Andy Capaloff
April 20, 2014

Why Big Data Needs Big Collaboration

Yesterday’s Wisdom Applies As Much As Ever Today

“You can never solve a problem on the level on which it was created” Albert Einstein

“Judge a man by his Questions rather than his Answers” Voltaire

Data, Analysis and Questions

The dictionary definitions of Data, Analysis and Questions show the clearest of synergies.

Data Analysis Questions Defs

Indeed, the first two are always identified together.  But Data Analysis is more the victim of inbreeding than most fields, so the most in need of fresh infusions of ideas.

The Scope of the Big Data Dilemma

Whereas some are surely gathering Data because it is the thing to do, hoping they will figure out next steps later, others will have the plan of leveraging their Data to improve performance, or better yet, to gain competitive advantage.

Two very important questions:data-scientist

  1. Who turns Everest-sized mountains of Data into usable, meaningful statistics?
  2. Who turns those statistics into Action Plans?

Well the second of those two is something of a trick question.

Whoever is involved in making meaning of the final statistics, MUST be involved upstream also.  It’s a mistake to hand the reins over to IT for Data Gathering and Analysis, then simply take what you are given and run with that.  More on that a little later.

We’re accumulating the Data but we’re not using it!

Data Mining in BrainIn the article Big Data help wanted (badly): How to win the war for talent, McKinsey & Company discuss both the amount of Data being accumulated and the shortfall in talent to do anything about it in terms of detailed, useful analysis.

Chew on this nugget to help you see the scope of the problem:

“CMOs surveyed say they use marketing analytics just 29% of the time to make decisions, and a paltry 3% say that analytics contributes “very highly” to their company’s performance.”

3%!  Did anyone else just say “wow”?

Things that can be drawn from the stated problems

Sorry, folks, but this is a New World issue being dealt with using Old World solutions and it simply will not work.

We just cannot have the same line-in-the-sand delineation of process and participants with Big Data as those that Business has run on in other areas, where Marketing, Accounting, IT and other participants have clear boundaries.  This can only work if ALL interested parties are involved at all phases.

Time to break down the barriers!

I see two clear issues:

  1. The hiring process for Data Analysts is all wrong
  2. Data Analysis is a prime candidate for Questions by Committee, or Question Circles

Tackling The Big Data Talent Gap

The hiring process

So companies are now looking outside of the traditional solution of hiring holders of Computer Science, Statistics and Industrial Engineering Talent GapDegrees.

“Major companies are starting to turn to disciplines as diverse as physics, philosophy, psychology, economics, and even biostatistics to find Big Data talent.”

I wonder how those interviews are structured?  Fair guess that it’s the traditional manner in which one or more interviewers ask questions and the interviewee answers them.

Am I alone in seeing how wrong this is?

You are seeking to determine who might ask the best questions and are doing so by asking them questions!

Surely, it is they who should be doing the asking?  (a major ‘Duh!’ moment!)

If you want to determine a person’s analytical skills, present them with a problem and have them ask questions about it.  I could never figure out why this isn’t really, really obvious!

We’re All In This Together

All in this togetherNow to break down the barriers and get everyone involved from beginning to end:

I have written several articles touching on the importance of having more people involved in the Asking of Questions, including Why We Need To Expand The Big Data Questions Gene Pool.

The nugget to pull out of that in relation to this post is:

“Simply put, no one group of people can form a complete picture of anything, as no one group of people has a panoramic wisdom of experience.

Surely, nowhere is this more true than around Big Data.

You only need look at the likely components of the Data to realise that we need to adapt an old saying to modern times.

The commonly stated:          Nobody has all of the answers

Should be reworded as:       No one person has all of the questions

It’s a fundamental, simple shift of the onus.

In the case of Big Data, it’s a highly necessary shift!

Data Analysis Question Circles

Can one Data Scientist or Analyst possibly know as much as a Marketer, a money person, a Social Media person and whoever else might have a collaboration1vested interest in the analysis?  Not a chance, no matter how good they are!

Convene a group!  Get all of those with a vested interest, including the Data person, along with someone who has no associated subject matter expertise acting as facilitator, around a table for an hour.

Have each asking their own questions.

And don’t allow the collaboration to end there.

Tear down the traditional boundaries and keep every party in the loop from beginning to end.

Big Data is really, really BIG!  To achieve its potential usefulness, it needs the full buy in and involvement of everyone who stands to benefit from it.

If you liked this article, become a Curatti subscriber, comment below about aspects you find of particular interest, and make sure to come back! 


Image attribution: Featured image (Data Scientist):

Data Mining in Brain:

Talent Gap:

In this together:


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Andy Capaloff

Andy Capaloff is the COO of Curatti. Prior to moving into the world of Content Marketing, Social Media Management and the day-to-day running of a Digital Marketing company, Andy spent over 3 decades in various aspects of IT. It is here that he honed his writing and technical skills, and his ability to ask uncommon questions.