Data Analyst: Career, Salary, Growth, and Key Trends
03.03.2025
In this article:
1. Who is a Data Analyst, and Why Do Companies Need One?
2. Why Are Data Analysts Important for Businesses?
3. What Does a Data Analyst Do?
4. How Is a Data Analyst Different from a Business Analyst?
5. Types of Data Analysts and Their Specialisations
6. What Industries Need Data Analysts?
7. What Skills Do Data Analysts Need?
8. Data Analyst Salaries
9. How to Become a Data Analyst
10. Career Path of a Data Analyst
11. Pros and Cons of a Data Analytics Career
12. How to Find a Data Analyst: A Guide for HR and Employers
13. Key Data Analytics Trends for 2025
1. Who is a Data Analyst, and Why Do Companies Need One?
2. Why Are Data Analysts Important for Businesses?
3. What Does a Data Analyst Do?
4. How Is a Data Analyst Different from a Business Analyst?
5. Types of Data Analysts and Their Specialisations
6. What Industries Need Data Analysts?
7. What Skills Do Data Analysts Need?
8. Data Analyst Salaries
9. How to Become a Data Analyst
10. Career Path of a Data Analyst
11. Pros and Cons of a Data Analytics Career
12. How to Find a Data Analyst: A Guide for HR and Employers
13. Key Data Analytics Trends for 2025
Who is a Data Analyst, and Why Do Companies Need One?
A data analyst is a specialist who gathers, processes and analyses data. Their job is to find trends, predict outcomes, and support business decisions with real data instead of gut feeling. Over the years, this role has evolved from simple number crunching to becoming a key strategic function in companies.
Data analysts are often confused with product analysts and systems analysts, but their focus is different. Product analysts work on user experience, while systems analysts deal with technical aspects. A data analyst, on the other hand, focuses on raw data and uses statistical methods to uncover insights. Imagine a food delivery company. A data analyst studies order patterns and notices that most orders come in during the evening, while the afternoons are slow. To balance demand, they might suggest offering discounts for daytime orders. This helps optimize courier workload and boosts overall revenue.
When you're lost in analysts
Why Are Data Analysts Important for Businesses?
More and more companies are using data-driven strategies, meaning key business decisions are based on numbers, not intuition.
A data analyst helps businesses:
- Identify the most profitable products or services.
- Spot weaknesses in business processes.
- Measure the effectiveness of marketing campaigns.
- Reduce risks when launching new features or services.
For example, if a store owner wants to rethink their product lineup, a data analyst can study sales trends. They might find that 15 top-selling products generate 90% of the revenue, while some low-performing items actually lose money. Based on this analysis, the company can eliminate unprofitable products and optimize inventory.
Data analysts are valuable in any industry that works with data. Even small businesses analyse sales, customer behaviour, and revenue. If they don’t have a dedicated analyst, these tasks often fall to marketers, accountants, or managers.
What is Big Data?
Big Data refers to massive amounts of information that can’t be processed manually. Analysing it requires specialised tools and technologies like machine learning, cloud computing, and data processing algorithms.
How is Big Data Different from Regular Data?
Think about making the perfect cup of coffee. You experiment for a few days—changing the milk quantity, grind size, and bean variety. You take notes and, in the end, choose the best recipe. This is working with small data—it's easy to track and analyse manually.
Now, imagine a large coffee chain that wants to know what type of coffee its customers prefer. A Big Data analyst would collect thousands of order details—what drinks are ordered most, at what times, and which add-ons are popular. They might also analyse customer reviews, preparation speed, and even the average temperature of served coffee. This is far too much information to handle manually, so Big Data technologies are used.
Companies use Big Data to:
- Understand customer preferences and offer personalised products.
- Optimize processes, cutting costs and improving efficiency.
- Spot trends and predict demand for different products.
Big Data
What Does a Data Analyst Do?
Examples of a Data Analyst’s Responsibilities:
- Analysing user behaviour on a website. Identifying pain points and making recommendations to improve engagement, such as figuring out which pages attract visitors and where they tend to drop off.
- Optimising marketing campaigns. Assessing the effectiveness of different marketing channels and reallocating budgets to generate more leads.
- Forecasting demand. Examining sales data, trends, and external factors to predict which products will be in demand.
- A/B testing. Evaluating the impact of changes and determining their statistical significance.
- Optimising business processes. Identifying inefficiencies in logistics, finance, and workforce management to cut costs and improve performance.
Data analysts help businesses build effective data processing systems and make well-informed decisions based on real insights.
