#Measure Career Development – Beyond Analytics Ninjas

“Dude, where’s my Job?”

Avinash Kaushik posted on his, far more widely read, blog post a ‘Web Analytics Career Guide: From Zero to Hero In Five Steps!‘ which is well worth a read.

What about the slightly experienced web analyst? What are they supposed to think about companies being acquired, business units rolling up into BI and so forth?

How can a web analyst stay competitive in the job market today and in the future?

I am a big believe in continuing education, those same skills which get you well paid today won’t necessarily get you employed a couple years from now.

I know history is full of examples where strong dominant forces remained at the top of the food chain, where those forces were rewarded for being the same forever.

That being said, even an ‘Analysis Ninja’ might want to learn a new skill here or there.

  • What skills might the Ninja want to take a look at?
  • And, who the hell am I to give advice when I am ‘new’ to this industry?

Durable Skills

The first thing to keep in mind is that web analytics is not some secret dark art, which only a few people know and pass on after ritual initiation.

Web analytics is built on top of several different disciplines, having skill in the underlying discipline empowers people to use their portable skills in a new area.

To put it another way, if a salesperson stopped selling planes and started selling boats would you ask them if they changed careers?

My background is Econmetrics and Linguistics, which are 100% durable and portable to web analytics. I use both absolutely every single day.

The hardest problem I ever solved was from my Cal State Econometrics class, and that was at least 10 times harder than anything I have thus far seen in web analytics.

Instead of rewarding durable skills, all too often perishable skills, such as tool specific skills, are the ultimate arbiter of employment. This is not only an absolute shame, but also has effects on how individuals choose to pursue advancement of their careers.

Building out and expanding upon their durable skills, as opposed to perishable tool specific skills, is what I would hope analysts with experience would focus on. Not only for their own career development, but also to advance web analytics as a profession.

If we are satisfied doing the work of glorified button pushers and paper shufflers, we are begging to be replaced.

Secretaries Will be Secretaries

Pulling reports of raw data rarely shows a business story which is relevant or actionable. Visits are up, great . . . right?

Performing routine tasks that can be automated has always shown to be a good career move.

Business units already want the ability to tie dollars to performance; move this lever and experience this effect.

If you don’t believe me, check out Eric T. Peterson’s presentation, direct download, on the SAS website. Good stuff.

While SAS generously provides Eric’s presentation, they aren’t the only solution in this realm. My Twitter followers already know . . . .

Revolution Analytics I Heart R Sticker On Stroller

This type of analysis can be done, if you know:

Statistics/Econometrics/Linear Algebra

There are challenges to modeling, and working with a smart individual on these problems is a must. If the most advanced procedure you are regularly using is the CORREL function in Excel, that isn’t enough.

Having an understanding of the advanced techniques will shortly be, if not already is, an essential part of the analyst’s toolkit. Building out a model of the levers which drive the business KPI’s which you are studying will be a part of your daily tasks.

For me, it already is and I keep Kennedy’s ‘A Guide to Econometrics‘ on my desk at all times.

Start with the basics

  • How are discrete and continuous variables different?
  • How do I figure out if this distribution is normal?
  • What is a normal distribution?
  • Why is this important?

If you lack this skill set, don’t worry! Lots of great free resources out there, in particular I ,recommend MIT Open Courseware.

At the end of Tim Ash’s ‘Landing Page Optimization‘ he has a nice introduction a few statistical concepts. Re-read that section if you already own the book.


Recall the part of ‘The Joy of Stats‘ where Dr. Rosling is talking about the size of data in the Internet.  (note: if you haven’t watched ‘The Joy of Stats’ already . . . this may not be the right industry for you)

1 zettabyte is the size of the data on the Internet. A zettabyte is 1 billion terabytes . . . that’s a lot of disk drives.

When I see Jim Sterne (Jim Sterne!) taking part in a webinar talking about big data I feel confident that its going to be around for awhile. (further note: Why isn’t Jim Sterne featured on the home page rotating images? Seems like that might help get people signed up . . .)

What does that mean for the web analyst?

