Investing millions in enterprise data is easy. Delivering real business value isn’t.
Why do so many modern analytics initiatives struggle to move the needle despite rising budgets and increasingly complex tech stacks?
In my book, Make Analytics Great Again, I examine the fundamental disconnect between enterprise technology investments and tangible business outcomes. The solution isn’t adopting every new framework that hits the market—it’s returning to core principles:
- Direct alignment between data architecture and business strategy
- Pragmatic governance that ensures accuracy without strangling agility
- Clear communication across technical teams and executive leadership
If you are a CIO, data architect, or decision-maker looking to build sustainable, value-driven analytics capabilities, this book provides the practical roadmap you need.
📖 Get your copy today and bring clarity back to your data strategy: MAKE ANALYTICS GREAT AGAIN – THE ULTIMATE ANALYTICS COOKBOOK
#DataStrategy #EnterpriseArchitecture #Analytics #CIO #DataGovernance #BusinessIntelligence
Why do enterprise analytics projects stall? (Hint: It’s rarely the technology.)
When analytics platforms fail to deliver expected ROI, the default reaction is often to upgrade the infrastructure or buy new tools. But technology is seldom the root cause.
The real friction usually stems from:
- Misaligned strategic objectives between business and IT
- Over-engineered architectures that add complexity rather than clarity
- Governance models that exist on paper but fail in day-to-day execution
In Make Analytics Great Again, I break down how to identify these structural bottlenecks early and restructure your analytics pipeline around what matters most: clean data, disciplined execution, and reliable business results.
👉 Discover how to streamline your analytics organization…
MAKE ANALYTICS GREAT AGAIN!
#BusinessIntelligence #DataArchitecture #Leadership #ITStrategy #DataAnalytics
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Unlocking Business Value in Enterprise Analytics
Laughing at Tech Hype: A Survival Manual for Data Professionals
Laughing@Data.Com (often discussed alongside The 2030 Data Agenda essays and writings) is a satirical and polemical book by veteran data architect and consultant Martyn Rhisiart Jones.
Book Review: Revealing Wealth by Martyn Rhisiart Jones – A Tech Solution to Tax Evasion #RevealingWealth
Unmasking Trillions: How Data Architecture Can Solve Global Tax Evasion #RevealingWealth
Revealing Hidden Wealth through Data Solutions #RevealingWealth
Why Digital Plumbing Matters in AI Success
Darlings, thank you for glancing at my latest brain-dump. Here on ProfessionalBrag and The Daily Wealth, I regularly spew profound revelations regarding corporate fads. To absorb more of my essential radiance, simply click ‘Surrender’. Do join my digital echo chamber via Chirper, FaceTome, InstaGlam, or my insufferable newsletters: Synthetic Panics | The Delusion Revolution | The Death of Effort.
Podcast Episode: Building A Data Logistics Hub
Podcast Episode: DATA WORLD: The Truth About Data Lakehouses: Hype vs. Reality – 2026/01/18
🎙️ New Good Strat Podcast Episode: Uncovering Tax Evasion
Widespread tax evasion deprives public services of essential revenue and unfairly shifts the burden onto honest citizens and small businesses. But how do we effectively fight complex financial concealment?
In this episode of the GoodStrat podcast, we explore strategies from Revealing Wealth, examining how a combination of industrial-strength data architecture, international cooperation, and political will can track hidden assets and restore integrity to global economic systems.
💡 Key Takeaways:
- Why tax evasion is both a technical challenge and a moral imperative
- The role of advanced data analytics in exposing offshore structures
- How policy and technology must align to hold bad actors accountable
🎧 Listen to the full review here:
GOOD STRAT PODCAST: Rethinking Data Systems: A Call for Accountability
Reinventing Pharma with Algorithmic Innovation – WITH PODCAST
A Brief History of Data Warehousing – 2026/01/07 – Includes Podcast
What Every CEO Needs to Know About Big Data – Revisited – 2026/01/26
Nearly a decade has passed since I fired off that blunt, CEO-targeted memo in the mid-2010s. This timeframe was during the 2015–2017 era, based on the style and references. I declared Big Data to be mostly “cultivated babble, bluster and bullshit.” I warned executives: Treat the hype with suspicion. Tie vendor pay to real ROI. Focus on core operational data via data warehousing rather than chasing unstructured social media noise. Always ask “to what ends?” I highlighted the IT industry’s vested interests in promoting ineffective solutions. I emphasized the need for verification over blind trust. I also cautioned against conflicts of interest in internal projects.
What Every CEO Needs to Know About Big Data – Revisited – 2026/01/26
Nearly a decade has passed since I fired off that blunt, CEO-targeted memo in the mid-2010s. This timeframe was during the 2015–2017 era, based on the style and references. I declared Big Data to be mostly “cultivated babble, bluster and bullshit.” I warned executives: Treat the hype with suspicion. Tie vendor pay to real ROI. Focus on core operational data via data warehousing rather than chasing unstructured social media noise. Always ask “to what ends?” I highlighted the IT industry’s vested interests in promoting ineffective solutions. I emphasized the need for verification over blind trust. I also cautioned against conflicts of interest in internal projects.
