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).

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.

CDOIQ

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?

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.

	Masters	Self-taught (MOOCs)	Bootcamp
Learning	Theory-rich learning	Self-guided learning	Experiential learning
Teachers	Live university faculty professors	Recorded university faculty professors	Practicing data scientists
Outcome	Diploma	Certificate	Portfolio of projects
Duration	9 - 20 months	6 - 18 months (part-time)	2 - 3 months
Tuition	$20,000 - $70,000	$0 - $500	$0 - $14,000
Networking	1.5 years of social networking	Isolated; no in-person networking	Collaborative networking
Projects	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.

Tell Your Kids to be Data Scientists – Not Doctors

Recently I had the pleasure of being interviewed by John Phillips at CNBC about our data scientist salary study. His article, Why Your Kids Will Want to be Data Scientists, was published at the end of May, and in it he raised a very interesting point:

“According to Burtch Works’ 2014 study of salaries for data scientists… those responsible for a team of 1-3 earn [a median salary of] $140,000 and those responsible for a team of 10 or more earn $232,500.

By contrast, the mean average annual income for a lawyer in America was $131,990 in 2013, while doctors earned $183,940, according to data from the U.S. Bureau of Labor Statistics.”

Did you hear that? Data scientists earning more than doctors! For complete salary information for data scientists, Big Data professionals and market research professionals, download the full reports for free here. Salary is not the only reason however, that I would recommend encouraging your children to pursue statistics and coding over going to medical school.

How Data Scientists Are Supplementing Doctors

There are big changes happening in healthcare right now, and the implementation of EHR (electronic health records) in particular is a great example of how data scientists will be working with doctors in the future. The move to EHR is picking up steam, and the Center for Disease Control reports that 78% of office-based doctors are using EHR as of 2013, with that number only expected to grow as practices will face penalties for non-compliance. All of these electronic patient records spell out Big Data for the healthcare fields, and data scientists - like all quantitative folks - love data. These medical data could not only offer tremendous insights that change the face of modern medicine, but also offer rewarding opportunities to the data scientists who must decipher the data.

Patient care also stands to receive enormous benefits from data science. Venture capitalist Vinod Khosla was recently quoted at the Standford University School of Medicine’s Big Data in Biomedicine conference saying, “Humans are not good when 500 variables affect a disease. We are guided too much by opinions, not by statistical science.” While a doctor may be trained to look for many factors when diagnosing an ailment, some of these diseases are impossibly complex, and patients could stand to gain faster, safer treatment if left in the hands of a well-developed machine, or even a physician aided by one. For example, IBM’s Watson is already collaborating with Memorial Sloan Kettering Cancer Center to help doctors make better cancer treatment choices. The human interaction between patient and physician will continue to be important, but data scientists will have a measurable impact on the future of healthcare.

Career of the Future

One of my predictions for the analytics hiring market this year was that data scientists would be embedded in analytics groups, and with the internet of things, the increase in wearables, social media sentiment analysis and many more applications for data science, it’s no wonder this career has so much buzz around it. With the increase in demand, shortage of talent, high salaries and applications in every industry, data science is becoming a good option for career success. The road to that success begins with a strong early foundation in math, and (perhaps) some nudging from the parents. I can’t tell you how excited I was when my daughter, Becky (who started college this year at Macalester), changed her major to math, as I believe strongly that this is a career path that offers a bright future. Who knows, maybe one day I’ll have a data scientist in the family!

Job Market Explodes For Quantitative Students

With the market for quantitative candidates continuing to gain momentum, Burtch Works spoke with Jennifer Priestley, Professor of Statistics and Data Science at Kennesaw State University - and friend of Burtch Works - about the job market for quantitative students, the  beginnings of her MS in Applied Statistics program, and her thoughts on SAS vs. R.

Burtch Works: Tell me about the makeup of your class, do you find that many of your students are coming straight from undergrad or mid-career and retraining?

Jennifer Priestley: I would say 40% of our students come in direct from undergrad; the other 60% are coming in with work experience, and are doing a mid-career change. What’s fascinating to me is that we’re getting increasing numbers of MBA’s. For a long time, an MBA would suffice, and it almost didn’t matter where it came from, but those days are over. Maybe from the top five programs they still matter, but for the most part an MBA is a way to round out qualifications. It would be naïve to think that an undergrad degree in business and an MBA will get you somewhere.

