03 Monday Jun 2019
This is the first in a series of management talking points. The name Leadership 7s has been influenced by the great game of rugby, which is was the emblematic game of my youth and a passion of the home country of my Mom and Dad, Wales.
In fact, much of what I learned about leadership in my formative years came from fundamentally social influences such as rugby union.
So, today I want to address seven talking points that deal with aspects of leadership, coaching and management.
02 Wednesday Aug 2017
Posted in 4th generation Data Warehousing, All Data, Architecture, Big Data, Big Data Analytics, data architecture, Data governance, data management, Data Supply Framework, Data Warehouse, Data Warehousing, Good Strategy, information, Information Management, Information Supply Framework, IT strategy, Knowledge, knowledge management, Marketing, Martyn Jones, Martyn Richard Jones, Sales, structured intellectual capital, wisdom
Martyn Jones
Zurich, Switzerland – 3rd August 2017
If anyone can turn data into knowledge then that person is me.
Let me explain.
I am a data architecture and management professional. For more than three decades I have been acquiring knowledge and experience in the design and delivery of effective data and solutions architectures for a wide range of projects and in a wide range of (mainly large global or regional) enterprise clients.
Therefore I think I can reasonably presume to have built up quite a good personal body of knowledge when it comes to applied data architecture and management.
As well as being a professional in the management and architecture of data, I have also considerable knowledge and experience in the areas of information management, artificial intelligence and knowledge management (structured intellectual capital).
So, what about turning data or information into knowledge? Continue reading
18 Saturday Mar 2017
Posted in 4th generation Data Warehousing, All Data, Big Data, Big Data 7s, Big Data Analytics, business strategy, dark data, data architecture, Data governance, Data Lake, data management, Data Mart, data science, Data Supply Framework, Data Warehouse, Data Warehousing, Good Strategy, information, Information Management, Information Supply Framework, IT strategy, pig data, Strategy, The Amazing Big Data Challenge, The Big Data Contrarians
Martyn Richard Jones
Carmarthen 18th March 2017
This is a second collection of snippets; ideas and occurrences that are aimed to provoke critical thought and engagement.
As I stated in the first of these articles, “Most people who know me, at least in a professional capacity, will know that I am no unconditional supporter of Data Warehousing, never mind the more plague ridden swamps inhabited by Big Data, Data Lakes, Travelling IoT, Dodgy Data Science, Instant Data Scientists (just add water) and New-Age Machine Learning. So, many will not be surprised by this heterogeneous post-modern pastiche of ideas, impressions and bagatelles.”
I have extended the original scope of these posts to include any thought on themes of strategy, data, technology, process, business and humanity. Continue reading
26 Thursday Jan 2017
Posted in 4th generation Data Warehousing, All Data, Big Data, Big Data 7s, Big Data Analytics, business strategy, dark data, data architecture, Data governance, Data Lake, Data Supply Framework, Extract, Good Strategy, goodstart, governance, Information Management, Information Supply Frameowrk, Information Supply Framework, IT strategy, Strategy, The Big Data Contrarians
Martyn Richard Jones
Mountain View, 22nd January 2015
This article is the first in a series of articles that discuss aspects of the use of architectural patterns in the Cambriano Information Supply Framework 3.0
The term architectural pattern may sound grand, misleading or daunting, but it’s really quite a simple concept. It’s like writing a function in a programming language to log in to a database, check that the connection is alive and working and report back the success of the connection request. If that function can be reused either in the same application development, in the same IT shop or in IT in general (e.g. Java code to connect and test the connection to SQL Server) then it’s well on its way to becoming an architectural pattern. Of course, there are much more sophisticated architectural patterns. But generally a pattern is a simplified and generic template for address a generally occurring problem. But as with much in architecture, less usually turns out to be more.
