What does a data analyst do? Roles, skills, tools & salary in 2026

A data analyst collects, cleans, analyses and visualises data to help organisations make better business decisions. Their work typically involves writing SQL queries, cleaning datasets, building dashboards, identifying trends, answering business questions and communicating insights to stakeholders. In 2026, many data analysts also use Python, Power BI or Tableau, and AI-assisted tools to automate repetitive analysis and explore data faster.

Have you ever realized that when you buy a product, you get bombarded with recommendations for other similar products?

Or that you mostly get ads for products or services that you have a certain affinity to?
Or have you noticed the prices for an airline ticket increasing as you check it over and over again?

All of this happens because of an algorithmically driven process in place that is enriched through data. The people that oversee the whole process, from collecting this data, to stalking the user’s intent, to deriving conclusions are known as data analysts.

A data analyst turns raw data into actionable insights that help organisations make informed decisions.

If we define it in a more generic way, 'A data analyst is someone who collects, cleans, and analyzes big unstructured data sets to solve business and real-world problems.'

They're the ones making sense of the quintillions of terabytes of data circulating all around us, be it the number of sales a particular ad triggered to the number of risks and frauds detected in the case of banking and insurance companies.

Basically, where there are numbers, there's data and when there's data, there are data analysts and data scientists.

Demand for data analysts

Data analytics continues to be used across technology, finance, healthcare, e-commerce, consulting, marketing and other industries. The role is also evolving as AI tools automate parts of data preparation, reporting and analysis.

Current job postings show that employers still expect analysts to combine core analytics skills with business understanding. For example, recent LinkedIn listings for data analyst roles mention combinations of SQL, Python, Power BI/Tableau, data modelling, dashboards and AI/ML exposure.

This means the modern data analyst is not simply a person who creates spreadsheets or dashboards. They increasingly need to understand the complete journey from business question → data → analysis → insight → decision.

Therefore, it's no news that a lot of youngsters are looking to break into the field of data. We've already talked about what skills you need and how you can get there in our previous article.

The rising demand for data analyst

The demand for data analysts has increased rapidly in recent years, reflecting the growing importance of data-driven decision-making across various industries. This can be attributed to several key factors:

Business Intelligence: Companies recognise that data-driven insights can lead to more informed decisions, improved efficiency, and a competitive edge. Data analysts play a pivotal role in providing these insights.

Emerging Technologies: The rapid development of data analytics tools and technologies, including machine learning and artificial intelligence, has created new opportunities for data analysts to harness the power of data.

Industry Diversity: The demand for data analysts extends beyond traditional sectors like finance and IT. Industries such as healthcare, e-commerce, marketing, and agriculture increasingly rely on data analysts to optimise operations and strategy.

Data Security and Compliance: As data privacy regulations become more important, organisations require data analysts to ensure data compliance and protect sensitive information.

Global Reach: The demand for data analysts is not limited to specific regions or countries. It is a global trend, making data analysis a highly sought-after skill worldwide.

Here, we'll be sneaking into the day-to-day work of a data analyst and how exactly they impact the industry.

Why does data analytics matter?

In 2026, the value of analytics is also shifting from simply reporting what happened to helping teams understand why it happened, what may happen next and what action they should take. AI-assisted analytics can speed up tasks such as query generation, summarisation and exploration, but analysts still need to validate results, understand the business context and communicate the implications clearly.

And that's why it has wide implications in all fields of work today, from healthcare, agriculture, and weather forecasting to businesses, sports, risk detection, and advanced technologies. (Check out 5 real life applications of Data Analytics)

Yes! With analytics, we can not only increase business intelligence (future sales or marketing spending) but we can also predict climate change in the coming future.

With that being said, it's time now to take a look at the day-to-day operations/proceedings of a data analyst.

If we simply look at the job descriptions for data analysts, it generally entails the following responsibilities:

• Create, implement, and manage cutting-edge analytical systems that take complex issues and turn them into straightforward frameworks.
• Determine trends and development prospects by analyzing large, complicated data sets.
• Work closely with management to determine key performance indicators (KPIs) and business needs.
• Make reports that reflect best practices in data mining, analysis, and visualization.
• Define and execute data gathering and integration logic, choosing the best mix of techniques and tools from a predetermined technology stack to ensure the solution's maximum scalability and performance.
• As data models, measurements, and infrastructure are built, create and update documentation for these concepts.

