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Over the last couple of years, Big Data has been unavoidable. It’s not just big, it’s massive. If you throw a stone down the streets of London or New York, you’ve got as much a chance of hitting a big data guru as you do a social media guru. 

Undoubtedly, there is great power in data, but is Big Data all it’s cracked up to be

50% of my brain thinks Big Data is great, and 50% of me thinks it’s a neologism.  I’ve found it difficult to reconcile all of the varying information out there about it.

So join me for the first part of a two-part series looking at Big Data. In part one, I’ll look at Three reasons why Big Data is a big load of baloney. And next week in part two, I’ll look at Three reasons why Big Data is awesome.

1. Big trends are trendy

My pet rock still hasn’t moved, and my Tickle-Me-Elmo still won’t shut up. And also, Big Data is big, at least according to Google Trends: 


Source

Some other terms once synonymous with the inter-web were pretty trendy too. Remember this one?


Source 

The adoption curve of the term “web 2.0” looks quite similar to where we are now with Big Data.  And yet, if you still use the term “web 2.0” in your job, then you probably think the Fresh Prince still lives in West Philadelphia.  (He doesn’t.) 

The thing about Big Data is that it really isn’t anything new. Cluster analyses, propensity modelling, neural networks and the like have been in use in the marketing sphere for quite some time. 

The phrase used a few years ago for this sort of stuff was 'business intelligence'


Source

But now, we don’t care about business intelligence anymore. Who needs intelligence? It’s over-rated.  Like Goethe said, “All intelligent thoughts have already been thought”.

And yet, Big Data is everywhere. Why shouldn’t it be? It’s BIG. However, you ask 10 people what Big Data means, you’ll get 10 answers, none of which make much sense.

Maybe it’s because of this:


Source 

We’ve all seen Moneyball and read Nate Silver’s blog. There are people out there who are better at statistics than you. And this is scary.

So what’s the solution?  Throw a bunch of money at Big Data, whatever it is, and sleep soundly knowing that you’ve gainfully employed a math graduate. 

And therefore, Big Data is a big load of baloney.

2. Missing one V

Gartner defines Big Data as requiring Three V’s: Volume, Velocity, and Variety. So let’s look at this a bit deeper.

Volume of data: for sure, there’s loads of data out there. Huge amounts. Check.

Velocity of data: yep, data is moved around in large quantities faster than ever before. Check.

Variety of data: in most digital marketing ecosystems, there are the following types of data (yes, I know there are more, but for the sake of argument bear with me):

  • Site stats.
  • Email engagement stats.
  • Mobile/SMS stats.
  • Past purchases.
  • Demographics, preferences etc.

And within each of these, the options are finite. For example, in email, most people measure (at the very least) opens, clicks and conversions. That’s three types of data. 

And for all of the other areas above it’s the same. For the sake of argument, let’s say that we’ve got 30 types of data in total.

This is the thing. 30 types of structured data.  Processing this data doesn’t require a super-computer, it simply requires robust statistical methodology. 

So, if you’re a digital marketer, what you actually have is 'a few sets of structured, small data', not 'Big Data'.

And therefore, Big Data is a big load of baloney. 

3. You can perfectly predict the past

With the beginning of the National Hockey League’s 2013-14 season fast approaching, I’ve been spending a lot of time lately trying to determine the best bets to place on the eventual winner. 

And of course, it seems Big Data is the best route to my next million dollars. (Btw if anyone is interested in joining my hockey pool then drop me a line - go-live is 1st October!)

I downloaded as many team statistics as I could from last season and embedded them into a spreadsheet. It included rudimentary statistics such as Goals For and Goals Against, right through to Winning % when trailing after two periods, CORSI 5v5, and defensive zone exit rate. 

Then I ran a multiple regression and removed non-causal variables. I perfected the model such that the formula spat out expected point totals that were on average within 0.5 points of the actual result. 

When I plugged in the raw data from the previous season, the outputted expected results weren’t even close to the actual results.

This is a perfect case of what is called 'over-fitting'.

When you have a lot of data, the urge is to use all of it and create an uber-complex, bullet-proof formula. Take all of your data points and find the trendline that touches everything.  But there’s an inherent problem with this – all you’ve done is create a formula to perfectly predict the past. 

The risks that come with an over-fitted model are twofold:

  1. You are assuming that the future will be the same as the past.
  2. Adding or removing variables becomes extremely difficult and risky.