Key Stages of a Data Analyst’s Work
- 1Data Collection. A data analyst gathers information from multiple sources—internal databases, external services, reports, CRM systems—and organizes it into a structured format for analysis.
- 2Cleaning and Preparation. Before data can be analysed, it needs to be cleaned. This includes removing duplicates, fixing errors, filling in missing values, and standardizing formats. In fact, data cleaning often takes up 80% of an analyst’s time.
- 3Analysis and Interpretation. Big Data analysts look for patterns, build mathematical models, test hypotheses, and identify factors that influence key business metrics.
- 4Visualisation and Reporting. Results are presented in a clear and accessible way using charts, graphs, and dashboards, helping decision-makers quickly grasp the insights.
- 5Making Recommendations. A data analyst doesn’t just report the numbers—they suggest actionable steps. For example, if sales of a product are declining, they might recommend adjusting the marketing strategy or improving the website’s user experience.
How Is a Data Analyst Different from a Business Analyst?
Although their titles sound similar, their roles are quite different:
Data analyst
Business analyst
Focuses on business processes, defines problems, gathers requirements, and helps implement solutions
Focuses on numbers, statistics, and algorithms. Works with large datasets, identify trends, and make predictions
In simple terms:
- A data analyst answers "What happened and why?"
- A business analyst answers "What should we do next?"
Types of Data Analysts and Their Specialisations
Data analytics covers many industries, and specialists focus on different aspects of data depending on their field.
- 1Business Analysts analyse business processes, identify issues, and suggest optimisations. They also automate reporting, implement IT systems, test hypotheses, and model risks.
- 2Marketing Analysts study the market, competitors, and consumer behaviour. They evaluate ad campaign performance, predict trends, and optimise marketing budgets.
- 3Financial Analysts analyse financial performance, forecast business growth, assess risks, and calculate return on investment.
- 4Product Analysts track user behaviour within a product, analyse key metrics, enhance functionality, and improve user experience.
- 5Web Analysts examine user interactions with a website, studies behavioural patterns, evaluate page depth and user journeys, and optimise conversion rates.
- 6Game Analysts study player behaviour, optimise monetisation, balance gameplay, and improve user retention.
- 7System Analysts translate business requirements into technical tasks, optimise IT systems, databases, and APIs, and ensure the correct functioning of information systems.
- 8UX Analysts analyse user experience, study interactions with websites and apps, and help improve usability.
- 9Marketplace Analysts analyse sales and product promotion strategies on e-commerce marketplaces.
- 10HR Analysts assess recruitment efficiency, employee turnover, and motivation.
What Industries Need Data Analysts?
Data analysts are in demand across virtually every industry, with job opportunities in IT, finance, retail, and healthcare.
- 1IT and Tech CompaniesTech giants like Google, Meta, and Amazon use analytics to:
- Personalise content on social media and streaming services.
- Improve search engine performance.
- Develop artificial intelligence algorithms.
- 2Finance and BankingBanks and financial institutions rely on data analytics for:
- Assessing customers' creditworthiness.
- Detecting fraudulent transactions.
- Forecasting financial risks.
- 3Retail and E-commerceStores and online platforms use data to:
- Optimise inventory and stock management.
- Analyse customer behaviour.
- Personalise promotions and discounts.
- 4Marketing and AdvertisingMarketers leverage analytics to:
- Measure ad campaign effectiveness.
- Identify the most profitable target audiences.
- Predict trends.
- 5Manufacturing and LogisticsCompanies handling physical goods use analytics for:
- Optimising supply chains.
- Monitoring equipment performance.
- Forecasting demand.
- 6HealthcareData analytics in medicine is used for:
- Early disease detection.
- Optimising hospital operations.
- Developing personalised medicine and pharmaceuticals.
- 7Gaming IndustryGaming companies analyse data to:
- Improve game balance.
- Predict player behaviour.
- Optimise in-game monetisation.
What Skills Do Data Analysts Need?
To effectively work with data and provide value to businesses, analysts need a mix of hard skills and soft skills.
Hard Skills
Soft Skills
- Critical thinking. Evaluating data accuracy and spotting errors.
- Attention to detail. Identifying duplicates, anomalies, and inconsistencies.
- Communication skills. Explaining complex data insights in simple terms to marketers, managers, and executives.
- Teamwork. Collaborating with different departments to maximize the value of data.
- Adaptability & willingness to learn. Keeping up with new tools, methods, and trends in data analytics.
What Skills Do Beginners Need?
