What do data analysts do
Business Intelligence
May 27, 2025
Explore the vital role of data analysts in transforming raw data into actionable insights that drive business growth and enhance decision-making.
Data analysts turn raw data into insights that help businesses make informed decisions. They collect, clean, and analyze data to identify trends, solve problems, and improve performance. Here's a quick overview of what they do:
Data Collection and Cleaning: Gather data from multiple sources, fix errors, and organize it for analysis.
Data Analysis: Use statistical tools and techniques to find patterns, correlations, and insights.
Data Visualization: Create dashboards and reports to present findings in an easy-to-understand format.
Business Impact: Help companies optimize operations, understand customer behavior, and make data-driven decisions.
Data analysts use tools like SQL, Python, and Excel, alongside visualization software like Tableau, to simplify complex datasets. Their work drives better decision-making, boosts revenue, and reduces costs across industries like retail, healthcare, and eCommerce.
Want to learn more? The article dives deeper into their daily tasks, required skills, and how they help businesses grow in a data-driven world.
What Does a Data Analyst Actually Do? (2024)
Main Job Duties of Data Analysts
Data analysts play a crucial role in turning raw data into valuable insights that drive business decisions. Their work revolves around three main areas, each requiring specific tools and expertise to ensure data is effectively used to achieve organizational goals.
Data Collection and Cleaning
Every successful data project starts with gathering and preparing the data. In fact, data analysts dedicate a significant portion of their time - around 60–80% - to cleaning and organizing data [3].
Data collection involves pulling information from various sources like databases, CRM systems, web analytics platforms, and surveys. However, this step often comes with challenges. Data from different sources might have inconsistent formats, duplicate entries, or mismatched labels, making it hard to work with.
The cleaning process addresses these issues by removing irrelevant entries, fixing inconsistencies, standardizing formats, and filling in gaps where data is missing. Missing values are either removed or estimated based on existing patterns. Data transformation is another key step, where information is converted into compatible formats to ensure smooth integration across systems.
To maintain high standards, analysts evaluate data quality based on five key factors: validity, accuracy, completeness, consistency, and uniformity [2]. They often rely on standardized templates to streamline the cleaning process and ensure reliable results.
Data Analysis and Interpretation
After the data is cleaned and structured, analysts dive into the heart of their work: uncovering insights. Using statistical and mathematical techniques, they transform raw numbers into actionable knowledge.
Depending on the type of data and the questions being asked, analysts use different methods like descriptive, diagnostic, predictive, or prescriptive analysis [4]. Quantitative analysis deals with numerical data, while qualitative analysis focuses on unstructured information, such as customer feedback or open-ended survey responses [5]. Analysts might write SQL queries to extract specific data sets, use statistical tools to find correlations, or employ advanced platforms to process large datasets efficiently.
For example, retail businesses analyze sales trends to identify seasonal patterns, segment customers to create targeted marketing campaigns, and evaluate product performance to highlight top sellers. Meanwhile, healthcare organizations use data to uncover risk factors and assess the effectiveness of treatments [6].
"Data interpretation enables informed decision-making by providing evidence-based insights." - Hady ElHady, Author at 10XSheets [6]
Once insights are identified, analysts translate complex findings into clear, understandable formats that decision-makers can use.
Data Visualization and Reporting
The final step in a data analyst's workflow is presenting their findings in a way that resonates with non-technical stakeholders. This is where data visualization comes in, turning complex datasets into clear, impactful stories.
Using tools like Querio, analysts create interactive dashboards that allow users - even those without technical expertise - to explore data easily. These dashboards are dynamic, updating automatically as new data becomes available, ensuring stakeholders always have access to the latest insights.
To create effective visualizations, analysts consider their audience's level of expertise and choose the most appropriate chart types. Line charts are great for showing trends over time, bar charts work well for comparing categories, and pie charts are useful for illustrating proportions [7][8].
Best practices for visualization include keeping designs simple, using consistent color schemes, adding interactive features like filters, and providing clear labels and reference points [9][10]. Analysts also ensure dashboards are accessible, user-friendly, and responsive across different devices.