So despite there being lots of data out there, the dominant strategy is to focus on the causal variables. In the hockey allegory above, while I won’t reveal my secrets, two of the stronger predictors of eventual success are goal differential and shot differential. 

Not rocket science, I know – if you take more shots than your opponents you’ll generally score more goals than your opponents.  However, I did learn to remove strictly correlative variables (such as Faceoff Win %, PDO and punches thrown). 

Instead of focusing on Big Data and its gillions of variables, I’m instead focusing on a small amount of variables that actually matter.

Within your organisation, what are your causal variables?  By looking at all the Big Data available to you, you run the risk of the truly valuable signals being obfuscated by irrelevant correlates. 

And therefore, Big Data is a big load of baloney. 

Disagree? 

I do too. Well, 50% of me does. Feel free to elaborate on your point of view in the comments section below.

And, join me in about a week’s time back here on Econsultancy’s blog for my follow up post, "Three reasons why Big Data is awesome”.  

Parry Malm

Published 2 September, 2013 by Parry Malm

Parry Malm is the CEO of Phrasee and a contributor to Econsultancy. Connect with him on LinkedInTwitter or Google+.

23 more posts from this author

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Niki Grant

Big data is just data! We haven't all become number-illiterate overnight. It is beyond me why everyone is suddenly freaking out about 'big data' as if it's some huge issue. We've always managed in the past - everyone just needs to chill out and get on with it!

over 2 years ago

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Jay Izso

Great article! Because it is so on target! It appears we have run out of "buzzwords" so we have recycle things that have already been out there so it sounds "new"...let's face it "big data" is like "content marketing"...it has always been around and most everyone has been apart of it in one form or another.

over 2 years ago

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Prashant Rohilla

I think Big data is just another channel to gain amazing insights about your business. You cannot mainstream it.

over 2 years ago

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Rajesh

BIG DATA is data and like all data its baloney.

However, analysing reams of data and cutting the fluff either mathematically or statistically helps you get to the core. This process converts data to knowledge. Leave it at that, then you are obviously over analysing the past with almost zero guarantee of a different output in the future.

It's when focused quantitative input or knowledge is overlapped with qualitative value - this baloney data becomes INSIGHT

I am sure you next post will deal with this in some form or the other.

Obviously I am a big fan of BIG DATA or business Intelligence....

over 2 years ago

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Edward Chung

Totally agree with the author. Big Data has fast become a cliche. I have attended several conferences titled 'Big Data that' and 'Big Data this'. However what I found was that they were still talking about web analytics, business intelligence, data analysis, etc. They just replaced the word 'data' with 'big data'.

over 2 years ago

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Eddie Prentice

I know you are only at half time on this Parry but a slightly different perspective ...

"Big Data" has become such a big a discussion point because it is so much more than what we see in ecommerce and digital marketing. Predictive analytics in crime prevention, healthcare, education, transport and all aspects of public life is having a massive influence at policy level leading to a significant impact on people's day-to-day lives. There is so much more yet to come.

over 2 years ago

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Klynn Alibocus, Customer Experience Strategy Manager at AXA WealthEnterprise

IMHO Big Data is just a term for a digital trend that seeks to explain and contextualise the continual and exponential growth in data from multiple different sources and types.

I've read a number of articles that almost implies that you have to start by auditing and integrating systems and analysing the outputs that then may determine some useful insights.

But businesses should start with the "Question(s)" they are trying to get answers to first and with a clear understanding in the value that answer will bring the business and its customers. This will then help to tightly define your businesses "Big Data" strategy and tactical plans moving forward.

over 2 years ago

Parry Malm

Parry Malm, CEO at Phrasee Ltd.

Hi all - cheers for all the comments! Clearly a topic that generates some strong opinions.

@Rajesh - you've got it in one. Stay tuned for part 2!

@Edward Chung - quite like your tautological perspective, where "big data" is actually "data." Nice!

@Klynn - couldn't agree more. Trying to find hidden trends in great masses of data is the proverbial needle in a haystack. The questions for which we seek answers are the key. Sort of like the Socratic method, except way less philosophical and way more capitalist :)

over 2 years ago

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Jim Linkhauer

I was pulling for you, but in the end I can't agree. Big Data is a big deal no matter what you call it. The reason it is different is that you can carve as many pieces of small data out of what is out there to do just about any kind of analysis. You are limited by your own creativity and the rules of math. The reason Big Data is a HUGE deal is because it is sooo mobile. Remember when it took an hour to download IE 4 over your dial-up?... same file might take a minute nowadays. Further, that speed is also used in the archiving process sooo.. AGAIN... we accumulate more data every day than ever before. soo... I say let's keep the baloney on our sandwiches and keep analyzing Big Data no matter what the buzz-word users call it.

over 2 years ago

Parry Malm

Parry Malm, CEO at Phrasee Ltd.