BW: I’ve heard similar things from other programs about the influx of MBA students. Do you get many international students in your program?

JP: Some, but not as many as I’ve heard in other programs. Many of the students are first generation college students, so upon taking their first course here they will be the most educated in their family. Our students are motivated, driven, and hungry for success. We have a 0% unemployment rate, and when these undergraduate students are placed into white collar, professional jobs making $50,000 a year, they’ll be making more money than their parents. That’s what inspires me to do this, because you’ve fundamentally changed their path and lineage. They may start their education at X County Comprehensive High School, where only 3% of students even go to college at all, and by the time they graduate our program they’re working as a business analyst at Equifax. That’s a huge step.

BW: Can you elaborate on how the Master’s program came about? When did it start, and how did you choose what to include in the program?

JP: It launched in 2006. When we started, we looked hard at what other universities in the regional footprint were doing. We didn’t want to go head to head with Georgia Tech, University of Georgia, or Emory. We wanted to do something in statistics that didn’t compete with them, since we didn’t want to be competitive - we wanted to be complementary.’ So we started with a blank sheet of paper, and created our program to fill the gap. We wanted to build the program so that our students could graduate, and the next day walk into a Fortune 500 company and add value. The entire curriculum was SAS based, and programming in base SAS. We also stress the importance of learning how to extract, transport, load (ETL) and cleanse your own data. Statisticians don’t have the luxury of being a “data diva”.

Statisticians can’t just create models; they have to do computer science. The days of getting pristine data sets are long gone, so students need to know how to do these things. Basically, we were teaching data science before it was cool. In our program, students spend weeks in some classes just cleaning data before they learn the modeling techniques. We recognized a gap in the current education system, and combined the idea of ETL and cleaning with the mathematics and the statistics.

BW: What are some of the other challenges that your program aims to prepare students for?

JP: A big point in our class is communication skills. Nobody cares what you did if it is so complex that you can’t translate it. If you can’t explain what you did to a marketing major, then you can’t improve the decision making process, and you may need to consider hanging it up. The people who are going to take your stuff and do something with it are on the business side. As you move up the chain, you will have 5 minutes to explain what you’ve found to a c-suite executive who doesn’t care about your incredible skills, they just care about how it will profit the organization and affect the bottom line. You need to be able to convert the results into something meaningful, and if you can’t then it will reflect on you as a professional.

BW: How has the career outlook for your students changed over the past 3-5 years?

Exploded. Completely exploded. Like I said, we have a 0% unemployment rate. Companies will contact me, saying “Can you send us resumes for your students? We’re trying to hire for internships.” The press says there are no internships out there, but we actually don’t have enough students to place. When we launched in 2006, and basically we didn’t know, ‘if we built it would they come’. But, we graduated our first cohort in 2008, and they did well. I would say starting salaries in the $60,000-70,000 range, which is not bad. Now, we place kids in the $90,000 to six-figure range, so an amazing increase in salaries.

Another indication is the number of applicants that come to us. When we started the program we kind of said whomever wants to apply, we’ll let them all in. Now? We have so many applicants that we receive four applications for every slot. We also took the GRE requirements way up. Up until last year they had to be above the median for us to look at the application, and now they must be in the 75^th percentile for us to consider the application. Keep in mind, as I said, we don’t have that many international students.

BW: So I’ve been hearing a lot about SAS Day at KSU, and it seems like a fascinating concept. When did that start, and how has it evolved over the past few years?

JP: It started in 2007. We work very closely with SAS Institute, so the year after we launched the program I got a call from a sales representative for SAS. He does business development for Fortune 500’s like Delta Airlines, Equifax, all companies in the region, and he said to me, “I’m always challenged with, once they get a multi-million dollar infrastructure with SAS, the next question is always: How do I find people to run it?” It’s easy to find someone with 5+ years of SAS experience for $200,000. What’s harder is finding someone with 2-3 years experience for $50,000-100,000. He said, “You have a natural pool of talent, and we have a built-in demand from companies.”

It eventually evolved into what we have now, where the first half of the event is the student poster competition, with students creating a poster for a SAS-based project, and the alumni come back and judge. We probably had 70 posters last year. We look for the perfect intersection of correct math and an elegant, efficient, and visually appealing presentation. Students set up a booth, and companies can visit the students and drop off their business cards. It’s basically a reverse career fair.

