What does a data analyst actually do?

Stakeholders want data analysts to answer their questions now, please—or, even better, yesterday. Marketing needs a funnel breakdown by Friday, and leadership wants a dashboard that basically runs itself. The ad-hoc requests don’t stop—and neither does the work of scoping, cleaning, analyzing, and explaining the data that sits behind each one.

Data analysts sit at the center of how businesses make decisions. And with AI taking on more of the tedious work, the role is becoming less about pulling data and more about knowing what to do with it.

This guide breaks down what data analysts do, which skills matter most, and what the essential analyst toolkit looks like—so you can understand the role or level up in it. 

Key insights

• A data analyst plays a critical role because their insights drive business decisions

• Technical skills like SQL and data visualization matter, but so do softer skills like critical thinking and communication with stakeholders

• The data analyst’s toolkit spans 3 categories—analytics, visualization, and warehouses and pipelines. How well the tools integrate makes a bigger difference in their workflow than any individual feature.

What is a data analyst? 

A data analyst turns raw data into decisions. They collect data, make sense of it, and translate it into actionable answers or recommendations.

The role interacts with almost every function in an organization. One week, an analyst might help the marketing team understand why a campaign’s conversions tanked. The next, they might show the product team where users drop off—or build a self-service dashboard for leadership. 

Technical skills matter. So does business judgment. The best analysts know not just how to work with data, but also which questions are worth asking about it. 

Data analyst vs. data scientist vs. data engineer

At smaller companies, one person might cover all data responsibilities. But larger companies usually have three distinct data roles that work closely together:

What does a data analyst do each day? 

A data analyst’s workflow isn’t linear. Most analysts juggle recurring tasks, spur-of-the-moment requests, and long-term projects all at once. But most of what they do falls into a handful of core tasks—and each demands a different set of skills.

1. Scope, find, and explore data 

Before digging into the data, a good analyst digs into the question being asked. What decision will this inform? What does a ‘drop in conversions’ actually mean to the person asking—and over what time period? Getting the context wrong means coming up with the right answer to the wrong question.

Once the scope is clear, they find the data. That means they identify the right sources and analyze them for gaps and inconsistencies. Depending on the project, they might look at

• Behavioral data that captures how users interact with their website, using an AI-powered customer experience intelligence platform like Contentsquare

• Customer relationship management (CRM) data, using a platform like Salesforce that tracks customer information and transactions 

• Product data that tracks multi-session, cross-device user journeys, using Contentsquare’s product analytics tools

  

  Source the product analytics data you need from Contentsquare

🧰 Skills and knowledge needed:  

• Critical thinking to examine a request before jumping to the data

• Familiarity with SQL to pull the right data sets from databases

• An understanding of the business context to assist with problem-solving

• Data management skills to collect and organize your data

2. Build the analysis

With the scope defined and the data in hand, it’s time to build the analyses that answer the question. 

That might mean: 

• Isolating specific customer segments—like new visitors vs. returning customers, or mobile vs. desktop users—to understand how they behave differently

• Determining the custom metrics that matter to the business (not just what’s easy to track)

• Comparing how the behavior of different cohorts changes over time

• Spotting new trends in the data and figuring out why those shifts happen

This is the part of the job that requires the most technical skill—and the most judgment about which approach will work best. 

🧰 Skills and knowledge needed: 

• Strong programming skills in R or Python to build and manipulate the data sets

• An understanding of statistics to know which analytical method to use and how to interpret the results

• Analytical skills to determine the right way to arrange the data and spot patterns

💡Pro tip: make your analysis easier with Contentsquare. Create custom segments and metrics that everyone on your team can access and use. Less time spent reinventing the wheel means more time for actual analysis. 

Create a list of custom segments with Contentsquare

3. Export data and go deeper

A data analyst can’t answer every question within a single platform. Sometimes their analysis needs a bit more horsepower:

• A more complex model than the analytical platform can run

• A longer historical data set than it stores

• A connection across multiple sources that only makes sense inside a data warehouse

For example, an analyst might combine behavioral data with CRM data in Snowflake to understand which customer segment dropped off the most. 

🧰 Skills and knowledge needed: 

• Data pipeline and warehouse knowledge to understand how data moves between systems and work within platforms like Snowflake or BigQuery

• Quantitative data analysis skills, so you can apply more advanced analytical methods once you’ve exported your data

💡 Pro tip: get your data out as cleanly and efficiently as possible with a direct, automatic integration. While manual CSV exports work in a pinch, they don’t scale. They’re slow—and a version control headache waiting to happen. 

