01 Tuesday Dec 2015
The Digital Document Lifecycle
MARTYN RICHARD JONES
This is a story of the life of a digital document. Its purpose is to explain the process of analysing, designing, building, testing and delivering content rich business artefacts in today’s digital age.
13 Friday Nov 2015
22 Wednesday Jul 2015
Posted in Big Data, Consider this, good start, Good Strat, goodstart, goodstrat
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As a child, I adored the USA rock band the Eagles, especially the musical talents of Joe Walsh. This explains the inspiration behind the title of this piece.
So, what’s going down at Ashley Madison?
Never heard of them? Off your radar? Surely not?
That stretches the bounds of incredulity. As even the people in Singapore’s Media Development Authority have heard of them. They even described their business site this way “it promotes adultery and disregards family values”, and subsequently will not allow them to operate in Singapore. Well, what a turn-up for the books.
On a more serious note, and as you might know, (from Wikipedia or some other ‘sites’,) Ashley Madison is a Canadian-based online dating service and social networking service marketed to people who are married or in a committed relationship. Its slogan is “Life is short. Have an affair.” It seems, if we are to believe various reports doing the rounds, that their Big Data has been compromised, big time.
Yes, I know, how could that possibly have happened, right?
According to some reports, Adison Mashley have around 37 million clients in the Big Data pool, and large caches of it have allegedly been stolen after an apparently successful hacking attempt was carried out. According to Krebs On Security, data stolen from the web site in question “have been posted online by an individual or group that claims to have completely compromised the company’s user databases, financial records and other proprietary information.”
But, again I ask, how can this happen?
I am not an avid fan of Big Data technology for core business use, and given the level of Big Data technology maturity, it sounds like a dopey idea. But each to their own.
What I will state is that my database management experience has tended to be associated with database technologies that can only be hacked as part of an inside job i.e. where people either know user IDs, passwords, IP addresses and layers of protection etc. or know of someone who does. Either someone who is a friend, part of the family (no, not that type of ‘family’) or someone who can be blackmailed into divulging the required access paths and security check workarounds.
However, taking a broader and more permissive view of this alleged hackerisation of Big Data, do we write it up as a Big Data success, i.e. The Amazing Big Data Affair? Put it down to a technical glitch and community faux pas? Or do we take a jaundiced view of the whole thing and keep it real? I await with baited breath for the enlightened opinions of the Big Data gurus.
Mitch ‘n’ Andy are not unfamiliar with ‘issues’ related to the use of people’s data. The Daily Dot carried a piece from contributing writer S. E. Smith with the headline ‘Why Ashley Madison is cheating on its users with Big Data’ in that piece, Smith states that “Like pretty much every other website on Earth, Ashley Madison spies on its users and crunches the data in a variety of ways to increase the bottom line.”
Belinda Luscombe writing in Time confirmed these suspicions with a piece titled ‘Cheaters’ Dating Site Ashley Madison Spied on Its Users’. She wrote:
In a study to be presented at the 109th Annual Meeting of the American Sociological Association in San Francisco on Saturday Aug. 16, Eric Anderson, a professor at the University of Winchester in England claims that women who seek extra-marital affairs usually still love their husbands and are cheating instead of divorcing, because they need more passion. “It is very clear that our model of having sex and love with just one other person for life has failed— and it has failed massively,” says Anderson.
“How does he know this? Because he spied on the conversations women were having on Ashley Madison, a website created for the purpose of having an affair. Professor Anderson, who as it turns out is a the “chief science officer” at Ashley Madison, looked at more than 4,000 conversations that 100 women were having with potential paramours. “I monitored their conversation with men on the website, without their knowing that I was monitoring and analyzing their conversations,” he says. “The men did not know either.”
Elsewhere, and as reported on Wikipedia, “Trish McDermott, a consultant who helped found Match.com, accused Ashley Madison of being a “business built on the back of broken hearts, ruined marriages, and damaged families.”
Wow, wow, and triple wow! What a way to run a dance hall!
Maybe they should reconsider their slogan, making it more snappy and apposite. How about “Life is short, we pimp your Big Data” as a starter? So go ahead, make your own and post it below. Have fun.
Many thanks for reading.
Oh, and one last thing before I go… GOOD-AD: Join The Big Data Contrarianshttps://www.linkedin.com/grp/home?gid=8338976
21 Tuesday Jul 2015
Posted in Big Data, Consider this, good start, Good Strat, goodstart, goodstrat, Strategy
Bore da, pobl dda. A hyfryd dydd ‘Big Data’* i bawb.