BW: What kind of companies go to SAS Day, and how many of them?

JP: Of the 200 or so people who came last year, I’d say there were about 50-70 different companies represented. Companies like Autotrader, Deloitte, Blue Cross Blue Shield, Equifax, KPMG, Coca-Cola, The Southern Company, Maxum Insurance, Teradata, CarMax, State Farm, Aspen Consulting, Midtown Consulting Group, Slalom Consulting, and AT&T.

BW: Do you have any examples of other schools that do this? Is this a common thing?

JP: SAS had said they wanted to partner with other schools, and I’ve heard of similar events at Texas A&M, UConn, and University of Illinois. I know University of Alabama has something similar, like a Data Analytics Week that’s aligned with the business school.

BW: What do you think of the popular debate about SAS vs. R?

JP: It’s not even a debate. We don’t see SAS and R as competition. They’re complimentary, and a lot of companies use both. Although we partner with SAS, we still teach both programs. I’d say we’re 70-80% SAS dominant, but we teach R also, and we encourage our students to know both. That’s our philosophy. We’ve actually had an R Day also for 2 years now; the next one will be in the fall.

I would say the results of your survey, 65% SAS and 35% R, is a perfect example of what we use.

The Deep Dive: SAS vs. R

Last month I conducted a quick “flash survey” of my network to quantify the preferences of the Burtch Works network, and asked: Which do you prefer to use, R or SAS? I posted the initial results on my blog a few weeks ago, and as promised during the webinar for our Data Scientist Salary Study, we've finished up our deeper dive analysis of the data from over 1,000 respondents. Whether you think the results are predictable or surprising I’d love to hear your thoughts in the comments below, as the conversation has been pretty lively so far. Without further ado, here are the greatly anticipated results!

As many theorized, respondents with five or less years of experience were the most likely to favor R.

Although SAS was favored by most education levels, amongst PhD respondents R was almost even with SAS. In looking at PhD respondents with five or less years of experience, R was decidedly more popular than SAS.

In most regions of the United States SAS was the preferred tool, however on the West Coast R is favored over SAS.

In almost every industry SAS is preferred over R, except for Tech, Telecom and Gaming companies. Retail, Pharma/Healthcare, and Financial Services have the strongest preference for SAS.

Tips for Hiring Data Scientists

This post is contributed by Frank Lo, an experienced data science professional and friend of Burtch Works. Frank is currently the Head of Data Science at Wayfair, as well as the founder of DataJobs.com.

 There is so much hoopla around the need to hire data scientists -- but amid all the frenzy, I notice a major disjoint between what companies think they need and what they actually need to leverage data science for business value. I come from the background of leading a data science team; on top of cultivating the team and diving into the nitty-gritty of data, I spend a lot of time recruiting, trying to find new rock-star talent. I'd like to share a few things I've learned along the way around what to look for.

  Look for quant experts with business hustle.

 First, let's think about what is data science. It is not only a combination of technical and quantitative disciplines, but also the acumen to leverage STEM skills to transform business. Too often, we focus too much on pure engineering/math ability, kicking business smarts to the wayside. On the contrary, I think business acumen is one of the most important traits of effective data scientists -- so much so that I filter out strong tech/math candidates if they have trouble thinking through the business applications of their quantitative work. Ultimately data scientists create value by being consultants to the business. Data mining and predictive analytics by themselves are not the point, but rather the means to enable intelligent strategy development. Look for people who are good at all of the above, including business.

  The #1 intangible is intellectual curiosity.

 The spirit of data science is discovery. Given a mountain of data, what inferences can we make? What truth is revealed or predicted? The strongest data scientists are motivated by this curiosity to explore data in very creative ways. When I recruit for my own team, I look for people who are not only good at answering questions, but who want to ask their own questions. This genuine inquisitiveness is rocket fuel in driving a data scientist's search for meaningful discoveries in data. It is so critical to the role that we turn away candidates who cannot demonstrate that they are brimming with intellectual curiosity.

 How to screen for this intangible? One interview question I ask everyone: "tell me about a data science project or investigation that you initiated on your own, outside of school and work." I look for candidates who can respond to this at length, diving into quant, tech, or business problems they were so intrigued by that they carved out their own time to pursue. It is a great signal they will thrive in a data science role.

  Don't filter candidates based on degree.