The ultimate goal is to present data in a way that supports decision-making. By turning raw data into clear, evidence-based insights, analysts empower organizations to act confidently and strategically. Ongoing feedback from users helps refine these visualizations, making them even more effective over time.
Required Tools and Skills for Data Analysts
To thrive as a data analyst, you need a mix of technical know-how, modern tools, and strong interpersonal skills. With the U.S. Bureau of Labor Statistics predicting a 23% growth in the data analyst job market by 2032, these skills are more important than ever for staying competitive [11].
Technical Skills You Need
A solid foundation in SQL and statistical programming languages like Python or R is essential. As Scott W. O'Connor puts it, "SQL, or Structured Query Language, is the industry-standard database language essential for all data analysts" [11]. While R is tailored specifically for analytics, Python stands out as a versatile language capable of handling complex tasks that go beyond what tools like Excel can manage [11][12].
Data visualization is another must-have skill. Analysts need to turn complicated datasets into clear, visually compelling stories that resonate with stakeholders [11][12]. Alongside this, expertise in data cleaning ensures the accuracy and reliability of analyses [13].
As businesses increasingly migrate to the cloud, familiarity with data governance, security protocols, and cloud computing environments is becoming more critical [13].
Main Tools for Data Analysis
Once equipped with technical skills, analysts rely on specialized tools to make sense of massive amounts of data. With 328.77 million terabytes of data generated daily, the right tools can turn overwhelming information into actionable insights [15].
Querio, for example, uses AI to simplify data queries. Instead of writing complex SQL commands, analysts can ask questions in natural language and get instant responses through dynamic dashboards [15]. This tool eliminates the technical barrier, making data analysis more accessible and efficient.
Traditional tools like Python and Microsoft Excel remain indispensable. Python's flexibility makes it a go-to for data analysis and scientific computing, while Excel's user-friendly interface ensures it remains a staple for many professionals [15]. Meanwhile, AI tools are transforming the field by automating repetitive tasks, allowing analysts to focus on strategic insights [15][16].
Modern AI solutions take things further by enabling conversational interactions with data. As Kishan Chetan explains, "AI agents are powered by LLMs. That means that the whole interaction is far more conversational, and its reasoning goes well beyond a rule-based system" [14]. These tools also enhance analysis through predictive and prescriptive analytics, anomaly detection, natural language processing, and more [16].
Communication and Teamwork Skills
Technical skills are only part of the equation. The ability to communicate findings effectively is what bridges the gap between analysis and action. Analysts need to explain data models and insights in ways that make sense to all stakeholders [17][18]. Collaborative work environments further help analysts refine their skills and navigate challenges with confidence [17].
Data storytelling is especially important. For example, when presenting sales data, a skilled analyst doesn’t just share numbers - they dig into the reasons behind trends, craft a narrative, and offer actionable recommendations [17].
"Data Analysis is worthless without a story or a point of view. Data's value is directly tied to the story it tells. Creating this type of value enables executives to make meaningful decisions. But data 'analysis' presented without a story or a point of view is lazy." - Rebecca Mark, Advisor to Regulated Startups [19]
To sharpen communication skills, analysts can explore data storytelling frameworks, take courses in data visualization and business writing, and practice presenting their findings to peers for feedback [17]. As Sabeen Ditta, Business & Marketing Operations Specialist, notes: "The objective is the starting and focal point throughout a project for me. Once I know what I am aiming for, I simplify this to overarching questions that can evolve into strategic actions, and be asked at each stage moving towards a solution" [19].
How Data Analysts Help Business Growth
Data analysts play a pivotal role in turning raw data into insights that drive growth, streamline operations, and enhance competitiveness. By diving into key analytical practices, we can see how their work directly influences business success.
Supporting Decision-Making with Data
Data analysts do more than just crunch numbers - they shape strategic decisions. In fact, 79% of businesses view data analytics as essential to their future success, and 62% credit it with improving their decision-making [25].
