What is Data Analytics?

Data analytics is the process of analyzing raw data to find trends and answer questions. It has a broad scope across the field. This process encompasses multiple techniques and goals that can vary by industry.

The data analytics process includes components that support a variety of initiatives. By combining these components, a successful data analytics initiative can help answer business questions related to historical trends, future predictions and decision making.

Generally, this process begins with descriptive analytics. This is the process of describing historical trends and summarizing data. Another part of data analytics is advanced analytics. This process leverages advanced tools to uncover patterns/relationships in large datasets, enabling predictions and trend identification.

The availability of machine learning tools, large datasets, and more affordable computing power has enabled the use of these techniques and others across many industries. Big data analytics helps businesses draw meaningful conclusions from complex, diverse data sources.

Types of Data Analytics

Data analytics is a broad field. There are four primary types of data analytics: descriptive, diagnostic, predictive and prescriptive analytics. Each type has a different goal and place in the data analysis process. These are also the primary data analytics applications in business.

• Descriptive analytics helps answer the question “What happened?” For a business, this can be used to describe outcomes to stakeholders. By developing key performance indicators (KPIs), descriptive analysis strategies can help track successes or failures. For example, metrics such as return on investment (ROI) are often used. Specialized metrics can also be developed to track industry-specific performance. This process requires the collection of relevant data, data processing, analysis and visualization. Together, this can provide essential insight into past performance.
• Diagnostic analytics helps answer questions about why things happened. These techniques supplement basic descriptive analytics. They take the findings from descriptive analytics and dig deeper to find the causes behind trends and outcomes. Key performance indicators are further analyzed to determine why they improved or declined. This analysis could look like the following steps:
  • Identify anomalies in the data. These may be unexpected changes in a metric or a particular market.
  • Collect data related to these anomalies.
  • Implement statistical techniques to find relationships and trends that explain the anomalies.
• Predictive analytics helps answer questions about what will happen in the future. These techniques use historical data to identify trends and determine if they are likely to reoccur or change. Predictive analytical tools and techniques include a variety of statistical and machine learning techniques, such as neural networks, decision trees and regression.
• Prescriptive analytics helps answer questions about what should be done. Using predictive analytics insights, data-driven decisions can be made, even in the face of uncertainty. Prescriptive analytics techniques rely on machine learning to identify patterns in large datasets.

These types of data analytics provide the insights businesses need to make effective, efficient decisions. Used together, they provide a comprehensive understanding of a company’s needs and opportunities.

What is the Role of Data Analytics?

Data analysts work at the intersection of information technology, statistics and business. They combine these fields in order to help businesses and organizations succeed. A major goal for data analysts is to increase efficiency and improve performance by discovering patterns in data.

The work of a data analyst involves working with data throughout the data analysis pipeline. The primary steps in the data analytics process are data mining, data management, statistical analysis and data presentation. The balance of these steps depend on the data being used and the goal of the analysis.

Data mining is an important step for many data analytics tasks. This involves extracting data from unstructured data sources. These may include written text, large complex databases or raw sensor data. The key components in this step are to extract, transform and load data (often called ETL). ETL converts raw data into a useful and manageable format and prepares data for storage and analysis.

Data management or data warehousing is another key aspect of a data analyst’s job. Data warehousing involves designing and implementing databases that allow easy access to clean data. This step generally involves creating and managing SQL databases. Non-relational and NoSQL databases are also common.

Statistical analysis enables analysts to uncover insights in data. Both statistics and machine learning techniques are used to analyze data and create statistical models that reveal trends. These models can then be applied to new data to make predictions and inform decision-making. Statistical programming languages such as R or Python (with pandas) are essential to this process. In addition, open-source libraries and packages such as TensorFlow enable advanced analysis.

The final step in many data analytics processes is data presentation. This step prepares insights for sharing with stakeholders. Data visualization is an important tool in this step. Compelling visualizations can help tell the story of the data, helping executives and managers understand the importance of these insights.

Why is Data Analytics Important?

The applications of data analytics are broad. For example, data analytics, automation/AI can optimize efficiency across many industries. Improving performance enables businesses to succeed in an increasingly competitive world.

Data analytics has an important role in the banking and finance industries, where it is used to predict market trends and assess risk. Credit scores are an example of data analytics that affects consumers. These scores use many data points to determine lending risk. Data analytics is also used to detect and prevent fraud to improve efficiency and reduce risk for financial institutions.

The use of data analytics goes beyond maximizing profits and ROI. Data analytics can provide critical information for healthcare (health informatics), crime prevention and environmental protection. For example, an MIT researcher describes real conservation ML work like wildlife monitoring with computer vision, drone-based reef monitoring, elephant re-identification, and multimodal satellite + camera fusion.

The use of data analytics in healthcare is already widespread. Predicting patient outcomes, efficiently allocating funding and improving diagnostic techniques are just a few examples of how data analytics is revolutionizing healthcare. The pharmaceutical industry is also using machine learning. For instance, drug discovery is a complex process with many variables that machine learning can streamline. Pharmaceutical companies also use data analytics to understand the drug market and predict sales.

The internet of things (IoT) creates high-volume sensor data, and analytics/ML are often applied on top of that data. Together, these devices provide a great opportunity for data analytics. IoT devices contain sensors that collect meaningful data points for their operation. Devices like the Nest thermostat track movement and temperature to regulate heating and cooling. Smart devices like this can use data to learn from and predict your behavior. This will deliver advanced home automation that adapts to your lifestyle.

The applications of data analytics are seemingly endless. More and more data is being collected every day—this presents new opportunities to apply data analytics to more parts of business, science and everyday life.

If you are interested in data analytics and considering earning a degree, you may check our lists of online master’s in data science programs and online master’s in business analytics programs.

Information last updated: December 2025