There is a notion out there that the best data scientists are the ones with Ph.D's. From my experience interviewing and evaluating candidates, it is my opinion that academic degree is the last consideration that matters. Some Ph.D candidates have very well-rounded skills and do become top performers. Though, I've found that many others find themselves mentally stuck far down the academic rabbit hole, and have difficulty translating their focused depth into value in a business environment. My point is: degree by itself is a very incomplete indicator, so don't filter candidates on it. Consider all academic backgrounds, zeroing in on well-rounded skills paired? with the right intangibles (i.e. intellectual curiosity, business acuity, etc). You'll find that many star data scientists are self-taught hackers and mathematicians, who may not have wanted to sacrifice work experience for academic credentials, but have a very complete data science knowledge base.

 Clearly, there is a lot to consider in how to hire solid data science talent. The reality is that data science is very multidisciplinary; you're looking for a blend of skills that can reveal itself across a wide range of academic backgrounds and professional experiences. But as long as you have a solid understanding for the nuance in what makes a good data scientist, you'll have an easier time trying to identify the right people for your team.

Keep an eye out for our Data Scientist Salary Study - a follow up to the landmark Burtch Works Study: Salaries of Big Data Professionals - which should be released in the next month.

My Predictions for the 2014 Analytics Hiring Market

Thanks in part to the media coverage of Big Data, analytics has become inescapable. To those who have worked in and around this industry for years this information is nothing new - merely more visible. Every day I’m hearing about more and more businesses warming to the idea that there are actionable insights readily available in their data if they know how to use it.

It is an exciting time to be a statistician, data scientist, or analytics professional, and if you thought 2013 was big, 2014 promises to be even bigger. Without further ado, here are my top predictions for the analytics hiring market:

1.)  Real-time analytics becomes increasingly important to business intelligence – Industries like the gaming industry already give their customers incentives in real-time and retailers are quickly moving toward real-time couponing. Those of you with real-time data experience will be in high-demand for these growing roles.

2.) Data scientists will be embedded in analytics groups – In addition to analysts who can build complex models, there will be an increasing need for data scientists, who with their background in analytics and computer science can wrangle massive, unstructured data sets. Companies hoping to extract the full benefit from their data sets will need a combination of both groups.

3.) If you don’t have digital you will be a dinosaur! - If your skill set doesn’t already include digital analytics your career plan should be to get experience and soon. Without it your skills will become outdated and obsolete.

4.) Sentiment analysis will continue to erode traditional survey methods – Use of social media data and other “free sources” has the opportunity to overtake traditional survey methods, which will impact the marketing research talent landscape.

5.)  Corporations will bring on internal staff to replace high paid consultants- Consultants got the ball rolling, and now companies will seek to take on full time staff.

6.) However, this will not adversely affect the consulting industry – Demand is so high for analytics experts (and continuing to grow) that there will still be companies looking to small and large consulting firms. Despite some companies hiring full time staff, the consulting industry will continue to experience growth. Heavy travel schedules will continue to be the norm for these opportunities.

7.) The trend towards wearables and PEDs will create new positions- If the buzz from CES2014 is to be believed, the wearables trend is increasing. As with any new technology that creates massive amounts of data, analytics positions will be created in the industry to make sense of and capitalize on all that information.

8.) Startups’ grip on the hiring market will loosen- I have spoken with candidates who have worked with many failed startups, and I think that this year the tendency towards hopping from failed startup to failed startup will begin to shake out. Candidates who have been through the failed startup cycle will begin to realize that to build a career it will be important to learn from an established group.

9.) Graduates from the new analytics programs will begin to hit the job market - This is not so much a prediction as it is inevitable, and I am very interested to see how well these new programs have prepared their students to enter the quantitative workforce.

What trends do you think we will see in the analytics hiring market? Let me know what you think in the comments below.

 

 

Burtch Works' Most Popular Social Media Posts from 2013

2013 was a busy year for Burtch Works’ social media accounts, and I wanted to take the time to revisit some of our most popular links this year. 

Our top links include blog posts on topics from resume writing to data science wannabes, as well as links to our original research, including salary studies and a flash survey of our network. 

Here is our social media year in review:

1.) The consulting trend is really gathering steam but the opportunity is not for everyone, which I addressed in Should You Take That Consulting Role? Here's Why or Why Not.

2.) With networking being more important than ever and holiday parties around the corner these 18 easy conversation starters from Careerealism that I posted were perfectly timed.

