Tag Archives: Analytics

Getting business value from data? Commercial analytics is where it’s at

29 Tuesday Mar 2016

Posted by in 4th generation Data Warehousing, All Data, Analytics, Ask Martyn, Big Data, Big Data 7s, Big Data Analytics, Commercial Analytics, dark data, data architecture, Data governance, Data Lake, data management, data science, Data Supply Framework, Data Warehouse, Data Warehousing, Martyn does, Martyn Jones, Martyn Richard Jones, pig data, sentiment analysis, The Amazing Big Data Challenge, The Big Data Contrarians

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Martyn Richard Jones

Overview

I first became involved in commercial analytics in the eighties. First, through my involvement in customer segmentation and data visualisation, principally in banking but also in energy, manufacturing and the chemical industry. It also emerged later, in conjunction with my activities at the Sperry European Centre for AI, and was centred on pricing and yield management applications developed using a combination of statistical techniques, expert system technology and data centre architectures all tightly integrated within a 4GL development and delivery environment. This provided a comprehensive and seamless scenario building, hypothesis testing and reporting capability. Continue reading →

Free Business Analytics Content –Thanks to Wikipedia – Part 5

17 Thursday Mar 2016

Posted by in 4th generation Data Warehousing, Analytics, Ask Martyn, Big Data Analytics, data science, Martyn does, Martyn Jones, Martyn Richard Jones, statistics

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Why buy when you can get it for free?

Back at you! Here is the fifth fantastic delivery of an amazing and fabulous selection of free and widely available business analytics learning content, which has been prepared… just for you. Continue reading →

Can you read? Be honest, now

15 Tuesday Mar 2016

Posted by in 4th generation Data Warehousing, Ask Martyn, business strategy, Good Strat, Good Strategy, goodstrat, Marty does, Martyn does, Martyn Jones, Martyn Richard Jones, Strategy

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Martyn Richard Jones

When you have read this, if indeed you read it all, will I have failed to convey the essence of what I am trying to get at? Will a confusion of entropy win the battle? Will the wheel of fortune turn in my favour, or will I fail to connect and communicate effectively?

Let’s give it a spin and see what happens. Continue reading →

The Big Data Contrarians: The Agora for Big Data dialogue

11 Wednesday Nov 2015

Posted by in 4th generation Data Warehousing, All Data, Analytics, Big Data, statistics, The Big Data Contrarians

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“In fact men will fight for a superstition quite as quickly as for a living truth – often more so, since a superstition is so intangible you cannot get at it to refute it, but truth is a point of view, and so is changeable.”

Hypatia

On the 1st of July, I decided to set up a professional group on LinkedIn in order to create a hype free Agora for Big Data dialogue. I called the group The Big Data Contrarians and although it is a closed group, all those with interest in an open, informed and honest exchange of ideas on data, from whatever angle they are coming from, are very welcome to join in. (URL:  http://www.linkedin.com/grp/home?gid=8338976)

So, why is the group called The Big Data Contrarians and not something more generic, such as The Data Contrarians?

It was a hard call, but in the end, I decided to go with the spirit of the times, for as overhyped and laden down with tripe as it might be, and the name stuck. This doesn’t mean that the group is limited to airing views, opinion and speculation just about Big Data. There’s a wealth of related data out there, and it’s not all as big as Google’s stash. Indeed, one of my pet projects for the world of Big Data and the Internet of Things is in discovering, designing and developing ways to reduce the Big Data footprint, early and often, without losing the essence of what the data might be able to tell us.

I believe that The Big Data Contrarians group fulfils a number of useful functions:

  1. To alert people to interesting but ultimately dubious Big Data claims
  2. To share lessons learned, good sense and practical data and Big Data principles
  3. To connect professionals in overlapping disciplines, for example, in statistics, data architecture and data management, project management, solutions architecture, database administration, data science, risk management, technical, management and executive management roles, and a long list of etceteras.
  4. To educate, inform and entertain each other about the practical world of data and Big Data
  5. To weigh up the pros and cons of data technologies and techniques applied to solving business challenges
  6. To minimise the hype surrounding Big Data, covered in part by the first item, but more so
  7. To engender critical thinking, healthy scepticism and reasoned contrarianism

Of course, these are simply suggestions, and what may not be false today, may be true sometime in the future, and vice versa. Nevertheless, that encapsulates the general spirit.