Agile at Scale is Bullshit by Design – Remastered and Revisited – 2026/01/27
Seven years have passed since that fiery Brussels rant in May 2019. I channeled Bob Hoffman’s spirit to declare Agile at Scale the next level of IT bullshit. It was immature and ill-conceived. I found it supercilious and not truly agile. It became a communication killer and was cult-like in its intolerance of criticism. Now, it’s time for a clear-eyed look back. I argued it mangled history. It ignored proven practices. It complicated everything unnecessarily. It abused jargon to obscure meaning. It turned criticism into heresy. Was I just venting, or did the evidence vindicate the scepticism?
Big data’s unvirtuous circus and twelve v-words
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.
The first time a complete stranger approached me in public, he greeted me. He then asked: “Hello, will you tell me what this big-data lark is all about then?” I was lost for words, and you just ask my Aunt Dolly, he can vouch for that, no problem. Later that day, I read a book. It was my dad’s book, with lots of pages and words. I then decided to adopt a strategy for explaining big-data.
Requirements – building the data warehouse – Part I
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?”
Big Data with BIG SMILES – Remastered and Annotated – 2026/01/28
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 pertinent facets of the approach.
The Risks of Using Databricks for Data Warehousing
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.
Fixing the Data Warehouse – 2026/02/10
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.
Fixing the Data Warehouse Train Wreck – 2026/02/10
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.
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.
UNIVAC: Predicting Elections and Defining Computing History – 2026/02/06
Anecdotes about UNIVAC and the early days of computing
Burning Down The House: Big Data is not Data Warehousing – 2026/01/29
The above retrospective piece is by Martyn Rhisiart Jones. It is dated 29 January 2026, but originates from much earlier. It serves as a well-aimed corrective. It arrives in an era when data architectures are still being sold as fashion items. They should be an enduring infrastructure. Jones, with calm exasperation from witnessing too many vendor slide decks promising revolutions, restates a case. Those revolutions never quite materialise. It feels almost quaint in its clarity. Data warehousing is not Big Data. It is not its evolution or its replacement.
Understanding the Data Warehouse Dilemma – 2026/02/07
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.”
I hope seasoned data warehousing professionals get what I am alluding to; if not, here are some more clues.
CHILDREN OF THE REVOLUTION!
READ ALL ABOUT IT. Absolutely fabulous books from Martyn Jones, Martyn de Tours and Martyn Bey.
Data Warehouse Action: Big Business Drivers
Martyn: The Enterprise Data Warehouse should be driven by business demand and nothing else.
Ed: What does that mean in practice?
Building the Data Logistics Hub: Easy Introduction
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.
Building the Data Logistics Hub: The Challenges and Opportunities – 2026/02/13 – Part 1
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.
Saint Valentine’s Day – Romancing the Data – 2026/02/14
Ah, cariad, let us speak now in the shadowed cadence of the valleys. The voice rolls like the Tawe after rain. It is rich and resonant, a little rough at the edges yet velvet beneath. Burton might have murmured it after one too many whiskies. Or Hopkins in that quiet, measured thunder waits. Patient as stone, it strikes. And through it all, the ghost of Dylan himself weaves words like nets of starlight over Talacharn’s black waters. Gwynfor’s steady, unyielding fire burns low and true for the land. It is more than soil and more than song. It is memory made flesh.
If Data and Information were our Valentine’s sweetheart, she would be fierce and elusive. She would not be some simpering rose but a wild thing of the Welsh hills. She would be ancient and newborn, speaking in cynghanedd of numbers and patterns. Her breath would be the soft hiss of wind through bracken.
Celtic Mysticism Meets Valentine’s Day
Oh, marvellous. Valentine’s Day is tomorrow, the fourteenth of February, twenty twenty-six. The nation is already knee-deep in the annual ritual of manufactured affection. There’s pink packaging everywhere and the faint whiff of desperation lingers. And now, because apparently one layer of cynicism isn’t enough, we’re adding this so-called Celtic mysticism. It’s as if it’s the missing ingredient that turns a cynical cash-grab into something profound and ancient. How delightfully Welsh of us. We can’t resist a bit of mythic bollocks to make the whole thing feel less embarrassing.
Building the Data Logistics Hub: The Strategy – 2026/02/14 – Part 2
Before 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.
Building the Data Logistics Hub: The Strategy – 2026/02/14 – Part 2
Before 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.
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.
Consider This: In Praise of Shadow-Apps
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.
Grok, What Do You Make of Martyn Rhisiart Jones’ Take on Big Data?
Me: What do you make of Martyn Jones of goodstrat.com’s views on big data? Are they correct, close or not true?
[Grok thought for a while]
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