5 Big Data Myths Debunked

So you heard about this thing called big data, and how it’s going to rock your  world. But beyond all this yay saying and data praying, what is actually going on and what should you buckle up for ? 


An edgy perspective on  the subject: 5 Big Data myths debunked


Myth 1: Big Data is big 

Nope. Big is relative. The ‘Big’ is marketing lingo, an attention grabber, as if we should automatically be afraid of scary unknown big things. Big actually means ‘more difficult to access and query than we are used to’. Yes, at some point you will probably need new tools. But does that make your data big, or your legacy tools and systems old? Telecom operators have been analyzing CDR’s (call detail records) for over fifteen years now, often using hundreds of millions of records. Sounds big? Heck no, it will fit on a USB flash drive that is available on Amazon for under $10, how can you call that big? Most people and companies are not working with Big Data, just data. If you want to talk about big data problems, ask the guys at NASA, planning missions where around 24TB of data will be streamed. Every day. From outer space



Myth 2: Big Data is a technology thing

Wrong, it is a paradigm shift in business models. And up until the new models are a common thing,  Big Data will  keep its adjective ‘Big’. Don’t be fooled by the traditional soft- and hardware vendors that are the first ones to step up to sell you Big Data Solutions. This doesn’t mean it’s an IT thing, don’t confuse the message with the messenger.  

Big data is about acting smart, and right now about changing  organizations to be smart, to have a competitive advantage with a better understanding and serving of customer needs. There, I said it; customer needs. And there’s your shift in business models: first comes the customer need, then the product. Successful companies of the future will not look for customers to sell their products to, but look for products that meet their customers’ needs.

It will go from 1. The understanding, to 2. The meeting - and 3. even the creation of new customer needs. How is it possible to create new customer needs you ask?  Well, with new technologies come new latent customer needs, it is inevitable. Remember standing bend over a map spread out on the trunk of your car? And How navigation software dramatically changed the driving experience? Now compare the first devices to today’s standards.. would you still be satisfied with a navigation system that can’t even tell you where the nearest gas station is, or doesn’t use live  traffic information? Well in a couple of years, you will feel the same way about the solution that cannot even suggest tomorrow’s best departure time for you, giving you an extra 15 minutes of sleep. All this is not about technology, it is about serving customer needs with technology that has been around for a while. 


Myth 3: There is an enormous shortage in analytical talent and experienced analysts. 

A lot of companies are having a hard time recruiting the right analytically skilled people. The quest for analysts and data scientists is a hard one, with consultancy agencies acclaiming that message in advertorials everywhere. Although there can never be enough analytical talent, and yes there is a shortage,  an important part of the problem lies somewhere else.

Your company is not interesting enough

Nice pic, but in the non-StockPhoto reality you are probably another boring company..

Nice pic, but in the non-StockPhoto reality you are probably another boring company..

Let’s be honest, your company’s corporate website might show pictures of young and good-looking people, working on an apparently fun business problem while pointing at a computer.. however in the non-Photostock reality you probably are a boring company that sells boring products and have boring problems to solve. So how on earth can you compete with interesting start-ups and cool, tech savvy companies? Well, just create interesting problems. Create multidisciplinary  teams where analytical talent is not only (mis)used as support. Allow them to create the interesting problems. Be like Google, facilitate them in spending time on their own projects. If managed correctly, you might end up with your next big thing this way. 

It is not your fault you are selling insurances, but the blame is on you if you hire analysts to only do some 1.0 direct marketing. Appealing to engineers and the analytical employee with coding skills will be less difficult when you provide the right challenges.

..A Playstation and a foosball table won’t do.

You already hired them, they are working in the wrong department

And I bet it is the IT department.. All your STEM ( Science, Technology, Engineering, Mathematics) skills are in one place. Nicely hidden away on a separate floor, or in some sort of ‘incompany quarantine’. So besides a big mentality- and organizational change,  there are two things you can do.  You either teach your marketing staff analytical skills, or you teach your analytical  staff marketing skills. I suggest you try the last. Bring them in on marketing and sales meetings. Again, not just as on demand support, but in the (co) driver’s seat.  You will be surprised how much creativity you can unleash.

Data Analytics still  is unnecessarily complex

A data analyst loves to analyze data, not the hardship of accessing the data. Are programming skills required to create cool tools, models and applications?  Or are they an absolute necessity because otherwise 90% of the time would be filled by meeting with the IT department? Data analysts work on the frontiers of data. That means the data is by definition not structural, seldom relational and hardly quickly accessible. The company providing a plug and play like sandbox solution for all company data will leap an important part of the analytical gap. 


Myth 4: Big Data is social data 

Social is data’s super sexy showcase. It will often start with social data, not only because it (still) is up for  grabs and there is a lot of it, but also because everything with a like button on it  appeals to marketers, creative companies and more than the usual suspects (yep IT, BI and CRM, I mean you guys). Since it is a fat chance that you – oh dear reader - are working for Facebook or Google, social data will mainly be big for them. Data lies at the core of their business model.  And although it is all about liking and sharing, they don’t like to share.

So don’t forget to have a focus on your own ‘big' data. Got a central cash register system, a website with a large volume of clicks and views (and it’s up to you if you decide to label it large or ‘big’) or even better, have check-ins, sensor data or some other sort of activity generated data? Even better! That’s your big data!   Got none of the above? An internet connection, web scraper and the blogosphere will do fine as well.  Wherever there are actions and transactions or interactions and the capability to store them, there is data.  You can make it as big as you like.


Myth 5: Big Data is a hype

Stop the definition debate. Who cares. Everybody agrees on the possibilities and disruptive force data can have. The era of data has already begun. 


What we are sensing is merely the urge of a breeze, storm is coming. 

Sanne Steegstra