This was written by Jennifer Marsh, connect with her,
As more devices connect to the cloud, an estimated 6 billion consumers will interact with data every day. The increasing reliance on the Internet of Things (IoT) and mobile technology powers much of the constant interaction with big data. Companies can leverage this interaction to improve marketing, make predictions on revenue and consumer interests, enhance cybersecurity, and advance technology overall. The prominent people behind these efforts are data analyst professionals who understand corporate interests and use programming and data models to provide insights into business development.
What Does a Data Analyst Do?
Before diving into a data analyst role, it’s important to distinguish a data analyst from a data scientist, often conflated by candidates and experts alike.
A data scientist trains data models for use with machine algorithms to produce predictions. They often program in Python and R to visualize data in charts and graphs and refine programs that display information to analysts.
A data analyst takes the output from machine predictions and translates the information into action-based suggestions for future business decisions. The types of recommendations made by a data analyst depend on the business and data.
For example, a data scientist could use previous sales data to determine seasonal interest in products in the e-commerce business. A data analyst takes these predictions and selects the products for next season’s e-commerce sales. Identifying patterns in data—such as consumer buying patterns on an e-commerce website—is the primary role of a data scientist job description.
Soft skills are also necessary for data analysts to excel in their careers.
Data analysts often collaborate with others, including data scientists, business executives, their team, and business analysts. Together, the group provides reports for business improvements and decision-making. Communication skills are also necessary for conveying ideas and information to other professionals within the organization.
What Tools Do Data Analysts Use?
Because a data analyst interprets data and does not usually train models, this role requires much less programming and even mathematical skills. The most critical skill data analysts can have is understanding their employer’s goals and matching those goals with data-based decision-making. To perform this job, the data analyst uses several tools.
A few tools commonly used are:
Microsoft Excel: This spreadsheet software provides reports to executives and graphs out predictions.
SQL: The Structured Query Language (SQL) searches data to filter and order information based on the data analyst’s requirements.
Web traffic analytic dashboards: Tools such as Google Analytics provide the data analyst with the information needed to make changes to site layouts, colors, and design to improve user experiences and increase traffic and sales.
Business intelligence tools: These tools pull data from a database and display information to inform business procedures.
Visualization tools: These tools turn raw data into easily understandable images (e.g., graphs) that can be used in reports so that executives and other decision-makers can understand data analyst interpretations and suggestions.
R or Python languages: Data analysts don’t always need to know programming languages, but having that skillset improves their marketability and career opportunities.
Should I Become a Data Analyst?
Choosing a career is a personal decision, but a data analyst career is predicted to be one of the hottest jobs in the near future. California University collected information on this career’s future, showing an estimated 11.5 million new data analyst jobs in 2026 with an average salary of $120,931 annually.
Before you decide to focus efforts on data analytics as a career, it’s important to note that you should be intensely interested in math-based studies, understand statistics well, and have at least a minor interest in computer programming and data science. More importantly, you need business acumen and communication skills to determine the main goals in a specific industry and the ability to discuss them across organizational stakeholders.
Every data analyst has their particular focus and strengths, but you must have a strong interest in data, algorithms, and the math behind the predictive analysis. Candidates can also start in data analytics to build a future career in data science to build algorithms used in AI and ML. Any candidate looking for a strong career with high income and longevity should consider data analytics as a career.