What does a data analyst do on a typical day?

A data analyst's day is rarely spent doing only one type of task. The exact mix depends on the company and role. An analyst in a product team may spend more time analysing user behaviour, while a finance analyst may focus on revenue, costs and forecasting. Let's understand them step-by-step:

Defining the objective

First things first, a data analyst needs to identify the question and the objective of the analysis. In a way, this could be the trickiest part of the work they do.

An analyst needs to have a strong grasp of the business model, its inner workings, the way it functions, and its goals down the line.

Many a time, the problem that seems obvious to the eye might not be the real problem.

Let's say your company wants to boost its revenue but they're facing a certain roadblock. The senior management and product team decides to launch a new product suite to increase the average weight of consumption (AWOC).

They start spending resources on analyzing product demand and modeling a new product. On the other hand, with a bit more research from different angles, you the analyst realize that the real problem lies in the marketing of the existing products, resulting in low customer engagement and retention.

If you bring this insight to light, your company might find that investing in marketing training would boost revenue at much lower spending.

You could end up saving the company thousands of dollars, and possibly get appraisals from the senior management.

It's just a hypothetical case but it shows the importance of observing the data from different angles and identifying the right question. As an analyst, you need to keep all eyes open.

Sounds interesting? Let's move on to the next step.

Collecting Data

Data can come in two forms - Quantitative data (like marketing figures, and the number of customers gained) and Qualitative data (such as customer reviews).

Furthermore, depending on the type of company you're working in, data can either come as first-party data (information your organization has collected directly), second-party data (information you've obtained through another company's first-party data), or third-party data which can come from numerous sources by a third party.

The first step is to define data quality criteria to measure and check if the data is accurate, consistent, and reliable. In case the data is inaccurate, it can result in bad decisions and ineffective strategies by the company (as we saw in the example above)

Collection of data can be done in various ways including but not limited to, online surveys and polls, website analytics, social media engagement, existing customer database, and so on.

Establishing data processes

Setting up a process or a framework is vital to all kinds of work. Just like a football team's coach builds the formation and strategy for the game, a data analyst also needs a process for organizing data.

Oftentimes, the data is raw, complex, and unstructured and needs an analyst to break it down into relevant, and useful information. Analysts work with data engineers and architects in the team to execute process changes, update systems, and build better documentation techniques.

Data Cleaning

Data cleaning remains an important part of an analyst's work, but the amount of time varies significantly by organisation, dataset and role. Analysts may need to remove duplicates, handle missing values, standardise formats, validate records and investigate unusual values before analysis begins.

In 2026, AI-assisted tools can speed up some repetitive cleaning and exploration tasks, but analysts still need to validate transformations and understand how changes to the data affect the final result.

After you've cleaned, you need to validate the data if it meets your requirements or not. Otherwise, you'll have to backtrack and have a go at it again through a different approach.

This is the iterative part of data analysis, but one that's absolutely essential. It's no surprise that companies would want people who can rigor through and clean the data before running algorithms.

Conducting Analysis

Modern analysts may also use AI-assisted analytics to generate hypotheses, explore datasets, explain trends and speed up repetitive analysis. However, AI-generated findings should be treated as a starting point rather than automatically accepted conclusions. Analysts remain responsible for validating the data, methodology and business interpretation.

These are the four main types of analysis:

• Descriptive analytics
• Diagnostic analytics
• Predictive analytics
• Prescriptive analytics

Let’s look at them one-by-one.

Descriptive Analytics

It answers the question, "What happened?"

Descriptive analytics involves using current and past data to mark trends and patterns. It is the simplest form of analysis and your company would probably use it on a daily basis. It uses essential statistical tools like Excel, and data visualization tools like Tableau to help parse data and find relations between variables.

One example would be how the streaming platform Netflix gathers data on consumers' in-platform behavior and determine which series or movies to display on the home screen.

Diagnostic Analytics

It answers, "Why did something happen?"

Diagnostic analytics is the next logical step after you've used descriptive analytics to find trends.

It involves techniques such as drill-down, data discovery, data mining, and finding correlations.

