A quiet disquiet has settled over the once-confident corridors of enterprise data. What was, not so long ago, regarded as a rigorous and rather specialised craft, data warehousing and business intelligence, now frequently presents itself in a more casual, even improvisational guise. A growing number of senior executives, technology directors and indeed practitioners themselves confess to a mounting discomfort with the quality, and at times the sheer quantity, of self-proclaimed experts who populate the field.
This GigaOm report is a classic vendor-sponsored benchmark. Fivetran picked the competitors and shaped the test scope. GigaOm (as explicitly disclosed) executed it “as-is” with “compatible configurations subject to judgment.” It’s marketing material presented as independent research. It has several structural weaknesses. These weaknesses make the 77–95% cost savings claim highly misleading for most real organisations.
1. It only measures ingestion compute, not true TCO.
The report repeatedly calls itself a “TCO report,” but it explicitly excludes:
The Skunkworks Collective – Martyn, Alba, Afi, Lila, and Coco
Madrid, Wednesday 14th January 2026
Right, listen. If you’ve ever sat through one of those AI conferences, you know the ones. Some bloke in a black polo neck stands on stage. He’s clearly never met a mirror he didn’t like. He says, “We’re on the cusp of AGI.” It sounds as if he’s just invented gravity. Then you’ll know the particular flavour of despair I’m talking about.
As we venture further into 2026, the landscape of enterprise artificial intelligence has undergone a subtle but profound shift. The once-dazzling promise of autonomous AI agents has matured into something more prosaic. These self-directed digital entities can orchestrate tasks from customer engagement to complex data integration. Yet, they are no less pervasive. They are no longer novelties confined to experimental labs; they inhabit boardrooms, back offices and supply chains alike.
Should All Business Data Requirements Be Justified in Business Terms?
A Pseudo-Debate
Motion:All business data requirements must be justified in business terms. Potential business utility should be a deciding factor in whether they are fulfilled.
For the Motion:Sir Afilonius Rex Against the Motion:Martyn Rhisiart Jones
The origins of data warehousing are often pinned to the late 1980s, when the term “business data warehouse” first appeared in an influential IBM Systems Journal article by researchers Barry Devlin and Paul Murphy. I was based in Birmingham at that time, and I also wrote a similar foundational document on Information Centres for Sperry Univac.
Yet, as with many technological breakthroughs, the story is far richer and older than the conventional narrative suggests. The foundational components of what we now recognise as a data warehouse were quietly taking shape as early as the 1960s, driven by the need to organise, integrate, and analyse growing volumes of business information in an era of punch cards, magnetic tape, and the first mainframes.
Nine things that really, really shouldn’t be use cases for AI. Delivered by slowly dismantling a bad idea like it’s a poorly constructed IKEA wardrobe. Rant about bourgeois nonsense with surreal fury. Explain why the whole thing is politically ridiculous. Just stare at the absurdity until it cracks. This is like Marnie Listicle Barr on crack.
Weaving the Dragon’s Data: A Welsh-Inspired Tale for Enterprise Architects in the New Year – 2026/01/01
The calendar turns to a fresh page in this crisp January of 2026. We, enterprise data and information architects, stand at a new threshold. Another year welcomes us, brimming with digital transformations. Data lakes swell like the River Taff after a storm. Information architectures evolve like the ancient mountain tops of Snowdonia. But amidst the algorithms and schemas, let’s pause for a moment of whimsy. What if we drew inspiration from the misty realms of Welsh myths and legends? Wales, that land of dragons and bards, offers a tapestry of stories. These stories mirror our quests: taming chaotic data into structured wisdom. They are about preserving cultural legacies in vast repositories. They ignite innovation from the sparks of history. In this Happy New Year ode, we’ll begin a narrative journey through Welsh lore. It is infused with the spirits of its iconic figures. Dylan Thomas, Dannie Abse, Richard Burton, Shirley Bassey, Paul Robeson, and Gwynfor Evans are part of this infusion. These spirits illuminate the art of data stewardship.
Top 5 Trends Shaping the Future of Data, Analytics, and AI Leadership in 2026
I’ve had the privilege of leading a groundbreaking global study in partnership with a strategic client. During this study, I spoke with many key Chief Data Officers (CDOs). I also engaged with Chief Data & Analytics Officers (CDAOs) and AI executives across industries and geographies. The insights reveal how organisations are changing their data and AI strategies. They aim to drive real business value in an increasingly complex world.