What's the Difference Between Data Analytics & Data Science?

By Ally Heinrich on June 18, 2026

If you gathered today’s business leaders and asked them to identify the biggest difference between doing business in the 20th versus 21st century, many would likely give the same answer: data.

Fueled by the rise of social media, smartphones, and other digital innovations, organizations now generate and collect more data than ever before. The challenge is no longer accessing information—it's knowing how to leverage it to drive better decisions.

This surge has sparked a race among organizations to transform raw data into meaningful insights and measurable results. At the same time, professionals across industries are looking to strengthen their data skills to remain competitive, unlock growth opportunities, and future-proof their careers in an increasingly data-driven economy.

As Harvard Business School Professor Iavor Bojinov, who co-leads the online course Data Science and AI for Decision Making with HBS Professor Karim Lakhani, explains, “Data became more of a record of what happened. It became a way to anticipate what might happen next.”

If you’re new to the world of data, two terms you’ll likely encounter are “data analytics” and “data science.” While they're closely related, they serve different purposes and require distinct skill sets. Understanding the difference can help you determine which path best aligns with your interests and career aspirations.

Data Analytics vs. Data Science

What Is Data Analytics?

Data analytics refers to the process of examining data to answer questions, uncover insights, and identify patterns that can inform decision-making. This is done using an array of tools, techniques, and frameworks that vary depending on the type of analysis conducted.

The four primary types of data analytics include:

Descriptive analytics, which examines data to understand and describe what happened
Diagnostic analytics, which goes deeper and seeks to understand the why behind what happened
Predictive analytics, which relies on historical data, past trends, and assumptions to estimate what may happen in the future
Prescriptive analytics, which recommends actions an individual or organization should take to reach future targets or goals

When applied in a business setting, data analytics is often referred to as business analytics. The goal of business analytics is to transform data into actionable insights that organizations can use to inform strategy, improve performance, and make better decisions.

Data analytics can be leveraged in various ways. Here are two examples to consider:

Forecasting Demand for Capacity Planning

A common application of data analytics is forecasting future demand. As described in the online course Business Analytics, taught by HBS Professor Jan Hammond, “forecasting is used when an organization needs to plan for volume.” By analyzing historical data and identifying patterns, organizations can make evidence-based decisions about budgeting, inventory, staffing, and resource allocation.

Consider an e-commerce company trying to scale—an example outlined in Business Analytics. Each week, leaders gather from marketing, customer experience, operations, analytics, and third-party logistics to determine how much capacity they need to meet customer demand.

The challenge isn't simply predicting whether demand will increase or decrease. It's deciding how to allocate resources when future demand is uncertain. If the company understaffs fulfillment or customer support, orders may be delayed and service levels could suffer. If it overstaffs, operating costs could rise unnecessarily.

By using forecasts to estimate potential demand scenarios, leaders can evaluate trade-offs, plan staffing levels, coordinate with logistics partners, and make more informed decisions about how to allocate resources.

Evaluating Alternatives Through A/B Testing

Organizations frequently leverage data analytics to test ideas and determine which approach is most effective. A popular tool often used to accomplish this is A/B testing.

An A/B test is a controlled experiment used to compare two alternatives. One group experiences the current version (the control group), while the other experiences a modified version (the treatment group). By comparing outcomes between the two, organizations can determine whether a change leads to better results.

To get the most out of an A/B test, according to Business Analytics, organizations should “isolate a single change, tie success to a measurable outcome that directly informs the decision, and evaluate results over a defined window.”

For example, a company interested in increasing website registrations might test two versions of its sign-up page. The only difference is that one features a call-to-action button saying, “Create Your Account,” while the other reads, “Get Started Today.” Visitors are randomly assigned a version, and the company tracks the percentage of those who register over two weeks.

Because only one element changed and success is tied to a specific metric—completed registrations—the company can determine which performs better. Rather than relying on intuition or personal preference, leaders can use data to evaluate alternatives and identify the option most likely to achieve their goals.

This approach helps organizations reduce uncertainty, improve performance, and make more confident decisions across marketing, product development, and customer experience initiatives.

What Is Data Science?

While data analytics focuses on understanding data and generating insights, data science centers on collecting, preparing, modeling, and analyzing data to build predictive capabilities and solve complex problems.