19 Thursday Jan 2017
Posted in 4th generation Data Warehousing, All Data, Big Data, Big Data 7s, Big Data Analytics, dark data, data architecture, Data governance, Data Lake, data management, data science, Data Supply Framework, Data Warehouse, Data Warehousing, disinformation, governance, information, Information Management, Information Supply Frameowrk, Information Supply Framework, pig data, The Amazing Big Data Challenge, The Big Data Contrarians
Marty Richard Jones
Mountain View, 19th January 2017
“I’ve been accused of vulgarity. I say that’s bullshit.” – Mel Brooks
If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians. Continue reading
02 Wednesday Mar 2016
Posted in 4th generation Data Warehousing, Consider this, Data Supply Framework, Data Warehouse, Data Warehousing, DW 3.0, EDW, Excellence, Good Strat, Good Strategy, Information Management, Information Supply Frameowrk, Knowledge, leadership, Methodology, Professional Networking, The Big Data Contrarians
A data warehousing superhero is something to beNot all that glitters is Big Data, and Big Data has a long way to go before it can deliver anything like the same satisfying results, tangible benefits and organisational agility that a properly implemented Inmon Enterprise Data Warehouse can provide.
Therefore, I have a question for you.
23 Tuesday Feb 2016
In the beginning was the Big Iron, the Big Data, and the Big Data Plan.
And then came the Big Data Assumptions.
And the Big Data Assumptions were without form.
And the Big Data Plan was without substance.
And the Big Iron was without movement.
And the Big Data was without velocity, variety and volume.
And darkness was upon the face of the data workers.
And they spoke amongst themselves, saying: “Big Data, is a crock of shit, and it stinketh mucho”.
And the data workers went unto their Data Supervisors and said: “This here Big Data is a pile of putrid crappy keech”, for they were from Govan, and continued, “and none may abide the odour thereof”.
And the Data Supervisors went unto their Information Managers, saying: “Big Data is a container of excrement, and it is very strong, such that none may abide by it.”
And the Information Managers went unto their Business Directors, saying: “This here Big Data doodoo is a vessel of fertilizer, and none may abide its strength.”
And the Business Directors spoke amongst themselves, saying to one another: “Big Data contains that which aids plant growth, and it is very powerful.”
And the Vice Presidents went unto the President, saying unto him: “This new Big Data will actively promote the growth and vigour of the company, with powerful effects.”
And the President looked upon the Big Iron, the Big Data and the Big Data Plan, and saw that they were good.
Many thanks for reading
Join The Big Data Contrarians
21 Sunday Feb 2016
Posted in 4th generation Data Warehousing, Big Data, Big Data 7s, Data Warehouse, Data Warehousing, Good Strat, Good Strategy, goodstart, goodstrat, Information Management, Information Supply Frameowrk, Information Supply Framework, Inmon, Martyn Jones, Martyn Richard Jones, Strategy, The Big Data Contrarians
NB THIS IS FICTION
All characters appearing in this work are fictitious. Any resemblance to real persons, living or dead, is purely coincidental.
Data warehousing, what is she like?
Although the answers are probably obvious, and to be honest, compared to the Big Data hype-circus this is a walk in the park, I have often wondered why Data Warehousing attracts such a surfeit of lazy, socially inept and shallow-thinking chancers.
I could go on about this at length, about how I convened a meeting recently (held on the outskirts of Bornheim, a small town in Germany ) to discuss how to move rapidly forward with a new strategic data-warehousing project, and how, whilst putting aside the crass impertinence and barely-disguised arrogance of my guests, I was still amazed by the unabashed and brazen snow-job that I was subjected to.
I imagined that this was a deliberate tactic used in the craven hope that I would be overcome by the depth and breadth of their ‘inside knowledge’, and would consent to having my workshop hijacked and reframed
But, I wasn’t having any of that. I know bluff and bluster when I see it, and as a reformed bullshitter I will not willingly accept bullshit from anyone else.
So, as their bullshit came in fast and furious I started making notes, and thinking of the most adequate response that a Project Manager could make in the circumstances, but I soon tired of note taking and was rapidly becoming irritated by a total lack of empathy and an utter lack of engagement.