Big Data

The existing web analytics tools largely do all the data querying for you. Select this check box, push that button there and OK data downloaded.

Building out all those cool models which are going to make your company more money can only be enabled with the correct implementation on the back end.

If, however, you let the DBA’s of the world run it you might end up with an overly complex snowflake schema which produces very nice precision while recall and performance both suck.

Hadoop is free for crying out loud, download the Virtual Machine from Cloudera and give it a go. It isn’t for everyone but if you walk through the exercises you can at least get an understanding of big data.

The key benefits in my professional experience of the Hadoop platform are:

  • Flexibility of data analysis
    • I’m not stuck in a particular schema
  • Scale like crazy
    • Stuff it to the ears with data and keep going, still runs great!

Too lazy Not enough time to pin the VM? Just take a look at Orbitz using R and Hadoop to optimize hotel search.

Get an idea of the trade offs of different solutions, so the next time an implementation rolls around you have something to contribute to the conversation.


1 zettabyte of data still blows me away, as my next thought is how in the world is that data going to get into a database?

For my regular data, sure it isn’t a problem to set up a cron job which downloads and then performs the necessary ETL steps. That’s old school.

What about on a Tuesday afternoon, when I need some data which is available somewhere but not in my database?

In this instance, I wouldn’t describe creating an IT ticket a ‘self-starter’ solution.

Programming Languages

ZDnet has a blog post on the fact that companies are ‘overwhelmed’ with unstructured data; this sounds like job security to those people who know how to apply structure to data on the fly.

As the data volume increases beyond human comprehension, the likelihood that data which is essential to your daily job function will be outside the existing structure is also increasing. There is only one reasonable way to get this done in a timely fashion:

Learn a programming language, which is certainly not a trivial task.

I taught myself Python for exactly this purpose and have reaped the rewards numerous times over.

Python is relatively easy to learn, and there is an entire book FREELY available to read on the Internet which will teach you the language if you start at the first page and work through to the end.


  • Who has lots of free time at their work?
  • Who doesn’t have to work with non-quants?

Business Management

We are dealing with too many requests from too many business units, and the minute you start producing models those same business units just may start to have unrealistic expectations of the returns from those models.

Managing expectations, business relationships and all other aspects of a corporate existence is critical for every single analyst.

Aside from B-School there are some excellent resources on the MIT Open Courseware site, as well as numerous books on business management out there.

I also really like Thornton May’s ‘The New Know‘ for managing analytics specifically.


So you navigated the maze. You used quant methods on big data, which you queried with a programming language, on a project you guided to success.

Hoorah! Post the results on the wall of your workspace and then . . .

How the hell are you going to get the C level executives to listen to you?

Business Communication

Effectively communicating the insights to individuals who aren’t as excited about topic model classification as you are is required for you to advance.

Right around the same business section on management are solid communication books.

While you’re in the section, take a peek at a sales book or two. You know, if you want to sell your insights internally.

Just sayin.

Transformational Data

Of course people will experience success without mastery of these skills, people who have moved up in their organizations sufficiently such that they supervise this sort of work.

I would argue even they need an understanding of what is possible, but not everyone would agree. If you are in that crowd at the top of the food chain, more power to you and enjoy the nice life.

If, on the other hand, you aren’t at the top of the food chain keep in mind there is a population of individuals who are busting their ass on all of these points just hoping to catch a break and get into web analytics.

Still not sure?

Any company who needs to add a real go-getter should give Ed Fine a look; Ehren Cheung and Pandu Truhandito are both working right now, but bookmark them just in case.

Ed’s very interested in working on more projects related to web analytics, he brings a very impressive skill set which is directly portable over to web analytics.


Feel free to holler at me on Twitter @MichaelDHealy, drop me an email at mdh [at] michaeldhealy [dot] com or post a comment.

Technorati Tags: Analytics, Career Development, Econometrics, Linguistics, Measure, Statistics

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5 Responses to #Measure Career Development – Beyond Analytics Ninjas

  1. Pingback: » The Analyst Skills Gap: It's NOT Lack of Stats and Econometrics | Tim Wilson's Blog at Web Analytics Demystified

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