22 Thursday Jun 2017
“Knowledge is the capacity to give correct answers to questions.”
“There is no well trodden path that takes a straight line from symbols, through data to knowledge and wisdom. This is just some nonsense invented by the IT industry.” – Martyn Jones
We may define data as being the symbolic representation of value or conversely of something which has no attached value. Data may represent, among other things, time, money, resources or worldly objects.
31 Wednesday Aug 2016
Posted in 4th generation Data Warehousing, All Data, Ask Martyn, Big Data, Big Data 7s, Big Data Analytics, Cambriano, dark data, data architecture, Data governance, Data Lake, data management, data science, Data Supply Framework, Data Warehouse, Data Warehousing, good start, Good Strat, Good Strategy, goodstart, goodstartegy, goodstrat, Martyn does, Martyn Jones, Martyn Richard Jones, pig data, The Amazing Big Data Challenge, The Big Data Contrarians
Martyn Richard Jones
Lora del Rio, 31st August 2016
Big data has arrived. Big Data is here for keeps. Big Data is the future.
Despite some of the malicious, mendacious and malodorous words of naysayers, sceptics and contrarians, the world of big data and big data analytics is replete with totally amazing and fabulous success stories.
Big Data gurus are often accused of not delivering coherent, cohesive and verifiable accounts of Big Data successes. Which is understandable but at the same time a pity. So here, to illustrate this miraculous and remarkable turnaround, I give you not three but seven of the many Big Data success stories that I could have casually grabbed out of the ether.
First, we take a trip to Glasgow to discover the leveraging of Big Data in alternative investments. Then we pass over to Boston to explore the magic of Big Data at Universal Legal. We venture through Switzerland and innovative marketing. Explore the heights of Dongalong Creek. Have a word with the good folks at Heisenberg Labs. Then round it off with a quick in-depth summary of Big Data at Choppers. So, here we go…
29 Monday Aug 2016
Posted in 4th generation Data Warehousing, All Data, Ask Martyn, Big Data, Big Data 7s, Big Data Analytics, business, Business Intelligence, business strategy, dark data, data architecture, Data governance, Data Lake, data management, good start, Good Strat, Good Strategy, goodstart, goodstartegy, goodstrat, Martyn does, Martyn Jones, Martyn Richard Jones
Once upon a time, there was a mountain known as Peak 15. Very little was known about it. Then in 1852, surveyors found it was the highest in the world, and they named it Everest.
As with other significant challenges that we can identify in life, many people have been driven by a passionate desire to conquer peaks all around the world. This is just one illustration of those of us who can identify their significant challenges and rise to them. This sharp focus, determination and courage turns ordinary citizens into people who are invariably on a mission. People who know what they want. Continue reading
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.
19 Wednesday Aug 2015
Posted in Big Data, good start, goodstart, goodstrat
If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians:
Join The Big Data Contrarians here: https://www.linkedin.com/grp/home?gid=8338976
Many thanks.
To the layperson anxious for answers to complicated questions, the very idea of bringing together sets of disparate data and turning it into precious insights may seem like magic, a modern day alchemy, a goal placed well beyond the grasp of mere mortals. Fortunately, this is no longer the case, thanks in part to bagatelle-proportioned advances in Big Data and Big Data analytics and massive advances in imagination; we are able to look into the past, the present and the future, with absolute certainty. Continue reading
16 Sunday Aug 2015
Posted in Big Data, Consider this, good start, goodstart, Strategy
Tags
Big Data, cynicism, data management, fakes, good start, goodstart, gurus, Martyn Jones, Martyn Richard Jones, Strategy
Why so many ‘fake’ Big Data Gurus?
Where do you all come from?
Where do you all come from?
All your integrity’s gone
Now tell me, where do you all come from?
