Business Intelligence

Data Science vs Machine Learning: Which is Best for Your Business?

Aug 8, 2022

There are many different aspects to business intelligence that business leaders need to know about to grow strategically. For example, data science vs machine learning.

Andrew ConradSr Content Writer
Data Science vs Machine Learning: Which is Best for Your Business?

As a business leader in a technical field, you are living and working in an exciting time and space. New technological breakthroughs seem to happen every day, and almost anything seems possible. But you need analytics professionals in your organization who can harness this technology effectively to perform transformational tasks like data mining and predictive analytics, and your resources are limited.

Expanding your business intelligence team to uncover more insights from your data is always a good idea, but should you expand by utilizing data science or machine learning?We talked to real business leaders with expertise in this field to get their perspective on when you should hire a data scientist and when you should hire a machine learning specialist. In this article, we’ll break down what they had to say and provide tips to help you grow your analytics team as effectively as possible by using both data science and machine learning.

Data science vs machine learning: what’s the difference? While both concepts involve data, computer science, and technology, the main similarities mostly end there. Data science is a blanket term that encompasses almost anything involving the analysis of data, while machine learning is a specific application of data science that uses artificial intelligence (AI) to systematically improve an automated task or set of tasks by leveraging data. We’ll dig deeper into these concepts below.

What is data science?

Data science is the study of large sets of data in order to extract strategic insights based on patterns, historic trends, and statistical analysis. These insights can then be used to make better business decisions, hit KPIs, and ultimately achieve organizational goals. Modern data science typically relies on software, algorithms, data models, artificial intelligence, and machine learning to amplify the efforts of human data scientists.


A sales data dashboard in Phocas data analysis software (Source)

Want to hear data science explained by an actual data scientist? Check out this video:

Data science explained by a real data scientist (Source)

So, what does a day in the life of a data scientist look like?

Here are a few examples of what data scientists can do to help your team:

  • Studying massive sets of data (big data) and isolating relevant business insights based on that data.

  • Preparing data visualizations based on the most important data insights, and presenting those visualizations to relevant stakeholders.

  • Working with machine learning specialists to ideate and plan machine learning algorithms that will best leverage available data sets.

What is machine learning?

Machine learning is a type of artificial intelligence that uses algorithms and computer models to progressively become better at a specific task or set of tasks by processing large amounts of data.


An issue tracking model in Knowi machine learning software (Source)

In other words, machine learning relies on data science, and the two concepts work together to solve business problems by leveraging data and advanced technology.

“Data science helps define new problems, which can then be solved using machine learning techniques,” says Mark Daoust, founder and CEO of Quiet Light, a Charlotte, North Carolina-based online business brokerage. “However, machine learning cannot exist without data science because the data must first be prepared to create, train, and test the model.”

mark daoust

Mark Daoust (Source)

As a complex topic, it is difficult to sufficiently describe machine learning in just a few sentences. To further illuminate the concept of machine learning, here is a one-minute video of a Harvard professor explaining the subject as it relates to medicine:

Machine learning explained (Source)

Wondering what machine learning specialists do all day?

A machine learning engineer is a tech professional who designs, programs, and maintains the computer systems that host machine learning applications.

Here are a few examples of how machine learning specialists can help solve business problems:

  • Designing a machine learning algorithm that can process large sets of data to forecast predictions based on that historic data.

  • Designing a machine learning model that can identify and extract isolated strings of data from large unstructured data sets. For example, extracting only phone numbers from a massive pool of unprocessed data.

  • Generating a machine learning tool that can recommend queries for business analysts to investigate based on previous queries.

What’s the difference between data scientists and machine learning engineers?

Now for the important part. If you’re trying to scale up your analytics team, should you hire a data analyst or a machine learning engineer?

We asked tech startup leaders this question, and here’s what they had to say:

1. Start with versatile data scientists, then hire machine learning specialists as necessary

When in doubt, it’s a good idea to start with general data scientists before expanding your analytics team to include more specialized roles, like machine learning engineers.

“For your first 5-10 hires in an analytics org, I'd shy away from hiring machine learning specialists, unless your company is producing a machine learning product,” says Shri Ganeshram, CEO and founder of Awning, a San Francisco-based real estate investment platform. “The most successful early hires I've seen on an analytics team are what I'd consider "data scientists" or "data engineers" … They're business-minded and business-focused. They can think about data problems in the context of the customer.”

shri ganeshram

Shri Ganeshram (Source)

In other words, build a small team of data scientists that can surface data problems to be solved, then, once you have a direction, it’s time to bring in the machine learning specialists to tackle those problems.

2. Think about data scientists and machine learning specialists as data partners

Even for early stage startups and businesses with very limited resources, thinking about data scientists and machine learning specialists as an either/or proposition is a bad strategy.