@Jim I suppose that's the other extreme. Hope you'll make it back next week for the counter-post :)

over 2 years ago

Mike Smee

Mike Smee, Business Development Country Manager (UK) at Devatics

Missing one V?...Not everyone is!

You’ve missed some very crucial data types out of the equation! What about unstructured data from social feeds subjected to sentiment analysis?...What about mass competitor pricing / promotion information harvested from the web and then factored into pricing, promotion and keyword bids!! All of these are possible with the right tools and combined with the data types listed would likely give rise to many more interesting correlations and hence much more actionable insight!!!

over 2 years ago

Parry Malm

Parry Malm, CEO at Phrasee Ltd.

@Mike cheers for the feedback and I understand your points. I'm guessing based upon your job title/company you've got a specific use case in mind, which I'm sure is quite an interesting one.

But I'm not convinced that "interesting correlations" are what astute statisticians will concern themselves with, regardless of their allegiance to Big Data.

over 2 years ago

Mike Smee

Mike Smee, Business Development Country Manager (UK) at Devatics

I don’t have a example within the context of my own current job, but I am aware a small company in Cardiff (called Eysys) who claim to be doing exactly what I highlight in terms of incorporating social feeds and real-time competitor analysis into the big-data mix with some claimed very interesting results!

I do take your point about “interesting correlations” but sometimes when we discover new approaches we fall into the trap of trying to apply them in an old way! Where Big Data comes into it’s own is in answering questions and testing hypotheses perhaps nobody thought to ask before!...Some of these will probably be things that nobody cares about, but there are many documented cases from predicting shopping patterns to flu outbreaks where very meaningful and valuable (as in £) insights have been gleaned!

over 2 years ago

Ashley Friedlein

Ashley Friedlein, Founder, Econsultancy & President, Centaur Marketing at Econsultancy, Centaur MarketingStaff

Great post Parry. I'm looking forward to part II. Like you I'm somewhat conflicted on the topic. I'm a great believer in data in the value of data-driven decision making but I don't find 'big data' a helpful phrase at all. I expect it to go the way of 'web 2.0'.

over 2 years ago

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Peter Odryna

This has to be one of the more entertaining articles I've read this morning. Especially Parry's followup comment:

@Klynn - couldn't agree more. Trying to find hidden trends in great masses of data is the proverbial needle in a haystack. The questions for which we seek answers are the key. Sort of like the Socratic method, except way less philosophical and way more capitalist :)

Ignorance truly is bliss. Bravo!

over 2 years ago

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Scott Valentine

I'm in the same boat as you Perry - one foot in and one foot out of the trend. I look forward to reading part 2 to determine which side of the fence I feel most comfortable at this point.

Any article that uses hockey to illustrate statistical analytics is alright by me.

over 2 years ago

Jacob Ajwani

Jacob Ajwani, VP of Strategy at Yieldify.com

For your upcoming article "3 ways Big Data is awesome" you can reference a specific article I wrote for e-tailers.

http://econsultancy.com/us/blog/11410-retail-marketers-how-to-turn-data-into-cash

over 2 years ago

Parry Malm

Parry Malm, CEO at Phrasee Ltd.

@Mike sounds like you've got a pretty interesting use case there. I'll check that company out, cheers for the heads up.

@Ashley cheers for the shout-out. And kudos to the Econsultancy team for not producing a "How marketers should use big data" report, even though you'd have made a quick buck :)

@Scott yep, there's been quite a bit of "moneypuck" in the last few years. My next article will look at a specific use case from the NHL as well... stay tuned!

@Jacob nice one - will give it a good read when I have a sec.

over 2 years ago

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Andrew

'Big data' has only ever been an unhelpful buzzword anyway. An obsession of CTOs and CIOs who just use statistics of justify their existence.

Big data is just data.

The only thing that's arguably changed is database and data collection technologies have made it affordable for companies to amass far more data that they know how to handle. And more than is useful for them.

