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To set the context straight away, this blog article is about pitching your ideas (not baseballs).  And as a professional data scientist you will continually be coming up with ideas, however it is rare to be in an environment where you can continually convince others that your ideas are actually good ideas.  Whether you are trying to convince investors to buy shares or trying to convince your management to allocate budget there are some core fundamentals that need to be address. 

Let’s face it, data science is hard to explain.  

Now this might be largely due to my lack of experience in conversing in general chit-chat, however the other factors at play here are that data science is new, data science sounds intellectual and data science doesn’t fit into any of the existing main industries.

We all have an initial knee jerk to anything new, this comes from our primal instincts and the ancestorial wiring of our brain.  Back when we were all cavemen, anything new was cause for alarm, because they hadn’t performed their “is it safe” test.  It is only after they’d performed their “is it safe” test (usually done with a series of poking and proding) that they’d know whether to be happy or not about the thing that is new.  So because data science is so new we are cautious about it and in the first instance things to be cautious about are best avoided.

If we do get over the first initial knee-jerk and they haven't gone to sort their socks, the next challenge is that the preconcpetions general society has about the word “scientist”.  Media and general communication depict the scientist as a highly intelectual person.  A person standing there in a lab coat straining their brain to quantitatively measure the relationships between physical phenonemon.  Now whenever we meet someone new what happens is that first we run the “is it safe” test, and then the next thing that we do is compare the person we are talking with, with ourselves.  And if we find out that this person is “better than me” it triggers a whole lot of other primal instincts using ancestorial wires in the brain which generally lead to the feeling of being annoyed.

Leading on from the “better than me” analysis, if we do get past this, there is still another challenge ahead.  The following analysis is on the context of what is being said and is the “relevant to me” analysis.  We live in such a busy world that we don’t have time to listen and talk about things that are seemingly not “relevant to me”.  The difficulty arises when you see that data science deosn’t have its own domain to belong to.  It crosses the boundries of Information and Technology, Business and Science.  So unless the data scientist role can be put into context for the person you are talking to, their “relevant to me” analysis will usually result in them wanting to talk about something else.

So, where does this leave us data scientists when wanting to tell everyone we meet and yell from the talest building that “I am a Data Scientist!”.  

Don’t even try!

Now of course it wont be this way for ever, in time, we will all come to know what a data scientist is and these initial challenges will be greatly reduced.  And even today, there will be situations and environments that provide an exception to this rule.  So at the moment it is best to call yourself something other than a “data scientist” and just adopt the data science methodology on the sly.  If your a computer scientist, entrepreneur, database administrator, software engineer, data curator, be just that and use the data science way of working to bring fulfilment and passion into being that.  

One more thing.

If you are looking to branch out and do more networking to expand into new market opportunities, you may find the following guide on how to get your personal web presence into gear.

Here is how I built this wordpress site from scratch (including the webserver) to kick off my web presence as a Data Warehouse Architect. 

1. If you don't have it grab Virtual Box from here >>
2. If you don't have one lying around grab a Ubuntu Distro from here >>
3. Run up your new Ubuntu server VM (Instructions can be found here >>)
4. Configure as a LAMP stack (Instructions can be found here >>, but basically it is apt-get install lamp-server^) 
5. Configure WordPress (Instructions can be found here >>, but basically it is apt-get install wordpress and read the readmes)
6. Buy a domain name and point it to your IP (I used Net Registery)
7. Mine content and create your news page (I used Paper.li) 
8. Publish and embed your CV (I used Google Docs)
9. Populate your blog with quality, unique content
10. Get known as the expert in this space (Still under development, but I'll keep you posted)

Check out the end product here >>

Btw, the total work effort required was 4 hours and total cost was AU$39.90, and if you want to skip the first 5 steps let me know an I can give you the VM vdi file.

Talk again soon,

Troy.

On Tuesday 21st February 2012, I attended the Oracle Day 2012 event held in Brisbane.  The one message that came through the strongest for me was that innovation is fast becoming the number one key for success.  

The top buzzword on the day emphasizes this, everyone was talking about the emergence of the “innovation worker”.  From information I absorbed on the day (plus some Googling) my understanding of the term is that the “innovation worker” is the new improved “knowledge worker”.  So whats the difference?  Well simply put, the main components of the “knowledge worker” are analyzing and collaborating, however the main components of the “innovation worker” are analyzing, collaborating, executing and innovating.  

So lets look at these two new components, executing and innovating.  

Executing, what does that mean?  I was disappointed to discover that executing didn't mean the middle ages type execution, rather, it refers to the ability to create something to completion.  A large part of being able to bring something to completion is ensuring you have the knowledge and skills to get the job done.  So in order to execute, if you don’t know or have something that you need, then you go learn it or get it.  And there are two reasons why this approach has become within the realm of a normal worker.  Firstly, it is because the information is now there, if you want to know anything about anything you can either get it directly from the Internet of find some that can get it for you.  And secondly, it is because more and more people are realising that a lot can be learned from “doing the work” yourself.  The experiential knowledge that comes with “getting your hands dirty” can increase the effectiveness and efficiency of your next task or decision exponentially.  By going through the execution process, the experience itself, will provide insight into how and where to go next.  This is commonly referred to by artists that sculpt or paint as “the resistance of the medium”.  The “knowledge worker” has become tired of just thinking in fragments, now they want to execute and create the whole. 

