At a time when vast amounts of data are generated every second, organizations face a critical challenge: how to structure, interpret, and use that data to make better decisions. Data alone has little value without the ability to extract meaningful insights and translate them into action.
This enormous volume of data, known as big data, has led to a greater demand for skilled data science professionals. According to the US Bureau of Labor Statistics, employment of data scientists is expected to rise 15 percent by 2029. Yet, to harness the power of data and AI, it isn’t necessary to be a data scientist.
Anyone with access to data can reap its benefits. Data science can be used to gain knowledge about behaviors and processes, write algorithms that process large amounts of information quickly and efficiently, enhance the security and privacy of sensitive data, and guide data-driven decision-making.
In a world overflowing with data, understanding how to interpret it, speak its language, and leverage it effectively can be invaluable throughout your career. Here’s a primer on what data science is and how you can use it in business.
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What Is Data Science?
In the online course Data Science and AI for Decision Making, co-led by Harvard Business School professors Karim Lakhani and Iavor Bojinov, Lakhani defines data science as “the broad discipline of extracting insights from data using statistical methods, programming, and technical expertise. It focuses on transforming raw data into a practical understanding that informs decisions.”
Data science is often confused with data analytics, a related discipline that also relies on data to generate insights. While the two fields share many similarities and are both valuable in the workplace, data analytics focuses primarily on examining and interpreting existing data, whereas data science encompasses a broader range of activities aimed at generating insights and building predictive capabilities.
In practice, data science involves:
Formulating hypotheses
Running experiments to gather data
Assessing data quality
Cleaning and streamlining datasets
Organizing and structuring data for analysis
To support these activities, data scientists often develop algorithms using programming languages, such as Python and SQL, to collect, process, and analyze large datasets.
When designed and tested effectively, these algorithms can identify patterns and trends humans might otherwise overlook, while significantly accelerating the process of gathering and analyzing information.
These capabilities have become increasingly important as organizations adopt AI technologies, many of which are built on the same data science principles used to collect, analyze, and learn from data.
“One thing I wish more people understood about AI in business is that AI is not magic—it's math, and code, and algorithms, and models, and data," says Hanspeter Pfister, a professor at the Harvard School of Engineering and Applied Sciences who's featured in Data Science and AI for Decision Making. "So, understanding the technical fundamentals of what's behind AI, in my opinion, is extremely important.”
Whether you're working directly with data or leading teams that do, understanding data science fundamentals can help you identify opportunities, evaluate trade-offs, and make more informed business decisions. Here are five ways organizations use data science and AI to create business value.
5 Business Applications for Data Science
1. Gain Customer Insights
Data about your customers can reveal valuable information about their behaviors, preferences, needs, and motivations. With customer data flowing from websites, mobile apps, transactions, surveys, and other AI-powered platforms, a foundational understanding of data science can help organizations turn information into actionable insights.
Consider Orchardio, a fictional fresh-food delivery company explored in Data Science and AI for Decision Making. In the depicted scenario, the company expands from a single-farm operation into a regional business, and its leadership faces a challenge: staying close to its customer base and making intentional decisions about how to meet their needs.
Existing categories, such as “registered versus guest” and “new versus returning,” were too limited to capture the full range of customer behavior. Instead, learners are encouraged to contemplate a different question: "What if we stop assuming how to group our customers—and let the data show us?”
To explore that possibility, the course details a data science technique known as clustering, which identifies natural groupings of customers based on shared behaviors. In this imagined scenario, by analyzing data on how customers spent, ordered, customized their grocery boxes, and interacted with the platform, Orchadio uncovered distinct customer segments that traditional categories failed to reveal.
“Used well, clustering transforms noise into meaning and meaning into action,” Lakhani emphasizes in Data Science and AI for Decision Making. “It guides product design, marketing strategies, and customer support decisions.”
Understanding who your customers are and what motivates them can help ensure your product meets their job to be done and inform how you personalize outreach, marketing tactics, and resource-allocation strategy.
2. Detect Risks and Anomalies
Organizations can also use data science to strengthen security and identify risks before they become costly problems.
One technique used for this purpose is anomaly detection, a form of unsupervised machine learning. As Lakhani explains in Data Science and AI for Decision Making, “anomaly detection spots unusual events, such as fraud or unexpected shifts in demand.”
Edward McFowland III, an assistant professor at HBS who's highlighted in Data Science and AI for Decision Making, defines it as “detecting when a system deviates away from its expected or desired behavior.”
While anomaly detection is often associated with fraud, its applications extend far beyond financial crime. Unexpected changes in customer behavior, operations, supply chains, or cybersecurity systems can all signal emerging risks or opportunities. Identifying these shifts can prompt important questions, including:
Why is customer behavior shifting?
What factors are driving the change?
What actions should the organization take in response?
According to McFowland, anomaly detection helps organizations answer those questions by processing large volumes of information and surfacing patterns that might otherwise go unnoticed.
