Understanding the Job of a Data Analyst Par :Daisy Robinson, Eboka Peter January 17, 2023 Updated March 24, 2023 Estimated reading time: 7 minutes. From predicting inventory levels, determining stocking capacity and even mapping out efficient product delivery routes, organizations have long used data to help achieve their business objectives. However, since the invention of the internet, which allowed for ease of collecting data, it has become an even more critical part of a business's daily operations. And this led to an increase in demand for trained data professionals who can put all this data to good use. But what do Data Analysts do? We break down all aspects of working as a Data Analyst to help you determine if it's the right fit for you. Who is a Data Analyst? In simple terms, a Data Analyst is someone who, through self-learning or formal education, has acquired the skills and knowledge to collect, sort, analyze and interpret large datasets, often to uncover insights that organizations can use to drive their business objectives. Data analysts look for patterns, relationships, and other insights to help businesses propel their ideas forward, stand out from the competition, and make changes within the company. The process of analyzing data typically moves through five phases, including: - Asking The Right Questions - Data Collection - Data Cleaning - Analyzing The Data - Interpreting The Results As a data analyst, you can do any number of these tasks on any given day. Remember that a large part of the job is critical thinking and working with numbers, so make sure that you enjoy both. The Difference Between a Data Analyst and a Data Scientist The terms data analyst and data scientist are often used interchangeably, but there is some difference between both professions. The focus of a data analyst involves using existing tools and methods to examine past data to infer insights. Key stakeholders then use these insights for future decision-making. The data scientist plays a slightly more advanced and complex role than an analyst. A data analyst might collect, sort, clean, analyze and present data. At the same time, a scientist will be responsible for devising new methods for collecting, cleaning and visualizing that data. A scientist will often require a Master's or Ph.D. in a related field. Although these roles work with data, they use them differently. It's essential to understand the difference so you can enrol in the right programs to educate yourself and set yourself up for a career as a data scientist or data analyst. A Day In The Life Of A Data Analyst Data Analysts work with data, extracting insights that management uses to achieve business objectives. But what does that look like daily? Schedules and Tasks: Every project will begin with working with your supervisor and the broader team of analysts to set schedules and prioritize tasks. This will involve identifying projects, data sets of interest, potential models and processes, etc., and assigning these tasks to individuals and team members. Data Collection: Before you begin working with data, it must first be collected. One of your tasks as a Data Analyst will be gathering data from primary and secondary sources. This data might have been collected internally in the organization or might have come from third-party data vendors. Data cleaning: Raw data will have errors such as double entries, entries that fall outside the range of interest, incomplete information etc. The data analyst's job is to filter and clean up this data. This helps guarantee that your analysis and insights will not have errors and impact business decisions based on those insights. Data Analysis and Interpretation: Once the data has been collected, sorted, filtered and cleaned, it is time for the data analyst to identify, study and translate trends from the data into a format understandable to less-savvy decision-makers. Data Visualization and Presentation: Once the data analyst has identified the patterns and trends in the data, it is their duty to present this information in a format and language accessible to stakeholders. Report Production: The data analyst also has to produce reports and brochures detailing the finer details and conclusions of their data investigations. These reports might be stand-alone or included in broader corporate releases and communications. Build and Maintain Dashboards: A Data Analyst works with other analysts and general staff within the organization. Part of your job might involve building and maintaining dashboards to handle internal data requests. Build and Maintain Records: You must maintain a record of all your investigations. This might involve documentation about the methods, models and visualizations, as well as the various databases you worked on. This will ensure that information is easily accessible within your organization. What Skills Do You Need to Become a Data Analyst? To succeed as a Data Analyst, you'll need a set of skills which are crucial to the work you'll do. These may be classified into technical and soft skills. Technical Skills Technical skills comprise the knowledge you will acquire from a Data Analytics education, either through a specialized degree, a Bootcamp or self-study. These include: Data Skills: As a Data Analyst, you will work primarily with large data volumes. You will need to know how to collect, clean and analyze data. You will need to be able to interpret this data and infer insights from it. You will also have to communicate these insights to stakeholders and key decision-makers. This might involve creating easy-to-understand presentations and building and maintaining databases and internal data portals. Software Skills: The work of a Data Analyst is heavily reliant on the use of software. Some of these are mainstream software like various spreadsheet programs such as Google Sheets and Microsoft Excel. In contrast, some of the software you will need to learn, such as Matlab and PowerBI, have been explicitly designed for the Data field. Programming Skills: Though software will do most of the heavy lifting in your work as a Data Analyst, you will