“Data scientists can help businesses build a model, fine-tune algorithms and present large amounts of data in a (useful) manner,” says Andreas Grant, founder of Networks Hardware, a Stockholm-based network hardware website. “On the other hand, machine learning specialists can take the lead from there and use that data to deploy machine learning products.”

andreas grant

Andreas Grant (Source)

In other words, you need data scientists to gather, prepare, and analyze data. Once that is in place, this data is ready to be used by machine learning specialists to make the most of that data. You can’t have one without the other, and asking a data scientist to switch hats and become machine learning specialists, or vice versa, is not an efficient use of resources.

“I have been part of companies that wanted to hire people who are both skilled in machine learning engineering and data science,” says Grant. “It’s better to hire a specialist in one area to get things done in an efficient manner.”

Ideally, if you have the resources and the directional clarity, you should start your analytics team with both data scientists and machine learning specialists.

“If you have a good sense of what you are trying to solve with a literate founder, product team, or (business analytics) team, then the answer is actually (to deploy) the right (machine learning) engineer working in parallel (with data scientists) from onset,” says Cory Janssen, cofounder and co-CEO of AltaML, a Calgary-based AI and machine learning developer.

cory janssen

Cory Janssen (Source)

3. Examine the “Why?” before adding to your analytics team

Trying to decide whether to hire a data scientist or machine learning specialist for your analytics team without first considering your next big project would be akin to selecting one tool for a home renovation without knowing what room of the house you’re working on. A reciprocating saw is a powerful tool, but you probably don’t need one to repaint the master bedroom.

“A startup leader needs to understand why they need a data science team, what business problems the team can help with, and what data they have or can collect that will support those decisions,” says Andrew Engel, chief data scientist at Rasgo, a San Diego-based machine learning developer. “A startup leader should look to hire a data science leader with a track record of driving business value. This leader can help with the questions above and set up the team for success once they begin expansion. Even as they begin expanding the team, the focus should be on generalists until the team has generated enough value to the company and roadblocks appear in the process that prevent project success."

Andrew Engel

Andrew Engel (Source)

In other words, think long and hard about the strategic goals you have in mind for your analytics team, and then hire a data leader that can help get you there.

4. Think of data scientists as multi-tools, and machine learning specialists as scalpels

There’s no right or wrong answer when it comes to adding a data scientist or machine learning specialist to your analytics team, because both roles are so valuable. But they are valuable in different ways. Data scientists generally have a diverse skill set and can be used to tackle a wide variety of problems. Machine learning specialists, on the other hand, are trained to tackle these problems in a very specific way.

“If you are looking for a person that can conduct exploratory data analysis, or infer results from data … then you are looking for a data scientist,” says Steven Randazzo, head of innovation at Altruistic, a London-based IT services hub. “Your data scientist can help with problem formulation, with data wrangling, and building and applying a model to test a particular hypothesis. Alternatively, your machine learning engineer is solely focused on applying a model to a production environment. They can build a model, test it, then deploy it.”


Steven Randazzo (Source)

In this section, we heard from several different tech leaders from a range of businesses. Here are a few key takeaways to summarize what we learned:

  1. Before hiring anyone else, make sure you have an experienced data leader in place to help align your data team with organizational goals.

  2. Start with general data scientists to build datasets and surface problems that need solutions.

  3. Once you have the data and a problem to solve, it’s time to add machine learning specialists.

  4. Use data scientists and machine learning specialists as partners working together to achieve organizational goals.


Make sure your data analytics team is equipped with the right software

Now that you have a gameplan for building out your data analytics team, it’s a good time to make sure you have the right software in place to help them do their job as effectively as possible. After all, the most skilled craftspeople in the world are useless without the right tools.

Here are a few relevant directories to browse, whether you’re adding a data scientist or a machine learning specialist to bolster your analytics team:

You can also browse our Category Leaders for business intelligence software to explore 15 different business intelligence software products that stand out in the market based on verified user reviews.


GetApp’s Category Leaders in Business Intelligence for 2022 (Source)

And if learning more about the differences between data science and machine learning has you hungry for more knowledge, we have you covered with our library of business intelligence resources. Here are a few recent articles to read next:


The applications selected in this article are examples to show a feature in context and are not intended as endorsements or recommendations. They have been obtained from sources believed to be reliable at the time of publication.

About the author

Andrew Conrad

Sr Content Writer
Andrew Conrad is a senior content writer at GetApp, covering business intelligence, retail, and construction, among other markets.

As a seven-time award winner in the Maryland, Delaware, D.C. and Suburban Newspapers of America editorial contests, Andrew’s work has been featured in the Baltimore Sun and PSFK. He lives in Austin with his wife, son, and their rescue dog, Piper.
Visit author's page