“Anomaly detection's job is to take large amounts of data and find these patterns and present them to you to help you see what you couldn't have seen before," McFowland says in Data Science and AI for Decision Making. "This needle in the haystack idea.”
From identifying fraudulent transactions and operational issues to detecting cybersecurity threats and shifts in customer behavior, anomaly detection helps organizations identify unusual patterns early and respond with greater precision.
While algorithms can surface these insights at scale, human judgment remains essential for interpreting their significance and determining the appropriate course of action.
3. Forecast Future Outcomes
Data science helps organizations make more informed decisions by identifying patterns in historical data, forecasting future demand, and reducing uncertainty. Rather than relying solely on intuition, businesses can use predictive models to make decisions that are more precise, scalable, and evidence-based.
Consider Ramp, a financial technology company explored in Data Science and AI for Decision Making. As the company expanded globally, it faced a critical financial challenge: determining how much liquidity to keep on hand at any given time. Holding too little cash could lead to failed transactions and a poor customer experience, which could damage customer trust.
“Initially, Ramp’s liquidity planning relied on intuition and heuristics,” Bojinov says in Data Science and AI for Decision Making. Teams reviewed recent demand and trusted their “gut feel” to anticipate seasonal fluctuations and market conditions. While this approach worked when the company was smaller, it became difficult to manage as the transaction volumes and operational complexity grew.
To improve financial forecasting, Ramp turned to machine learning. By analyzing historical transaction data and customer behavior patterns, the company built models that could predict future liquidity needs with greater accuracy. The goal was not to eliminate doubt entirely, but to make better decisions in the face of it.
“As the company scaled, so did the cost of getting those predictions wrong," Bojinov explains in Data Science and AI for Decision Making. "Forecasting became a core capability—not just for day-to-day operations, but for making confident, strategic decisions under uncertainty."
Whether managing cash flow, inventory, staffing, or growth investments, data science can help leaders plan more effectively and respond to changing conditions with greater confidence.
4. Streamline Operations
Another powerful application of data science is streamlining operations. As organizations grow, they generate enormous amounts of data—from inventory levels and supplier networks to logistics, fulfillment, and customer activity. While this information contains valuable insights, its sheer volume and complexity often make it difficult to analyze manually.
Data science helps organizations transform that information into a strategic asset. By uncovering inefficiencies, identifying patterns, and improving forecasts, businesses can optimize workflows and make faster, more informed decisions.
The fictional Orchardio offers a helpful example. In the scenario described in Data Science and AI for Decision Making, the company invested in systems as it scaled to improve operational efficiency and support growth. Describes Bojinov, the business “built forecasting models to match inventory with demand—using historical data, seasonality, and farm supply to reduce waste and improve planning.”
This integrated view can allow companies like Orchardio to move beyond simply tracking what's already happened and begin anticipating future needs, such as inventory shortages, demand fluctuations, and restocking requirements.
5. Test and Improve Business Strategies
Data Science doesn’t just help organizations understand what's happened—it helps them determine what actions actually work. Through experimentation and A/B testing, companies can evaluate new products, features, and AI systems using real-world data.
As Bojinov explains in Data Science and AI for Decision Making, “experimentation gives you evidence, not just predictions, but real data and real outcomes.” This is important because a feature that performs well in development or testing environments may behave very differently once it's exposed to real customers.
Yelp, mentioned in Data Science and AI for Decision Making, originally hypothesized that requiring advertisements to respect the geographic boundaries users selected on a map would improve ad relevance on its platform.
Rather than relying on intuition, the company conducted a randomized experiment to measure the impact. The results revealed that click-through rates increased significantly, indicating users found ads more relevant. Yet, it also revealed unintended consequences: ad inventory declined, reducing overall revenue in some segments.
Looking deeper into the data uncovered a vital insight. While the change reduced revenue for local service searches, it improved performance for non-local searches. This led Yelp to make a targeted rollout rather than a company-wide deployment.
“The goal of experimentation is not just to produce an average uplift but to learn where and why the effect occurs,” Bojinov notes in Data Science and AI for Decision Making.
By using data science to design experiments and evaluate outcomes, organizations can uncover what works, minimize costly missteps, and make decisions backed by data instead of guesswork.
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Using Data Science at Your Organization
When paired with critical thinking and business expertise, data science transforms information into impact. From anticipating demand and streamlining operations to strengthening security and uncovering new opportunities, data-driven approaches help organizations make smarter decisions and create lasting value.
You don't need to be a data scientist to harness these capabilities. Understanding how to evaluate data, uncover insights, and draw informed conclusions can help you solve problems more effectively and drive results in virtually any role.
Are you interested in learning how organizations use data science and AI to solve real business challenges? Explore Data Science and AI for Decision Making—one of our digital transformation and AI courses—or download our free course flowchart to find the course that best aligns with your career goals.
This post was updated on June 11, 2026. It was originally published on January 14, 2021.