What should be the centre of focus is tools and mechanisms for finding the insight in data - visualization, mining, analytics tools and technologies.

over 2 years ago

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Matt Lovell, Group Head of Customer Insight & Analytics at Thomas Cook Airlines

Data sets have invariably got larger but for me, the main reason that senior bods in companies are now so obsessed with 'Big Data' is because they are so aware of how poorly they are managing any of their data currently.

Ask any company whether they really understand how their customers engage with their website, what they are looking for and whether they find it / purchase it and you'd get a lot of nervous faces as the response that comes back is a very unconvincing 'obviously...'. The same is true of marketing activity, historical customer data or any form of interactions that users may have with a brands web / mobile presence, social media or indeed real life (be it store or call centre focused) existence.

As a result, when they are told that they need to take all of these data source, combined them together and find an answer to the meaning of the world and everything in it they panic and many look to pay some ridiculously overpaid statistical consultants to give them the answer.

The problem is unless you know what questions you want to ask, all the data in the world is useless to you and all you will end up with, in the words of Benjamin Disraeli from way back in the 19th century (popularised by Mark Twain) is lies, damned lies, and statistics...

over 2 years ago

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Anthony

When techies say Big Data they usually mean 1TB+ of unstructured "data lake". Hadoop lets you filter and structure that great mass into under a terrabyte of data which can then be loaded into your data warehouse... you might filter it some more and do final analysis in Excel or SAS.

If you can load it straight into a business intelligence data warehouse or even Excel(!) it's not "big data"! It's just data - structured and relatively small. "Big data" is Google streaming eveything you ever do on your Android phone into its data lake so that sometime in the future the data can be analyzed and used for marketing. It's a fundamentally different approach from business intelligence or basic data analysis.

Of course, the buzzword gets applied to existing data analysis because it's trendy.

over 2 years ago

Ashley Friedlein

Ashley Friedlein, Founder, Econsultancy & President, Centaur Marketing at Econsultancy, Centaur MarketingStaff

@Andrew yes, it is indeed the case that the 'barrier/cost to entry' for doing more sophisticated analysis on larger data sets has come right down. But it relates to the next point

@Matt entirely agree. The technology isn't really the problem, or the size of the data. It's knowing what you want and why in the first place that's much harder. Knowing the right questions to ask. Having the resources to actually do anything about it IF you find anything useful. Lots of interesting stuff but not always actionable or useful.

@Anthony yes, there is a 'size' element to data's big-ness. But in my experience there are *very* few organisations who are anywhere close to having to worry about Google-scale data activities. To Matt's point there are plenty of other 'basic' things yet to address before we worry about this level of data?

over 2 years ago

Parry Malm

Parry Malm, CEO at Phrasee Ltd.

Interestingly, even massive dot.coms have limited need for big data:

"Three production traces – from Facebook, Bing and Yahoo! – show a heavy-tailed distribution of [data processing] job sizes. Small interactive jobs dominate by count while large production jobs consume the most resources."

Source: http://www.cs.berkeley.edu/~istoica/classes/cs294/11/papers/pacman-draft.pdf

So even the companies with the most digital data in the world appear to have limited need for Big Data processing - they need lots of processing power, but the biggest count is small jobs.

over 2 years ago

Ashley Friedlein

Ashley Friedlein, Founder, Econsultancy & President, Centaur Marketing at Econsultancy, Centaur MarketingStaff

@Parry - I was heartened, when speaking to a recent ex-Amazon-ite, to hear, when I asked about their wondrous internal data/systems, that it was actually 'slow, manual, not connected...'. That made me feel better ;)

over 2 years ago

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Matt Lovell, Group Head of Customer Insight & Analytics at Thomas Cook Airlines

@Ashley - It's so true though. Having worked at and with a number of media agencies over the years who claimed to have invested in huge systems to allow them to provide complex attribution algorithms for their clients or provide 'accurate' predictions for their clients based on an array of external factors, as soon as you actually scratched below the surface you either found it was all hype or a group of very dedicated analysts working night and day to churn through everything.

The biggest problem with it all (which was actually raised at the Round Table Econsultancy hosted earlier in the year) is that the myths about Big Data are made even worse by mistruths that are spread about what 'the best in the business' are actually doing...

over 2 years ago

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Neil Biehn

Parry,
It's as if you are reading my mind. I am giving a webinar in less than 24 hours on this exact subject -

http://info.pros.com/NewV_Register.html

I make a very similar point in WIRED:

http://insights.wired.com/profiles/blogs/the-missing-v-s-in-big-data-viability-and-value

People who actually do this stuff on a day to day basis understand the difference between marketing hype and the reality of making a big impact.

