If you’re looking to leverage your data—whether it’s to make an important business decision, optimize your marketing, or forecast sales and growth—you might be wondering what kind of data professional you need. Do you need a full-blown data scientist who’s skilled with R, Python, machine learning, and statistical modeling? Or, do you need a data analyst who can dive into your data with SQL and pull visualizations and insights you can put to use?

The answer depends on a few things: the current state of your data, what you need from that data, and your overall business goals.

What’s the difference between a data analyst and data scientist?

Data scientists and data analysts aren’t interchangeable, but they do both have a common goal: to draw insights from data. While their skills will overlap (in many ways, data scientists are advanced analysts), generally data scientists will have a broader and deeper skill set, especially when it comes to their business acumen. They’ll have technical knowledge an analyst won’t necessarily need on a day-to-day basis, such as deep familiarity with Hadoop, advanced statistical modeling, and machine learning.

Both professionals can transform data into answers business owners need to make better decisions, but what they’re starting with and the skills required to reach those answers will vary. Data analysts can answer your business questions, but data scientists can help you formulate new questions to drive the business forward. And when it comes to complexity, chances are you’ll need a data scientist.

Before we help you determine which is right for your project, let’s quickly look at what each does.

Data Analysts

Data analysts take known data and glean actionable insights and answers to specific questions you have about your data. These are the pros who funnel insights from data into industries like education, healthcare, and travel to help businesses like airlines and hospitals run better, and deliver better service to customers.

Their value lies in their ability to make data (for example, data that’s been input into a CRM or exported from Google Analytics) more usable for you and your company. Generally, an analyst will

  • Clean and sort data
  • Uncover new patterns and correlations
  • Find actionable insights and package them up for business use
  • Use visualizations and interactive dashboards to present findings
  • Query data to meet specific needs
  • Create reports for key stakeholders

When it comes to unstructured data, analysts may work with a data scientist or data engineer to get help pulling new data sets for analysis.

Data Scientists

Why are most data scientists able to charge almost double the rate or salary as a data analyst? Data scientists have a broader and deeper skill set, especially when it comes to their business acumen. These pros create algorithms and models businesses use to predict future sales, make critical decisions, or launch products. They’re able to do more with more difficult data, including

  • Mining large amounts of structured or unstructured data
  • Data warehousing
  • Advanced programming, with R, SQL, Python, MatLab, and SAS
  • Statistical modeling
  • Develop machine learning and predictive analytics models
  • Work with the Hadoop ecosystem, including Hive and Pig
  • Formulating important business questions and hypotheses, then testing validity with math and statistics

A big difference is their ability to work with more complex, unstructured data—as in, data your company either doesn’t currently understand or can’t work with because it’s from multiple disconnected sources. If an analyst is primarily working with your “known data,” a data scientist is equipped to work with any of your company’s data that isn’t known or currently understood.

When a business is making a critical decision, data scientists play a key role. They test theories and hypotheses, the results of which become eye-opening insights key stakeholders can use to make predict outcomes and make more informed decisions.


Which Do You Need?

Let’s look at a few questions to get you started:

  • What data are you analyzing?
  • How much of that data do you have? How much will it grow?
  • What’s the current state of that data? Is it structured and sorted, or highly unstructured?
  • What do you need from the data? Is this mission-critical, highly sensitive, or more informational?
  • Do you need algorithms or models designed to help you wrangle your data?

A good rule of thumb? Discuss your goals for your data with a professional. Chances are they’ll be able to assess if your project is in their wheelhouse, or if it requires more advanced skills. Get started by writing a thorough job post that clearly outlines your requirements here.