For those just starting out, the essential skills include:
- SQL. The foundation for working with databases and writing queries.
- Excel & Google Sheets. Pivot tables, data analysis, and automation.
- Statistics. Basic principles of data analysis.
- Data visualisation. Creating charts and dashboards with Power BI and Tableau.
- Python. Basic data handling and process automation.
Depending on specialisation, additional skills like Google Analytics, CRM systems, machine learning, and cloud services may also be useful.
Data Analyst Salaries
We collected salary data from the past year using information from platforms like Indeed and Glassdoor. The table presents average values, which may vary depending on the company, location, job level, and other factors.
How to Become a Data Analyst
You can enter the field through university education, online courses, self-learning, or internships. A data analyst's knowledge spans programming, databases, business analytics, and artificial intelligence.
Where to Study Data Analytics?
1. Universities and Colleges
There isn’t always a direct “Data Analytics” major, but relevant knowledge can be gained from these fields:
- Applied Mathematics and Computer Science. Algorithms, statistics, and machine learning.
- Business Informatics. Data management and its application in business.
- Software Engineering. Software development and data analysis fundamentals.
- Statistics. In-depth study of data analysis methods.
2. Online Courses and Self-Learning
A faster way to enter the profession is through educational platforms offering analytics courses.
Popular Paid Courses:
- Google Data Analytics Professional Certificate (Coursera). 6 months, it covers SQL, spreadsheets, R programming, Tableau, and data visualisation.
- IBM Data Analyst Professional Certificate (Coursera). 4 months, it includes training in Excel, SQL, Python, and data visualisation.
- Data Analyst with Python Career Track (DataCamp). Comprehensive track focusing on Python, data manipulation (pandas), statistical analysis, and visualisation.
Free Courses:
- Introduction to Data Analytics (IBM) (Coursera). It covers data types, data gathering, and visualisation techniques.
- Data Analysis for Social Scientists (edX - MIT). It introduces probability, statistics, and data analysis techniques, with a focus on social sciences.
- Learn SQL (Codecademy). Interactive SQL training, covering database queries and management.
- Interactive SQL Training. SQLZoo offers hands-on exercises to practice SQL queries. Kaggle is a platform offering datasets and exercises to build SQL and data analysis skills.
3. Internships and Practical Experience
Once you grasp the basics, hands-on experience is crucial. Many companies offer internships for beginners:
- Indeed – Job board listing remote and entry-level data analyst internships.
- ZipRecruiter – Internship listings across various industries, including remote options.
- Speedy Mentors – Internship programs for entry-level data analysts, with mentorship and real-world projects.
- RemoteRocketship – Focuses on remote data analyst internships for beginners.
Best Resources for Learning Data Analytics
Books
- "Data Science from Scratch" – Joel Grus. A beginner-friendly introduction.
- "Data Analysis in Any Industry" – Kirill Eremenko. Practical applications.
- "Statistics for Everyone" – Sarah Boslaugh. Covers statistical fundamentals.
- "Statistics and Cats" – Vladimir Savelev. Fun and simple explanations.
Online Platforms
- Kaggle – Hands-on data analysis challenges.
- DataCamp – Interactive courses in SQL, Python, and statistics.
- Habr – Articles on industry trends and best practices.
Tips for Beginners
- 1Master the core tools: SQL, Python, BI systems, and statistics.
- 2Build a portfolio: Upload projects on GitHub or Kaggle.
- 3Get real-world experience: Apply for internships and participate in challenges.
- 4Develop analytical thinking: Understand how data drives business decisions.
Transitioning into Data Analytics
Many professionals shift into data analytics from related fields since they already work with numbers, data, and logic.
Career Path of a Data Analyst
1. Junior Data Analyst
Experience: 0–2 years
Main Responsibilities:
- Collecting, cleaning, and preparing data.
- Writing simple SQL queries.
- Creating reports and visualisations.
Key Skills for Junior Analysts:
- 1SQL basics (queries, table joins).
- 2Excel & Google Sheets (pivot tables, formulas, automation).
- 3Python or R (Pandas, NumPy).
- 4Data visualization (Power BI, Tableau, Google Data Studio).
- 5Basic statistics & A/B testing.
How to Level Up:
- Deepen SQL knowledge (query optimisation).
- Automate analysis with Python or R.
- Work on projects involving advanced data analysis.
2. Middle Data Analyst
Experience: 2–5 years
Main Responsibilities:
- Conducting in-depth data analysis and identifying patterns.
- Developing predictive models and analytics frameworks.