Irritated as I was, I still tried putting things back on the rails. Therefore, I continued to be as engaging and constructive as one should, whilst internally suppressing the urge to ask ‘what the feck is going on here?’ So, I talked about lifting, shifting and dropping a legacy data warehouse and marts from one box to another – thinking that this would be the minimum that Data Warehouse experts could engage with – and the need to get estimates of the effort required to do so (you know, things like roles and number of days, Big ballpark estimate stuff). The ‘Data Warehouse Architect’ and I use that term loosely, went off on a tangent. Vague, fuzzy and disjointed. The architect threw in some nonsensical vagaries about the need for Master Data Management to be an integral component of any future data warehouse. I half-managed to avoid the incredulous Jeremy Paxman look of ‘what on earth are you talking about?’ just as the gathered augmented the assault on MDM with a call to Information Lifecycle arms. Therefore, when things were becoming even weirder, the weird turned pro, with the train kicked off the tracks, rolled down the hill, and then set on fire, by so-called professionals, passing themselves off as supine yobs, and reciting, in close harmony, “Proof of Concept! Proof of Concept! What about the f****** proof of Concept! Uh? Uh? Uh?”
Well, well, well… what a way to run a dance hall!
We were opening up all technological fronts, apart from the ones that would actually be relevant. I felt like a PG Tips chimp getting bananas and cups of tea thrown in their general direction. I was Martyn, the Project Chimp, plaintively calling out “‘ere mate, do you know this is not the way to do Data Warehousing”, and half-expecting a response along the lines of “you plan it, Son, we’ll muddle along “. I didn’t get a response, all I got was what looked like the human equivalent (if there is such a thing) of a page-fault, a glazing-over of the eyes and a rapid reboot into full-on bullshit mode.
I could go on and on about this all day, but I would rather not. Just the day in the life of a PM tasked with getting sense, sensibility, and work out of profane variations on the theme of Blackadder’s stupid Prince George. “I don’t need Inmon or Kimball, I know data! And… I have been to Ikea!” Sorry, that was just an example of how utterly obtuse things can get on the front-line of Data Warehousing.
So, to close, I would like to pose a question, one that goes beyond Data Warehousing and Big Data. Do people have the same or similar issues in other parts of IT or indeed in other businesses and technical related activities?
Bamberg
22nd September 2012
17 Tuesday Nov 2015
Many people come up to me in the street and beg me to write about the truths, myths and unwise things said about Big Data. I am offered gifts of goats, partners and riches beyond the dreams of avarice just to pronounce on such things. I am not in the habit of bowing to such street-pressure, but I have finally come round to doing something, if only to placate the river of rose-petal bearing infants’ tears flowing past my abode.
12 Thursday Feb 2015
Posted in Big Data, Consider this, Good Strat, Information Management, Martyn Jones
Normally I would send such people to see a specialist – no, not a guru, but a sort of health specialist, but because this has happened to me so many times now, I eventually decided to put pen to paper, push the envelope, open up the kimono, and to record my advice for posterity and the great grandchildren.
So, here are my top seven tips for cashing in quick on the new big thing on the block.
1 – A business opportunity for faith
Like every new religion, trend or fad, Big Data has its own founding myths, theology and liturgy, and there is money to be made in it; loadsa lovely jubbly money. By predicating and evangelising Big Data you will be welcomed with open arms into the Big Data faith, and will receive all the attendant benefits that will miraculously and mysteriously fall upon you and your devout friends. Go on, I dare you. Be a Big Data guru, a shepherd to a flock of sheep, and enjoy the wealth, health and happiness that most surely will come your way. You too can look cool in red Prada slippers, a flattering and flowing gown and matching accessories.
2 – Acquire it, multiply it, weigh it, mark it up and sell it on
Simply stated, this is about acquiring other people’s data, by sacred means or profane, marking it up and then selling it on. The value you add is that you act as a trusted conduit, a conduit for good. You may care to enrich the data, swop the order of data, replicate and embellish data, make stuff up, etc. which all serves to ‘add value’ to the data. You may even consider adding nuggets of value to the data, just for kicks and giggles. My best friend’s favourite is injecting the good old ‘diaper and beer’ and ‘friends and family’ clichés into every Big Data collection, as it never fails to thrill, please and delight.