By identifying trends, understanding customer behavior, and spotting potential bottlenecks, analysts provide leadership with the tools to act decisively. Predictive analytics lets companies anticipate changes, enabling them to allocate resources wisely and seize emerging opportunities.
Take Elle Neal, Data Scientist at BPA Quality, for example. By transitioning from manual Excel processes to Python for data tasks, she saved her organization 48 hours monthly and boosted leads by 13X [24]. This demonstrates how data-driven strategies can simultaneously cut costs and increase revenue.
In healthcare, David Howell, Joint Director for Strategic Insight and Analytics at Surrey Heartlands Health and Care Partnership, developed a machine learning model capable of predicting 70-80% of emergency readmissions [24]. This kind of precision helps healthcare providers allocate resources effectively and improve patient care.
Improving Business Operations
Once data is cleaned and organized, analysts use it to uncover inefficiencies and pinpoint opportunities to improve operations. For instance, redesigning processes and integrating AI can cut costs by up to 25%, while AI-enabled predictions can reduce supply chain errors by 30–50% [27][29].
The telecommunications industry offers a great example. Using AI analytics, companies can answer critical questions like, "Are customers experiencing roaming issues?" or "Is the network stable?" These insights help reduce churn and increase ARPU (Average Revenue Per User) [29].
In eCommerce, analysts focus on optimizing conversion rates by identifying early-stage issues that might impact performance. This proactive approach prevents minor problems from escalating into costly setbacks [29]. It’s no wonder that 53% of businesses report revenue increases and 42% report cost reductions thanks to data analytics [25].
Modern tools like Querio make this process even faster, allowing analysts to ask operational questions in plain language and receive instant insights via dynamic dashboards. This quick turnaround means businesses can address problems and implement solutions without delay.
Understanding Customer Behavior
Customer insights are the foundation for personalized experiences, targeted marketing, and informed product development - all essential for growth. Analysts dig deep to uncover what customers do, why they do it, and what they’re likely to do next.
"Data analytics is integral to business because it allows leadership to create evidence-based strategy, understand customers to better target marketing initiatives, and increase overall productivity." - Penn LPS Online [23]
By analyzing trends and shopping patterns, analysts help businesses fine-tune their marketing efforts for maximum impact [23]. For example, Amazon uses data to power its recommendation engine, suggesting products based on browsing and purchase history, while Netflix leverages analytics to produce shows tailored to viewer preferences [1].
Analysts also identify untapped markets and potential growth areas by examining customer data and competitor behavior [20][22]. This helps businesses differentiate themselves and explore new opportunities.
With 95% of businesses recognizing the need to manage unstructured data [22], the importance of robust customer analysis is clear. Real-time tracking of customer feedback and product performance enables businesses to adapt quickly to shifting demands and maintain a competitive edge [21].
"I believe data analytics is the backbone of successful digital marketing. It provides the insights needed to understand your audience, personalize experiences, optimize campaigns, enhance content, improve retention, predict trends, and integrate efforts across channels." - Deepak Bansal, CEO & Founder, Clearpath Technology Pvt Ltd [26]
Beyond marketing, data analysis transforms product development and customer service. With 70% of analysts reporting that AI and automation enhance their effectiveness [28], businesses can process vast amounts of customer data and generate actionable insights faster than ever before.
Conclusion: The Growing Need for Data Analysts
The world of business is undergoing a major transformation, and data analysts are at the heart of it. As companies increasingly rely on data to make smarter decisions, the ability to turn raw numbers into actionable insights has become a critical advantage. Across industries, the demand for skilled data analysts is skyrocketing as organizations recognize the importance of data-driven strategies.
Key Takeaways
Data analysts play a vital role in connecting raw data to informed decision-making. Their work - ranging from data collection and cleaning to analysis, interpretation, and visualization - has a direct impact on a company’s ability to succeed. For instance, data-driven companies are 23 times more likely to excel at customer acquisition and 19 times more likely to sustain profitability [33].