- Automating reporting and optimising data workflows.
- Collaborating with business teams.
Key Skills for Mid-Level Analysts:
- 1Advanced SQL (indexes, optimisation, stored procedures).
- 2Statistical analysis (A/B testing, correlations).
- 3Basic machine learning (linear regression, clustering).
- 4Big Data tools (Hadoop, Spark, Google BigQuery).
How to Level Up:
- Learn advanced analytics and forecasting.
- Work with Big Data and cloud computing.
- Develop soft skills: presenting data and working with stakeholders.
- Mentor junior analysts.
3. Senior Data Analyst
Experience: 5+ years
Main Responsibilities:
- Developing complex analytical models.
- Managing data projects and strategies.
- Integrating machine learning into business processes.
- Mentoring and training junior analysts.
Key Skills for Junior Analysts:
- 1Expert-level SQL and analytics process optimisation.
- 2Advanced machine learning (classification, predictive modelling).
- 3Data architecture and analytics automation.
- 4Project management and strategic planning.
Next Steps for Senior Analysts:
- Head of Analytics – Leads an analytics team, oversees data strategy, and manages internal processes.
- Data Scientist – Focuses on machine learning, complex algorithms, and predictive analytics.
- Data Engineer – Designs and optimizes data infrastructure, databases, and data pipelines.
Pros and Cons of a Data Analytics Career
Like any profession, data analytics has its pros and cons.
Pros
- Fast entry. You can learn the basics in a year through online courses and internships. No IT degree is required.
- High salaries. Even entry-level analysts earn above average, and experienced professionals are highly sought after.
- Flexible work. Many analysts work remotely or in hybrid roles, allowing for better work-life balance.
- Strong career growth. Analysts can quickly move up to senior roles, transition into Data Science, or become Data Engineers.
- Industry versatility. Analytics is needed in finance, marketing, healthcare, retail, IT, manufacturing, and more.
- Collaborative learning. Many teams offer mentorship and professional development.
Cons
- Repetitive tasks. A large portion of the job involves data cleaning, writing SQL queries, and preparing reports, which can become monotonous.
- Constant learning. The field evolves quickly, requiring analysts to keep up with new tools and techniques.
- High responsibility. Analytical insights influence business strategy, and mistakes can lead to financial losses.
- Competitive job market. Demand for analysts is growing, but so is the number of professionals entering the field. A strong portfolio and hands-on experience are essential.
- Communication challenges. Analysts must translate complex data into clear insights for non-technical stakeholders, which isn't always easy.
How to Find a Data Analyst: A Guide for HR and Employers
A data analyst is one of the key roles in modern business, especially for companies dealing with large volumes of information. Finding the right specialist can be challenging—you need to consider not only technical skills but also the ability to communicate with business teams, explain complex concepts in simple terms, and adapt to change.
Where to Find Data Analysts
- LinkedIn
- Job boards (HH.ru, SuperJob, Habr Career)
- Professional platforms (Kaggle, GitHub)
- Telegram and Slack communities
- Internship and student programs – Large companies like Yandex, Sber, and Ozon train analysts, making these programs a great talent source.
- Hackathons and competitions – Participating in Kaggle challenges or Datathons helps identify skilled professionals.
- Referrals – Many companies hire data analysts through colleague recommendations.
- Recruiting agencies – IT recruitment firms like Lucky Hunter can help find top talent.
How to Evaluate a Candidate
Assessing a data analyst is not just about testing technical skills—it’s about understanding how they think, analyse information, and collaborate with teams.
- 1Technical SkillsGive the candidate a practical task, such as writing an SQL query, conducting basic data analysis, or creating a dashboard. This will show how well they handle tools and apply their skills in practice.
- 2Logical Thinking and Problem-SolvingAsk real-world business questions. For example: "How would you analyse a decline in sales or an increase in customer churn?" Pay attention not just to the answer, but to their approach—what data they would use, which metrics they would check, and how they would structure the analysis.
- 3Communication SkillsA good analyst must do more than understand numbers—they need to explain them clearly. Ask them to describe a complex concept, such as correlation, in simple terms or present their analysis in a way that a non-technical audience would understand.
- 4Motivation and Willingness to LearnData analytics is a fast-evolving field. Ask candidates what courses they have taken, what technologies they are learning, and how they stay updated in the industry.
How to Evaluate a Data Analyst?
Common Hiring Mistakes
- 1Expecting the "Perfect" CandidateThere is no one-size-fits-all analyst. Expecting someone to be an expert in SQL, Python, Big Data, machine learning, and business analytics all at once is unrealistic. Define the most critical skills for the specific role.