Innovating, means to continually introduce something new.  The increasing number of non-hierarchical organisational structures and the growing popularity (and success) of self managed teams is allowing this component to be a normal part of the work we do.  Why do non-hierarchical organisational structures and self managed teams allow for innovation?  Because within non-hierarchical organisational structures and self managed teams the worker experiences a heightened level of freedom.  Freedom is a key ingredient when it comes to being innovative.  Workers feel empowered to offer new suggestions and try new things.  The “knowledge workers” are being set free and they are gathering together to build cultures that embrace change and breed innovation.
 
The emergence of the “innovation worker” coincides with the emergence of the Data Scientist role.

The “knowledge worker” to “innovation worker” transformation is seen as workers transform to the Data Scientist role from roles such as statistician, programmer, data manager, data analyst, engineer, librarian, and the list goes on.

If you feel you are stuck in a “knowledge worker” role and would like to start transitioning to a “innovation worker” role, I’d be interested to hear what you need to help you along the way.  I’ve created this survey to collect data on what people want in a Data Scientist Training course.  

Please contribute and answer all 5 questions, because I appreciate your opinion.

Being “innovative” is risky business.  And you don’t have to look past the meaning of the word to realize why.  The word “innovative” is derived from the Latin word innovare which means "to renew or change".  The human brain seems to be wired to have a involuntary response to change, which is along the lines of “WHAT THE...!, somethings not right here”.  And it doesn’t seem to matter how much preparation has been performed in setting up for the change, the initial response is the same (“something is not right”).  Following this initial response comes our instinctive nature to place it into either the “good” or “bad” category.  When we are talking about changes involving digital data and software we also have a fair bit of cognitive bias to overcome, due to the years of bad memories of loss of valuable data, loss of countless hours converting formats and unmeasurable amounts of brain-strain due to things consistently changing.  Therefore, it is very likely that a change (when experienced) gets immediately placed in the “bad” category by those experiencing it, and stays there until it proves itself otherwise.  In addition, our tainted memories provide an untapped energy for our cat-like minds to be ready for the pounce once something goes wrong, and seems to exclaim “AH HUH!, I knew this was a bad idea!” every time a minor challenge emerges.  And conversely when we see an improvement its more of a hesitant “hmm okay, this might be better... I’ll keep trying it and see”.  So to be labeled as “innovative”, when dealing with digital data and software, it seems, that a fair bit of convincing needs to happen before the change is honored with the precursor of “a change for the better” status.


The Data Scientists’ 3 step process for combating this is:

1. Lead by example in embracing change.  Next time you find yourself reacting to a change, observe your mind habitually labelling it as “good” or “bad” and then move to take a “neutral” perspective before making the final judgement.  It still might end up in the “bad” category but giving it a neutral playing field allows for a more accurate evaluation.  By embracing change you see it for what it really is.

2. Let the change be initiated by the users.  It might feel counterintuitive but in some cases its best to let sleeping dogs lie.  If no-one is complaining about any problems then don’t “wake up the dog” so to speak.  This is not to say that you should hide anything from anyone, but make sure you’ve got user support before “looking the dog in the eyes”. 

3. Present the value of the change wherever and whenever possible.  Getting reminded of why a change was initiated is always a good thing, especially if unforeseen challenges have emerged.  It provides an opportunity to validate that the value the change brings is still worth pursuing. 


Using this 3 step process you can reduce the risk of implementing a positive change only to have it seen by those that use it as a negative change.  Technology is advancing at such a rate that it is out running the evolution of our hard wired circuitry of the human brain.  The data scientist role is perfectly positioned to initiate positive change, however, you don’t need to be a data scientist before you start practicing The Way of a Data Scientist.  

The Data Scientist Culture contains the following principles:
1. Embrace change.
2. Learn to love learning.
3. Create transparently.
4. Serve to inspire.
5. Teach for action and transformation.

Hi Everyone,

Welcome to Data Scientists.Net.  I started this site so that I can expand my data scientist social network, you see, I believe that knowledge is the new currency and that old saying "its not what you know, it who you know" now has a new connotation and that is that *its impossible for one person to know enough to get anything really done!*  That might a bit of an extreme statement but my point is that *together more can be achieved*.  Gone are the days when I could just sit in my cave and rely on the knowledge I'd accumulated from from past experiences and knowledge available on the internet to solve problems.  The world is changing at such a rapid pace, these days, I've got to get out into the world and experience it live and in real time (what a drag).  But actually its not all that bad, once I get over my self sabotaging fears and insecurities that have built up over the years due to lack of human interaction (and I mean "normal" human interaction not interactions between my fellow nerd and geek friends), its actually kinda cool.  Finding and solving challenges in real time the data scientists way.  And what I've discovered is that that is where the real innovation and creativity happens... I'd rather be in a real time, uncommon-collaboration with some epidemiologists, genealogists, climatologist or [insertwordhere]ologist, working on solving the challenges of the world that couldn't be solved without the freely and opening exchange of everyone's unique expertise and real time participation of all those involved. 

My aim is to update regularly, share openly, inspire intellectually and play my part in making a difference in the world.

Troy