Success in this field requires a mix of technical and soft skills. Proficiency in tools like SQL, Python, and Tableau is essential, but so is the ability to communicate findings clearly and align them with business goals. Analysts who understand the broader business context can ensure their insights lead to meaningful results.
Real-world examples highlight the power of effective data analysis. UPS, for example, used its ORION system to cut delivery routes by over 10 million miles annually, saving approximately $400 million in fuel costs each year [34]. Meanwhile, Zara’s use of advanced analytics allowed the company to adjust production quickly based on customer preferences, contributing to its €20 billion in sales in 2022 [34].
The financial rewards for data analysts reflect their growing importance. Salaries have risen significantly, with the average now reaching $111,000 [30]. Additionally, 87% of analysts report feeling more strategically valuable than ever before [30]. These figures underscore the increasing value of this profession in today’s data-centric world.
Future of Data Analysis
The field of data analysis is evolving rapidly, driven by advancements in technology and the growing needs of businesses. According to the US Bureau of Labor Statistics, the job market for data analysts is expected to grow by 23% by 2032 [30], far outpacing the average for other professions. This demand spans industries like healthcare, retail, and beyond, as companies seek to leverage the vast amounts of data at their disposal.
Artificial intelligence is also reshaping the way analysts work. Seventy percent of professionals in the field say AI tools make their jobs more efficient [30]. Platforms like Querio, which allow users to ask natural language questions and receive instant insights, are revolutionizing workflows by cutting down on time spent on repetitive tasks. Without these tools, analysts can lose the equivalent of 60 workdays annually to routine queries [32].
With the global data sphere projected to surpass 175 zettabytes by 2025 [31], the need for experts who can navigate this complexity and deliver actionable insights has never been greater. For companies aiming to remain competitive, investing in skilled data analysts and cutting-edge analytics platforms isn’t just a smart move - it’s a necessity for thriving in today’s data-driven economy.
FAQs
What key technical skills are essential for a data analyst to succeed?
To thrive as a data analyst, building a solid base of technical skills is essential. Here are some of the key areas to focus on:
SQL: A must-have for querying and managing databases effectively.
Microsoft Excel: Ideal for organizing, analyzing, and manipulating data.
Python or R: These programming languages are invaluable for statistical analysis and automating workflows.
Data Visualization Tools: Platforms like Tableau or Looker help transform data into clear, actionable visuals.
ETL Processes: Understanding how to Extract, Transform, and Load data ensures you can prepare and manage datasets efficiently.
Mastering these tools and techniques equips analysts to uncover insights, support business strategies, and make confident, data-backed decisions.
What is the role of a data analyst in improving business decisions?
The Role of Data Analysts in Business Success
Data analysts are essential in steering businesses toward smarter, data-driven decisions. By gathering, organizing, and interpreting vast amounts of data, they uncover patterns and insights that shape strategies and streamline operations. Their expertise enables companies to predict market trends, better understand customer preferences, and reduce potential risks.
Equipped with tools like SQL, Tableau, and AI-driven analytics platforms, data analysts transform complex datasets into easy-to-understand visuals and dashboards. These tools help stakeholders quickly grasp the most important takeaways, enabling faster and more informed decision-making. In essence, data analysts play a key role in helping businesses boost performance, enhance efficiency, and remain competitive in today’s fast-paced world.
How has data analysis helped businesses grow or save money in real-world examples?
How Data Analysis Transforms Businesses
Data analysis has become a powerful tool for businesses, helping them grow while cutting costs through smarter, more informed decision-making. Take MGM Resorts International, for instance. By diving into customer data, they improved the overall guest experience and refined their pricing strategies. This allowed them to target promotions more effectively and manage inventory with precision, ultimately boosting their annual revenue by over $100 million.
Starbucks offers another great example. By examining purchasing patterns, the company optimized its supply chain, cutting down on waste and improving inventory management. These changes not only saved money but also increased profitability. These cases show how data-driven approaches can lead to impressive outcomes for organizations.