This post was updated with current data by Brianna Hansen.
Sitting atop a mountainous treasure trove of data analytics, most businesses are hungry for people who can take a massive data set and turn it into something meaningful—and potentially lucrative.
Whether it’s pinpointing new sources of revenue, or predicting the next, best product feature, businesses are depending on data scientists to derive valuable and actionable insights.
In other words, Big Data means big money!
A data scientist's salary is among the highest in tech—nationally averaging about $113,000 a year, according to Glassdoor. And if you’re living in Silicon Valley, CA, that base salary shoots up to $140,000. Harvard Business Review boldly claimed data scientists the "Sexiest Job of the 21st Century."
And there’s no shortage of demand for data scientists. According to a study conducted by Quanthub, 67% of companies were predicted to expand their data science team in 2020.
What is a data scientist?
First off, let’s start by asking: what does a data scientist do?
Data scientists gather and analyze large sets of structured and unstructured data. Through computer science, statistics, and mathematics, they analyze, process, and package insightful data to inform key decisions within organizations.
Some common technical skills of data scientists include:
- Apache Spark
- Code Quality
- Data Modeling
- Data Visualization
- Data Wrangling
- Machine Learning
- System Design
- Technical Communication
Comparing data scientist vs. software engineer, data engineer, business analyst
Many roles in Big Data have overlapping qualities. However, it's important to differentiate between the different roles—especially if you’re considering a career in data science. Each role has individual values and are often complementary to one another, making up a strong Big Data team.
Here's a high-level visual that helps differentiate the roles by a few of their core functions, inspired by a great post from Kevin SchmidtBiz.
Data Scientist vs. Software Engineer
Data science focuses on data gathering and processing, whereas software engineering focuses on the development of products, applications, and capabilities for users. While both roles do require programming experience, data science involves more statistics and machine learning, and software engineering focuses more on coding languages.
Data Scientist vs. Business Analyst
While data science is largely rooted in statistics, data modeling, analytics, and algorithms, a business analyst’s strength lie in their ability to effectively communicate across teams and make key business decisions.
They communicate well with both the data scientist and C-suite—as well as sales and marketing—to make data-driven decisions faster. The best business analysts also have skills in statistics so they can glean interesting insights and trends from past behavior.
Data Scientist vs. Data Engineer
While data scientists dig into the research and visualization of data, data engineers ensure data flows correctly through the pipeline. They're typically software engineers who can build a strong foundation for data scientists or analysts to think critically about the data.
The biggest misconception about data scientists
Data scientists are truly valuable in extrapolating, analyzing, and finding patterns in existing data using statistics and machine learning. But there's often a big gap between expectations and the reality of what data science can do for your business. After speaking with a few highly sought-after data scientists, here's what we found:
There’s no single, magical formula data scientists use to dig up the right patterns from any given data set. The key to leveraging Big Data is having the creativity and curiosity to ask the right questions relevant to each unique business model.
“Data scientists understand the process of analyzing and finding commonalities in data, but they don't have the necessary business context to determine which commonalities or anomalies can actually help the business,” says David Giannetto, author of Big Social Mobile. In other words, any ordinary data mining expert can pull data, but the top-tiered data scientists should spot trends and valuable insights that help the business.
However, data mining alone is simply not enough to move the business forward.
“There's an expectation that collecting large quantities of data will make an organization smarter,” says Mark Schwarz, VP of Data Science at Square Root. “In reality, that often leads to decision paralysis and the inability to actually move the needle within the business.”
Brian Lange, a data scientist at Datascope Analytics, believes while data scientists can, in many cases, make better predictions with more data at their disposal than ever before, they aren't immune to the inherent pitfalls that come with making predictions.
"Overfitting (the statistics equivalent of missing the forest for the trees), false assumptions, sample bias—these are all things that aren't solved by the quantity of data you throw at the problem," he says.
So what should companies do instead?
"Companies should be focused on uncovering the right information,” Schwarz says. Average data experts can pull data based on a number of factors handed to them, but the truly valuable data scientists have the strong business acumen to proactively focus on data that will actually change behavior.
What motivates data scientists? It’s not what you think
While every data scientist is different, Lange says that most data scientists aren't easily impressed by fancy, expensive enterprise software.
"We love having fast computers and blank checks to use on cloud computing bills, but when it comes to software, many of us prefer working with languages and tools that are completely free, like Python, R, PostgreSQL, and ElasticSearch," Lange says.
The value of hiring data scientists
Every business wants to collect more and more data. But that data is only valuable if it fuels profitable business decisions.
Data scientists are crucial in making sense of data by conducting research, asking open-ended questions, and finding trends that impact the bottom line.
Before you build out your Big Data team, prioritize your Big Data needs in a way that makes sense for your business. And keep in mind that, while Big Data can be pivotal in boosting business, it's crucial to empower your data scientists and analysts with the right business context to propel your business forward.