Last year, Computing, a United Kingdom-based publication for IT leaders, quoted Harvard Business Review’s recent naming of data science as the “sexiest job of the 21st century.” Who would have thought? Traditionally, jobs like data science and software engineering have been associated with a lot of things, but being sexy wasn’t generally one of them. So why the shift?
We’ve all read about the shortage of data scientists, and subsequent neon, flashing arrows pointing people to choose this career to make some fast money. After all, nearly every industry could use workers skilled in this field, and they need them fast or all the good data they are collecting will go to waste. Data scientists unlock the data and find the much-needed insights contained therein. So let’s find some newbies and get to work! Wait…
The Computing article headline clearly spells out the problem with this model: “Data scientists are in-demand and well paid - so why is there a skills gap?” Why, indeed? As the market demands these skills, novices are rushing to gain the necessary certifications and educational components to apply for open, lucrative positions. But they have no real-world experience to apply to their new positions, resulting in a talent gap that can bring disastrous results to any business. To paraphrase The Rime of the Ancient Mariner: water, water, everywhere, but not a drop to drink.
After all, the data scientist’s role is vital to business success. The article says “As problem solvers and analysts, data scientists are the professionals identifying patterns, noticing trends and making new discoveries, often working with real-time data, machine learning and AI…These highly-skilled professionals interrogate and make sense of the information available, making a significant contribution to the company’s performance.” They must take their science, math and analytics skills and couple them with a strong business sense. It is this combination of expertise and practicality that create value.
Companies are finding it challenging to find individuals who meet this required balance, so either their Senior Data Scientist roles are left unclaimed (and business decisions must be made without the benefit of rigorous data analysis) or they are hiring greenhorns who could cause more problems than benefits. Demand is greater than supply. The article says “Could it be the case that the specialism is diluted as everyone knows a little about interpreting data - just enough to get by? And what is the danger if we don’t fill this skills gap?” Other than the obvious dangers of not having data insights to make important business decisions, this can trickle down to a negative growth curve and economic impacts on a greater scale. One model that can provide a bridge for companies wishing to get the most out of their data is what we call a “data science consultancy” model. In the consultancy model, companies hire a team of specialized experts to complement in-house efforts. This model reduces risk, accelerates project timelines, and brings experience and skills to the table that would inefficient to maintain full-time. This model has proven to be quite successful in the legal, architecture, and marketing spaces—where “boutique consulting firms” of highly-skilled experts work hand-in-hand with in-house teams to help companies plan, execute, and succeed at ambitious projects.
Pacific Data Science offers a team of experienced data scientists, project managers, and full-stack software engineers to help companies fill the very real skills gap that Computing writes about. From designing data engineering workflows and executing technical work, to setting up a sustainable system and assisting with training and hiring, our team puts data to work for business success.