When it comes to Big Data, some people accuse me of being akin to a Luddite. Nothing could be further from the truth. Not that the facts matter. In the age of superficiality and surfaces there is as much wilfully cultivated obliviousness as there is unashamed and unabashed term abuse. Add the prevailing underlying current of anti-intellectualism into the mix, and we have an explosive combination that manifests itself in the alliterative combination of bluff, bluster and banality.
— JOIN THE BIG DATA CONTRARIANS: http://www.linkedin.com/grp/home?gid=8338976
I was reticent about writing this article, because it’s a bit like arguing against the irrational, self-interested and wilfully obtuse. Or as Ben Goldacre would have it, “You cannot reason people out of a position that they did not reason themselves into.” Therefore, a lot of care needed to be exercised. Indeed, Mark Twain once stated, “Never argue with stupid people, they will drag you down to their level and then beat you with experience.” Now, I wouldn’t go that far, and I do try to be nicely diplomatic, most of the time, but I can see where he was coming from.
Anyway, without more ado let’s get a handle on what a Luddite is, in terms I hope that most will understand.
According to Wikipedia (yes, I know) The Luddites were:
“19th-century English textile workers who protested against newly developed labour-economizing technologies from 1811 to 1816. The stocking frames, spinning frames and power looms introduced during the Industrial Revolution threatened to replace the artisans with less-skilled, low-wage labourers, leaving them without work.”
So why do I get a feeling that some people think that I am a Big Data Luddite?
Here is Peter Powell of PDP Consulting Pty Ltd putting me in my place below the line on my piece titled 7 Amazing Big Data Myths:
“With all due respect – your post does sound a little like what I could envisage an exchange between a man riding a horse and a man driving one if the first automobiles….sorry.”
Although a respectable knowledge of the technology and its evolution would inform otherwise, I assume that this means that I would be the “man riding a horse”… An interesting piece of conjecture indeed, even if hat in lacks in accuracy is made up for by the inexplicable certainty of belief. Still, it’s fascinating to discover just how many ‘experts’ think that this stuff – the sort of stuff I was doing in the mid to late eighties at Sperry and later Unisys – is bleeding edge innovation,
Sassoon Kosian a Sr. Director of Data Science at AIG, had this to tell me on my piece entitled Amazing Big Data Success Stories:
“Yes, cynical indeed… here is another amazing Big Data success story. You go on your computer, type in any search phrase and get instantaneous and highly relevant results. It is so amazing that a word has been coined. Guess what that is…”
What to say? There goes a person who seems to believe that the history of search starts and ends with the Google web search engine. Something slightly less than a munificently inapposite comment, only outdone by its tragically disconnected banality.
More recently, Bernice Blaar had this to say about my take on Big Data in general and The Big Data Contrarians in particular.
“Master Jones may well be the great and ethical strategy data architecture and management guru that the chattering-class Guardian-reading wine-sipping luvvies drool over, but he is also a brazen Big Data Luddite. No, actually far worse than a Luddite, he`s a Neddite, because with his ‘facts’ and ‘logic’ (what a laugh, you can prove anything with facts, can’t you [tou}???) he is undermining the very foundation of the Big Data work, shirk and skive ethic that has been so hardily fought for by the likes of self-sacrificing champions and evangelists of the Big Data revolution, to wit, such as those bold, proud and fine upstanding members Bernard Marr, Martin Fowler and Tom Davenport, for example, and the brave sycophants that worship at their feet. Martyn is worse than Bob Hoffman, Dave Trott, Jeremy Hardy, Mark Steel, Tab C Nesbitt and Bill Inmon, all rolled into one. He may be a great strategist, but I wouldn’t hire him. Contrarian Luddite!”
And then followed it up with this broadside:
“The Big Data Contrarians group are nothing more than a bunch of over-educated clown-shoes who are trying to scupper the hard-work of decent people out to earn a crust from leveraging the promise of a bright future. In a decent society of capital and consumers, they would be banned off the face of the internets.”
How does one reciprocate such flattering flatulence? How can one possible respond to such a long concatenation of meaningless clichés? Though to be fair, I quite liked being referred to as a Neddite, whatever that is.
Anyway, to set the record straight, this is where I stand.