As with other groups, we can all contribute discussions, comments, links to other material advertise jobs and promote what our businesses, and what we ourselves are doing.

In particular, I would be interested in hearing the facts about real-life Big Data success stories. I want to see things that have some value, not just ambiguous references to what might work or allusions to success stories that are just too successful to talk about in anything but the vaguest or the most venal, pompous or preposterous of terms. I want simple, clear and coherent, and I’m sure I’m not alone in this desire. I want to hear:

  1. We looked at this data. Yes, provide details of the data that you used, the data objects, attributes, data structures and business and technical meta-data. It doesn’t need to be exhaustive, it just needs to be sufficiently complete to understand exactly what is going on.
  2. These are our pain points and business questions. There’s no need to be coy, we can just spell it out.
  3. These are the business questions converted to Big Data analytics. Yes, let’s see details of the search, sum and compare parameters, etc. after all, this is about understanding and spreading the ‘word’. So let’s just make it happen.
  4. What outcomes from Big Data analytics did we get, if any, actually directly influenced business decisions, and in what way.
  5. The truth about ROI. How much did you spend? How much did you gain? What benefits accrued? What disruption ensued?
  6. Did your Big Data initiative go live?
  7. Is your Big Data success story still in production?

I like these seven bullet-points, because they are essentially the types of things I can talk about when it comes to Data Warehousing and Risk Management and Reporting. I also think that the time has come for us to move on to less prosaic and woolly allusions to Big Data successes, and indeed share the skinny on both real Big Data successes and real Big Data failures.

Because that’s how we all progress, right?

Many thanks for reading, and don’t forget, please join The Big Data Contrarians.

Who’s afraid of the Big Data Contrarians? Here’s 500 reasons not to be

11 Wednesday Nov 2015

Posted by in 4th generation Data Warehousing, All Data, Big Data, Business Intelligence, Cambriano, Consider this, Good Strategy, Strategy

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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.

When I first started The Big Data Contrarians group on LinkedIn I was thinking that maybe we would get 100 members within three or four months. Well, I was mistaken. Since the 1st of July, the membership ranks of The Big Data Contrarians has risen to over 500 members. However, it’s not about the quantity it’s about the quality, and The Big Data Contrarians is ‘the nicest Big Data community that you are ever likely to encoun Continue reading →

The Million-Dollar Big Data Briefing

11 Wednesday Nov 2015

Posted by in 4th generation Data Warehousing, agile, Big Data, Consider this, Data Lake, Martyn Jones, Martyn Richard Jones

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If you enjoy this piece or find it useful then please consider joining The Big Data Contrarians: https://www.linkedin.com/grp/home?gid=8338976

Many thanks, Martyn.

Big Data, together with Cloud computing, the Internet of Things and Machine Learning, are topics that are very much to the fore in contemporary trends in Information Management. But is Big Data really the revolution that people have been waiting for or is it simply about the next steps in the evolution of business data architecture and management?  Continue reading →

On Not Knowing Sentiment Analysis

12 Tuesday May 2015

Posted by in Big Data, Big Data Analytics, Consider this, good start, goodstart, sentiment analysis

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If you know all about Sentiment Analysis, you’ve come to the right place. Because I don’t have a clue if what I know about it is accurate or not.

I started to do a bit research into this Sentiment Analysis lark, in particular with the theoretical idea of using it to analyse and draw conclusions from comments on Pulse – assuming that this is what it can be used for.

To begin at the beginning, which is good place to start, I read the piece on Wikipedia, and this was how it began:

“Sentiment analysis (also known as opinion mining) refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials.

