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Cool, thanks for the details.

First, the good news:
You might already realize it, but this is a tremendous field to be in. The opportunity is absolutely massive. To put it simply, I’ll say that the world (companies, institutions, and soon, individuals) are currently generating more data than we can analyze. And year-over-year we’re generating data at a faster rate.

People who are excellent at analyzing data will have lots of high-salary, high-benefit opportunities (as it is, if you have the right skill set, it’s common to get contacted by Apple, Google, Facebook, Amazon; these companies all need skilled analytics workers).

Now, the challenge:
Learning analytics is hard.

Game plan:

Short Term:

In the short term you should focus on data visualization and “visual communication.” This means, communicating with charts, graphs, and images in place of excessive words. I won’t go into the details, but the human mind is wired for visual inputs. We don’t process spreadsheets, tables, and prose that well. However, our brains are sort of wired for visual inputs. The phrase “a picture speaks a thousand words” is fairly accurate.

I agree that “storytelling” is necessary, but I sometimes dislike it because I think it confuses what we’re actually doing. Let me unpack that term a little: storytelling actually means 1. finding valuable insights, 2. communicating valuable insights.

In the early stages of your career, the easiest way to find insights and communicate them is with visualization. (note that machine learning is also awesome for finding insightful information; it will be extremely difficult to teach yourself ML though, so hold off on that until you can take a class and have a mentor at work.)

That said, here’s what you should focus on:

1. Master the “Big 3” visualizations, with all their variations
i. Bar Chart
ii. Line Chart
iii. Scatterplot

What’s important is not just being able to do them, but being able to create them fast, accurately, and knowing when to use them. 80%+ of all reporting can be done with these 3 charts and their variants.

2. Learn conceptually how each visualization functions as a tool: when to use them, why, how they are best implemented, etc.
Nathan Yao’s Data Points is pretty good for this
Stephen Few’s books are also informative, but I like his material less than Yao’s.

3. Upgrade your tools
If you want to really develop in this career path, you have to move beyond Excel. Excel is great for quick-and-dirty tasks, but for a true analytics professional, it’s not a primary tool. (It doesn’t scale well at all, it’s functionality is limited, it’s more error prone, difficult to automate.)

Here are my two favorite tools, which I highly recommend. These are the tools that I wish I knew when I started:

Tableau, R

i. Tableau
Pros:
Great for rapidly creating lots of visualizations (simple charts and graphs, as well as some exotic ones).
Great for creating dashboards (you need to have Tableau Server for this). Dashboards can take some work off of your plate if you learn to automate the process and can convince your business partners to accept an online dashboard instead of a weekly/monthly/quarterly powerpoint.

Cons:
Automation can be difficult.
Tableau is bad at data wrangling. I really dislike doing any sort of data cleaning, merging, transformation in Tableau. Tableau just isn’t great at those tasks.

ii. R
Pros: Free and highly functional for data analytics. It’s very functionality is centered around analyzing data.
Cons: The learning curve is a bit steep. It takes time.


4. Master Presentation Design
Because your deliverables are mostly PowerPoint presentations (PPTs), you should really learn slide design. Honestly, if you do this right, you’ll be ahead of most analysts; most presentations are not well designed.

i. Presentation Zen, by Garr Reynolds

ii. Clear and to the Point, by Stephen Kosslyn





In the medium to long term, you’ll need to learn “data wrangling” (gathering, combining, re-shaping data).
I’d highly recommend learning SQL and R’s “plyr” package.


If you’re serious about analytics, you should start reading my blog. I’m writing about how to learn analytics step-by-step, and I’ll eventually cover all of these above topics (data visualization, R, Tableau, data wrangling, presentation design).

Also, if you have specific questions, stop by the blog and contact me on the “Contact” page.

All the best,

sharpsightlabs.com

As everybody else here says in different ways, do these three steps
1: find out the business problems people are trying to solve by going to where they work and sitting down with them. LITERALLY where they work. The gemba, as it's called
2: this will tell you the decisions they need to make.
3: this will tell you the data they need to make these decisions. A handy heuristic, once you've started providing data ask the recipients what decisions they COULDN'T make if they stopped getting it. If they couldn't think of any, you're providing the wrong data.