A contrarian is a person who takes up a contrary position, especially a position that is opposed to that of the majority, regardless of how unpopular it may be.
Like others, I am a Big Data Contrarian, not because I am contrary to the effective use of large volumes, varieties and velocities of data, but because I am contrary to the vast quantities of hype, disinformation and biased mendaciousness surrounding aspects of Big Data and some of the attendant technologies and service providers that go with the terrain. I don’t mind people guilding the lily (to use an English aphorism for exaggeration), but I do draw the line at straight out deception., which could lead to unintended consequences, such as creating false expectations, diverting scarce resources to wasteful projects or doing people out of a livelihood. That’s just not tight.
Does that make me a Luddite (or a Neddite)? I don’t think so, but do make sure that your opinion is your own and is arrived at through reason, not some other persons bullying hype. As I wrote elsewhere some moments ago “If you have to lie like an ethically challenged weasel to sell Big Data then clearly there is something amiss.”
As always I would love to hear your opinions and comments on this subject and others, and also please feel free to reach out and connect, so we can keep the conversation going, here on LinkedIn or elsewhere (such as Twitter).
Many thanks for reading.
— JOIN THE BIG DATA CONTRARIANS: http://www.linkedin.com/grp/home?gid=8338976
Photograph: Delegates at my Big Data Summer Camp in Carmarthen (Wales).
*Data mawr
20 Monday Jul 2015
Posted in Big Data, Big Data Analytics, Consider this, Data Warehousing, good start, Good Strat, goodstart, goodstrat
Okay, before we get started I have to declare the real intent for posting this piece. It is to get you to join The Big Data Contrarians professional group here on LinkedIn.
To apply to join the best Big Data community on the web simply navigate to this address http://www.linkedin.com/grp/home?gid=8338976 (or paste it into your browser) and request membership, the process is quick and painless and well worth the effort.
Now for the rest of the news…
There are many common misconceptions amongst the Big Data collective about Data Warehousing. There are common fallacies that need clearing up in order avoid unnecessary confusion, avoidable risks and the damaging perpetuation of disinformation.
In the dim and distant past of business IT, the best information that senior executives could expect from their computer systems were operational reports typically indicating what went right or wrong or somewhere in between. Applied statistical brilliance made up for what data processing lacked in processing power, up to a point, because even heavy lifting statistics requires computing horsepower, which in those days was really a question of serious capital expenditure, which not all companies were willing to commit to.
Then, and curiously coincidentally, people around the world started to posit the need for using data and information to address significant business challenges, to act as input into the processes of strategy formulation, choice and execution. Reports would no longer just be for the Financial Directors or the paper collectors, but would support serious business decision making.
Many initiatives sprang up to meet the top-level decision-making data requirements; they were invariably expensive attempts, with variable outcomes. Some approaches were quite successful, but far too many failed, until the advent of Data Warehousing.
Back then, most of the data that could potentially aid decision-making was in operational systems. Both an advantage and a problem. Data in operational systems was like having data in gaol. Getting data into operational systems was relatively easy, getting it out and moving it around was a nightmare. However, one of the advantages of operational data is that it was generally stored in a structured format, even if data quality was frequently of a dubious nature, and ideas such as subject orientation and integration were far from being widespread.
Of course, data also came in from external sources, but usually via operational databases as well. An example of such data is instrument pricing in financial services.
Therefore, briefly, a lot of Data Warehousing started as a means to provide data to support strategic decision-making. Data Warehousing ways not about counting cakes, widgets or people, which was the purview of operational reporting, or to measure sentiment, likes or mouse behaviour, but to assist senior executives, address the significant business challenges of the day.
Bill Inmon, the father of Data Warehousing, defines it as being “a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision making process.”
Subject Oriented: The data in the Data Warehouse is organised conceptually (the big canvas), logically (detailing the big picture and) and physically (detailing how it is implemented) by subjects of interest to the business, such as customer and product.
The thing to remember about subject areas is that they are not created ad-hoc by IT according to the sentiments of the time, e.g. during requirements gathering, but through a deeper understanding of the business, its processes and its pertinent business subject areas.
Integrated: All data entering the data warehouse is subject to normalisation and integration rules and constraints to ensure that the data stored is consistently and contextually unambiguous.
Time Variant: Time variance gives us the ability to view and contrast data from multiple viewpoints over time. It is an essential element in the organisation of data within the data warehouse and dependent data marts.
