What does an analyst data do
Business Intelligence
May 23, 2025
Learn how data analysts convert raw data into valuable insights, driving better business decisions across various industries.
Data analysts turn raw data into useful insights that help businesses make better decisions. They find patterns, solve problems, and guide strategies using data. Here’s a quick breakdown of their work:
Collect and Combine Data: Gather information from various sources like databases or tools and merge it for analysis.
Clean and Prepare Data: Fix errors, standardize formats, and ensure data quality before analysis.
Analyze and Report: Use statistical methods to uncover trends, create visualizations, and present findings to stakeholders.
Key Skills:
Business Understanding: Linking data insights to business goals.
Communication: Explaining complex findings in simple terms.
Industries Benefiting from Data Analysts:
Healthcare: Improve patient care.
Retail: Predict sales trends.
Finance: Manage risks.
Supply Chain: Optimize logistics.
With the rise of AI tools, data analysts can now automate repetitive tasks, saving time and focusing on deeper insights. Their role is critical as businesses increasingly rely on data to grow.
What Does a Data Analyst Actually Do? (2024)
Main Tasks of Data Analysts
Data analysts play a vital role in turning raw data into insights that drive business decisions. Their work involves a series of interconnected tasks aimed at delivering meaningful and actionable results.
Getting and Combining Data
One of the first steps in a data analyst's workflow is gathering and merging data from various sources. This can include internal databases, CRM systems, and even external tools. Interestingly, companies today manage over 100 data tools on average, a sharp increase from just 16 tools in 2017 [3].
To make sense of this data, analysts rely on techniques like:
Data integration: For system-wide analysis.
Data blending: To provide flexible, comparative insights.
Data joining: To combine related datasets into a single, cohesive view.
Data Cleanup and Processing
Cleaning and preparing data is a time-intensive but essential part of the process. In fact, analysts typically spend 70–90% of their time on this step [1]. And for good reason - poor data quality costs U.S. businesses an average of $12.9 million every year [2].
The cleanup process involves several key steps:
Error Detection and Correction
Analysts identify and fix a wide range of issues, from simple typos to more complex structural problems.
Standardization
Data from different sources often comes in various formats. Analysts standardize elements like date formats, measurement units, and categorical variables to ensure consistency.
Quality Assurance
Once cleaned, the data undergoes validation to ensure it meets quality standards and aligns with business rules.
With clean and standardized data in hand, analysts can move on to uncovering actionable insights through statistical methods.
Analysis and Results Delivery
The final steps in a data analyst's process involve analyzing the prepared data, creating visualizations, and presenting findings in clear, concise reports. These insights are then shared with stakeholders to support decision-making.
For instance, an analysis by AltexSoft of airline sales data revealed a systematic pricing error where two tickets were being recorded as one. This discovery led to immediate corrections and improved sales accuracy [2].
The quality of these analyses can have a direct impact on business success. By identifying opportunities, solving problems, and guiding decisions with data-backed evidence, analysts enable organizations to operate more effectively and efficiently.
Key Skills for Data Analysts
To excel as a data analyst, you need a balanced mix of technical know-how, business understanding, and strong interpersonal skills. With the U.S. Bureau of Labor Statistics predicting a 23% increase in data analyst positions by 2032 [4], it's clear that demand for these professionals is on the rise. Let's dive into the essential skills that help data analysts turn raw data into actionable insights.
Technical Skills
A solid technical foundation is critical for tackling today’s data challenges. Research shows that companies using data insights outperform competitors by 85% in sales growth [9]. That’s a compelling reason to sharpen your technical abilities.
Here are some must-have technical skills:
SQL Expertise: SQL is the backbone of data analysis. It’s essential for querying databases, transforming data, and pulling out meaningful insights.
Programming Proficiency: Python is a go-to language for data manipulation, analysis, and automation. Its versatility makes it indispensable.
Data Visualization: Crafting clear, engaging visualizations is key to telling a story with data. In fact, by 2022, 20% of analytic applications incorporated machine learning to enhance their capabilities [5].
Business Knowledge
Technical skills alone won’t cut it. To make an impact, data analysts need to understand the business context behind the numbers.
"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" [7].
This highlights the importance of connecting data insights to real-world business strategies. When analysts grasp the bigger picture, they can help organizations make smarter, more informed decisions.
Team Skills
Collaboration is where technical expertise meets practical application. Data analysts often act as a bridge between technical teams and business stakeholders, ensuring everyone is on the same page.
"Clear communication enables me to convey complex information and resolve issues promptly" [6].
Critical teamwork skills include:
Cross-functional Communication: Collaborating with different departments to align on objectives and deliverables.
Presentation Skills: Translating technical findings into actionable recommendations that everyone can understand.
Problem-solving: Using both technical and business knowledge to tackle challenging issues and find effective solutions.
AI Tools for Data Analysis
With data expected to grow by 150% by 2025 [10], analysts face mounting complexity in managing and interpreting this flood of information. AI-powered tools are stepping in to simplify workflows and make sense of this data deluge.
AI-Powered Data Queries
AI is revolutionizing how analysts interact with data, thanks to natural language processing (NLP). Instead of writing complex database queries, analysts can now use conversational language to explore datasets with ease.