Have a read of what people have found before you. They've written it up so you can learn it quicker than they did.
I'd define myself as a systems thinker, and so what I'd recommend is skewed towards that way of thinking
This guy is good and has a book coming out soon specifically about how data can provide value when analysed properly.
https://www.leanblog.org/tag/process-behavior-charts/
This guy writes well about something most analysts have never heard of it, it's worth an hour of your time and is invaluable. Trust me, browse it.
There's HUGE amounts of brilliant stuff online. One of the best books I've ever read on this is Understanding Variation, https://www.amazon.co.uk/Understanding-Variation-Key-Managing-Chaos/dp/0945320531
It's short, oriented to problem solving and process understanding, and eminently practical.

Your job sounds brilliant by the way, but don't get bogged down in learning software packages. People who receive your output need to know what it MEANS. This can often be left out of fancy pretty graphs. Analysis should produce insight, not just the workings out. Get to know the business and be a business person, not just a data person. Data has no meaning stripped of context, and you should steep yourself in context.

It is definitely worthwile, by looking at the analytics of the website you can drive insights on what went right/what went wrong with your current sites ==> you will learn about what to do and what not to do for building the new versions.

I do not know book specifically appliable to small business analytics but in the analytics section I can recommend warmly Web Analytics 2.0 which will give a good basis with examples valid for both small & large businesses https://www.amazon.com/Web-Analytics-2-0-Accountability-Centricity/dp/0470529393

Note that if the businesses in question have low traffic, i.e. not enough data to reach statistically relevant insights, you should rather learn about UI,UX and UX design for your specific industry. A nice starter website would be https://goodui.org/

I run the web analytics. I keep tabs on what all our online visitors are doing domestically and internationally.

Yes, the margins are indeed low - fuel is just over 60% of our overall operating cost. We have analysts in revenue management, partnerships, vacation packages, IT, marketing, sales, engineering, finance, pretty much everywhere.

I started reading the book in the URL below called "Analytics at Work." I enjoy the tone of the writing, and I also find their assessment of common data issues within organizations to be spot-on.

http://www.amazon.com/Analytics-Work-Smarter-Decisions-Results/dp/1422177696/ref=sr_1_2?ie=UTF8&qid=1368846748&sr=8-2&keywords=analytics

I'm a little biased (full disclosure: I helped edit this one at our company), but we released a book last month focused on Google Analytics and Tag Manager that I think is comprehensive and accessible to a novice. Comes with plenty of examples on implementation.

Take a look here. By Jonathan Weber.

Sounds like you need a lesson on the concept itself. I highly recommend reading https://www.amazon.com/Web-Analytics-2-0-Accountability-Centricity/dp/0470529393

Read [Web Analytics 2.0 by Avinash Kaushik] (http://www.amazon.com/Web-Analytics-2-0-Accountability-Centricity/dp/0470529393)

Avinash was/is an analytics evangelist for Google and used to head analytics at Intuit. The book will give you a great grounding in online measurement and has examples of a bunch of reports.

I'd also grab a copy of the [Excel Bible] (http://www.amazon.com/s/ref=nb_sb_noss_2?url=search-alias%3Dstripbooks&field-keywords=excel+bible) for whatever year you're using. It gives you scenarios for using different functions and macros and examples of everything.

All the analytics packages have training you can take, just look around their sites.

“Competing on Analytics” is a classic.

Competing on Analytics: Updated, with a New Introduction: The New Science of Winning https://www.amazon.com/dp/1633693724/ref=cm_sw_r_cp_api_iDdSBbNABWMN6

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking https://www.amazon.com/dp/1449361323/ref=cm_sw_r_cp_api_8EdSBbKH69PFN

https://www.amazon.co.uk/LaMetric-Time-Wi-Fi-Clock-Smart/dp/B017N5FP0E - These are cool and quite a bit cheaper than SMIRL and FLAPIT. Plus it is all digital and can display loads of information from different sources.

I would definetly suggest going with Greco's book. I'm a solutions engineer so I only do the implementation, but, I use this book when I need to get around the tool.
http://www.amazon.com/The-Adobe-SiteCatalyst-Handbook-Insiders/dp/032185991X

Many organizations have poor Adobe Analytics implementations that have devolved over time. I suggest checking out my book on the product (https://www.amazon.com/Adobe-SiteCatalyst-Handbook-Insiders-Guide/dp/032185991X) or you can check out free Adobe Analytics blogs here: http://analyticsdemystified.com/category/adobe-analytics/

https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1449319793

My university uses "Marketing Models: Multivariate Statistics and Marketing Analytics" by Lacobucci along with a bunch of books on SPSS.

PS: This is broader and pure 'marketing analytics' and not just Google Analytics.