One use case of diagnostic analysis is to decode customer behavior. Specifically, in the case of subscription-based services, it helps a great deal in customer retention. It explains the reason why departing customers cancel their subscriptions.

Predictive Analytics

It answers, "What's likely to happen in the future".

As the name suggests, it's used to predict future events across industries using historical data.

It can be either conducted manually or using machine learning algorithms.

Predictive analytics can be seen in many places, from your weekly weather forecast to early detection of allergic reactions in the health industry.

In our previous article '5 real-world examples of data analytics' we talked about how Walmart uses predictive analytics to hone loyalty programs for different customer segments and to forecast the demand for over 500 million products.

Prescriptive analytics

It is logically the last step in conducting an analysis. After you've described trends, found out the reason, and made data-driven predictions, the next step is to decide on the action plan. 'What should we do next' is the bottom line of prescriptive analytics.

It uses machine learning algorithms, specifically, "if and else" statements to process enormous amounts of data quickly and effectively.

However, it's important to note that everything can't be left to the algorithms. A major chunk of prescriptive analytics should involve human judgement and inference. We're the ones making the decisions, aren't we?

Producing Reports

Now, it's time to communicate your results to the stakeholders- could be your senior management or a third party. It's not as simple as just putting up some numbers and pointers on a blank paper.

This is where your data visualization skills come into play. How you're able to create lively narratives and stories from a given chunk of data will decide the direction of your company.

As an analyst, you will be creating graphs, pie charts, dashboards, documents, and presentations to deliver the narrative in an effective way.

This step can be easy to overlook, but it's as important as the analysis itself. You need to share your findings in a way that is less time-consuming and easily understandable to the non-technical people in your team.

Data Analyst Toolkit in 2026

Recent data analyst job postings reinforce this combination. Current listings mention SQL alongside Python and BI tools such as Power BI or Tableau, while some roles also list AI/ML knowledge as an advantage.

Data Analyst Salary in India by Experience

Note: Data analyst salaries vary by experience, location, company, industry, skills and job title. These figures are indicative ranges, not guaranteed salaries.

Want to build Data Analytics skills with AI?

If you're looking for a structured way to build skills across SQL, Python, data analytics, AI-assisted analysis and modern AI workflows, the Certification Program in Data Analytics with AI and Gen AI by E&ICT Academy, IIT Roorkee is designed around these areas.

The 6-month online program combines data foundations with SQL, Python, AI-assisted analytics, Generative AI and multi-agent systems, along with hands-on projects and a portfolio-ready capstone. The program requires around 8–10 hours per week and is open to pursuing college students and graduates.

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Skills needed to ecome a Data Analyst in 2026

Frequently Asked Questions

What does a data analyst do?

A data analyst collects, cleans, analyses and visualises data to answer business questions and support better decision-making. Their work can include SQL queries, data cleaning, dashboards, statistical analysis and presenting insights to stakeholders.

What skills do you need to become a data analyst?

Important skills include SQL, Excel or Google Sheets, statistics, data visualisation, business understanding and communication. Python and AI-assisted analytics are increasingly useful for automation and deeper analysis.

What tools do data analysts use in 2026?

Common tools include SQL, Excel or Google Sheets, Python, Power BI, Tableau and AI-assisted analytics tools. The exact stack varies by company and role. Recent job postings show combinations of SQL, Python and BI tools such as Power BI and Tableau.

How much does a data analyst earn in India?

Salary varies by experience, location, company and skills. Current 2026 data shows an average base salary of about ₹6.57 lakh per year on Indeed, while experienced analysts can earn significantly more.

Is Python necessary for a data analyst?

Not every data analyst role requires Python, but it is increasingly valuable for data cleaning, automation, analysis and working with larger or more complex datasets. SQL and a BI tool remain particularly important for many analyst roles.

Do data analysts need AI skills in 2026?

AI skills are increasingly useful, especially for automating repetitive tasks, generating draft SQL, exploring datasets and summarising findings. However, analysts still need to validate AI outputs and understand the underlying data and business context.

What is the difference between a data analyst and a data scientist?

Data analysts generally focus on analysing existing data, reporting insights and supporting business decisions. Data scientists typically work on more advanced statistical modelling, machine learning and predictive problems, although responsibilities can overlap depending on the organisation.