also need to understand scripting and database languages such as SQL and Python. Knowing how to speak the language of a database system will help you use and manipulate that database more effectively and efficiently. SOFT SKILLS The second set of skills needed by a Data Analyst is soft skills. Your job as a Data Analyst involves working with teams and communicating insights to less savvy decision-makers. You will need all the necessary skills to work, collaborate and communicate with other people: Communication Skills: Data analysts work in teams. Having excellent communication skills will aid in collaborating effectively. You will also have to communicate complex information to others who may not be as data-savvy as you. Understanding how to communicate complex information as simply as possible is one of the crucial skills required by a data analyst. Curiosity: To succeed as a Data Analyst, you must have a degree of curiosity. Curiosity lets you know what questions to ask, why, and how to extract the results from a data set. Curiosity will help you ask questions that lead to unique insights and innovative solutions. Analytical skills: Having curiosity and knowing what questions to ask and why are the first step. The next step will be understanding how to extract information based on those questions. Analytical skills will help you query and extrapolate unique insights from often large and confusing data sets. Collaboration Skills: The projects you will work on involve large volumes of stored data. More often than not, you will be working on only a tiny part of a much larger project. Collaboration skills are essential if you must work closely with teammates. This will allow for easy brainstorming and cross-fertilization of ideas and ensure coherence and consistency across all project parts. Presentation Skills: Finally, an essential aspect of your job will mean communicating insights to decision-makers. This might be a presentation to a larger group beyond the team. Having good presentation skills will go a long way in sharing your ideas. Want to dive deeper? We have a full feature on How To Become a Data Analyst Do Data Analysts Need To Be Good At Math? If you have a math background, you’ll have a leg up on some of your competitors, helping you stand out in the long run. Even an adjacent industry, like finance, can be advantageous. If numbers aren’t your forte, but you know you have a passion for tech, then look at other career paths. Web development and Cyber Security are less mathematically demanding but fun and exciting careers as well. That being said, you don’t have to be a math wizard to have a successful career as a data analyst. What data analysts do involves following a set of logical steps. A solid understanding of business and the business world will be much more valuable than being a mathematician. Is Being A Data Analyst a Stressful Job? Every job has its form of stress. Never-ending deadlines, miscommunication, and constant new information are common stressors across jobs and industries. It also depends on your employer, the company culture, and stress triggers. As with any job, some aspects of your role can cause stress. Below are some everyday stressors that you should be aware of if you're considering becoming a data analyst: Continuously learn on your own You have to be comfortable working alone as a data analyst. Troubleshooting and problem-solving, which you can usually only do alone, will take up much of your time. You also have to be willing to work many hours to improve your communication skills, business knowledge, understanding of data, and ability to balance tasks. Little direction on how to do tasks As data is something only some can easily understand, you may get tasks thrown at you with little direction on achieving that goal. Suppose you like a lot of structure and order in your job. In that case, you'll probably find data analysis quite stressful. A constant state of problem-solving Data, especially when there are large volumes, might appear confusing. And often, it's poorly put together and difficult to understand. You'll spend a lot of your time-solving puzzles, gathering missed information, and learning how to talk to people who aren't in data to get the information you need. This can be stressful if you don't enjoy challenges. Lots of patience is needed. Until you get to a point where you understand the data you're analyzing, a lot of patience, attention to detail, and trial and error are necessary to complete your job. Make sure you work well under this type of pressure to ensure a successful career as a data analyst. Responsibility Although most people don't think about how important data is regularly, it's critical to propelling any business forward. Any task you get to analyze data - small or large, is vital to that company's success. Stakeholders rely on your findings to help them make decisions. The numbers and insights you give them will help make those crucial decisions easier Communication is key Finding the right piece of information can take a lot of digging. You will spend a lot of time talking to various people to gather new data. Communicating effectively with people outside of your field will make your job easier. As a data analyst, you also have to speak with stakeholders and other executives, who may be stressed, so it's essential to have patience when communicating with them. Start Your Data Analytics Career with Lighthouse Labs During this 6-week online data analytics course, you’ll learn how to think and present like a data analyst. You’ll have real world datasets to analyze and will learn how to master commonly used programs such as MS Excel and Tableau. Or if you're ready to jump right in, join the Data Analytics Program to launch your career into the stratsophere. Begin Your Career Journey Here On top of dedicated instructors teaching you, you’ll have access to a mentor that you can contact any time on Slack, so all your questions can be answered in real time. This course is for anyone from executives to early stage professionals who want to better understand data for their business decisions.