Generally speaking, sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document. The attitude may be his or her judgment or evaluation (see appraisal theory), affective state (that is to say, the emotional state of the author when writing), or the intended emotional communication (that is to say, the emotional effect the author wishes to have on the reader).” Source: Wikipedia Link:http://en.wikipedia.org/wiki/Sentiment_analysis

Well, that’s a fairly intuitive description. I could have almost have guessed as much.

But, back to the aim of analysing sentiment in Pulse comments, where to start and what to do.

What would sentiment analysis make of these:

On the death of an IT-business celebrity. What would sentiment analysis make of the very emotive comments of desolation, sadness and poignancy of people who didn’t personally know the departed, even remotely, or maybe didn’t even know of them until after they had ‘shuffled off life’s mortal coil’? How would that work? What would sentiment analysis make of the maudlin aphorisms, surrogate grief and bizarre sorrow of people separated by more degrees than Kofi Anan and Mork from Ork.  What additional insight does sentiment analysis tell us when these comments are analysed along with the body of the text and other comments that triggers these comments?

In a similar vein, how does sentiment analysis catch instances of sycophancy? Especially considering the fact that some of it is so ‘in your face’ and blatant that it often times seems to be a bad parody of a bad parody. “Oh, Ricky, why are you such a sexy brainbox?” How does it work in those situations?

Worse than that is the preening, gushing and obtuse texts of massive, errm… fabulators[i]. If it wasn’t about Big Data or Strategy or IT, it would be about something else, usually about the writer themselves. “I give Rafa and Rodge tips on tennis! I went to the University of the Universe and got a first! I challenged Superman to a race, and won! I have read the entire works of Dan Brown, 25 times…Neeeh!” What would sentiment analysis do with that sort of gold?

Also, what does sentiment analysis do with texts so ambiguously daft that they could mean anything? Okay, it might be able to pick up a few trigger words here or there, “rubbish”, “of”, “load”, “a”, “what”, etc. However, how does it know when “excellent” is being used in a way that means anything but excellent? For example, “Excellent Big Data job there”, with the silent “if you want a job doing properly then do it yourself”.

Finally, for the purpose of this little piece, what would sentiment analysis do with term abuse, if it could actually identify it? Going back to the use of the terms such as Big Data or Strategy, how can sentiment analysis discern between the dopey and wrong-headed use of the term, and when it is actually being used in a coherent, cohesive and consistent way, in line more or less with its formal definition? I suppose we can always write a mountain of rules to help us out:

If topic in focus of piece is strategy

And context of topic is business

And author of piece is Richard Rumelt

Then the credibility of text is good (with a certainty of 100%)

But you and try and maintain a rule base with isntances like that. It soon becomes a management nightmare.

Alternatively, maybe it could be used to analyse this text. It’ll have its work cut out, that’s for sure. Does sentiment analysis do sarcasm and cynicsm?

Anyway! I bet you might know how this sentiment analysis works, don’t you? On the other hand, if not, then it will be someone else who ‘knows’. But of course, all will not be revealed, because it’s a secret so powerful, that in the wrong hands it could be used to dominate the entire galaxy.

Only joking; and many thanks for reading.

[i]To engage in the composition of fables or stories, especially those featuring a strong element of fantasy: “a land which … had given itself up to dreaming, to fabulating, to tale-telling” (Lawrence Durrell).

lang: en_US

Consider this: Big Data and the Pot of Tea

17 Tuesday Feb 2015

Posted by in Big Data, Consider this, Good Strat, Good Strategy, Martyn Jones, Martyn Richard Jones, Strategy

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To begin at the beginning

Hold this thought: Big Data is King.

Is there just nothing that Big Data isn’t capable of fixing? From terrorism, world hunger, Ebola, HIV, fraud, money laundering and hiring the ‘right’ people through to winning the lottery, curing hangovers, arranging entrapment and finding the love of your life. Big Data is King. Continue reading →

Big Data, a promised land where the Big Bucks grow

12 Thursday Feb 2015

Posted by in Big Data, Consider this, Good Strat, Information Management, Martyn Jones

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Consider this. Many people come up to me in the street, and, apropos of nothing, they ask me how they can make money from Big Data.

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.

That’s it folks!

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