3.) Apparently everyone is thinking about retooling their resume, because my latest blog post Need to Rewrite Your Resume? Four Tips Before You Submit is one of the most popular posts on our social media this year.

4.) Are you a real data scientist? Or just a Data Wannabe?

5.) McKinsey put out an informative infographic on Big Data and ROI Big Data Big Profits.

6.) Lou Adler once again published a great article on LinkedIn with 5 Things You Must Not Do In an Interview, and 5 Things You Must.

7.) The Burtch Works Study: Salaries of Big Data Professionals was released in July, and Silicon Angle ran a story about lucrative salaries for foreign-born analysts.

8.) Our Marketing Research Salary Study was released in October, both salary studies and their webinar presentations are available here.

9.) Back in April I addressed the lack of urgency to hire in Help Wanted But Hiring Slow.

10.) While catching up on TEDxTalks in September I watched Big Data, Small World by Dr Kirk Borne which is definitely worth your time. He also maintains a very active twitter presence and was voted the #1 influencer on Big Data by Onalytica.

11.) In a guest post on my blog at the beginning of November, Burtch Works’ entry-level recruiting specialists shared their advice in How to Get Your First Analytics Job.

12.) Researchers at the University of Pennsylvania analyzed social media Big Data to create word clouds showing commonalities by age, gender & personality. Fascinating stuff!

13.) Forbes published my article Five Ways Marketers Can Keep Quants From Quitting in December.

14.) We tallied results from our Flash Survey for analytics professionals about how many are approached via LinkedIn about new job opportunities and how often.

15.) What Design Thinking Can Teach Analytics Professionals was an attention-grabbing article from Data Informed.

With all the attention focused on Big Data, I expect next year to be even busier as more companies look beyond the buzzword and start seeing returns on their investments. The analytics hiring market will continue to heat up, so keep an eye out for my 2014 hiring predictions blog in the beginning of January. For more career advice, blog posts and industry news throughout the year be sure to follow Burtch Works on LinkedIn and Twitter. Happy Holidays everyone!

Kaggle Flourishes by Embracing the Competitive Nature of the Data Scientist

The emergence and success of Kaggle, a website dedicated to matching number crunchers with companies in need, shows us that no matter how challenging the problem there is always someone willing and eager to tackle it head on.

We’ve been hearing more and more about Kaggle in various capacities: candidates posting their Kaggle ranks on their LinkedIn profiles, students or recent grads using the site to gain experience with unstructured data, or even companies asking for candidates’ Kaggle rankings in job postings. For an aspiring data scientist with little to no experience this could be a invaluable tool to gain experience with real-world big, messy data.

Kaggle has grown in popularity from 25,000 to over 100,000 members in a little over a year and received attention from numerous publications such as The New York Times and Forbes about its unique service. Quantitative professionals from all over the globe can compete for prize money by offering intellectual property including any algorithms, models, solutions etc.

In addition to earning money for their services, Quants have the opportunity to hone their skills and see how their solutions and skills rank against other members. For companies who cannot afford or do not need a full time analytics staff, Kaggle will match you with a team based on your specific initiative that you can hire on a contract basis.  According to the founders of Kaggle, their intention was to inspire great performance by inciting competition amongst the members.

The competitions are divided into sections, with one dedicated to “Getting Started” which offers entries and students looking to test their skills on real data sets an opportunity to see how they compare to working data scientists worldwide. There have also been competitions from Belkin, Amazon and Expedia for more advanced participants.

Most recently, Facebook is using Kaggle to host a recruiting competition. Could this site completely revolutionize the way Quants learn, share, compete or job-hunt within the community? As anyone keeping abreast of Big Data news will tell you, we’re moving into the age of digital everything- socializing, interviewing, learning- why not digital sports for the data scientists too?

Your Webinar Questions Answered Part 2

Thank you again to those of you who attended my webinar presentation on The Burtch Works Study - a comprehensive look at the salaries and demographics of Big Data professionals. In case you missed it, The Burtch Works Study and webinar presentation are available here. 

Due to the volume of questions I received during the webinar (we had over 750 registrants) I decided to share some of my insights on my blog in two parts, Part 1 is available here.

10.) Are you seeing salaries for non-Big Data (e.g., market research, smaller scale database or CRM analysts) increasing as a side effect of Big Data demand?