From ‘Where Do You All Come From‘ by Mott the Hoople Continue reading
03 Monday Aug 2015
Posted in Big Data, Consider this, good start, goodstart, Martyn Jones, Strategy
If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians:
Join The Big Data Contrarians here: https://www.linkedin.com/grp/home?gid=8338976
Many thanks.
Now everyone is doing Big Data you don’t want to be the odd one out, right? Of course not.
Now, if you are serious about looking at Big Data from a business perspective then I will try and lend you some advice. If you are doing it from an IT or technology perspective, then I wish you good luck, and I hope that your Big Data initiative doesn’t turn into another tech crash-and-burn show.
Now some Big Data pros are telling us that the place to start with Big Data is with strategy. Now, I’m too polite to call this out as abject bullshit, even though it is, and will instead content myself by offering an alternative and simple approach to approaching and addressing Big Data.
My first piece of advice is this. DON’T START WITH STRATEGY!
Strategy is a coherent, cohesive and executable response to a significant challenge.
Strategy is not a definition of objective, a wish list of what you are trying to achieve or aspirational goals of a nebulous nature. No, strategy is not the objective but a means of reaching that objective. Strategy is real, tangible and executable. Strategy is doing.
So what is a Big Data strategy?
If a company is looking at the Big Data options, the last place they should want to start out from is from strategy. That is as silly idea as they come. Starting with strategy on the road to formulating viable responses to significant challenges and opportunities is like saying that before we choose strategic options and a realisable strategy, then you must have a strategy in place.
Strategy is not working out what you want to achieve. That sort of thing should happen prior to any strategic work. Neither is strategy an exercise in establishing starting points, nor formulating questions nor understanding the challenges. All of this should come well before the major strategy aspects even kicks-in.
Big Data strategy is a realisable, tangible and manageable response to a significant challenge, one that depends heavily on the availability, usability and credibility of Big Data (or Very Large Data Bases) and the business value of processing that Big Data.
So, a word of advice. If you are thinking of embarking on a Big Data initiative, do not start with strategy. That is a really daft place to start.
Start here instead. With real business imperatives. This is where you are thinking about the big and significant challenges to the business, and how, at a high level of abstraction, you could go about meeting those challenges. Here you identify your challenges and your responses, aligned to your objectives.
If you can identify business imperatives that make it absolutely necessary to include elements of Big Data, then go forward with that mandatory requirement in mind. If not, then don’t try to shoe-horn Big Data into a place where it really isn’t needed or wanted. Because if you go against the grain in this way it may well hurt you and your business, in more ways than you bargained for.
In order to go out looking for data requirements driven by business imperatives, we really need to know what we are looking for.
What we are looking for maybe highly tangible or less so. We may have to derive the data we are looking for by refining, aggregating, enriching, filtering and cleansing. Therefore, with those and other aspects in mind, we can go out and find what we need.
From looking at the data requirements, you should have a good idea of potential sources of that data. Agility in this aspect is predicated on the premise that one knows the systems on the IT landscape, the business processes and all the potential sources of data – at a high level at least. So, this is not the sort of work you can do remotely with little or no knowledge of the clients business, IT setup, processes or culture.
But anyway, after you identify the sources you move on to the next step.
Here you discuss aspects of the data you require with the database / application platform owners to ensure that:
So far so good. Once passed these hurdles (and don’t forget this is a super-simplification) we move in to the next.
So, now we know:
Therefore, we go ahead and create a proof of concept or three. Simples!
However, make sure that all prototypes are governed by these simple timeless guidelines:
You run your proof of concept. You analyse, assess and represent your outcomes. You socialise, present and interpret.
When you’ve done that you are in now in a good position to estimate the usefulness of the exercise, from both a qualitative and quantitative perspective.