For instance, Querio's AI data agent allows analysts to:
Ask questions in plain English
Get instant suggestions for queries
Navigate data relationships seamlessly
Automatically generate precise SQL queries
This natural language approach saves time and ensures accuracy in data retrieval. By automating query writing, analysts can focus on real-time reporting and dynamic insights.
Live Dashboard Creation
AI-powered dashboards are changing the game for data visualization and reporting. These tools not only reduce operational costs - by as much as 54% [10] - but also make it easier to keep track of key metrics.
Here’s what these dashboards bring to the table:
Smart Visualization Suggestions: AI identifies patterns in the data and recommends the best chart types.
Real-Time Updates: Metrics and KPIs refresh automatically, keeping dashboards current.
Contextual Insights: AI explains trends and highlights anomalies, providing deeper understanding.
For example, Tableau AI uses generative AI to simplify data analysis and make insights accessible to more users [11]. Beyond creating visuals, AI also handles data preparation and transformation, taking much of the manual effort out of the process.
Automated Analysis Steps
AI is streamlining the more tedious aspects of data analysis, particularly during preparation, which can take up to 40% of a project’s time [12]. Tasks like cleaning, transforming, and integrating data are now automated, freeing up analysts for higher-value work.
These tools also enhance industry-specific workflows. In supply chain management, for example, AI-powered analysis has cut forecasting errors by 30% to 50% [13]. By automating repetitive tasks and improving accuracy, AI enables analysts to focus on strategic insights and decision-making, even with massive datasets.
Conclusion: How Data Analysts Help Businesses Grow
Data analysts play a key role in driving business growth by transforming raw data into actionable insights. Companies that embrace data-driven strategies are three times more effective in decision-making, often seeing revenue increases of 10-15% and notable market expansion [14][15]. The growing importance of this field is reflected in its median annual salary of $99,410 [8].
"Big data is already being used to improve operational efficiency, and the ability to make informed decisions based on the very latest up-to-the-moment information is rapidly becoming the mainstream norm." – Randy Bean, CEO and managing partner of consultancy firm NewVantage Partners [14]
The impact of data analysis is evident in the success stories of major companies. For example, Google's data-driven management training boosted manager favorability scores from 83% to 88%. Amazon's recommendation engine, powered by analytics, drives a staggering 35% of its consumer purchases. Similarly, Starbucks uses demographic and traffic data to strategically optimize store locations [14]. However, a surprising 68% of available data remains untapped by many organizations, revealing a significant opportunity for improvement with advanced AI tools [15].
AI-powered platforms like Querio are stepping in to address this gap, helping analysts save up to 8 hours per week on routine data tasks [16][17]. By streamlining workflows, these tools enable analysts to focus on high-impact activities that drive growth, such as:
Optimizing operations and reducing costs
Enhancing customer engagement with personalized strategies
Improving forecasting for better decision-making
Detecting threats and managing risks in real time
Maximizing marketing efficiency through data insights
The future of data analytics is incredibly promising. The global market is expected to reach $132.9 billion by 2026 [18]. Organizations that adopt data-driven strategies report productivity gains of up to 63%, while moving to advanced analytics can increase profitability by an impressive 81% [18]. These figures highlight how data analysts directly contribute to business success, offering strategic insights that shape smarter decisions and stronger outcomes.
FAQs
How do data analysts ensure their data is accurate and reliable?
Data analysts play a crucial role in maintaining the accuracy and reliability of information by adhering to a few essential practices. First, they focus on data cleaning - a process that involves eliminating duplicates, fixing errors, and standardizing formats to ensure consistency throughout the dataset. This step lays the foundation for dependable analysis.
They also conduct regular data validation and audits to catch and resolve any inconsistencies, ensuring the data aligns with established quality standards. On top of that, analysts keep a close eye on data quality metrics, which allows them to quickly spot and address potential issues as they arise.
By following these practices, analysts ensure their work is built on reliable and actionable data, enabling businesses to make smarter, data-driven decisions.
What tools and technologies do data analysts use to automate repetitive tasks?
Data analysts use a range of tools and technologies to make repetitive tasks easier and work more efficiently. For data manipulation and analysis, Python, R, and SQL are go-to choices. When it comes to creating visualizations, tools like Tableau and Microsoft Power BI are widely used to automate reporting and build dashboards. Platforms such as Alteryx and DataRobot take it a step further by streamlining workflows, automating tasks like data cleaning, transformation, and even predictive modeling.
These tools do more than just save time - they improve the accuracy and consistency of data analysis. This allows analysts to dedicate more energy to uncovering insights that can directly influence business decisions.
How do data analysts turn raw data into actionable business strategies?
Data analysts play a key role in turning raw data into practical strategies that businesses can act on. They meticulously gather, clean, and analyze data from multiple sources, using statistical techniques and specialized tools to uncover trends, patterns, and insights that align with business objectives.
To ensure the data tells a clear story, analysts often create visualizations and dashboards with tools like Tableau or Power BI. These visuals simplify complex information, making it easier for stakeholders to understand and use in decision-making. By presenting their findings effectively, analysts empower businesses to adopt data-driven strategies that boost efficiency, streamline operations, and fuel growth.