What does a data analyst do

A data analyst turns raw data into clear insights that help businesses make better decisions. They collect, clean, analyze, and visualize data to uncover patterns and trends. This role is essential in today’s data-driven world, where companies rely on analytics to grow and solve problems.

Key Responsibilities:

• Data Collection: Gather data from surveys, databases, APIs, or other sources.

• Data Cleaning: Fix errors, remove duplicates, and prepare data for analysis.

• Analysis: Spot trends, run statistical tests, and interpret results.

• Visualization: Build dashboards, charts, and reports to present findings.

Tools They Use:

• SQL: Query and manage databases.

• Python/R: Analyze and automate data tasks.

• Tableau/Power BI: Create visual dashboards.

• AI Tools: Automate analysis and predict trends.

Skills Needed:

• Technical skills like SQL and Python.

• Problem-solving to address business challenges.

• Communication to explain findings clearly.

Why It Matters: Data analysts help businesses save time, cut costs, and make smarter decisions. For example, companies like Starbucks and PepsiCo use data analysis to improve strategies and boost growth.

Want to learn more? Keep reading to dive into their daily tasks, tools, and real-world examples.

What Does a Data Analyst Actually Do?

Daily Work of a Data Analyst

A data analyst's role revolves around turning raw data into meaningful insights that drive business decisions. This involves a structured process of collecting, preparing, and analyzing data. Let’s break down what this looks like in practice.

Data Collection Methods

Data collection is the foundation of a data analyst’s work. Analysts gather datasets from various sources, ensuring they are both reliable and comprehensive.

For instance, in March 2023, Spotify achieved a significant reduction in its email bounce rate by integrating an Email Verification API. This improvement enhanced email deliverability and boosted revenue (Source: Mailchimp Case Studies, 2023).

Data Preparation Steps

Before diving into analysis, data must be cleaned and structured - a process that takes up a significant portion of an analyst's time. Studies reveal that analysts and data scientists dedicate about 80% of their time to this phase [2].

1. Data Cleaning

   • Removing duplicate entries

   • Correcting formatting issues

   • Addressing missing values

   • Identifying and handling outliers

   • Resolving corrupted data

2. Data Transformation

   • Standardizing formats for consistency

   • Normalizing values to ensure comparability

   • Creating uniform categories

   • Structuring data to align with analysis needs

"When using data, most people agree that your insights and analysis are only as good as the data you are using. Essentially, garbage data in is garbage analysis out." - Tableau [3]

Data Analysis and Visualization

Once the data is prepared, the focus shifts to uncovering patterns and presenting findings in a way that stakeholders can easily understand. Visual content is particularly effective - our brains process visuals 60,000 times faster than plain text and numbers [4].

Key activities during this phase include:

• Spotting trends and conducting statistical analyses

• Building interactive dashboards

• Producing reports tailored for decision-makers

For example, the Texas Rangers' sales and marketing teams used time series analysis to capitalize on seasonal trends. This approach improved their forecasting and enabled more informed decisions [5].

To visualize data, analysts rely on tools that allow them to create:

• Interactive dashboards

• Real-time data streams

• Customized reports

• Predictive models

These visualizations and insights equip businesses to make decisions backed by solid data, ensuring more accurate and impactful outcomes.

Tools Data Analysts Use

Data analysts rely on a variety of tools to transform raw data into actionable insights. These tools are essential for handling the complexities of their daily tasks and ensuring they can deliver accurate, meaningful results.

Database Query Tools

At the heart of data analysis is SQL (Structured Query Language), a critical tool for extracting and manipulating data from relational databases [6]. With SQL, analysts can:

• Filter through massive datasets

• Aggregate data to uncover trends

• Combine multiple data sources for deeper insights

• Transform raw data into usable formats

As one expert puts it:

"SQL (Structured Query Language) is an indispensable tool for data analysts, providing a powerful way to query and manipulate data stored in relational databases." [6]

For those looking to simplify SQL's complexity, tools like Querio allow users to write natural language queries while retaining SQL's powerful functionality, making database interactions more intuitive.

Data Display Tools

Data visualization tools play a crucial role in turning complex datasets into clear, digestible visuals. These tools help analysts communicate their findings effectively to both technical and non-technical audiences. Some key visualization features include:

To maximize impact, analysts should ensure visualizations align with the audience's technical expertise, use clear labels and titles, and provide proper context [7].

AI Tools for Data Analysis

AI-powered tools are revolutionizing the field of data analysis, helping organizations save up to 54% in costs after implementation [11]. These tools bring automation and intelligence to the forefront, making data analysis faster and more accessible.

Ash Sharma, AI Product Leader at Amazon, highlights the potential of AI:

"AI can help make sense of the large volume of analytics that are produced and never get in front of stakeholders. Natural language and conversational interfaces are a great value add for non-technical audiences to query an AI system - 'which customer segment had the largest repeat usage last week?' - to quickly get insights and disseminate them throughout the organization." [9]

Querio, for example, leverages AI to provide:

• Natural language querying for ease of use

• Automated detection of patterns in data

• Predictive analytics to forecast trends

• Real-time insights for quicker decision-making

While AI tools can drastically reduce analysis time - sometimes by as much as 50% [8] - it's essential for organizations to maintain high data quality and ensure proper user training to fully capitalize on these tools [10].

Required Data Analyst Skills

Data analysts must have a mix of technical know-how, analytical thinking, and strong communication abilities. With the job market for data analysts expected to grow by 23% by 2032 [12], these skills are more essential than ever. They enable professionals to effectively use the tools and methods discussed earlier in this article.

