Big Data Mythbusters: Part 2

Our two-part Data series continues with 3 more big data myths.

Myth 4: “Big Data is a technology challenge”

Rubbish, it is about bridging the gap between opportunistic short-termism and the creation of such a sound understanding of all consequences of a decision that sustainable strategies can be developed and improved.

The technologies are mainly there and many of the ones that aren’t there are either free, or comparatively inexpensive. What used to be a big issue has now become a surmountable problem with only a few days of implementation time and a few thousand Pounds/Dollars/Euros, etc. investment. If you take data loading and massaging for example, you needed a busload of API’s and an eternity to figure out relationships before you could run the sausage factory of information. Well, there are tools out there that do these things for you now and they do not break the bank.

So, technology is no longer the bottleneck and neither is investment in technology, or even retiring legacy systems.

It is entirely about asking the right questions and figuring out the leanest possible approach (ideally agile) to get started on this journey. Business people, you are back in the driver’s seat, don’t let anyone fool you. Technology people, ask more often why something should be done to understand the real motivation and you can probably add much more value in the long run.

Myth-Busted

Myth 5: The Five V’s are all shiny and new

Baahhh… The 3 C’s are as good and still valid. Current, correct and complete is all you need. Yes, scalability of systems should be a non-hinderance and you need a certain level of quality management, but what else is new? Some poor bloke paid some strategy consultant a tonne of money to come up with something cool that he thought his board would buy into. They tried to reinvent the wheel and there you have it.

Just make sure that your data has been recently created, has been assessed and rated for quality and is actually all the data you need and you are good to go. Just keep an eye out for anomalies and make sure that they are not caused by flaws in the dataset and you can save yourselves a lot of time and money.

Myth-Busted

Myth 6: Agile is for Developers only

I am a mean person. You got me! I’ve kept the funniest for the very end. Let me give you an example of why I think this is so funny:

You are planning to cross a multi-lane street and cars come. What do you do?

• Carry on with your planned trajectory because you had it all mapped out in your head?
• Turn around and run away?
• Or, readjust your pace and your direction in a way that allows you to safely cross the street?

Now, a tram comes, followed by a cab and a set of bicycles… Same thing, you work with what you’ve got as your ‘epical’ idea is both to survive (uninjured), and to get to the other side.

Why do you want to go to the other side? You were hungry. Ok, got it.

But, with all the back and forth and adjusting you are now thirsty as well, so the food stall you had in mind is not good enough anymore. You would rather go to the beer garden next door (good idea by the way). Luckily, you see the beer garden before the tram, cab and bicycles cross your path! You adjust some more, make it safely and enjoy an adult refreshment to reward you for your efforts.

Now folks — call this agile, call it nimble, or just pure common sense.

It certainly is not only a thing for techies, it is something we consciously, or subconsciously do all the time. For some very weird reason we just don’t apply our everyday smarts when we plan projects and shoot for a future state. We somehow expect the future to stand still, whilst we are trying to shape everything accordingly.

One of my favourite sayings is: “Life is what happens while you are busy making plans.”

Agile is absolutely not just for developers, it is common sense and we need to be considerate and flexible with the right level of speed and consistency in our approach. Then, the big tasks ahead will be solvable, one piece at a time.

Myth-Busted

This is basically it! I will not talk about “one size fits all”, “data = information”, or “get a cool set of tools and you are sorted.”. These are just too obviously flawed to even be considered worthy of proper busting.

Take it from a German: Do not over-engineer your solution for Big Data & Analytics. Keep it lean, keep it mean and make it your competition-killing-machine ;-)

Go MythBusters.

P.S. I hope this mini-series has put things into perspective. I really wanted to keep it a light read as our lives are stressful enough without the consultant babble. This clearly isn’t a “how-to” guide, this was simply to put things into perspective.

In our next post, my colleague will talk to you about some simple benefits and useful ways on how to measure and benchmark agility, which will then provide a practical use case for you.

About Florian: With more than 15 years of experience Florian has a broad and deep knowledge of the digital space. He is passionate about tying all initiatives back to the strategic objectives which facilitate usable outcomes for our clients. Florian has studied Insurance Economics and Business IT Management at MIT.