LB:  Not yet, but we’ll keep an eye out since we cover those areas too.

11.) I am working in a company that is just beginning to realize the benefits of big data; any suggestions for how to further present the benefits of data and Big Data professionals to a company that so far has not thought it was worth the cost? (Especially how to negotiate salary for data professionals.)

LB: Great question! The payoff for investing in the right Big Data professional is the difference between an ineffective, costly foray into Big Data and a successful and brand-strengthening opportunity. I would highly recommend investigating and presenting specific insights related to your industry. There are new examples every day of companies who are using this wealth of information to their advantage. I would also emphasize that soon the time to gain an edge on the competition will have passed and your company may find itself trying to catch up instead of being a front-runner in the industry. Be diligent about staying abreast of new developments and make sure to network. When you are given the chance, tackle a small project first, rather than going after a big, lengthy project that might not show any results for months. Choose a smaller project where you can quickly get some encouraging reports  that will help to show  that this is worth investing more time (and money!) in, and allow you more time to go after a bigger, more influential project. As for salary negotiations, a couple of my contacts have already used excerpts from our study as support for the case within their organizations for higher salaries within analytics.

12.) When it comes to recruiting candidates, does age play a role as gender does?

LB: In my opinion it has more to do with attitude, energy, commitment and the ability to change and grow. Companies want to hire someone who is capable of adapting to the changes happening every day in the industry and who will be able to capitalize on new opportunities.

13.) Story Telling is a big part of Analytics in any organization. It is an art and a science...Can a Data Scientist play this role?

LB:  Absolutely. Story-telling and strong communication skills are critical for success in analytics. The most successful people in this role can work in a quantitative, technical way as well as selling their ideas to a non-technical audience. I wouldn’t say that one quality is necessarily better to have than the other, but having both makes you a very strong candidate.

14.) There seems to be a lot of tension within and around corporations these days with regards to where the data resides and where it should reside. The data belongs to the company but within the company where does it belong? And if the bulk of a company’s customer-centric data is moved out of IT does this mean the people who work in IT are less valuable or expendable?

LB: You're right in that there's tension in the industry around this issue, mainly because it's become front and center. From my perspective, I think it's extremely important that the relationship between the head of IT and the head of analytics is a strong one. If data is moved out of IT I don’t think that makes IT people more expendable.  Anyone that works for a corporation is expendable, and the best way to manage this is to ensure that your skills are up-to-date and marketable in the long run.

15.) Why is it never a good idea to accept a counter offer?  I understand that a few people would be annoyed, but isn't that a common outcome?

LB: The number one reason people make the decision to leave is because they want a new challenge or a new environment. Money sometimes plays a part but should not be the only reason. If money is the number one reason you should talk to your boss before going on the market because accepting counter offers is generally frowned upon. It’s a small world and the last thing you want to do is burn any bridges during a transition.

16.)  Many of the Analytics Management roles I see posted require deep SAS skills, but do you see growth in roles for those who understand analytics and the applications to solve business problems? Ideally managing a team of analytic professionals but focusing more on strategic goals rather than modeling and being an analytic coach?

LB:  I am definitely noticing growth in this area, which I call "Analytics Management" - people who are savvy with numbers and insights, but don't necessarily have the deep modeling and statistical skills of traditional analytics professionals. The most valuable however, can manage, lead and coach with hands-on skills.

17.)  I am definitely noticing growth in this area, which I call "Analytics Management" - people who are savvy with numbers and insights, but don't necessarily have the deep modeling and statistical skills of traditional analytics professionals. The most valuable however, can manage, lead and coach with hands-on skills.

LB: I would advise identifying what specifically is making you unhappy so you can possibly come to a resolution with your boss. If a satisfying compromise can’t be made, then perhaps it’s time to update your LinkedIn profile and start looking!

18.)  I’ve established myself as a strong candidate and am receiving offers, but I want to make sure that I land the ideal fit: I don't want to get locked into a job that will exhaust me and my family!

LB:  It’s a great question because it’s a difficult one, especially when you are in demand! Considering your desire for work/life balance, as general rule avoid consulting, finance or startups and make sure to discuss your concern with your potential hiring manager to get a feel for the company culture. Feel free to reach out us if we can help you evaluate a role, even if it’s not through us!

At Burtch Works we are always looking for trends within the analytics field, so be sure to check back and see if we have posted any new research results!