What Does a Data Analyst Do? (Ultimate Guide)

What is Data Analytics?

Data analytics is the discipline of organizing, interpreting and displaying data in a way that’s useful to a business. Although the field has evolved a lot over the past few decades, the central aim of data analytics has always been gathering valuable insight from data. The data itself can include quantitative or qualitative data, and these can be further divided into areas like categorical data (language, gender, etc.), ordinal data (ordered ratings like customer satisfaction), etc. No matter the type of data, analytical methods help to process and make sense of oftentimes complex information .

Consumer facing companies often use data analysis in order to plan their product distribution. For example, companies can analyze sales data from all the different locations where their products are sold. By finding the average sales volume over a given period in each location, companies can plan ahead and send the right amount of product to each store. Using data in this way can help reduce spending and waste. Companies can also use prior customer purchase data to plan their marketing campaigns, and only target customers who are likely to buy their product.

While data analysis is commonly associated with areas like marketing, it's also starting to be used more and more in healthcare. For instance, predictive analytics can help with fast decision making in the hospital. Algorithms can use health data to identify the highest risk patients and help decide which patients to prioritize in an overwhelmed ICU ward. Similarly, analytics can help spot early warning signs for declining health, and identify patients who need to be moved to an emergency ward; at Ysbyty Gwynedd Hospital in the UK, this new technology led to an 80% reduction in cardiac arrest.

Data Analyst Job Description

A data analyst uses analytical tools and methods to transform sometimes confusing, disorganized or disparate data into information that’s readable and easy to understand. Although responsibilities might vary based on position and industry, data analysts are often in charge of gathering data, organizing it, and pinpointing trends and patterns.

In the later stages of a project, data analysts produce visualizations and reports, and communicate their findings to leadership. For highly technical roles, setting up the actual infrastructure to gather data can be a major component of the job, as well as maintaining databases and using programming languages to perform equations. For roles that are less technical, most data analysts still need to be skilled in Microsoft Excel and SQL.

What Data Analysts Do on a Daily Basis

Data analysts are an invaluable part of any organization and juggle a number of different day-to-day tasks that vary depending on the needs of their company. Here are a few responsibilities you might need to fulfill as a data analyst:

• Gathering Data - Whether an analyst collects the data themselves or coordinates with others in the organization, there needs to be efficient processes in place to compile data. Data might come from tracking tools like Google Analytics, company surveys, email marketing software, or any number of sources. In the healthcare space, analysts use patient records, clinical trial data, data from insurance claims, and other pieces of information to improve patient care and health outcomes. Using algorithms and other tools, healthcare professionals can plan and execute the right decisions faster and save lives. Hospitals and other healthcare institutions also use data to plan ahead for important purchases like personal protective equipment (PPE) and reduce supply shortages.

• Designing and Maintaining Databases - Many data analysts need to establish processes for storing, retrieving and categorizing data, as well as address any structural issues or coding errors within databases. Depending on the role, they might work on this individually, or collaborate with an engineer.

• Cleaning Data - Although not the most glamorous part of the role, data must be cleaned of duplicates, errors, and unnecessary information. This can be done manually, but often involves using automated software or programming.

• Using Tools to Interpret Data - Statistical programs are an important part of any analyst's toolbox. Data analysts use software like SAS and Excel or programming languages like Python to discover patterns and insights and make sense of large amounts of data.

• Documenting and Improving Processes - Data analysts work with others in the organization, such as engineers and business leaders, to create data policies, improve data collection and develop thorough documentation.

• Presenting to Stakeholders - Data doesn’t mean much if it doesn’t lead to meaningful action or business decisions. Data analysts format and visualize data to make it easier to understand, and present their findings to key stakeholders.

Where Data Analysts Work

Data analysts are present in almost every industry you can imagine. Banks and financial firms need data analysts for financial reporting and forecasting. Healthcare institutions like hospitals need analysts to organize large amounts of patient information, advance patient care, streamline insurance processes and improve efficiency. Healthcare data analysis can also be used to improve treatment; analyzing large datasets of tumor samples and looking at how cancer proteins react to various treatments, for example, can help speed up the search for a cure.

National and local governments use big data and statistical analysis on everything from weather forecasting to crime prevention and national security efforts. Varied fields like telecom, real estate and even agriculture all employ data analysts.

In a given organization, data analysts may work on their own, as an auxiliary member of another team like marketing , logistics, or on a dedicated data team. In some healthcare organizations, analysts report directly to upper management. Data analysts often work directly with more senior data team members like data scientists or data engineers.

5 Technical Skills Every Data Analyst Should Have

1. Math & Statistical Skills

Most data analysis tasks require some skill in math and statistics. While you won’t necessarily need the advanced mathematical skills required of a data scientist, using formulas and operations will be important for your day-to-day work. Even if your statistical tools do much of the actual calculations, you’ll need to understand math to know how formulas are being used and how to interpret the results.

2. Excel Proficiency

Although most office workers know how to use Excel, data analysts need to be highly proficient in the software and be able to use more advanced functions. Analytical and modeling techniques in the program will help you arrange data and better notice patterns and trends. Formulas like COUNTA will help you notice gaps so you can clean data, while formulas like VLOOKUP display values from different columns so you can access information more easily.