3 – Anything can be anything
The good thing about making money from Big Data is that it doesn’t need to be anything to do with Big Data. Make a 20GB Enterprise Data Warehouse? Call it a Big Data success. Sell 20 boxes of dodgy doughnuts down the alternative market? Proclaim a Big Data triumph. Sell your digital porn stash to your best mate? Point to the incredible invisible hand of the Big Data market at work. See what I’m doing there. Anything can be anything, and you too can cash in on that opportunity, big time.
4 – Big Data Patronage
Tense, nervous headaches? Do you like making up stories about Big Data, or for that matter anything else? Are you a natural born fibber but are strapped for cash? Then worry no longer. If you get a Big Data patron you will be sorted for ‘life’; get two and you’ll be sorted for the afterlife as well. With a Big Data patron you can get the most tenuous, crappiest and superficial of pieces published, promoted and vaunted – globally. Can’t make it up yourself, then outsource and offshore it, after all, just get the keywords right for SEO ranking and the gullible will flock to you in droves. The down side of this profession is that you will be targeted for writing half-truths, quarter-truths and downright lies, and you will be pilloried as a purveyor of rank hyperbole. But don’t worry, take heart and never lose the faith, you will be in good company. As one Big Data guru was want to say ” If you repeat a lie often enough, people will believe it, and you will even come to believe it yourself.” Amen! brother.
5 – Big Data Certification
By 2016 there will be global demand for 30 billion Big Data professionals. Are you prepared to cash in on that inevitability? No? Then consider this.
One of my best friends makes his living as a completely phony Big Data Scientist. For two hundred bucks he can make you a Data Scientist or a Big Data guru. Some guys give you an education but this guy gives you immediate access to high paying jobs, sex and a life in the city. Moreover, for an extra 250 bucks you can also become a certified Big Data Trainer, which will allow you to do unto others what has been done unto you.
6 – Creative Technology Reuse
Big Data has heralded in the biggest innovations known in the history of computing, and arguably in the entire history of humankind. One of those new inventions has been the now widely acclaimed and revolutionary ‘flat file data base’ (FFDB), and this has been accompanied with developments in low level operating system primitives that allow for the processing of these collections and hierarchies of FFDBs. So, if one has a mind to do so, one can get some real business leverage off of these new tendencies by borrowing 21st century technology found in old operating system hacks from the sixties and seventies and eighties and nineties and… Well, the point is that in order to get serious funding it is no longer good enough to have a half page business plan, it is also necessary to eke out ‘stuff’ that works within the new paradigms of Big Data and Big Data Analytics. For my next venture I will be looking for serious funding for my ‘Arbitrary Dawdle Down Data Street’ (AD3S) Big Data Analytics platform, a platform designed to support virtual 1k bit processing and the massively parallel provision of global regular expression search and match (S&M), concatenation and listing, and cooperative data-driven and streamed data extraction and reporting. I’m hoping to attract the attention of governments, the EU, the Manic Street Preachers, the UN, China, Vladimir Putin, the DOD, HP, Oracle, Gartner, Lana Del Rey, Deloitte and IBM. So, this is going to be absolutely massive. Word!
7 – Big Data Brokerage
According to leading management consultants and industry watchers Gartner, McKinsey and Deloitte, data needs to be managed and accounted like any other asset, such as money. To get into a similar view-point requires a massive leap of faith, but it is a conversion that might drive dividends. One avenue to be explored in eking out value from the apparently massively valuable Big Data lakes, silos and pools is through the operation of a Big Data Brokerage. A Big Data Brokerage is a business whose main responsibility is to be an intermediary that puts Big Data buyers and Big Data sellers together in order to facilitate a transaction. Big Data Brokerage companies are compensated via commission after the Big Data transaction has been successfully completed. They may also charge introductory fees. Just imagine the wealth of business opportunities in that. You could become the Goldman Sachs of data.
I hope you enjoyed this piece and would be pleased to hear your views on this and other subjects.
Whilst I understand the attraction and even the need of creating a new and significant growth industry, I would also advise a degree of restraint, and whilst I see that “Big Data” (the consideration of the potential value of All Data) has its allure, I also think that some good sense and informed caution should also prevail.
Thank you so much for reading.
Martyn Richard Jones
GOOD STRATEGY 