Tuesday, August 19, 2014

CAP: The Certification Program for Analytics Professionals

Scott Nestler, an experienced operations research analyst transitioning from the Army to the private sector, Chair of the Analytics Certification Board at INFORMS, and friend to Burtch Works, shares some thoughts on the Certified Analytics Professional (CAP) program.

CAP, Schmap. What’s That?

The Certified Analytics Professional (CAP) program has been in existence since the spring of 2013, after a couple of years in development.  Since launch, participation has steadily grown; over 10% of the Fortune 100 companies now have at least one CAP on their staff.  Here is some information about the program that might be of interest to both individuals seeking to distinguish themselves from the crowd and employers searching for qualified analytics talent.

It’s Not Just an Exam (There are Four Other E’s)

Many of the questions we receive are about the Exam that is part of the CAP certification program.  While exams are understandably a source of concern to many, there are other components to the program.  Collectively, we refer to these as “The 5 E’s.”  The others include 3-7 years of Experience in the analytics field, with respect to Education, at least a Bachelor’s degree.  Additionally, there is a requirement for the verification of Effectiveness of soft skills by a current or former employer or client. Finally, certificants must agree to a Code of Ethics, as described in this CIO Magazine article.  Therefore, CAP is a well-rounded program of continuous professional development, not just a one-time event.

Now, about that practice-based Exam … you get 3 hours to complete 100 multiple-choice questions, based on typical tasks performed and knowledge applied by analytics professionals. The Job Task Analysis identifies 7 domains as shown in the table below; weights represent the number of questions in each area.  It is available as a computer-based test at over 700 locations through Kryterion.  Pencil-and-paper exams are available in conjunction with INFORMS conferences.  Oh, and you get to use a 4-function calculator that we provide.

A Brief Primer on INFORMS

INFORMS stands for the Institute for Operations Research and Management Science; a professional organization with over 11,000 highly educated (50% PhD, 95% earned or pursuing MS) members.  About half are academics while the remainder is split between practitioners and students. While based in the United States, about 20% of the membership is located in Europe or Asia.

Although INFORMS has been viewed by some as being traditional and academic in nature, that has changed since 2010, when it “caught the Analytics bug.” The CAP certification program is just one of several analytics-related initiatives at INFORMS, including a newly-developed Analytics Maturity Model, Analytics Magazine, an annual Business Analytics Conference, and Analytics Continuing Education Courses. Also, the semi-autonomous Analytics Certification Board includes members from across industry, academia, and government, including some well-known personalities in the analytics field, e.g. Tom Davenport (IIA), Bill Franks (Teradata), Jeanne Harris (Accenture), Kathy Kilmer (Disney), and Jack Levis (UPS).

More Information, Please

Those interested in more information about CAP certification can check out the CAP Candidate Brochure or Candidate Handbook, while employers might be interested in looking at the Employer Guide to CAP.  If you have additional questions, please contact me, Scott Nestler, (acb@informs.org) or Louise Wehrle, the INFORMS Certification Manager (certification@informs.org).

Sunday, August 10, 2014

Data Literacy in the C-Suite is Not a Fad, Neither is Data

The conversation around Big Data has mostly shifted from “what is it?” to “how do we handle it?” and with this shift there has been much excitement around data scientists. But while data scientists are adept at many things, a large enterprise hoping to truly capitalize on the value in their data needs more than a team of brilliant data scientists – it needs a strategic leader capable of governing and managing the data, with the authority to enact strategy across departments.

At some organizations this has involved appointing a Chief Data Officer, and many more have appointed a senior leadership position with the same focus – but without elevating the role to the C-suite. Although the individual may not be called a CDO, it is more about the scope of responsibility than the title itself. Someone in the organization has to be ultimately responsible for the data.

Although many have been quick to brush this latest addition to the C-suite as just another fad, David Linthicum addresses this skepticism aptly when he writes:

“I’m not a big fan of creating positions around trends in technology.  Back in the day, we had the chief object officer, chief PC officer, chief Web officers, you name it.  However, data is not a trend.  It’s systemic to what a business is, and thus the focus on managing it better, and centrally, is a positive step.”

Data is not a fad. In fact, data is exponentially increasing every day, hour, and second of the day, for every business. This means many things: increasing data management challenges, increasing opportunities to better understand customers, increasing privacy concerns, increasing advantages for marketing, and much more. Of the many uncertainties surrounding Big Data, its existence now (I’m referring to the data itself, not the buzzword) and going forward should not be one of them. When the conversation surrounding Big Data dies down, it will most likely be because massive data has become the new normal, not because it has disappeared.


I was invited by Peter Anlyan to speak as a panelist at MIT’s Chief Data Officer and Information Quality Symposium (CDOIQ) in July, discussing how the industry is bridging the talent gap in analytics and data science. As we in the industry are all well aware, there is more focus than ever before on quantitative professionals, but the shortage of qualified analytics professionals and data scientists  has made hiring a significant challenge for many companies.

The talent shortage is great enough, in fact, that some company representatives at the symposium expressed concern about sending their teams to Master’s programs for deeper training, lest they be poached away, defeating the investment of time and resources. While high attrition may be a frustrating symptom of the times, I’m not sure they have a choice.

