Book Binge – December Edition

I typically spend the end of my year self reflecting on how things have gone – both the good and the bad.  Usually that leads me to this thoughtful place of “I need more books.”  For some reason to me books are instant inspiration and a great alternative to binge streaming.  They remind me of the people I want to be, the challenges I want to battle and conquer, and seamlessly entangle themselves into whatever it is I am currently experiencing.

Here are 3 of my binges this month:

First up: You are a Badass: How to Stop Doubting Your Greatness and Start Living Your Life by Jen Sincero

This is a really great read.  Despite the title being a little melodramatic (I don’t really believe that I’m not already a super badass, or that my greatness isn’t already infiltrating the world), Jen writes in a style that is very easy to understand.  She breaks down several “self help” concepts in an analytical fashion that reveals itself through words that actually make sense.  There’s a fair amount of brash language as well, something I appreciate in writing.

Backstory on this purchase:  I actually bought a copy of this book for me and 2 fellow data warriors.  I wanted it to serve as a reminder that we are badasses and can persevere in a world where we’re sometimes misunderstood.

To contradict all the positiveness I learned from Jen Sincero, I then purchased this guy: The Subtle Art of not Giving a F*ck by Mark Manson.  (Maybe there’s a theme here: I like books with profanity on the cover?)

Despite the title, it isn’t about how you can be indifferent to everything in the world – definitely not a guide on how to detach from everything going on.  Instead it’s a book designed to help you prioritize the important things, see suffering as a growth opportunity, and figure out what suffering you like to do on a repeated basis.  I’m still working my way through this one, but I appreciate some of the basic principles that we all need to hear.  Namely that the human condition IS to be in a constant state of solving problems and suffering and fixing, improving, overcoming.  That there is no finish line, and when you reach your goal you don’t achieve confetti and prizes (maybe you do), but instead you get a whole slew of new problems to battle.

Last book of the month is more data related.  It’s good old Tableau Your Data by Dan Murray + Interworks team.

I was inspired to buy this after I met Dan (way back in March of 2016).  I’ve had the book for several months, but wanted to give it a shout out for being my friend.  I’ve had some sticky challenges regarding Tableau Server this month and the language, organized layout, and approach to deployment have been the reinforcement (read as: validation) I’ve needed at times in an otherwise turbulent sea.

More realistically – I try to buy at least 1 book a month.  So I’m hoping to break in some good 2017 habits of doing small recaps on what I’ve read and the imprint new (or revisited) reads leave behind.

How do you add value through data analytics?

I recently read this article that really ignited a lot of thoughts that often swirl around in my mind.  If you were to ask me what my drive is, it’s making data-informed, data-driven decisions.  My mechanism for this is through data visualization.  More broadly than that, it is communicating complex ideas in a visual manner.  Often when you take an idea and paint it into a picture people can connect more deeply to it and it becomes the catalyst for change.

All that being said – I’ve encountered a sobering problem.  Those on the more “analytical” side of the industry sometimes fail to see the value in the communication aspect of data analytics.  They’ve become mired down by the concept that knowing statistical programming languages, database theory, and structured query language are the most important aspects of the process.  While I don’t discount the significance of these tools (and the ability to utilize them correctly), I can’t be completely on board with it.

We’ve all sat in a meeting that is born out of one idea: how do we get better.  We don’t get better by writing the most clever and efficient SQL query.  We get better by talking through and really understanding what it IS we’re trying to measure.  When we say X what do we mean?  How do we define X.  Defining X is the hard part – pulling it out of the database, not as difficult.  If you can get really good at definitions, it becomes intuitive when you start trying to incorporate it into your business initiatives.

As we continue to evolve in the business world, I highly encourage those from both ends of the spectrum to try and meet somewhere in the middle.  We have an unbelievable amount of technical tools at our disposal, yet quite often you step into a business who is still trying to figure out HOW to measure the most basic of metrics.  Let’s stop and consider how this happened and work on achieving excellence and improvement through the marriage of business and technical acumen – with artistry and creativity thrown in there for good measure.

The Float Plot

One of the more interesting aspects of data visualization is how new visualization methods are created.  There are several substantial charts, graphs, and plots out there that visualization artists typically rely on.

As I’ve spent time reading more about data visualization, I started thinking about potential visualizations out there that could be added into the toolkit.  Here’s the first one that I’ve come up with: The Float Plot.

The idea behind the float plot is simple.  Plot one value that has some sort of range of good/acceptable/bad values and use color banding to display where it falls.  It works well with percentage values.

I’ve also made a version that incorporates peers.  Peers could be previous time period values or they could be less important categories.  The version with peers reminds me somewhat of a dot plot, but I particularly appreciate the difference in size to distinguish the important data point.

What’s also great about the Float Plot is that it doesn’t have to take up much space.  It looks great scaled short vertically or narrow horizontally.

Enjoy the visualization on my Tableau public profile here.

Statistical Process Control Charts

I’ve had this idea for a while now – create a blog post and video tutorial discussing what Statistical Process Control is and how to use different Control Chart “tests” in Tableau.

I’ve spent a significant portion of my professional career in business process improvement and always like it when I can integrate techniques learned from a discipline derived from industrial engineering and apply it in a broader sense.

It also gives me a great chance to brush up on my knowledge and learn how to order my thoughts for presenting to a wide audience.  And let’s not forget: an opportunity to showcase data visualization and Tableau as the delivery mechanism of these insights to my end users.

So why Statistical Process Control?  Well it’s a great way to use the data you have and apply different tests to start early detection.  Several of the rules out there are aimed at finding “out-of-control,” non-normal, or repetitive parts within a stream of data.  Different rules have been developed based on how we might be able to detect them.

The video tutorial above goes through the first 3 Western Electric rules.  Full details on Western Electric via Wikipedia: here.

Rule 1: Very basic, uses the principle of a bell curve to put a spotlight on points that are above or below the Upper Control Limit (UCL) or Lower Control Limit (LCL) also known as +/- 3 standard deviations from the mean.  These are essentially outlier data points that don’t fall within our typical span of 99.7%.

Rule 2: Takes into consideration surrounding observations.  Looking at 3 consecutive observations are 2 out of 3 above or below the 2 SD mark from the average.  In this rule the observations must be on the same side of the average line when beyond 2 SD.  Since we’re at 95% at 2 SD, having 2 out of 3 in a set in that range could signal an issue.

Rule 3: Starts to consider even more data points within a collection of observations.  In this scenario we’re now looking for 4 out of 5 observations +/- 1 SD from the average.  Again, we’re retaining the positioning above/below the average line throughout the 5 points.  This one really shows the emergence of a trend.

I applied the first 3 rules to my own calorie data to see detect any potential issues.  It’s very interesting to see the results.  For my own particular data set, Rule 3 was of significant value.  Having it in line as the new daily data funnels in could prevent me from going on a “streak” of either over or under consuming.

 

Interact with the full version on my Tableau Public profile here.