A New Approach To Training Data & Analytic Talent


A new approachIt should come as no surprise that Data Science & Analytics has become one of the hottest career choices of the day. It’s nearly impossible to do anything without encountering some type of content referencing AI or Machine Learning. These buzzwords no longer signify a competitive advantage but rather represent table stakes in brand messaging. More surprising is how agnostic this brand messaging is; nearly every type of business is referencing how AI & Machine Learning is augmenting their operations. As a direct result the demand for qualified talent has grown at a tremendous rate and is forecasted to continue growing over the next decade and beyond.

However there is a problem … The demand for qualified analytic talent out-weighs the supply of qualified candidates.

“65% of recruiters report that the biggest challenge in hiring is a shortage of talent." jobinvite

Effectively businesses are looking for individuals with confirmed Data Science & Analytics experience paired with subject matter expertise.

This problem is a result of two factors:

1) Educational systems fueling the pipeline for analytic talent are incomplete

2) Businesses lack the understanding of what Data Science is and the practical application

Explaining factor one: Educational systems fueling the pipeline for analytic talent are incomplete

In response to the growing popularity and demand for analytic talent a wide variety of educational platforms have come into existence. These include offerings such as degree and certificate programs from colleges & universities, online training programs, bootcamps, etc. These programs are great for educating students on the skills, concepts, and techniques for use in the classroom but businesses are looking for more.

“Sixty-one percent of tech hiring professionals agree that a four-year college degree in a technology-related field alone does not prepare job seekers to be successful in today’s workforce.” ICIMS

All businesses hiring analytic talent have one thing in common on their job postings, they all require candidates to have some level of prior experience. This also rings true for entry and junior level positions. Businesses want the best assurance possible to confirm their return on investment. A candidate with prior experience that takes 4–6 months to come up to speed costs much less than a candidate that takes 9–12 months. History has conditioned employers to consider experience as a leading indicator in forecasting time to value.

This problem is not new and has been solved for a number of other career types. Two examples are doctors and mechanics. Doctors, in basic terms, go to trade school (medical school) and then do a work study program (residency). They learn the skills, concepts, and techniques and then go and practice them in real world scenarios. Mechanics do the same, learn and then practice in real world scenarios.

This type of education and application model works on many levels. It services both the student and the employer by reinforcing the learned content and leveraging the information collected as a part of an evaluation system. Students learn where their strengths and weaknesses exist creating the opportunity for improvement and employers see the demonstration of desired skills.

Up until this point there were not many good options available to provide this type of education and application system in the Data & Analytics space. Historically students had to secure internships or work study programs as the primary options. In some cases companies would offer employee education programs that paired education with practical application. Unfortunately the supply of such opportunities is low.

SimDnA, a recent startup, brought to market a new product that solves for this problem. Their product, TradeCraft; creates simulated Real-World businesses and allows students to immerse themselves into Data & Analytic roles. The simulated businesses are modeled to be identical to actual companies all the way down to the data. Representative data is critical to the mission of this product by enabling students to learn the practical application of Data & Analytic skills. As students’ progress through simulations their skills and level of analytic role are evaluated creating an ideal environment for self-paced growth. An added wrinkle to the platform is the opportunity for employers to request candidates complete evaluation simulations. This results in more fact based information to drive hiring decisions.

Explaining factor two: Businesses lack the understanding of what Data Science is and the practical application

This statement applies to the vast majority of businesses but not all. Data & Analytics is a broad career discipline and has a broad set of applications within a business. Unfortunately the popularity of AI & Machine Learning has just about every business in existence thinking about only the data science applications. This is not correct and leads to costly failures.

Before a company thinks about Data Science they need to ask themselves some basic questions.

1) How sophisticated is your analytic environment?

2) How well do you track and monitor KPI’s?

3) How informed are decision makers on their three most critical monthly questions?

If a business is not able to answer all three of these questions… then they are not ready for Data Science. They need to focus on the more traditional applications of Data & Analytics. That would include analytic exercises such as:


Data Collection

Storage & Organization

Process Analysis

Portfolio Analysis

KPI Development

Business Intelligence

Successfully developing these traditional analytic exercises creates a baseline system of information by which a business can manage itself. Now a business is prepared to test, measure, and optimize. Without a foundation you are basically throwing darts off a cliff and hoping to hit a target on the other side of the planet.

Unfortunately this lack of understanding the practical application of Data & Analytics is only growing due to the emphasis on Data Science. It needs to be made clear that Data Science lives on the far right side of the analytic spectrum and deals heavily in prescriptive outcomes. Educational systems need to make sure that they are teaching skills, concepts, and techniques across the entire analytic spectrum and providing the proper context. This in turn produces an analytic employee that is better able to help businesses navigate the complex world of data.

About the Author: My name is Ion King and I am a 20 year veteran of Analytics and Information systems for both fortune 500 companies and startups. My career has centered around traditional consumer lending and Fintech. Presently, my focus is on helping others passionate about growing careers in Data Science & Analytics achieve their goals. Connect with me on LinkedIn or find more of my articles on medium.