Get started with Radius

We'll walk you through our Radius Revenue Platform and show how it can be used to blow past your revenue goals.

Demo Request Submitted

Thank you for you interest! One of our account executives will reach out shortly to schedule a demo with you.

Should You Build a Data Science Team?

Various organizations have advocated the need for every marketing team to hire a data scientist, however, is this claim feasible as companies scale?

In the past, data was primarily produced in enterprise systems. Today, we see streams of data emerging from unconventional sources such as social networks, blogs, web pages, and email. The mass proliferation of data provides marketers the ability to go beyond basic customer profiling to tap into the abundance of behavioral data and firmographic insights. However, this data is often chaotic and doesn’t slide neatly into pre-determined slots. While marketers want to be data-driven, they don’t have the time or the bandwidth to analyze the increasingly large amounts of data they now collect. As a response to this problem, organizations are clamoring to hire data scientists, who will merge, de-duplicate, and scrub their data for inaccuracies to pinpoint trends and relationships and ultimately drive revenue.

So should all organizations pencil in budget for a data scientist?

The rising cost of data scientists

Data science pulls its theories from a wide array of technical disciplines including statistics, mathematics, data engineering, and computer science. As result, finding qualified applicants can become a challenge. Data scientists also come with a hefty price tag, with an average annual cost of $100,000 per employee. The starting rate to hire a team of data engineers, machine learning experts, and data modelers would cost around $1 million. This doesn’t account for cross-training, skill redundancy, and transitions (in case a member of the team is recruited out).

big-data-big-paycheck

Building a data science team also means investing in a data science recruiter. Since the field is new, few recruiters have experience qualifying data science professionals or understanding the skills and background that make a strong candidate. The recent hype surrounding big data has also led many business analysts to rebrand themselves as data scientists, despite lacking key skills.

Translating intellectual curiosity into business results

Data scientists often have different goals than the executives that hire them. Companies want to focus on keeping their products competitive, serving customers better, and creating a unique innovation cycle. Meanwhile, data scientists like to build and research rather than create repeatable processes.

“There’s a pervasive belief that data science works by hiring smart people, turning them loose on data, and waiting for great things to happen.” – Ron Bodkin, Teradata

Another risk companies face is having their data scientists get stuck on secondary and tertiary problems that are interesting, but have little to no impact on their overall business goals. To deliver at a consistent pace, companies need to assign project managers to track the efforts of their data science teams. If your company lacks the solid infrastructure data scientists thrive in, it may be time to rethink your data needs.

Scaling & productizing data science

Most small and medium-sized companies lack the funding required to build large data teams and often resort to hiring one or two data scientists. Hiring one data scientist may seem attractive to your shareholders, but it isn’t enough to extract actionable insights from your unruly data. Instead, your new hire may spend most of their time collecting, preparing, and cleansing digital data.

“It’s an absolute myth that you can send an algorithm over raw data and have insights pop up.” – Jeffrey Heer, Trifacta

These companies also can’t provide long-term growth to keep data scientists around. Keeping top performers means building high-performing, cross-functional teams that include a variety of roles, and aligning them to directly support decision makers.

Instead, they should invest in their money in software solutions that are built on data to deliver actionable insights. These solutions make it possible for companies to operationalize data science without paying a fortune for a team.
data science team

So should you build a data science team? Yes, if you’re a large organization who has the time and money to recruit, hire, and assemble a team of data scientists. However, if you’re a small to mid sized company looking to increase revenue, your best bet is in software.

Learn how we’re using data science at Radius to drive ROI from our customers’ data.

 

Recommended Articles

Predictive Analytics
8 Expert Answers to "What is Predictive Analytics?"

When we commissioned Forrester Consulting to conduct a comprehensive survey of B2B marketing lead...

John Hurley

Predictive Analytics
Determining Your Predictive Marketing Use Case

With over 61% of B2B marketing leaders already implementing solutions, it’s clear that Predictive...

John Hurley

B2B Marketing
Pepsi vs Coke: Why Marketers Shouldn’t Be Fooled By the Technology 'Taste Test'

When it comes to competition, no two brands have had a more fierce, long-standing head-to-head ba...

John Hurley