A data analyst is a professional who collects, organises and analyses organisational data and presents the results through clear reports. The main purpose of the role is to transform raw data into meaningful information that supports business decisions. Data analysts may share their findings through tables, charts, dashboards and presentations. This helps managers and business teams make more measurable, consistent and data-driven decisions.
The responsibilities of a data analyst may vary according to the organisation and industry. Data cleaning, identifying missing or inaccurate records, monitoring performance indicators and preparing regular reports are common duties. Analysts may work with sales, marketing, finance, operations or customer experience data. They should also be able to communicate analytical findings clearly to business teams, not only to technical stakeholders.
A background in statistics, mathematics, computer engineering, industrial engineering, economics, business or similar fields can be advantageous for becoming a data analyst. However, the profession is not limited to graduates of specific departments. Technical skills, analytical thinking, problem-solving ability and practical project experience are also important in recruitment processes. Certificate programmes, portfolio projects and work with real datasets can therefore help candidates develop their capabilities.
Data analysts are generally expected to have knowledge of Excel, SQL, data visualisation tools and basic statistics. The ability to create reports and dashboards with tools such as Power BI, Tableau, Looker Studio or similar platforms is important for many roles. Python or R can provide an advantage in data cleaning, analysis and automation processes. Statistical tools such as SPSS may be used in certain industries, but they are not mandatory for every data analyst position.
Job requirements may differ depending on the seniority of the role and the organisation’s data maturity. Some entry-level positions may accept one year of experience, while more senior roles may require two or more years. Big data technologies, machine learning methods and advanced Python skills are more common in analytics, data science or senior data analyst positions. A successful data analyst needs communication skills, business understanding and the ability to interpret results correctly in addition to technical knowledge.
