Organizations in all industries increasingly rely on data to make critical business decisions—which new products to develop, new markets to enter, new investments to make, and new (or existing) customers to target. They also use data to identify inefficiencies and other business problems that need to be addressed.
In these organizations, the job of the data analyst is to assign a numerical value to these important business functions so performance can be assessed and compared over time. But the job involves more than just looking at numbers: An analyst also needs to know how to use data to enable an organization to make more informed decisions.
These roles are in high demand. Job openings for analysts are projected to grow by 23% between 2021 and 2031. Data analysts receive a median starting annual salary in the $108,000 range; however, success in the role can lead to senior positions with salaries exceeding $137,000.
What is analytics?
Analytics brings together theory and practice to identify and communicate data-driven insights that allow managers, stakeholders, and other executives in an organization to make more informed decisions. Experienced data analysts consider their work in a larger context, within their organization and in consideration of a wide range of external factors. Analysts are also able to account for the competitive environment, internal and external business interests, and the absence of certain datasets in the data-based recommendations that they make to stakeholders.
A Master of Professional Studies in Analytics prepares students for a career as a data analyst by covering the concepts of probability theory, statistical modeling, data visualization, predictive analytics, and risk management in the context of a business environment. In addition, a master’s degree in analytics equips students with the programming languages, database languages, and software programs that are vital to the day-to-day work of a data analyst.
Types of data analytics
Four types of data analytics build on each other to bring increasing value to an organization.
Descriptive analytics examines what happened in the past: monthly revenue, quarterly sales, yearly website traffic, and so on. These types of findings allow an organization to spot trends.
Diagnostic analytics considers why something happened by comparing descriptive datasets to identify dependencies and patterns. This helps an organization determine the cause of a positive or negative outcome.
Predictive analytics seeks to determine likely outcomes by detecting tendencies in descriptive and diagnostic analyses. This allows an organization to take proactive action—like reaching out to a customer who is unlikely to renew a contract, for example.
Prescriptive analytics attempts to identify what business action to take. While this type of analysis brings significant value in the ability to address potential problems or stay ahead of industry trends, it often requires the use of complex algorithms and advanced technology such as machine learning.