I did not want to touch in specific aspects of technology in this piece, in part, because I did not consider it a central issue in the theme of things. Of course, as part of creating proofs of concepts and pilot schemes you may want to experiment with the swatch (swaith? oh for auto-correction) of technologies out there. So go ahead and evaluate ‘Big Data’ technologies, and don’t forget, the answer to every Big Data technology question isn’t an automatic ‘Hadoop’. There are other valid Big Data technology options around, such as Lustre and GPFS, or even Oracle, Teradata or EXASol. Also, remember this, if all you are working on is a prototype, a proof of concept or a pilot then you can try and negotiate a free license with any of the major DBMS vendors for that initiative. So negotiate, bargain and get the most appropriate technologies with the best deals.
Finally I will leave you with three guidelines to consider:
Many thanks for reading.
In subsequent blog pieces I will be sharing my views on the evolution of information management in general, and the incorporation novel and innovative techniques, technologies and methods into well architected mainstream information supply frameworks, for primarily strategic and tactical objectives.
As always, please reach out and 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 you will even consider sending me a LinkedIn invite if you feel our data interests coincide. Also feel free to connect via Twitter, Facebook and the Cambriano Energy website.
For more on this and other topics, check out some of my other posts:
Absolutely Fabulous Big Data Roles – https://www.linkedin.com/pulse/absolutely-fabulous-big-data-roles-martyn-jones?trk=prof-post
Not banking on Big Data? – https://www.linkedin.com/pulse/banking-big-data-martyn-jones?trk=prof-post
10 amazing reasons to join The Big Data Contrarians –https://www.linkedin.com/pulse/10-amazing-reasons-join-big-data-contrarians-martyn-jones?trk=prof-post
Amazing Data Warehousing with Hadoop and Big Data –https://www.linkedin.com/pulse/cloudera-kimball-dw-building-disinformation-factory-martyn-jones?trk=prof-post
The Big Data Contrarians: The Agora for Big Data dialogue –https://www.linkedin.com/pulse/big-data-contrarians-agora-dialogue-martyn-jones?trk=mp-reader-card
The Big Data Shell Game – https://www.linkedin.com/pulse/big-data-shell-game-martyn-jones?trk=mp-reader-card
Aligning Data Warehousing and Big Data –https://www.linkedin.com/pulse/aligning-data-warehousing-big-martyn-jones?trk=mp-reader-card
Big Data Luddites – https://www.linkedin.com/pulse/big-data-luddites-martyn-jones?trk=mp-reader-card
Data Warehousing Explained to Big Data Friends –https://www.linkedin.com/pulse/data-warehousing-explained-big-friends-martyn-jones?trk=mp-reader-card
Big Data, a promised land where the Big Bucks grow –https://www.linkedin.com/pulse/big-data-promised-land-where-bucks-grow-martyn-jones-6023459994031177728?trk=mp-reader-card
The Big Data Contrarians – https://www.linkedin.com/pulse/big-data-contrarians-martyn-jones?trk=mp-reader-card
Is big data really for you? Things to consider before diving in –https://www.linkedin.com/pulse/big-data-really-you-things-consider-before-diving-martyn-jones?trk=mp-reader-card
Big Data Explained to My Grandchildren – https://www.linkedin.com/pulse/big-data-explained-my-grandchildren-martyn-jones?trk=mp-reader-card
If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians:
Join The Big Data Contrarians here: https://www.linkedin.com/grp/home?gid=8338976
Many thanks.
03 Monday Aug 2015
Posted in Big Data, Consider this, good start, goodstart, Martyn Jones, Strategy
Plus ça change, plus c’est la même chose.
Jean-Baptiste Alphonse Karr
I wrote a piece called ‘7 New Big Data Roles for 2015′. I published it on LinkedIn. Many people read it. Some people made suggestions. Others politely ignored it.
I listened to the suggestions, comment and criticisms, and revised the piece as a result.
So here, it is… I hope you like it. And if not, I might try again in six months’ time. Continue reading
26 Sunday Jul 2015
Posted in Big Data, Consider this, Data Warehousing, good start, goodstart, hadoop
Many thanks for reading, and don’t forget, please join The Big Data Contrarians.