Non-Volatile: The data warehouse represents structured and consistent snapshots of business data over time. Once a data snapshot is established, it is rarely if ever modified.
Management Decision Making: This is the principal focus of Data Warehousing, although Data Warehouses have secondary uses, such as complementing operational reporting and analysis.
In plain language, if what your business has or is planning to have does not fully satisfy the Inmon criteria then it probably is not a Data Warehouse, but another form of data-store.
The thing to remember about informed management decision making is that it needs to be as good as required but it does not need to achieve technical perfection. This observation underlies the fact that Data Warehouse is a business process, and not an obsessive search for zero defects or the application of so called ‘leading edge’ technologies – faddish, appropriate or not.
JOIN THE BIG DATA CONTRARIANS: http://www.linkedin.com/grp/home?gid=8338976
Before we delve into the meaning of Data Warehousing, there are a couple of terms that need to be understood first, so, by way of illustration:
Let’s follow the numbers in the simplification of the process.
This is a very simplified view, but it serves to convey the fundamental chain of events. The most important aspect being that we start (1) and end (7) with the user, and we fully involve them in the non-technical aspects of the process.
JOIN THE BIG DATA CONTRARIANS: http://www.linkedin.com/grp/home?gid=8338976
Essentially, a Data Warehouse is a business driven, enterprise centric and technology based solution for continual quality improvement in the sourcing, integration, packaging and delivery of data for strategic, tactical and operational modelling, reporting, visualisation and decision-making.
A data warehouse is business centric and nothing happens unless there is a business imperative for doing so. This means that there is no second-guessing the data requirements of the business users, and every piece of data in the data warehouse should be traceable to a tangible business requirement. This tangible business requirement is usually a departmental or process specific dimensional data model produced together in requirements workshops with the business. We build the Data Warehouse over time in iterative steps, based on the criteria that the requirements should be small enough to be delivered in a short timeframe and large enough to be significant.
Typically, a Data Warehouse iteration results in a new Data Mart or the revision of an existing Data Mart.
As we build up the collection of Data Marts, we are also building up the central logical store of data known as the Enterprise Data Warehouse that serves as a structured, coherent and cohesive central clearing area for data that supports enterprise decision making. Therefore, whilst we are addressing specific departmental and process requirements through Data Marts we are also building up an overall view of the enterprise data.
By technology, I mean technology in the broadest sense of techniques, methods, processes and tools, and not just a question of products, brands or badges.
Unfortunately, there is a popular misconception that Data Warehousing is primarily about competing popular and commercial available technology products. It isn’t, but they do play an important role.
The following is an example of a very high-level Data Warehouse architecture diagram.
Various methodologies support the building, expansion and maintenance of a Data Warehouse. Here is one example of a professional data integration methodology, produced, maintained and used by Cambriano Energy.
And here is an information value-chain map as used by Cambriano Energy as part of its Iter8 process management. There are alternatives, many of which do a satisfactory job.
Last but not least, this was (from memory) the way that Bill Inmon’s Prism Solutions ETL company used to view the iterative EDW building process.
JOIN THE BIG DATA CONTRARIANS: http://www.linkedin.com/grp/home?gid=8338976
At this point, I decided to cut short further explanations on aspects on Data Warehousing. However, if you have any question then please address them to me and I will do my best (or something close) to answer them.
Hold this thought for another time: If you think you can replace a Data Warehouse, that is not a Data Warehouse, with another approach to ‘Data Warehousing’ that doesn’t produce a Data Warehouse, for as fast and cheap as one can do it, then you still don’t have a Data Warehouse to show for all of your efforts. That is not a great place to be.
Therefore, you see, Data Warehousing was never about a haphazard approach to providing random structured, semi-structured and unstructured data of various qualities, provenance, volumes, varieties and velocities, to whomever was of a mind to want it.
Many thanks for reading.
If you want to connect then please send a request. I you have any questions or comments then fire them off below. Cheers :-)
Oh… and one last thing before I go… DON’T FORGET TO JOIN THE BIG DATA CONTRARIANS: http://www.linkedin.com/grp/home?gid=8338976
03 Friday Jul 2015
Posted in Big Data, community, Good Strat, goodstart, goodstrat
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Friends, peers and colleagues, lend me your bandwidth and 10 minutes of your time. Gather around and let me tell you about the greatest, most interesting and fantastically diverse Big Data and Data community right here in our very midst on this amazing LinkedIn community.
