What Do Data Analysts Do?

What Do Data Analysts Do

Gain insights into the key responsibilities of a data analyst role, plus the essential skills needed to succeed and the various career paths data analysts can travel down.

Reading Time 8 mins

In today’s data-driven world, organizations rely heavily on insights derived from data to make informed decisions and stay competitive in their industries. Data analysts are at the forefront of this data revolution. They are equipped with the ability to interpret complex datasets and extract valuable insights to help guide businesses toward smart decisions. 

In this blog post, we’ll answer the question “What do data analysts do?” by outlining the key data analyst duties and responsibilities and highlighting the essential skills and qualifications for the role. We’ll also explore the diverse impact of data analysts across various sectors and examine potential career paths for those interested in pursuing a career in the field. Whether you’re considering a career in data analytics or simply interested in understanding the role better, read on to find out more.

Data Analyst Job Description

A data analyst collects, cleans, analyzes, and interprets datasets to address questions or solve problems. They work across various industries, including business, finance, criminal justice, fashion, food, technology, science, medicine, healthcare, environment, and government.

Data Analyst Duties and Responsibilities

The process of analyzing data typically moves through these iterative phases:

Data gathering

This involves identifying data sources, and collecting, compiling, and organizing data for further analysis. Data analysts must prioritize accuracy, reliability, and high quality in the data they gather. They employ diverse tools and techniques, such as database inquiry and web scraping, to accomplish this task. 

Data cleaning

Data cleaning is the meticulous process of removing errors, inconsistencies, or inaccuracies from data to maintain its integrity. This involves handling missing values, outliers, and transforming data while ensuring consistency in formats. Data analysts also focus on resolving inconsistencies in values or labels, ensuring the accuracy and reliability of the dataset. They utilize a range of tools and techniques, including Python, R, SQL, or Excel for data cleaning. Data analysts often spend more time on data cleaning than on modeling or other analysis tasks.

Exploratory Data Analysis

Exploratory Data Analysis (EDA) involves examining and visualizing datasets to understand their structure, uncover patterns, detect anomalies, and identify relationships between variables. It involves descriptive statistical analysis and data visualization, utilizing tools such as R, Python, Tableau, and Excel. Insights gained from EDA can inform decisions to optimize business operations, enhance customer experience, and increase revenue. 

Data modeling

Data modeling enables the generalization of findings from a sample to a larger population or the formulation of predictions for future outcomes. For a data analyst, data modeling involves selecting or engineering relevant features, determining appropriate modeling techniques, constructing inferential or predictive models, and assessing model performance. Utilizing tools like R, Python, SAS, or Stata data analysts execute modeling tasks. These models can range from straightforward linear regression models to advanced machine learning models, depending on the nature of the data and the research question.

Data visualization

This involves creating visualizations such as charts, graphs, and dashboards to effectively communicate findings and presenting reports to stakeholders. Data analysts use tools such as Python and R visualization libraries, Tableau, Microsoft Power BI, and Microsoft Excel to create charts, graphs, and dashboards that convey complex information in a simple and easy-to-understand format. Data visualizations help stakeholders to easily discern patterns and trends in the data, facilitating informed, data-driven decision-making.

Decision support and business insight

Decision support and business insight are the ultimate goals of data analysis. Data analysts can offer actionable recommendations for business decision-makers that impact the bottom line. How? By analyzing data to identify patterns, trends, and correlations, which provide insights to support strategic decision-making for businesses. Data analysts optimize business operations, enhance customer experience, and increase revenue.

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Data Analyst Skills and Qualifications

Excelling in the data analysis field demands a blend of technical and soft skills, including:

  • Critical thinking: The ability to objectively evaluate information, analyze it from multiple perspectives, and make informed judgments.
  • Problem solving: Strong analytical and problem-solving skills to interpret complex data sets and extract meaningful insights.
  • Curiosity about data: A natural inclination to investigate, experiment, and learn from data, which can lead to new discoveries.
  • Attention to detail: Meticulous attention to detail and a methodical approach to data cleaning and analysis to ensure accuracy and reliability.
  • Communication skills: Strong written and verbal communication to convey complex findings clearly and concisely to both technical and non-technical stakeholders.
  • Basic mathematical abilities: A solid foundation in mathematics and statistics to identify the most suitable tools and analysis methods.
  • Technical proficiency: Proficiency with data analysis tools and programming languages like R, Python, SAS, and STATA, and database management tools such as Microsoft Excel and SQL for efficient data querying and data manipulation.
  • Data visualization: The ability to create clear and compelling visualizations such as charts, graphs, and dashboards to effectively communicate insights. Proficiency with visualization tools such as Python and R visualization libraries, Tableau, Power BI, and Excel.
  • Domain knowledge: Industry knowledge—healthcare, business, finance, or otherwise—to understand the context of data analysis within organizational goals and objectives.
  • Time management: Efficiently managing time and prioritizing tasks to meet deadlines and deliver high-quality analysis within timelines.
  • Adaptability: The ability to quickly adapt to new tools, technologies, and methodologies in the rapidly evolving field of data analytics.
  • Collaboration: The ability to work effectively in a team environment, share insights, and collaborate with colleagues from diverse backgrounds to solve complex problems

With these essential skills, you will have the necessary tools to excel in the field of data analytics.

