A few years ago, the Harvard Business Review named the data scientist the “sexiest job” of the 21st century. It has become a craze for all kinds of companies to hire data scientists, since in addition to being smart people, they look good to C-suite executives, shareholders and customers. The marketing industry is no different. But for all but the biggest companies, hiring in-house data science employees can be the wrong choice, given the company’s needs and resources. Using software is often the better choice. Here’s why.
1. Data scientists are best utilized for open research, which is often not what they are hired for
Data scientists – before the term data scientist™ became cool – pioneered the great technological breakthroughs in the past few decades at research universities and the research departments of large corporations like AT&T and Xerox. But for the majority of small and mid-size companies’ marketing needs, they are brought in to improve the bottom line, not conduct lots of research, according to Forbes.
Without a sizeable research component, the day-to-day data science tasks are quite repetitive and monotonous. The New York Times reported last year that 50 to 80 percent of data scientists’ work is what they call data munging, or cleaning up raw data to make it useable to run algorithms to analyze campaign response rate, etc.
“You spend a lot of your time being a data janitor, before you can get to the cool, sexy things that got you into the field in the first place.” – Matt Mohebbi, data scientist
It is uneconomical to pay exorbitant salaries – more on that later – for data science employees to do rote tasks that do not optimize their skills. It can also be frustrating and unfulfilling for these highly educated and intellectually curious professionals. Software saves the company money, the scientists boredom and everyone time.
2. Companies often lack the resources to take full advantage of a data scientist or team
Does your company have an existing data infrastructure, such as shared, searchable computer databases and system-level middleware software to store and organize information? According to Information Management, a poor or nonexistent data infrastructure will lead to inconsistent standards and more time spent on fixing administrative than productive work. Are you willing to invest time and money and manpower into building a good data infrastructure? If not, hiring data scientists is a waste of the company’s money, the scientists’ talents and generally a bit moot.
“As companies struggle to make sense of their increasingly big data, they’re laboring to figure out the morass of technologies necessary to become successful. However, many will remain stymied, because they keep trying to fit a necessarily fluid process of asking questions of one’s data with outmoded, rigid data infrastructure.” – TechRepublic
And now, going back to exorbitant salaries…
3. Data scientists’ upkeep is expensive
The figures are still rising too: the same survey by Burtch Works (a recruiting firm) found that salaries rose 8 percent on average in the last year.
In addition to data scientists’ skills, companies are paying for the ‘cool’ factor of the profession, whether they realize it or not. “The very term pushes their value up,” said Alex Cosmas, chief scientist at Booz Allen Hamilton. It’s quite literal: The White House’s first chief data scientist DJ Patil (one of the original coiners of the term) said that a person who simply changes their LinkedIn profile to “data scientist” will see a dramatic boost in recruiting messages and possibly even get a raise at their current job. There is also the cost of recruiting – companies that do not have an expert on hand would need to hire again, a specialist recruiter to assist with finding a qualified data scientist.
On the other hand, software is easier to hire and fire. It can be bought, renewed and cancelled much more flexibly and with less third-party involvement. No dental insurance policies to wrangle, no hard feelings.
4. Automation is the way of the future
“Over time, software will do more and more of what data scientists do today.” – Roland Cloutier, VP and chief security officer at ADP
The data scientist is a very important profession, but much of what companies hire them to do for marketing can be replicated with software. There is software that can post content across multiple social media platforms, software that visually represents information about consumers and more. Easypurl software, for example, can create and track large-scale email, SMS and mail campaigns, with many options to customize content and personalize it to the 1:1 marketing level, in a fully automated system. It also generates its own analysis reports with graphics.
This will only become more common as time goes on and more technological innovations are made. The New York Times used the example of how spreadsheets made financial math and basic modelling accessible to millions of laypeople. Moreover, technology offers the power of scale, something adding employees cannot.
“We’re trying to liberate people from data-wrangling. We want to free up their time and save them from going blind.” – Prakash Nanduri, chief executive of Paxata
Data scientists are a great fit for large, established companies investing heavily in research. But for smaller companies that are looking for easily adaptable marketing solutions on more conservative budgets, software is your best bet.
