What Does a Data Analyst Do?

A Data Analyst collects, cleans and interprets data to identify trends, explain business performance and support decisions.

Why are operating costs increasing? Which products generate the most revenue? Where could a business improve efficiency? These are the kinds of questions Data Analysts investigate.

Their work primarily focuses on descriptive analysis, using existing data to understand what has happened and why. By comparison, Data Scientists often build statistical and machine learning models to predict future outcomes.

If you’re exploring a career in Data & AI, understanding the responsibilities, skills and day-to-day work of a Data Analyst can help you decide whether the role matches your interests.

Data Analyst core responsibilities

Data Analysts work with information from different sources, checking its accuracy, examining patterns and presenting findings to the people who need them.

Their main responsibilities include:

• Collecting data: Gather information from databases, spreadsheets, customer systems and other business platforms.
• Cleaning and preparing data: Check for missing records, duplicates and inconsistencies that could affect the accuracy of the analysis.
• Querying and analysing data: Use SQL and statistical techniques to investigate business questions, such as comparing sales performance across regions.
• Identifying trends and anomalies: Investigate changes in performance, unexpected results and relationships within existing data.
• Building dashboards and reports: Create visualisations that allow stakeholders to track performance, compare results and monitor important business metrics.
• Working with business teams: Understand what colleagues need to know and turn their requests into questions that the available data can answer.
• Presenting findings and recommendations: Explain what the data shows, what the analysis can’t tell them and which actions the business should consider.

Data Analyst job descriptions can vary considerably between employers. Some positions concentrate on routine reporting and dashboard maintenance, while others involve investigating commercial problems and working directly with senior stakeholders. Looking beyond the job title helps you understand what the employer actually expects.

Data Analyst vs Data Scientist: descriptive vs predictive analysis

A Data Analyst explains what happened. A Data Scientist builds models to predict what might happen next.

Imagine an online retailer has lost 15% of its repeat customers over six months.

A Data Analyst digs into historical sales data to understand when customers stopped buying, which groups were most affected and whether the decline followed changes in pricing or purchasing behaviour.

A Data Scientist builds a predictive model to identify which customers are most likely to leave in the future.

The questions they answer

Data Analysts frequently work with established business goals. A marketing team might want to understand why campaign performance changed, or a finance team might need to investigate rising costs.

Data Scientists also investigate business problems, but their work places greater emphasis on developing statistical and machine learning models to identify relationships and predict outcomes.

The roles overlap. Experienced Data Analysts can define their own investigations and forecast trends, while Data Scientists also evaluate historical data to understand patterns.

The tools they use

Data Analysts commonly use SQL, Excel and business intelligence tools such as Power BI and Tableau to analyse information and report findings.

Data Scientists rely more heavily on Python, R, statistical modelling and machine learning frameworks to develop and evaluate predictive models.

Career progression

Data Science generally requires deeper programming, statistical and modelling expertise, although it isn’t automatically a more senior position.

Some professionals progress from Data Analysis into Data Science as they develop those skills. Others move into senior analytics, business intelligence or management roles.

For more detail on the modelling side of the profession, read What Does a Data Scientist Do?.

Key skills a Data Analyst needs

Data Analysts need technical skills to investigate information and the judgement to explain what their findings mean.

The core skills include:

• SQL and database querying: Write queries to extract, join and summarise data from relational databases.
• Excel and spreadsheet analysis: Use formulas, lookups and pivot tables to perform calculations, compare results and check data quality.
• Data visualisation: Build reports and interactive dashboards using Power BI or Tableau.
• Statistical analysis: Understand averages, distributions, correlation and other statistical concepts to interpret results accurately.
• Communication and stakeholder management: Understand business requirements, explain findings and present recommendations to technical and non-technical colleagues.
• Problem-solving and attention to detail: Investigate unexpected results, identify errors and check whether the evidence supports a conclusion.

Python is also useful for automating tasks and handling more complex analysis, although not every Data Analyst role requires it.

For a fuller breakdown of the technical skills, qualifications and routes into the profession, read How to Become a Data Analyst.

A day in the life of a Data Analyst

You open your laptop to find a message from the marketing team. Website traffic is up, but sales haven’t followed. They want to understand why.

You start by pulling data from the company’s reporting systems. Before investigating, you notice that some transactions have been recorded twice. You clean the dataset, check the figures against the previous reporting period and use SQL to compare traffic, conversions and sales.

By lunchtime, you’ve found something worth looking into. Traffic has increased, but most of the additional visitors are coming from a campaign with a much lower conversion rate.

You update the marketing dashboard to show the difference, then meet with the team to explain what you’ve found and discuss what they should investigate next.

The afternoon brings a different task: preparing a monthly performance report for senior management. Tomorrow, another team will have a different question to work out.

Which industries employ Data Analysts?

Data Analysts work across a wide range of industries, analysing everything from financial transactions to customer behaviour and public service performance.

Common employers include:

• Finance and investment banking: Study transactions, financial performance, customer behaviour and risk.
• Healthcare: Examine patient outcomes, service demand, operational efficiency and resource allocation.
• Retail and e-commerce: Track sales, purchasing behaviour, inventory and marketing performance.
• Marketing and advertising: Measure campaign results, website performance, audience engagement and conversion rates.
• Government and the public sector: Examine population data, public services, spending and policy outcomes.

Business questions change across sectors, but many technical skills transfer. An analyst experienced in SQL, reporting, and statistical analysis can apply those skills across industries while building relevant sector knowledge.

Summary and key takeaways

• Data Analysts investigate business performance. They collect, clean and analyse information to identify trends, explain results and support decisions.
• Descriptive analysis is central to the role. Data Analysts primarily explain what has happened, while Data Scientists more often develop models to predict future outcomes.
• The job combines technical and communication skills. SQL, Excel, data visualisation and statistical knowledge are important, alongside the ability to explain findings.
• Responsibilities vary between employers. Some roles concentrate on reporting, while others involve more complex analysis and stakeholder engagement.

Explore How to Become a Data Analyst for career routes or How Much Does a Data Analyst Make? for salary information.

Frequently Asked Questions

What is the difference between a Data Analyst and a Data Scientist?

Data Analysts primarily use descriptive analysis to understand existing data and explain what has happened. Data Scientists more often build statistical and machine learning models to predict future outcomes. The roles overlap, but Data Science generally requires more advanced programming, statistical modelling and machine learning skills.

Does a Data Analyst need to know Python?

Not always. SQL, Excel and data visualisation tools are more consistently required for entry-level Data Analyst positions. Python becomes useful for automating tasks, processing larger datasets and carrying out more complex analysis. Requirements depend on the employer and the role’s technical scope.

Is Data Analyst a good entry-level Data & AI role?

Yes. Data Analysis is one of the more accessible entry points into Data & AI because employers may consider candidates from different academic and professional backgrounds. You can develop core skills through courses, independent projects or related work experience, although entry-level positions can still be competitive.

What industries hire Data Analysts?

Data Analysts work in finance, healthcare, retail, e-commerce, marketing, government and the public sector. Organisations hire them to investigate performance, identify trends and analyse information that informs business decisions. The datasets and questions vary by industry, but skills such as SQL, statistical analysis and reporting transfer across sectors.