Career Paths for Data Analysts

As technology continues to advance, the range and volume of available data has grown exponentially. As a result, the ability to collect, arrange, and evaluate data has become essential in almost every industry. 

Data analysts are essential figures in fields such as business, finance, criminal justice, fashion, food, technology, science, medicine, healthcare, environment, and government, among others. Below are brief profiles of some of the most common job titles found in the field of data analysis. 

Business intelligence analysts analyze data to provide insights for strategic decision-making, utilizing data visualization tools to effectively communicate findings. They focus on improving efficiency and effectiveness in organizational processes, structures, and staff development using data.

Financial analysts use data to identify and evaluate investment opportunities, analyze revenue streams, and assess financial risks. They use this information to provide recommendations and insights to guide decision-making and maximize financial performance.

Operations analysts are responsible for improving a company’s overall performance by identifying and resolving technical, structural, and procedural issues. They focus on streamlining operations and increasing efficiency to improve the bottom line.

Marketing analysts study market trends and analyze data to help shape product offerings, price points, and target audience strategies. Their insights and findings play a crucial role in the development and implementation of effective marketing campaigns and strategies.

Healthcare analysts utilize data from various sources—including health records, cost reports, and patient surveys—to improve the quality of care provided by healthcare institutions. Their role involves using data analysis to enhance patient care, increase operational efficiency, and influence healthcare policy decisions.

Research data analysts gather, examine, and interpret data to aid research initiatives across various fields such as healthcare and social sciences. They work closely with researchers and utilize statistical tools for dataset analysis. Research data analysts generate reports and peer-reviewed publications to support evidence-based decision-making.

Data Analyst Career Advancement

A career as a data analyst can also pave the way to numerous other career opportunities. Based on their experience and the needs of the business, data analysts can progress into roles such as senior data analyst, data scientist, data engineer, data manager, or consultant.

Senior Data Analyst: With experience, data analysts can progress to senior roles where they handle more intricate projects and lead teams of analysts. They are also often responsible for mentoring junior analysts, shaping data strategy, and influencing business decisions with their insights.

Data Scientist: Transitioning into a data science role, data analysts can apply advanced statistical and machine learning techniques to solve more complex business problems. They develop innovative algorithms and predictive models, enhancing company performance and driving strategic decisions through future forecasting.

Data Engineer: Moving into a data engineering role, data analysts can work on designing and building data pipelines and infrastructure. They will ensure the scalability and reliability of these systems, enabling efficient data collection, storage, and analysis.

Data Manager: Transitioning into a data management role, data managers oversee the entire data lifecycle, from acquisition and storage to analysis and utilization. They handle data governance, database administration, strategic planning, team leadership, and stakeholder collaboration. 

Consultant: With several years of experience, data analysts can transition into a consulting role. They may work as a freelance contractor or for a consulting firm, serving a diverse range of clients. This role offers more variety in the type of analysis performed and increased flexibility.

Data Analyst Job Outlook

Data analysts are in high demand. The need for data analysts is rapidly growing across various industries, as organizations increasingly depend on data-driven insights for a competitive advantage. As of May 2024, the estimated annual salary for a data analyst in the United States is $91K, according to Glassdoor (although this figure can vary based on factors such as seniority, industry, and location).

The Future of Jobs Report from the World Economic Forum listed data analysts among the top high-demand jobs in 2023, predicting a growth rate of 30-35% from 2023 to 2027, potentially creating around 1.4 million jobs.

What Do Data Analysts Do? A Conclusion

Data analysts play a critical role in transforming raw data into actionable insights that drive business decisions and strategies. With a diverse skill set and a passion for problem-solving, individuals can thrive in this dynamic field and contribute to organizational success. Whether you’re just starting your career or looking to advance to higher-level roles, the field of data analysis offers ample opportunities for growth and development.

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Disclaimer: The information in this blog is current as of May 16, 2024. Current policies, offerings, procedures, and programs may differ.

About Aysu Erdemir

Aysu Erdemir is a research data scientist with an expertise in clinical research and advanced analytical skills. Holding a Ph.D. in Cognitive Psychology, she has spent over a decade untangling the intricacies…

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