The better option is to push structured data straight to your warehouse with a tool like Contentsquare’s Data Connect. Automatically send behavioral, session, and performance data directly to Snowflake, BigQuery, Redshift, or Databricks—and combine it with CRM data, transaction history, and other sources to get the full picture. Less data wrangling = more time for data analysis. 

4. Build dashboards and reports

Once the analysis is done, an analyst has to create a useful, accessible spot for it to live. A data analyst might create

• A one-off report for deeper dives into a specific question or time period

• A recurring dashboard, offering the most up-to-date views of essential metrics

A well-built dashboard doesn’t just display data. It answers questions that stakeholders want to know the answers to—before they even think to ask them. 

A data analyst spends much of their time creating, reviewing, and explaining dashboards, like these, in Contentsquare’s experience intelligence platform

🧰 Skills and knowledge needed:

• Data visualization—knowing which chart type communicates your findings most clearly

• Tool fluency to get the most out of the platform you’re working in. (If that platform is Contentsquare, Sense handles a lot of the heavy lifting by letting you ask questions in plain language instead of setting up everything manually.)

5. Communicate findings

Analysis that doesn’t land is analysis that doesn’t matter. 

Data analysts need to translate what the data shows into something a stakeholder can understand and act on. The format depends on the audience:

• A structured slideshow for a leadership team

• A concise Slack summary for a product manager

• A live dashboard walkthrough with time for questions 

The format changes. The challenge doesn’t—making complex findings feel clear and relevant. 

🧰 Skills and knowledge needed:

• Storytelling to frame your findings in a narrative that makes the ‘so what’ clear

• Communication skills to adapt a presentation depending on the audience and situation

• Stakeholder management to handle questions and pushback with confidence

3 types of tools a data analyst needs 

Data analysts rarely work from a single tool. The job spans too many different tasks for one platform to cover everything. Most analysts work from the best data analyst tools across 3 categories:

1. Analytics platforms

Data analysts spend most of their time in an analytics platform—makes sense, right? They’re the tools that let you explore data, build analyses, and surface insights. 

The best analytics platforms go beyond just storing data. Look for: 

• Custom analysis and segmentation: build cohort comparisons, trend analyses, and reusable metrics 

• AI-powered querying: ask questions in plain language

• Shareable reporting: share templates and dashboards with your team members, so they feel empowered to dig into the data, too

Contentsquare’s 360 experience intelligence covers all three. Contentsquare Charts is where you do your core analytical work. AI assistant Sense Chat lets you ask questions in natural language and get answers back instantly. And Sense Analyst handles more complex questions—give it a high-level prompt, and it builds an analysis plan, runs it, and delivers ready-to-use recommendations. 

As Christian Fiorelli, SVP of Global Ecommerce & CRM at Alexander Wang, puts it: “Team members who might’ve once relied on a data analyst or waited for a report can now self-serve insights in real time. It’s empowered everyone to be more curious and confident with data. Digital experience really is a team sport, and Sense is making sure everyone can play.”

2. Business intelligence (BI) and visualization tools

BI and visualization tools turn findings into dashboards and reports that stakeholders can understand and navigate on their own.

Some popular options include: 

• Metabase: lightweight open source analytics for teams that value simplicity

• Power BI: convenient business intelligence for organizations already running on the Microsoft stack

• Contentsquare: perfect for visualizations like heatmaps, funnel analysis, key performance indicator (KPI) graphs, and custom dashboards—with AI for ease of analyzing those visualizations

Visualize data such as funnel conversions, and let Contentsquare’s Sense handle the analysis to save time

3. Data warehouses and pipelines

When analysis needs to span multiple sources or handle large volumes of data, the warehouse is where that work happens. This category also includes the pipelines that get the data there in the first place. 

Key tools in this space include: 

• Warehouses like Snowflake or BigQuery

• Platforms for cleaning and modeling data, like dbt

• Automated data integration platforms like Fivetran

And if you’re using Contentsquare, you can link the Data Connect tool to your warehouse once and automatically export and sync your data. 

“Data Connect turns complex data from multiple sources into unified, actionable insights that provide direction for the overall development of our digital platforms. We no longer need to take a shot in the dark and can make smarter, more informed decisions."

Kevin Jakubec

Web Analytics & CRO Insights Manager

Less setup, more strategy: where the role is heading

The data analyst role has always been about driving decision-making through data. What’s changing is how much of the mechanical work—from repetitive queries to manual exports—that AI can handle. And contrary to the headlines, that’s not making analysts redundant. It’s making them more valuable.

Because when AI handles the grunt work, analysts can focus on the part of the job that actually requires a human—interpreting the meaning behind the numbers and making sure the insights lead somewhere. 

Frequently asked questions about data analysts