- 2Lack of Clear ObjectivesIf your company does not have a clear idea of what the analyst will do, they may end up working on scattered tasks without real business impact.
- 3Ignoring Soft SkillsTechnical expertise is crucial, but an analyst must also be able to communicate insights and work effectively with teams. Many resumes focus only on technical abilities, but without strong communication skills, an analyst’s impact in a business environment is limited.
- 4Underestimating the Learning CurveEven an experienced analyst needs time to understand company processes. A well-structured onboarding process helps them become productive faster.
Key Data Analytics Trends for 2025
In 2025, data analytics will continue to evolve with advancements in technology, stricter regulations, and a growing focus on automation. Here are the key trends shaping the field.
1. AI Governance: Managing Artificial Intelligence
As AI systems become more widespread, regulations around their use are increasing. The AI Act in the EU introduces strict transparency requirements, particularly in regulated industries like finance and healthcare.
Impact on Businesses:
- Companies must monitor what data is used in AI models to avoid privacy violations.
- Documentation of data sources and AI decision-making processes will be required.
- There will be increased demand for experts who can ensure compliance with AI regulations.
Impact on Analysts:
- Stronger emphasis on transparent reporting.
- Growing demand for specialists in ethical AI usage and data governance.
- New tools emerging for monitoring and auditing AI models
2. AI-Powered Analytics and Automation
Artificial intelligence is not just being regulated—it is also revolutionizing data analysis. Modern AI solutions can process data without complex SQL queries, raising questions about whether AI will replace human analysts.
How Businesses Benefit:
- Analytics becomes more accessible to employees without a technical background.
- Report preparation time decreases, leading to faster decision-making.
- AI-generated insights improve the efficiency of analytics teams.
Impact on Analysts:
- Reduction in routine data processing tasks.
- Increased demand for professionals who can configure AI analytics tools.
- More analysts transitioning into data engineering to integrate AI into company workflows.
3. The Rise of Data Products
Companies previously focused on Data Mesh, a decentralized data management approach. Now, the focus is shifting to Data Products—structured datasets designed as reusable business assets.
Impact on Analysts:
- Data is no longer just a resource—it becomes a product with clear business value.
- Increased importance of data governance, ensuring data quality, security, and accessibility.
- Easier management of decentralized data storage.
4. Data Monetisation: Turning Information into Revenue
Companies are not only using data internally but also selling datasets on emerging data marketplaces.
What’s Next in 2025?
- Businesses will develop and sell pre-packaged datasets.
- Demand will grow for professionals who can evaluate data quality and prepare it for commercial use.
- Stronger security measures will be required when transferring data between organisations.
5. Real-Time Analytics Becomes the Standard
Historically, businesses analysed data retrospectively. Now, there is a growing need for real-time analytics.
Where It’s Being Used:
- Banking – Instant fraud detection.
- Marketing – Automated budget adjustments for ad campaigns.
- Manufacturing – IoT devices streaming live operational data.
Impact on Analysts:
- Higher demand for experts in real-time data processing tools like Kafka, Flink, and Apache Pulsar.
- Shift from traditional analytics to predictive analytics, using models to improve business decisions.
- Increased use of AI tools for automated anomaly detection.
6. The Growth of Synthetic Data
To protect privacy, companies are increasingly using synthetic data—artificially generated datasets that mimic real ones without containing sensitive information.
Why This Matters:
- AI models can be trained without privacy concerns.
- Analysts gain access to high-quality data even in restricted industries.
- Data sharing between companies becomes safer and more efficient.
7. The Growing Importance of Data Governance
AI, machine learning, and analytics are only as good as the data behind them. In 2025, Data Governance will be at the core of company strategies.
Why This Is Crucial:
- Poor data quality costs companies millions in losses.
- Regulations around data storage and processing (GDPR, AI Act) are tightening.
- Effective AI implementation relies on structured, well-managed data.
The Analyst’s Role:
- Establishing data management standards.
- Ensuring data quality and security.
- Implementing Data Lineage tools to track data origins.
Data analytics is no longer just about processing information—it is a strategic business tool. Companies that build strong data strategies gain a competitive edge, and the demand for analysts who can adapt to new challenges continues to grow.
If your company is looking for experienced data analysts, AI specialists, or data engineers, as well as other IT professionals, Lucky Hunter can help you find top candidates who match your needs.
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Alexandra Godunova
Content Manager in Lucky Hunter
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