Data analyst vs. data scientist: what is the right solution for your project?

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 fully-fledged data scientist who is proficient in R, Python, machine learning, and statistical modeling? Or do you need a data analyst who can dive into your data with SQL and extract visualizations and insights that you can use?

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

What is the difference between a data analyst and a data scientist?

Data scientists and data analysts are not interchangeable, but they both have a common goal: to learn from data. While their skills overlap (in many ways, data scientists are advanced analysts), data scientists will generally have a broader and more in-depth skill set, especially when it comes to their business acumen. They will have technical knowledge that an analyst may not necessarily need on a daily basis, such as in-depth knowledge of Hadoop, advanced statistical modeling, and machine learning.

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

Before we help you figure out what’s right for your project, let’s take a quick 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 professionals who funnel insights from data into industries like education, healthcare and travel to help businesses like airlines and hospitals run better and provide better service to customers.

Their value lies in their ability to make data (for example, data entered into a CRM or exported from Google Analytics) more usable for you and your business. Typically, an analyst

  • Clean and sort data
  • Discover new models and correlations
  • Find actionable insights and consolidate it for business use
  • Use interactive visualizations and dashboards to present results
  • Query data to meet specific needs
  • Create reports for key stakeholders

When it comes to unstructured data, analysts can work with a data scientist or data engineer for help extracting 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 professionals create algorithms and models that companies use to predict future sales, make critical decisions, or launch products. They are able to do more with more difficult data, including

  • Extraction of 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
  • Formulate important business questions and hypotheses, then test for validity with math and statistics

A big difference is their ability to work with more complex and unstructured data– as in, data that your business does not currently understand or cannot use because it comes from multiple disconnected sources. If an analyst works primarily with your “known data,” a data scientist is equipped to work with any data in your business that is not known or currently understood.

When a business makes a critical decision, data scientists play a key role. They test theories and hypotheses, the results of which become revealing information that key stakeholders can use to predict outcomes and make more informed decisions.

What do you need ?

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

  • What data are you analyzing?
  • How much of this data do you have? How much will he grow up?
  • What is the current state of this data? Is it structured and sorted, or very unstructured?
  • What do you need the data for? Is it critical, highly sensitive, or more informative?
  • Do you need algorithms or models designed to help you scramble your data?

A good rule of thumb? Discuss your goals for your data with a professional. Chances are they will be able to assess whether your project is in their wheelhouse or requires more advanced skills. Start by writing a detailed job posting that clearly describes your needs here.

Sean N. Ayres