What a great read. I can't wait to hear about the other 50%.

-- Neil Biehn

over 2 years ago

Parry Malm

Parry Malm, CEO at Phrasee Ltd.

@Neil great read - thanks for the link. I signed up for the webinar but will likely have to watch it on catch-up (stupid day job)

You know, I wonder if there's a 6th V - Viagra - as it seems there's a "my data is THIS BIG" contest these days :)

over 2 years ago

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doug

People will do what they have always done. Big data is a fallacy. The job of marketers and other data miners is to determine what past behaviors accurately predict the future. The answer always lies in a few pieces of information, not thousands. Is this guy a good credit risk? Does he pay his bills on time? Simple as that.

Big Data sounds nice. It's great to through around in a meeting. It doesn't mean anything. What does? Relevant Data.

over 2 years ago

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Peter Rosenwald

That's telling it the way it is. Thanks.

"Baloney": I'd say more than 50%.

Remember Lester Wunderman's classic remark: "Data is an expense. Knowledge is a bargain."

The key question is: How much does response have to increase to justify the cost of collecting and using the 'big data'?

Anyone wanting a simple Excel model to make this determination should e-mail me at rosenwald@accountablemarketing.info with the words 'Model Please' and I'll happily send one so you can figure it out for yourself.

over 2 years ago

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Bill Gardner

Big Data is appropriate for business that are more or less static. Casinos are the perfect example of big data in play. Data or odds as the casino calls them are useful when trying to determine if you should be repeating behavior, but in of itself doesn't offer better approaches or ways to innovate or provide better customer experiences or any of the intangibles that most people associate with bettering a business.

But data does show where the most effective extractive efforts should be placed. It is no coincidence that in the past "data mining " was a term used so often. The mining of data is most profitable when an emphasis on one particular variable or another is more predictive than others, but that doesn't change it out of the category of looking at history.

What is critical after employing "big data " is how the info is implemented and whether or not the data is appropriately considered in the correct context. Its why data can tell us what the favorite flavor of ice cream in a particular locale is , but it can't tell what new flavor will be the rage next summer.

Generally data assessment is most useful where extraction and market share are the primary methods being improved and not organic growth. It is why people didn't want to manufacture tablets until Apple made them.

over 2 years ago

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Mark Goggin

this question has been asked and answered.

Target wanted to change the buying habits of women. To do that they set up a Predictive Analytics project to find women who were in their second trimester. They started with their own baby registry and added data points.

NYT had an article on this about a year ago. Very successful.

Amazon uses the data they get from what you buy. Works.

Finally, I have studied the Predictive Persuasion methods used by the Obama campaign. Here again, this worked. They had to find people who could be persuaded to vote for the President in Battleground States. Worked out. They found the voters they needed and figured out how to persuade them. Complicated. Expensive. But, ultimately, successful.

Bottom line is that Predictive Analytics, like everything else:
It Depends...

over 2 years ago

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Sriram

Very insightful comments from various participants. When I started delving into this area, I could draw parallels to the Six-Sigma concept where a specific business problem gets translated into statistical problem, analyzed using appropriate and relevant statistical solution is contextualized and Improvements Implemented.
Also like any Phenomena, Big Data is not a Panacea for all problems in the business world, but it has potential to address 'noteworthy' use cases. The key thing for all is to find those use cases.

happy hunting.....

over 2 years ago

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Terry Nugent

Like you, I am of two minds on the subject.

I think Big Data started out as a somehat meaningful concept in terms of data set size, but it was either originated or hijacked by vendors to (1) Sell products and services and (2) Put a fresh spin on old seminar topics. As a result, is has become a hackneyed buzzword that appears to be peaking.

However, we do have more data than ever, and one can glean insights from previously unavailable and unmanageable data sets (the Amazon and Obama campaign examples are good ones). Healthcare is also an area where Big Data is not only available but has great potential in drug research and epidemiology.