Luckily, the increase in MOOC’s (Massive Online Open Courses) and various bootcamps across the country could offer an alternative to companies not willing to risk investing in a time and money into a full-fledged Master’s program. The efficacy of those methods however, depends on the strength of the program as well as the learning style of the individual, as Irmak Sirer of Datascope Analytics noted in his guest post last week.

Having just read Karen O’Leonard’s report from Deloitte, Show Me the Money: How to Secure Funding for Your Talent Analytics Case  I was also eager to hear her thoughts on HR and talent analytics at CDOIQ, as well as attend some of the other events to hear more about the  development of the Chief Data Officer position. You can read more about Karen’s thoughts from CDOIQ here, and Gregory Piatetsky of kdnuggets also had some good insights from the symposium.

The Future of the C-Suite

My thoughts on the longevity of the CDO role are that the responsibilities are the important part, not the title. Gartner predicts that by 2015, 25% of large global organizations will have appointed Chief Data Officers, so it will be interesting to see if that holds true. If we’re predicting the future in C-Level hires though, perhaps it’s time for a Chief Analytics Officer to throw their hat in the ring?

Monday, August 4, 2014

Becoming a Data Scientist: Master’s Program, Bootcamp, or MOOCs?

This post is contributed by Irmak Sirer, a partner and data scientist at Datascope, where he solves business problems with data by designing analyses and interfaces. Irmak has helped companies across industries solve problems with data, from small companies to members of the Fortune 50. Working with Metis, Irmak is an instructor for their Data Science Bootcamp program which will be starting on September 2nd in New York City. You can read the full version of this post on the Datascope website.

One of the most frequent questions we hear, right behind “so, what exactly is a data scientist” or “what makes a great data scientist”, is “how do I become one? I should probably just get a Master’s, right?” Perhaps not anymore; rising costs, changing demand, and the Internet are disrupting this traditional path and providing two viable alternatives. At one extreme, self-learning through Massive Open Online Courses (MOOCs) give access to courses at an extremely low cost (often free), but leave it “as an exercise for the reader” to identify a suitable set of courses and tools to round out a coherent skillset. Bootcamps offer a middle ground where students can pay for a structured learning environment at a far more affordable rate compared with obtaining a Master’s Degree. So, “which path do I take?”

We think the answer to that question largely depends on the student. In some cases a student will prefer attending a bootcamp whereas in other cases a student will prefer receiving a Master’s at a university or taking university courses online through MOOCs.

Here at Datascope we see great benefits from the bootcamp format, so when Metis (a part of Kaplan) contacted us about partnering to design a data science bootcamp, we jumped at the opportunity. We thought we could take all these points we see as the advantages of the format, and elevate them as much as we could. So, we designed a course that would give aspiring data scientists a lot of experience with 4-5 projects, and a guided route of several core data science concepts and approaches. Participants can quickly build the necessary foundation without the burden of teaching herself everything or paying the handsome price of a Master’s program before realizing her dream job. If you’re interested, our Data Science Bootcamp program is starting on September 2 in New York (applications due by August 11), and you can learn more about it here.

Since there are many things to consider when choosing which program works best for you, in a separate post, we do a thought experiment to compare the three experiences for a fictitious aspiring data scientist named Audrey. For the sake of brevity, the following table summarizes our thinking about what each of these experiences is like and, more importantly, who they are ideally suited for.

Self-taught (MOOCs)
Theory-rich learning
Self-guided learning
Experiential learning
Live university faculty professors
Recorded university faculty professors
Practicing data scientists
Portfolio of projects
9 - 20 months
6 - 18 months (part-time)
2 - 3 months
$20,000 - $70,000
$0 - $500
$0 - $14,000
1.5 years of social networking
Isolated; no in-person networking
Collaborative networking
Internship + practicum projects
Projects on own time
Projects built in to experience
Job hunt
University-wide recruiting day
Self-driven job search
Hiring day organized by bootcamp; talent placement manager helps with hunt
Ideal for
People that enjoy immersing themselves in campus life and want to take time to let the new material absorb while learning in a structured environment with the full credentials of a University degree.
People that thrive with ambiguity and self-guided environments and are motivated enough to design their own curriculum around their own strengths and weaknesses.
People that want to switch or accelerate careers ASAP and want to have confidence that the switch will result in a job they will like while learning in a structured environment.

As technology increases the rate of change of society, the most successful workers will be those that can quickly shift to new specialties and learn on the job to meet market demands. In our opinion, the bootcamp format provides the benefits of personalization, credentialing, and social learning that a Master’s degree offers, but at an accelerated rate with experiential learning. Sure it is more expensive than being self-taught, but the connection with employers and the guided, experiential learning process increases your confidence to tackle the uncertain prospect of making a career switch.

To become a data scientist, you don’t need to have postgraduate degrees, or 20 years experience, or be proficient with every data-related technique and tool under the sun. What you need is to have enough baseline knowledge and experience, and the skill to constantly adapt and learn. Bootcamps, in our opinion, are the perfect medium for making the transition.