Some time back, Bill Inmon, the father of Data Warehousing, took the Hadoop vendor Cloudera to task for putting out some confusing advertising.
In recent times, Cloudera have linked up with Ralph Kimball, who, as some in the data world will know, has been an eternal ‘rival’ of Bill Inmon.
For some, the name of Ralph Kimball has become synonymous with dimensional modelling, and although the Kimball Group once stated that Ralph did not invent the original basic concepts of facts and dimensions, Ralph has contributed much to the development of dimensional modelling and the innovative use of SQL. Subsequently, the Kimball Group reassessed, and are now labelling Ralph as the “Dimensional modelling inventor”.
Kimball and Cloudera have collaborated on a number of initiatives, such as a webinar and slide set, with particular emphasis on the theme of Hadoop and Data Warehousing.
Now, I do not know whether this is intentional or accidental, but this collaboration has produced a lot of disingenuous claims and dubious comparisons, so much so, that I get the impression that building the DW Disinformation Factory is becoming a cottage industry in its own right.
Personally, I can see scenarios in which Big Data complements Enterprise Data warehousing, and I have explained my vision and possible architectures for these scenarios. However, what some Hadoop vendors are alluding to in the Data Warehousing space, is actually quite mischievous and misleading and is not constructive in the least, in fact, the biggest side-effect is to muddy the Big Data and Data Warehousing waters even further. That is not good, either for the industry or for the customers, or indeed, for the professionals.
In one piece of content from Cloudera, we can read that…
“Dr. Kimball explains how Hadoop can be both:
A destination data warehouse, and also
An efficient staging and ETL source for an existing data warehouse”
On the first point? No, Hadoop will not be replacing Teradata, Oracle, EXASol or any other high-performance relational database management system.
On the second point. Hadoop could support a data source for Data Warehousing, as can many other technologies. However, there is no such animal as an ETL source. There are data sources and data targets, extractions, transformations and loads, and all that cool data management, but ETL is a technology, not a source.
I think Big Data may have a big future; it depends on how deeply the internet development culture pervades enterprise application development. A lot of what Big Data addresses is about is making up for shortfalls created by badly architected web applications and shoddy application development, in which data use and data persistence were at best workaround bodges, rather than being well designed and coherent approaches to data management.
Maybe this is some why people have a hard time explaining why they are considering using Hadoop technologies for Big Data. What would a CEO say if it was brought to their attention that Hadoop was being used in their business simply to make up for the fact that their internet applications are really shoddy examples of analysis, design, architecture and management? More to the point, what would the shareholders say if they understood the full ramifications behind the need to use Hadoop?
In many cases, I think that Hadoop can be an indication that your IT organisation did something very wrong in the past, and that in these cases Hadoop is the price one pays when you one does not want to bite the bullet and admit that to screwing up, big time.
In my opinion, it would make more sense to replace applications built on faulty architectures with robust and well-architected applications, rather than fix a problem by overmedicating the patient. This would mean that data generated and used by these applications could simply dovetail into standard decision-support data platforms, such as the Enterprise Data Warehouse.
As for Cloudera and their bizarre and babbling baloney about Hadoop replacing the Data Warehouse? I suggest they read a book in the subject of Building the Data Warehouse, and maybe buck up their ideas a bit. As Bill Inmon stated “You would think that the executives of Cloudera would have familiarized themselves with what a data warehouse is.”
As for recognised data professionals and influencers who support such Hadoop tripe? The less said the better. Eh, Ralphie?
That stated, maybe Cloudera, Kimball and the Big Data flim-flam merchants simply don’t care.
So go ahead, “turbocharge your Porsche – buy an elephant.”
Many thanks for reading. Don’t forget, please join The Big Data Contrarians. The best Big Data community on the planet.
22 Wednesday Jul 2015
Posted in Big Data, Consider this, good start, Good Strat, goodstart, goodstrat
Tags
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