3. Data Visualization

Those in leadership often prefer to see easy to read charts and graphs of data, rather than a complex spreadsheet. Software like Tableau, Google Data Studio, and Jupyter Notebook allows you to use data to create easily digestible insights. Data visualization is important so different members of your team can easily grasp trends or correlations. Since there are so many available options (pie charts, area graphs, spiral plots, etc.), data analysts need to know which visual representation best matches the data and the intended audience.

4. Programming

Even though programming might not be the main focus of the role, it’s still an important part of data analysis. With a programming language, you can more efficiently analyze data, create dashboards and applications, and run statistical tests. While it’s true that you might not be required to code in some data analysis roles, it’s important to at least understand programming logic, know how to write some basic code, and understand how it fits into data science as a whole. You’ll need to learn a programming language in depth, like Python or R, in order to work with large datasets and have more control and flexibility when performing equations.

5. Database Tools

Database software like MySQL and Oracle allows you to store, manage and extract large amounts of data. Instead of manually sorting through potentially hundreds or thousands of spreadsheets and rows, databases allow you to more efficiently keep track of your data and organize it in a way that’s readily accessible. This role often uses relational databases, which allow you to select data from multiple tables and perform analysis.

Data Analyst Salary

The field of data analytics is going through a period of tremendous growth. Data analysts have the right combination of technical skills, communication skills, and creative ingenuity that makes them highly sought after for many different kinds of businesses. The high demand for skilled data analysts across almost every industry also means this position is generally well-compensated.

The average annual salary for data analysts is around $69,000 (Glassdoor). In New York, the average salary climbs up to around $84,000 per year. At the higher end, data analysts can make upwards of $125k (Built In NYC). Those interested in this field sometimes start their data analyst career and then move on to become data scientists after they gain more experience. Data scientists command even higher compensation, with an average salary of $132k in New York (Built in NYC).

Data Analyst vs. Data Scientist

While data analysts and data scientists work in the same field and can share some of the same responsibilities, they have very different roles and requirements. Simply put, data analysts gather, manage and visualize data for business intelligence insights, while data scientists build and implement more complex code and processes, and work with emerging technology like machine learning.

In their day-to-day work, data analysts may use many of the tools that data scientists develop. In turn, data scientists analyze the visualizations and statistical models that data analysts create and use them to understand more complex trends. Both roles often work closely together to achieve goals and produce useful reports for leaders in the organization. Data scientists need more advanced math and statistical skills than analysts and need to be adept at coding.

Data Analyst Education

There are a few different paths one can take to become a data analyst. It’s possible to find entry-level positions like assistant or junior data analyst right after graduating with a bachelor’s degree. Usually, aspiring analysts study data science, computer science, statistics or similar disciplines in college. After gaining work experience for a few years, recent grads can find roles as a data analyst.

For many, a Master’s Degree in Data Analytics is a smart career choice that can lead to better roles and higher pay. There are a few different scenarios where a master’s can lend a huge benefit. If you currently work or have a bachelor’s degree in an unrelated field, a Master’s in Data Analytics will give you the technical skills and formal training you’ll need to qualify for a position.

If you already have a relevant bachelor’s, a master’s will help your job applications stand out and prepare you to tackle more advanced projects at work. If you’re just starting out in your career, earning your master’s degree is a great way to qualify for higher level positions early on, and earn a higher salary right after graduation. And of course, the more advanced coursework that’s central to a master’s program can also prepare you for related positions, such as data scientist or statistician.

Data Analytics in Healthcare

There’s no question—data analytics is an extremely important component of health care in the 21st century. Hospitals, medical centers, government health agencies and other institutions use analytics to manage patient and client data, predict business and financial trends, and even identify patterns in public health. Because health care has become such a tech-oriented space, data analysts are hired to keep track of large amounts of data and help healthcare leaders improve patient care/outcomes and make challenging decisions. Big Data analytics, or the process of extracting information from massive amounts of data, is the next big trend in health care.

By using statistical models and other tools, healthcare data analysts are able to help healthcare organizations pinpoint exactly how to lower costs and improve patient satisfaction . Advanced data analysis can also have a major impact on better patient outcomes and health. For example, at SSM Health, predictive analytics using machine learning helped reduce the need for acute dialysis in certain patients.

From a public health perspective, predictive analytics allow governments and organizations to understand the spread of infectious disease in a population, and plan resources accordingly. During the height of the COVID-19 crisis, data visualization and analytics played an important role in effectively communicating how the crisis was unfolding. Intelligent use of data benefits us all and allows decision makers to respond to a health emergency effectively.

Master’s in Healthcare Data Analytics

If you’re interested in starting an exciting and dynamic career in healthcare data analytics, earning a master’s is one of the best career moves you can make. Whether you already have some experience in analytics or have a bachelor’s in a completely different discipline, a master’s program will give you the skills needed in the healthcare space today. Touro College Illinois offers a 30-credit online Master's in Healthcare Data Analytics that can be completed in as little as one year. 

As part of the program, you’ll learn in-demand technical skills around data mining, warehousing and visualization. You’ll be introduced to concepts in advanced topics like machine learning and programming, and learn how to navigate healthcare information systems. You’ll also explore how Big Data analytics is used in areas like disease research, insurance, and patient care.

Towards the end of the program, you’ll have the opportunity to find an internship in a healthcare data analytics role, and gain real-world experience applying what you’ve learned. As two of the fastest growing industries in the nation today, having a deep understanding of how healthcare and analytics intersect will prepare you for a long-lasting and fulfilling career.

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