Data Analyst
Turn raw data into clear decisions — collecting, querying, and visualising business data to answer operational questions and drive measurable improvement.
Data Analysts collect, clean, query, and visualise data to answer specific business questions and support operational decision-making. They sit between raw data and management decisions — translating numbers into charts, reports, and dashboards that make patterns visible and actions clear. While Data Scientists build predictive models and conduct experiments, Data Analysts focus on descriptive and diagnostic analysis: what happened, how much, where, and why. In practice this means SQL queries, Excel or Google Sheets pivot tables, Power BI or Tableau dashboards, and clear written or verbal reporting. The Data Analyst role is one of the most accessible entry points into the data career pathway — and one of the most in-demand positions in every sector of the Sri Lankan economy. Every commercial bank, telecomunications company, insurance firm, retailer, hospital, and government department generates operational data that needs to be understood. Specific Sri Lankan employers include all major banks (Commercial, Sampath, HNB, BOC), Dialog Axiata, SLT-Mobitel, John Keells Holdings, Cargills, Hemas, Aitken Spence, and the rapidly growing fintech and e-commerce sector. Data Analyst is the standard entry point for students who want a data career but are not yet ready for the full Data Scientist pathway — and many Data Analysts grow into Data Scientist, BI Developer, or Data Engineer roles within 2–3 years.
What a Data Analyst does daily
- Query databases using SQL to extract the data needed to answer specific business questions
- Clean and prepare data — removing duplicates, handling nulls, standardising formats, joining data from multiple tables
- Perform descriptive analysis — calculating totals, averages, growth rates, distributions, and trends across business metrics
- Build dashboards and reports in Power BI, Tableau, or Excel — making performance data visible and accessible to managers
- Conduct ad-hoc analysis — responding to specific questions from management ("which branches have the highest churn rate this quarter?")
- Monitor KPIs and alert the business when metrics deviate from expected ranges
- Prepare regular business reports — daily, weekly, monthly performance summaries for management review
- Present analytical findings to non-technical stakeholders — translating numbers into plain-language recommendations
- Work with Data Engineers and Data Scientists to understand available data and flag data quality issues
- Support A/B test analysis — compiling test results and summarising conclusions for product or marketing teams
Step-by-Step Career Roadmap
- Build a genuine comfort with numbers — percentages, ratios, averages, and reading charts and graphs are the foundation of all data analysis
- Start using Excel or Google Sheets seriously — learn how to create a table, apply formulas, and make a basic chart
- Develop curiosity about how businesses use data — bank interest calculations, sports league tables, school exam results, weather averages
- Practise presenting numbers in a story — "our class average went up from 67% to 74% because we revised harder; here is the chart" is exactly the kind of data communication a data analyst does professionally
- Learn basic ICT skills — file management, Google Workspace, basic typing speed
- Google Sheets class data analysis project
- Excel/Sheets tutorial (GCFGlobal.org free)
- Sports statistics tracking as a hobby
- School magazine or science fair data chart creation
- Data analysis is a practical skill — reading about it without doing it builds no actual competency; start making real spreadsheets with real data as early as possible