh, dw, protection, security, technology
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.
Continue reading12 Thu Feb 2026
Tags
agility, AI, Artificial Intelligence, Business, data hub, data-logictics-hub, data-on-demand, data-sharing, digital-marketing, dlh, protection, security, technology
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.
Continue reading11 Wed Feb 2026
Posted in Inform, educate and entertain.
Tags
AI, Artificial Intelligence, Business, Data Mart, Data Warehouse, digital-marketing, drivers, imperatives, technology, urgency
Martyn Rhisiart Jones
Madrid, Saturday 31st January 2026
Martyn: The Enterprise Data Warehouse should be driven by business demand and nothing else.
Ed: What does that mean in practice?
Continue reading11 Wed Feb 2026
Posted in Big Data, Consider this, Data Warehousing, Good Strat, hadoop, hdfs, Martyn Jones
Tags
Analytics, Big Data, books, Data Mart, Data Warehouse, Data Warehousing, enterprise data warehousing, Good Strat, Good Strategy, google, Martyn Jones, Martyn Richard Jones, News, sports, technology
Martyn Rhisiart Jones
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.
Continue reading09 Mon Feb 2026
Posted in Inform, educate and entertain.
Tags
AI, Artificial Intelligence, azure, cloud, databricks, scam, technology
Martyn Rhisiart Jones
Madrid, Monday 9th February 2026
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?”
Continue reading
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