what is a CAO?

In a grad program of 86 students, it’s hard to get to know every student particularly well. As such, reputations start to crop up as a kind of interpersonal shorthand. Thankfully these reputations are generally positive, even if they are a bit one-dimensional. There’s the Networking Master, the Class Clown, the Consultant. My best guess for my own reputation, based on conversations I’ve had with classmates, is Blog Girl. I appreciate this, because people feed me blog ideas every couple of days, which is helpful during dry spells.

However, I think I accidentally expanded that reputation. The other week, a speaker was helping us understand the differences between analytics job titles — consultant vs. analyst vs. data scientist vs. manager, etc. He asked if there were any other positions he hadn’t mentioned, and I raised my hand to ask about the title of CAO, or Chief Analytics Officer. I somehow didn’t realize this would mean I’d have to stand in front of the entire class and explain what I thought a CAO does. But I did, so now I’m the Student Who is Going to Sit in the C-Suite Right After Graduation. That estimation is about 10-15 years ahead of what I think is realistic, but I appreciate the vote of confidence.

So what is a CAO, exactly?
The role can vary depending on the organization. Additionally, it’s such a new concept that both our speaker and the majority of my classmates were wholly unfamiliar with it. (“Did you mean CIO? CEO?”) Ideally, the CAO sits on the board of directors and reports directly to the CEO. The CAO is responsible for envisioning and directing an organization-wide analytics strategic plan. Because so many organizations are at different points in their adoption of analytics, this is where many of the differences can crop up: one CAO may be building an analytics team from the ground up, whereas another may be steering a well-built, seasoned ship. Either way, the CAO needs to make sure that their organization is on the cutting edge of data usage and analysis, as well as making sure that all branches of the organization have access to analytics, preventing the data science geeks from getting siloed into a handful of business priorities.

Just as the CFO cannot be replaced by some combination of the CIO and CTO, the CAO warrants her own spot in the C-suite. Sure, finance and analytics both involve managing information through the use of technology, but there’s that pesky accounting stuff that only the CFO knows how to manage, and all kinds of statistical know-how merged with business acumen that only the CAO can adequately cover.

“Chief Analytics Officer” is not a hypothetical position.
These positions already exist in big organizations like AOL and Teradata, startups such as AllSource Analysis, and even the City of New York (although, a year and a half later, it appears the Michael Flowers is no longer holding the inaugural position).

With the accelerated adoption of analytics across industries, we can expect to see the CAO position become ubiquitous in the next decade. So who knows? Maybe you’ll see yours truly as the CAO of a startup in a year or two…


the dating game: finding your passion


Allow me to start off this post by saying that I am happily married and not, in any way, about to start posting about my romantic life. I’m instead referring to the process of deciding what type of job I’d like to have within analytics. Ever since I decided to start applying to analytics Master’s programs, people started asking me what I would like to do with such a degree.

Actually, scratch that. Most people asked me what can be done with an analytics degree *period*, because most people outside the industry don’t really “get” what analytics is (are?). You can tell them it’s mostly Big Data and you’ll get an “ohhhhh” of recognition, but mostly in the way that I go “ohhhhh” and nod in the universal symbol of “I understand, please move on” when someone explains a Game of Thrones reference to me: I actually know nothing about the subject matter but feel shamed into feigning recognition due to its popularity.

As a result, I’ve been talking a decent amount about what can be done with analytics, and a relatively small amount about what I'd like to do with analytics. I have a vague idea that I’m interested in helping make healthcare better, but can’t really narrow it down much more than that. 

One of the beautiful things about our program is that we get exposed to a variety of analytical techniques and their applications, almost as if each topic is a Bachelor on The Dating Game, or as if my fellow students and I are speed dating. Yes, we should be familiar with all of these tools, but I get the sense that we will gravitate toward some techniques more than others, and that this may influence the type of work we will do after graduation. (We’ll see in nine months whether or not this remains true) Some methods seem like they might be a chore to learn and use, while others I find fascinating - and each student’s impressions so far have been a little different. So far, I’m surprisingly interested in clustering and a little wearied by the idea of stab-in-the-dark predictive modeling.

We’re not diving deep into these subjects at the moment, just getting introduced. And with each one I find myself evaluating: “Do I like you, yes or no?”.

Further Reading: When it comes time to do the job search, we’ll find ourselves in a slightly different “dating” situation.