We have a new Big Data/Data group, and the group is aptly named The Big Data Contrarians, and yet it is neither a ‘me too’ group, of which there are too many to mention, or a ‘belief circle’, of which the less said, the better. Not, The Big Data Contrarians group is a place for cool opinion pieces, creative abrasion, practical insight and (within the realms of the possible) BS free comment.
However, before going into more detail about the group, I would like to digress for a moment.
Like many people, I take a lot of inspiration from outside my own professional spheres of practice, principles and technologies, and this is no less true when it comes to advertising.
Two of my real influencers – the real kind not the LinkedIn kind – are advertising legends Dave Trott (also author of Predatory Thinking) and Bob Hoffman (the Ad Contrarian), who are exceptionally experienced, talented and creative people, of the NoBS (no flim-flam) kind. Indeed, it was after reading some of Bob’s and Dave’s recent articles that I decided to get this group registered on LinkedIn, which, love it or loath it, is where many of us connect.
So, I hear you ask “What’s The Big Data Contrarians, Mart?”
Okay, to be fair, The Big Data Contrarians group is about far more than just being contrarian and a legitimate means of inciting discussion, for as reasonable as that is. It’s also about arguing against or openly rejecting mistakenly cherished and contrived Big Data beliefs and ‘institutions’ and established Big Data hype, speculation and opinion. It’s about separating Big Data fads, fantasises and folk-tales from Big Data reality.
What we seek to understand and convey is where, when, how and for what ends data (including Big Data) can be used to derive legitimate benefits. Moreover, stated from a position of reason and facts, and not simply projected as an issue of Big Data faith, speculation and clairvoyance.
On the other side, we can call out the Big Data hype for what it is, and just as Bob Hoffman calls out the social media and advertising BS babblers in his trade, this too lends a platform for people to do the same with the disreputable and dubious practices of Data gurus, courtesans and ‘influencers’.
“So, Mart, is being a Big Data Contrarian a bit like being a Big Data Luddite?”
Well, not really, but the problem with having so many people who are new to IT is that the past is a mystery top them, so anything that is new to them is actually taken as new, whether it is new or not.
Those who know will know that technologies of distributed file stores and search over unstructured data has been around for quite some time, and some of the “new” technologies that we big-up today, are actually simple developments of data technologies that go back to the seventies and eighties, or maybe even before.
However, this is not essentially about being anti-technology or even in advances in the application of technology, but of understanding that it isn’t helpful for the media, the big industry players and their indentured acolytes, to railroad, cajole and bully businesses into buying Big Data technology they don’t need, to solve Big Data problems and opportunities they don’t have.
That said, it’s up to the members of The Big Data Contrarians to decide on what shape the community should take, and as it is an open forum in democratic terms, the members have equal rights in presenting their own opinions, lessons learned and other insights.
So, if you haven’t yet drunk the Big Data kool-aid, come on down to The Big Data Contrarians, the place for everyone interested in Big Data/Data and its many potential uses.
Many thanks for reading.
Of course, this piece will also not feature on LinkedIn’s Big Data channel, because apparently that channel editor (naming no names) doesn’t like anyone raining on their particular Big Data flim-flam parade.
#BigData #BigDataChannel
22 Friday May 2015
Posted in business strategy, Consider this, Good Strat, goodstrat, IT strategy, Strategy
I used to work for an affable person from Chicago. His two favourite phrases were “Let’s talk strat” and “Brought your cheque book with you?”
There are many misconceptions about strategy. But, I particularly want to address two things:
So, without more ado, let’s get the baby off the ground.
Continue reading16 Saturday May 2015
Posted in Big Data, Consider this, Good Strat, Good Strategy, Martyn Richard Jones, Strategy
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Listen up Big Data playmates! The ubiquitous Big Data gurus, tied up in their regular chores of astroturfing mega-volumes, velocities and varieties of superficial flim flam, may not have noticed this, but, Hadoop is getting set up for one mighty fall – or a fast-tracked and vertiginous black run descent. Why do I say that? Well, let’s check the market. Continue reading
01 Friday May 2015
Posted in Big Data, Big Data Analytics, Consider this, Good Strat, goodstart
Simply stated, the best application of Big Data is in systems and methods that will significantly reduce the data footprint.
Why would we want to reduce the data footprint?