As Lakhani notes in Data Science and AI for Decision Making, data science “focuses on transforming raw data into practical understanding that informs decisions."

To achieve this, data scientists leverage a combination of technical tools and methodologies outlined in Data Science and AI for Decision Making, including:

Machine learning algorithms: Rather than rely on explicit rules for every decision, machine learning algorithms learn patterns from data through training. This enables organizations to identify relationships, generate predictions, and continuously improve performance as more data becomes available.
AI integration: By combining machine learning techniques with AI-powered systems, organizations can automate processes, improve efficiency, and enhance decision-making across business functions.
Data wrangling: Before data can be analyzed, it must be cleaned, structured, and organized. Data wrangling helps ensure information is accurate, consistent, and ready for analysis.
Statistical modeling: Organizations use statistical models—including regression, classification, and clustering techniques—to identify relationships between variables, test assumptions, and generate insights.
Programming: Languages such as Python, R, and SQL enable organizations to manage large datasets, build models, automate workflows, and analyze data more efficiently than through manual methods alone.

Together, these capabilities help data scientists build the infrastructure, models, and systems organizations rely on to generate insights and make data-driven decisions.

As Data Science and AI for Decision Making emphasizes, leaders don't need to become data scientists themselves. Instead, they need enough fluency to understand how data-driven systems create value, assess opportunities and risks, and make informed decisions about where to invest organizational resources.

Here are two examples of data science in action:

Building Predictive Models

One of the most common applications of data science is forecasting future outcomes through predictive modeling.

Ramp, a financial technology company explored in Data Science and AI for Decision Making, faced a significant challenge forecasting liquidity needs within the highly volatile cryptocurrency market. Holding too little liquidity risked transaction failures, while holding too much could leave valuable capital underutilized.

To address the challenge, Ramp constructed a regression-based forecasting model trained on historical data. It helped estimate future liquidity requirements based on user behavior, industry trends, and shifting global conditions.

As Bojinov explains in Data Science and AI for Decision Making, “The leadership team viewed this moment not merely as a scaling challenge, but as an opportunity to embed data science into Ramp’s DNA.”

Improving Diagnostic Accuracy

Data science is also transforming how organizations make complex, high-stakes decisions.

K Health, an AI-powered healthcare platform featured in Data Science and AI for Decision Making, provides a strong example. The company sought to improve how early diagnostic decisions are made across diverse patient cases.

As described by Lakhani in Data Science and AI for Decision Making, the platform was designed to “classify cases systematically, using past data to reduce uncertainty and surface the most probable conditions.” Its goal was to improve diagnostic accuracy—the ability to correctly predict a patient’s condition using clinical and personal data. By leveraging information from thousands of similar cases, clinicians could make more consistent and informed decisions.

The stakes were significant. Missing a serious condition could delay treatment, while escalating a low-risk case could increase costs and strain resources. To address these challenges, K Health evaluated not only predictive performance but also interpretability, clinical priorities, and economic impact.

“By focusing on data quality and model design from the outset, K Health was able to deliver a system that clinicians trust and patients find useful,” Lakhani says in Data Science and AI for Decision Making. “The results are tangible: greater diagnostic accuracy, faster access to care for patients, and less administrative burden for clinicians—all grounded in the company’s early investment in data, model design, and validation.”

K Health demonstrates how organizations can use data science to transform large volumes of data into practical insights that support more accurate, consistent, and informed decision-making.

Not an Either/Or Decision

While data analytics and data science are closely related, they serve distinct purposes. Data analytics focuses on extracting insights to support decision-making, while data science focuses on developing the models, systems, and methodologies that enable organizations to generate those insights and make predictions at scale.

Most organizations typically rely on both. For business professionals, the goal isn't necessarily to become a data analyst or data scientist. Rather, it's to develop enough fluency to understand how data can create value, evaluate opportunities and risks, and make more informed decisions.

Are you ready to strengthen your data-driven decision-making skills? Explore Business Analytics and Data Science and AI for Decision Making—two of our online digital transformation and AI courses. Not sure which course is the right fit? Download our free flowchart to find the program that best aligns with your goals.

This post was updated on June 18, 2026. It was originally published on January 5, 2021.

Author:

Ally Heinrich

Topics:

Digital Transformation & AI