The 4 Types of Data Analytics

Data is an increasingly-valuable asset for organizations across countless industries. It plays a pivotal role in decision-making, strategy formulation, and gaining a competitive edge. At Nichols College, we are proud to offer comprehensive degree programs in data analytics, equipping our students with the skills and knowledge needed to thrive in this rapidly evolving field.

Below, we explore the four essential types of data analytics: descriptive, diagnostic, predictive, and prescriptive. By understanding these different categories, you will gain insights into how you can help organizations leverage data to drive value, make informed decisions, and shape the future.

What is Data Analytics?

Data analytics refers to the process of examining, interpreting, and transforming raw data into meaningful insights. It involves the application of statistical methods, algorithms, and various analytical tools to collect, organize, and analyze data in order to discover valuable patterns, trends, and correlations. Data analytics plays a vital role in business decision-making, as it helps organizations make informed choices, solve complex problems, and uncover opportunities. Through this process, companies can gain a deeper understanding of their customers, optimize their operations, and drive overall business growth.

Data analytics is a multidisciplinary field that combines expertise in statistics, mathematics, computer science, and domain knowledge to extract actionable insights from data and derive value from the vast amounts of information available in today’s digital landscape.

Types of Data Analytics

Four primary types of data analytics enable organizations to leverage data effectively, gain insights, solve problems, and make more informed decisions at different levels of complexity and on different timelines. Utilizing these various types of data analytics empowers organizations to harness the power of their data and drive strategic decision-making.

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#1 – Descriptive Data Analysis

Descriptive data analysis involves examining, summarizing, and presenting historical data to gain insights and understand patterns or trends. It focuses on what has happened, providing a detailed overview of past events or situations.

#2 – Diagnostic Data Analytics

Diagnostic data analytics goes beyond descriptive analysis to understand the reasons behind specific outcomes or events. It involves digging deeper into data to identify the root causes of issues or anomalies, enabling organizations to make informed decisions and take corrective actions.

#3 – Predictive Data Analysis

Predictive data analysis employs statistical models and algorithms to forecast future outcomes or trends based on historical data patterns. By analyzing past data, organizations can make predictions and anticipate potential future scenarios, assisting in proactive decision-making and strategic planning.

#4 – Prescriptive Data Analytics

Prescriptive data analytics takes a step further than predictive analysis by not only predicting future outcomes but also recommending the best course of action to achieve desired goals. By considering various scenarios and constraints, organizations can optimize decision-making processes and determine the most effective actions to take for desired outcomes.

Careers in Data Analytics

There are many exciting career opportunities for those with a passion for data analytics. Choosing a career in this field means joining a fast-paced and evolving industry, where innovation and critical thinking are at the forefront of organizational success. Some of the most popular careers in data analytics include:

#1 – Data Scientist

Data scientists are responsible for extracting insights and generating actionable recommendations from complex and large data sets. They use statistical analysis, machine learning, and predictive modeling techniques to uncover patterns, solve complex business problems, and drive data-informed decision-making.

#2 – Data Analyst

Data analysts collect, clean, and analyze data to identify trends, patterns, and insights. They work with various tools and techniques to extract meaningful information from data, provide reports and visualizations, and help businesses understand the implications of their data for strategic planning and decision-making.

#3 – Business Intelligence Analyst

Business intelligence analysts gather and analyze data to provide valuable insights and support strategic business decisions. They utilize data visualization tools and techniques to create dashboards, reports, and data-driven KPIs to help organizations monitor performance, identify opportunities, and improve overall operational efficiency.

#4 – Data Engineer

Data engineers are responsible for designing, building, and managing the infrastructure required to handle large volumes of data. They develop data pipelines, construct data warehouses, and ensure data integrity and security. Data engineers work closely with data scientists and analysts to ensure the availability, reliability, and accessibility of data for analysis and decision-making.

Salaries for Data Analytics Careers

Salaries for data analytics careers are highly competitive and reflective of the increasing demand for skilled professionals in the field. Data scientists, with their expertise in advanced analytics and machine learning, often command some of the highest salaries in the industry.
According to, the estimated total pay for data scientists in 2023 is $152,138. But senior-level positions can reach even higher figures, with top companies paying well over $200,000 for senior-level data scientists.

In addition to high salaries, careers in data analytics offer diverse opportunities for professionals to leverage their analytical skills, work with cutting-edge technologies, and contribute to the growing field of data-driven decision-making. Learn more about this exciting field and the data analytics certificate offered by Nichols College today.