20 Friday Mar 2015
Posted in Big Data, Consider this, good start, Good Strat, goodstart, Martyn Jones
Most of us would probably like to work in a profession recognised for its legality, decency and honesty. At least I hope so. In my line of work, what we have right now is palpable evidence that the IT industry lacks a moral compass.
Imagine this. A major sensationalist tabloid pulls together a team of diverse journalists who are set to work on a national campaign to promote very high usage of sunbeds as a cure for cancer. Why? The newspaper owner’s son owns the sunbed franchise.
The health experts criticise the publisher for being irresponsible, unprofessional and lacking in scruples.
The public is mainly undecided, but many take the story on face value and adopt the fad. The intensive use of sunbeds sharply increases. Elsewhere, in unrelated news, the cases of skin cancer show a marked increase. Some blame it on EU legislation for bangers and bananas.
In spite of protests, the press campaign continues over many months.
Eventually, and based on the evidence of recognised health experts and bodies, the press regulatory association tries to get the offending publisher to temper their claims, but without any success. It is only when the government’s lawyers step in and threaten the newspaper owners with legal proceedings, do they freeze their campaign. Much later, the editor resigns and the board of directors issue a short apology on the back pages of their much vaunted organ.
We have that in IT. Our current sunbed cure for cancer, if you believe those who are ‘bigging it up’, is undoubtedly Big Data.
I occasionally post content to Linkedin, some of it (maybe even this piece) gets promoted through the Pulse Big Data channel. There are some reasonable pieces pinned to that channel, but unfortunately, for much of the time what we get is total and moronic Big Data astroturfing. Tantamount to the equivalent of Big Data’s very own Big Lie campaign.
The Linkedin Big Data channel reflects life, and it is full of self-aggrandising and shameless marketing guff, shot-through with scandalously flimsy promotions of tendentious success stories, specious claims of value, half-truths about realisable benefits and embarrassing conjecture about the importance of social media and internet logs.
What I am referring to mainly are superficially neutral (yet virally toxic) pieces placed in the public domain in order to promote Big Data at any cost.
Now let’s step back a bit.
For over 125 years, the Financial Times (FT) has built up a solid professional reputation for accurate reporting, reliable journalism and informative editorials. The FT is a newspaper trusted by its discerning readership and admired everywhere. In fact, I could not imagine their journalists writing about markets, securities and financial houses the same way that pundits elsewhere write about Big Data, Dark Data and the Internet of Things. Because the FT knows, that maintaining the trust of their readership is far more important than winning the short-term favours of a few market players.
So consider this; if we in IT cannot bring our standards of communicating with the public up to the levels of the financial industry, at minimum, you know what that means don’t you?
Exactly. The IT industry will have a far worse public image problem than the bankers and the solicitors currently have, and we all understand the general public appreciation of those professions.
Now, call me old fashioned, but for me that possibility is worthy of serious consideration, and especially by those in IT who confuse no holds barred pimping of fads, trends and technology, in which truth, decency and honesty are optional, for ethical, candid and informative analysis and reporting of the industry.
How will the industry take these criticisms?
To go back to the sunbed analogy what we will most certainly get comments in this vein:
Whilst those who rail against ‘the cancer curing advantages of sunbed use’ may be right – or at least partially right – the sunbed revolution will continue, just as the IT revolution industry has done, and in spite of people saying that the age of computing would be a passing mania.
So, when someone tells you “intensive sunbed use is just a dangerous fad”, what they actually mean to say is that we don’t need the term any more, as intensive sunbed use is here to stay, as are those who are shrewd, unprincipled and cynical enough to cash-in on the public’s gullibility and wilful stupidity when it comes to fads.
Yes, it does get that bad.
We have people who seemingly spend all their waking lives working out not-so-original ways and means of riddling the IT industry with vacuous bullshit, and what Big Data promotion has shown us clearly is that what we have palpable and comprehensive evidence that the IT industry in general lacks a moral compass.
Is that a reflection of IT, of those who create and manipulate IT fads, or of society in general?
Many thanks for reading.
As always, please share your questions, views and criticisms on this piece using the comment box below. I frequently write about strategy, organisational leadership and information technology topics, trends and tendencies. You are more than welcome to keep up with my posts by clicking the ‘Follow’ link and perhaps even send me aLinkedIn invite. Also feel free to connect via Twitter, Facebook and the Cambriano Energy website.
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