Finally, I do want to join your hockey pool even though it sounds like I will be shorthanded compared to your statistical power play.

over 2 years ago

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kelly

When I read a certain Davenport article that defined the term big data, the 3 components he espoused were volume, velocity, and utilization by the business unit rather than the IT unit. It made sense. Later, as your graph 1 adoption curve grew, I joined the group that find the terminology to be hype.Big data as well as data scientist need to be put to rest. As many have said in their posts, data is data, and knowledge workers know to make it sing. There simply needs to be sensible use of data to bring value to a business. I agree that it's discovery of the variables that matter and effective business execution based on that knowledge that makes it an exciting field to be in. I want to join your hockey pool too. Looking forward to the next post.

over 2 years ago

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Jonathan Bowen, Director at Datitude Ltd

I think the comparison with "Web 2.0" is interesting. As you say, no-one says "Web 2.0", do they, yet.....we're all living the Web 2.0 dream - Twitter, Facebook, commenting on eConsultancy blog posts and so on. Seems to me that the idea of Web 2.0 has been massively influential, even though the term is completely out of date now.

My guess is that Big Data will go the same way and the rise of the influence of data within the decision making process will continue at pace. But in 12, 24 months time, none of us will say "Big Data".

What's undeniable, IMHO, is that Big Data has 1) brought us software, platforms and techniques that make the processing of data much more efficient and far cheaper than before and 2) raised the profile of data, rendering guesses, opinions and hunches insufficient for quality decision making. As the wise man (W Edwards Deming) said, "In God we trust - all others bring data".

over 2 years ago

Parry Malm

Parry Malm, CEO at Phrasee Ltd.

And here's the follow up post if this horse isn't dead yet!

http://econsultancy.com/uk/blog/63365-three-reasons-why-big-data-is-awesome

over 2 years ago

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Keith Croall

Big data is just that big data. It just needs a better framework and toolset to manage it and the consumer doesn't really know it is "big data". We developed a big data application for tours with 197 million tours currently. It just looks like any other site, except it's in russian and uses big data technologies . If you're interested it can be seen under the SPO project at www.cubexsoftware.com/projects

over 2 years ago

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Guy Cuthbert, Managing Director at Atheon Analytics

For sure, "Big Data" is baloney, but big data, and the effective analysis of big data, makes perfect commercial sense. The key, therefore is to focus on understanding the meaning behind the masses of information businesses hold to unlock its potential, rather than competing with other organisations in data collection, which is often the mis-guided primary focus. In fact, medium-sized businesses should not be seduced by the technology industry’s hyperbole for ‘big data’. Instead, you should focus on using the data that is sitting in the palm of your hand more thoroughly, imaginatively and effectively.

All data worth keeping should be treated as a business asset; protected, nurtured and curated such that it is available in a timely, reliable and consistent manner. Adhering to this, business reporting should be focused on addressing genuinely *key* performance indicators, addressing Donald Rumsfeld’s ‘known knowns’:

“There are known knowns; there are things we know that we know, there are known unknowns; that is to say there are things that, we now know we don't know. But there are also unknown unknowns – there are things we do not know we don't know.”

Effective reporting highlights important metrics, and puts them in the context of what the organisation knows, understands or expects: previous performance, forecasts, budgets and plans. This allows directors and employees to contrast actual business performance with what is expected. Business reporting, however, does not address effectively the challenges of the ‘known unknowns’ or the ‘unknown unknowns’; to do so requires the effective application of data analytics.

Businesses must therefore apply analytics to their data – of any scale – in order to explore, discover and understand their ‘known unknowns’; those business questions that are well formulated, but whose answers are unclear.

Visual analytics is the swiftest and most accessible route into data which utilises our visual perception system and its innate pattern discovery; no-one needs a degree in statistics or mathematics to be an effective visual analyst. The best of the current breed of visual analytics software encourages exploration of data, of all forms and sizes, and enables simple, effective communication of insights discovered to non-technical audiences.

So is Big Data all baloney? For businesses who currently do not treat the small data they have as a valuable asset – absolutely. But does the right approach to data – big and small – contain huge opportunities for insight, innovation and transformation? Without a doubt.

Guy Cuthbert
Managing Director
Atheon Analytics

www.atheonanalytics.com

over 2 years ago

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Mikkel Lund, Service Platform Manager at DA-Desk

My personal definition of Big Data is external and unstructured.

Good examples from the transportation industry are weather and traffic information, public holidays, major events - which may all - and in combination - impact the performance of your assets.

"stupid is what stupid does" - well, the biggest crime would be to do nothing: you don't know what you don't know - so the more visibility, the better informed decisions you can make - even if a conclusion is that certain data has no impact.

Most companies I've worked with, however, have too little structure on their internal data - hence going for Big Data is at best premature, at worst distorting.

over 2 years ago

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