What is Data Science?

Data science is the practice of extracting actionable insights from large and complex datasets using a mix of statistics, programming, machine learning, and analytics. The goal is to identify patterns, build predictive models, and generate insights that drive smarter business decisions.

In short, data science turns raw data into useful knowledge—helping organizations improve efficiency, forecast future outcomes, and respond quickly to challenges and opportunities.

Data scientist vs. data analyst vs. data engineer

Data science is a broad field that involves multiple roles. While their work overlaps, each has a distinct focus:

• Data Scientist – Defines the questions to be answered, collects and prepares unstructured data, applies machine learning, and communicates insights to decision-makers. They focus heavily on modeling, predictive analytics, and experimentation.
• Data Analyst – Focuses on interpreting data to answer specific business questions, often provided by leadership. They use visualization, reporting, and statistical methods but typically do less coding and advanced modeling than data scientists.
• Data Engineer – Designs and manages the infrastructure that makes data usable. They build and maintain pipelines, data warehouses, and data lakes so analysts and scientists can access clean, reliable data.

Together, these roles ensure data flows smoothly, is well-analyzed, and supports actionable decision-making.

Data science and business intelligence

While business intelligence (BI) and data science both drive data-informed decisions, they differ in scope:

• BI – Analyzes historical and current data to understand what happened and why.
• Data Science – Uses statistical modeling, machine learning, and predictive methods to forecast what’s likely to happen next.

Think of BI as focusing on the past and present, while data science emphasizes the present and future.

Why is data science important?

Data science matters because it helps organizations:

• Make better decisions – By analyzing root causes, testing solutions, and presenting clear visuals for leaders.
• Predict outcomes – Using machine learning to forecast trends and identify risks before they escalate.
• Find hidden patterns – Detecting anomalies, fraud, or inefficiencies that might otherwise go unnoticed.
• Improve efficiency – Automating processes and enabling smarter allocation of resources.
• Enhance customer experiences – Personalizing recommendations and tailoring products or services based on behavior.

What you can do with data science

Data science applications are wide-ranging. Common use cases include:

• Detecting fraud or anomalies
• Classifying data (e.g., emails, inventory, documents)
• Delivering personalized recommendations
• Automating routine processes
• Forecasting trends and outcomes
• Segmenting customers or products
• Enabling recognition of faces, text, images, or audio
• Optimizing processes to reduce risk and maximize reward

How does data science work?

Because data science is such a large field that deals with a variety of tasks, it can be difficult to narrow down exactly how each question is answered. Generally, the data science process, also known as the data science lifecycle, involves these steps:

1. Capture

‍Data scientists gather raw structured and unstructured data using many different methods from all the relevant sources available. Tasks include: 

• Data acquisition
• Data entry
• Signal reception
• Data extraction

2. Maintain

‍Data scientists put raw data into a standardized format so that it can be used for analytics, machine learning, and other forms of modeling. Tasks include:

• Data cleansing
• Data processing
• Data staging
• Data warehousing
• Data architecture

3. Process

‍Data scientists examine the data to find patterns, ranges, and distributions of values and to check for biases. All of this information informs whether or not the data is suitable for predictive analytics, machine learning, and other analytical methodologies. Tasks include:

• Data mining
• Clustering and classification
• Data modeling 
• Data summarization

4. Analyze

‍Data scientists perform functions to extract insights from the data. Tasks include:

• Predictive analysis
• Regression
• Text mining
• Qualitative analysis

5. Communicate

‍Data scientists present their findings in data visualizations like reports and charts that make insights easy to understand. They help decision makers understand how findings will impact their business. Tasks include:

• Data reporting
• Data visualization
• Business intelligence
• Decision making

What should I look for in a data science tool?

The best data science tool for your organization should be accessible for both business users and data scientists. When everyone can harness the power of data science to make decisions, the entire organization benefits. 

Domo’s data science tools allow data science experts and business users alike to prepare data and create predictive models. Beginners can use drag-and-drop functions built into the extract, transform, load (ETL) process, including classification, clustering, forecasting, and predictions. Experts can combine the power and convenience of the Domo ETL process with the precision of data science with embedded R and Python scripting tiles. And, Domo users can take advantage of Domo’s automated machine learning solution, powered by Amazon SageMaker, to rapidly determine the best machine learning model for their data and then share those insights with their teams.

How do different industries use data science?

Every organization across industries can benefit from the insights and opportunities that data science brings. Data science helps make processes more efficient and helps improve the customer experience. Here are a few examples: 

• Airlines – Predicting flight delays and optimizing scheduling.
• Public Safety – Using statistical analysis to allocate resources effectively.
• Healthcare – Detecting diseases and improving treatment tools.
• Financial Services – Identifying fraud and managing risk.
• Streaming Platforms – Delivering personalized content recommendations.
• Logistics – Optimizing routes and improving delivery efficiency.
• Automotive – Powering real-time object detection in autonomous vehicles.

How will data science evolve in the future?

In the future, automated machine learning will be utilized more broadly to help enterprises achieve outcomes and understand the variants that drove impact. Data integration combined with domain knowledge tools will create even more opportunities to automate business processes. 

Additionally, productionizing data science will become easier for business users and analysts, requiring less core computer science, advanced statistics, and linear algebra skills. Tools for data scientists will expand, but more solutions for citizen data scientists will encompass end-to-end workflows to accelerate the data life cycle.