Accelerating Analytics: Decrease The Cost of Asking Useful Questions

After twenty years in the business, I am giving up on the idea of asking brilliant questions. They don’t exist. Ironically, most of the questions which have delivered serious money in the past tended to look like relatively dumb ones…

The first set of significant wins I had in my analytics career was in direct marketing, where I moved the campaign analysis process for a $5MM/year program in-house. From a technical perspective, this was pretty straightforward: write a SAS program to merge our mailing list with our customer file then aggregate response and sales data. Since a common key existed on both files (finders file number), it was a simple matter to join the files and summarize the data into an Excel Pivot table. Intern level stuff.

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Escaping The Walled Garden of Enterprise Analytics: Using R and Python For Data Analysis

In which an experienced analytics guy advises the younger generation to leave the walled garden of enterprise analytics tools and learn how to write code using a real programming language. Specifically advocating the use of R and Python for data analysis and related programming. But hey, I’m flexible on that point…

The use of COBOL cripples the mind; its teaching should, therefore, be regarded as a criminal offense.

– Dijkstra

I was taught a long time ago in some Management 101 course to sandwich constructive criticism between two compliments. So I’ll open with this statement:

SAS and the other BI vendors have done a nice job of bringing statistical computing techniques within the reach of the typical college graduate.

Now pull up a chair and grab yourself some popcorn, since I’m going to bite the hand that fed me for the first half of my career. I spent the first seven years of my career in roles involving significant usage of SAS and a variety of drag & drop query tools. The COBOL of the analytics world.

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Website Revenue Models: Real Revenue Statistics For Small Websites

Driven by a passionate desire to “scratch our own itch”, we released the first draft of a little project we’ve been working on this weekend. As our regular readers are aware, we built our first public website earlier this year. We started running ads earlier this summer (just to pay the server bills) and wanted some perspective on “what good looks like”. This is where things get furry: there aren’t any reliable and comprehensive public sources on revenue benchmarks for a small website. So we decided to build our own data set and create some benchmarks around how much a small website could earn. Here you go…

As you can see below, profit-per-visitor numbers vary widely. More after the jump…

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The Best Course I Ever Took: Combinatorics Problems

I sat quietly in the chair, number six or seven on George’s agenda of young hopefuls. The on campus recruiting process was a brutal cattle call. On the positive side, we had a large number of great companies coming to visit. Unfortunately, the vast majority of my 1500 classmates were tipped off about their arrival. Moo!

His eyes skimmed down my resume – and locked onto a phrase halfway down the page.

“Combinatorics Problems? What the heck is that?”

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DevOps Eye for the Analytics Guy

We may not be deploying cloud servers at the drop of a hat, but the analytics community can take a few lessons from our friends in the DevOps movement. DevOps, short for Developer Operations, has grown around the art of scripting and automating the process of setting up your infrastructure (servers, databases, etc.).

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Diving into R

Spent most of the past week working on my first “serious” R scripting project.

I’ve been using R for a couple of years, generally as a free minitab replacement (using the R Commander GUI interface) and adjunct to Python projects. Most of my analytics projects to date have been coded in Python, since they are generally heavy on data (eg. require good integration with our data warehouse, dynamic SQL) and require either custom statistics or heavy text parsing. The latest project is somewhat the reverse – involving a relatively small dataset (very painful to assemble, since it reconciles data from two different transaction systems, but small) that I’m going to run a bunch of prepackaged statistical studies and graphics again. Sounds like a perfect fit for R.

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Why Every Analyst Should Learn To Code

In recent years, I’ve seen a new breed of analyst emerge. Indeed, more than a few of my superiors have encouraged me to join their ranks. This new breed of analyst knows statistics and finance but prefers to leave the coding to others. They have “people skills”. They know how to sit in meetings and provide “thought leadership” and “design policy”. However, these analysts cannot pull the actual data to support their point of view!

This approach to the technical side of our profession is doing them a serious disservice. Here’s why:

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