Programming and Software Skills

Technical expertise forms the backbone of a data analyst's toolkit. As Scott W. O'Connor points out:

"SQL, or Structured Query Language, is the ubiquitous industry-standard database language and is possibly the most important analytical skill for data analysts to have." [12]

In addition to SQL, modern data analysts benefit from mastering other tools and platforms, which can be grouped into key categories:

These technical skills directly enhance the processes of gathering, preparing, and presenting data.

Problem-Solving Methods

Problem-solving is at the heart of a data analyst's role. Kevin Flerlage, a Data Visualization Consultant at Moxy Analytics, emphasizes:

"The key is to actually identify the problem first...the key is to listen, understand, and determine what the real problem is regardless of what the stakeholder tells us." [14]

This approach is critical, as 63% of recruiters prioritize problem-solving abilities when evaluating data professionals [13]. Great problem solvers dig deeper than surface-level data, uncovering root causes and addressing complex business challenges with precision.

Data Communication Skills

The ability to tell a compelling story with data can have a measurable impact. For instance, effective data storytelling helped TopNotch Learning improve customer retention by 15% in Q3 2024 [15]. Communication bridges the gap between complex analysis and actionable insights, building on the visualization and analytical tools mentioned earlier.

Consider these statistics:

• 65% of people are visual learners [15]

• 95% of businesses must manage and present unstructured data [15]

Edward R. Tufte sums up the importance of clear communication:

"Above all else, show the data." [15]

To excel in data communication, analysts should focus on:

• Designing clear and impactful visualizations that emphasize key findings

• Building narratives that connect data insights to business goals

• Strengthening written and verbal communication skills

• Actively listening to understand stakeholder needs and expectations

These skills not only make data analysts more effective but also ensure their work drives meaningful business outcomes.

Conclusion: The Business Value of Data Analysts

How Data Analysts Help Companies Grow

Data analysts play a key role in transforming raw data into insights that drive growth. With businesses managing an average of 163 terabytes of data [1], the demand for skilled analysts has skyrocketed. Their contributions are felt across industries, delivering measurable success.

One compelling example comes from Microsoft's Workplace Analytics team. After a 2015 office relocation, they analyzed employee calendar metadata. The result? A 46% reduction in meeting travel time, which translated to saving 100 hours weekly and about $520,000 annually in productivity gains [18].

PepsiCo also showcased the power of data analysis with its Pep Worx platform. Analysts pinpointed 24 million households likely to buy Quaker Overnight Oats, a strategy that drove 80% of the product’s sales growth in its first year [18].

Harvard Business School Professor Jan Hammond highlights the growing importance of data-driven decision-making:

"The ability to bring data-driven insights into decision-making is extremely powerful - all the more so given all the companies that can't hire enough people who have these capabilities. It's the way the world is going." [18]

Data analysts create value across multiple business areas, as shown below:

These figures highlight the broad and impactful role data analysts play in driving business success.

Using Querio for Data Analysis

Modern tools like Querio amplify the value that data analysts bring to the table. By combining AI-powered features with user-friendly interfaces, Querio simplifies the analysis process [19]. Analysts using Querio can generate reports up to 20 times faster, saving approximately 8 hours of work each week [19][20].

Querio offers features designed to streamline workflows, including:

• Direct database connections

• Natural language querying

• Dynamic dashboards

• Collaborative tools

With these tools, organizations can focus on three pillars of effective data analysis: maintaining high-quality data, ensuring accessibility, and promoting data literacy across teams [16]. By reducing technical hurdles, Querio allows analysts to concentrate on delivering actionable insights that drive growth.

FAQs

How do companies use data analysis to improve their business?

Data analysis plays a key role in helping companies make more informed decisions and achieve stronger outcomes. Take Netflix, for instance. They use data to tailor content recommendations to individual subscribers, keeping viewers engaged and more likely to stick around. Beyond that, by studying viewing patterns, Netflix identifies trends that guide the creation of hit shows, ensuring they resonate with their audience and drive up viewership.

Coca-Cola is another great example. They rely on data to spot consumer trends and fine-tune their marketing efforts. In one instance, analytics helped them adjust their advertising strategies, which not only improved customer retention but also boosted sales. These cases highlight how leveraging data can spark new ideas and help businesses stay ahead of the competition.

How do data analysts choose the best methods for collecting data on a project?

How Do Data Analysts Choose Data Collection Methods?

Data analysts begin by pinpointing the project's objectives and determining the type of data they need. This involves understanding the target audience, the nature of the data, and how precise the information must be. Popular collection methods include surveys, interviews, observational studies, and reviewing existing transactional data.

Practical factors like time constraints, budget limits, and available resources also play a big role in this decision-making process. By carefully balancing these considerations, analysts can select the most effective method to collect reliable data that fuels meaningful insights and informed decisions.

How do AI tools enhance data analysis and boost efficiency?

AI tools are changing the way data analysis is done, making it quicker, more precise, and easier to scale. They take over repetitive tasks like cleaning and processing data, allowing analysts to concentrate on drawing strategic insights and tackling complex problems. For instance, AI can swiftly process massive datasets and deliver real-time insights, enabling businesses to make timely, well-informed decisions.

By minimizing human error, AI ensures data interpretations are more accurate and free from bias. This not only boosts efficiency but also helps companies cut costs and stay ahead in the game by using smarter, data-driven strategies.

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