What Does a Data Analyst Do on a Daily Basis?

by | Data Analytics

In the digital age, data plays a significant role in helping organizations make informed decisions and drive growth. So, what does a data analyst do on a daily basis?

A data analyst’s day-to-day responsibilities include gathering, cleaning, and validating data from various sources, such as conducting surveys or using web scraping techniques.

Ultimately, a data analyst’s work enables organizations to make data-driven recommendations and decisions that contribute towards overall business success.

In this article, we’re going to take a look at the daily responsibilities of data analysts, skills and tools for the job, career challenges, and growth opportunities.

Let’s go!

Data is increasingly important for firms in the digital age since it aids in decision-making and promotes growth. So what does a data analyst actually do all day long?

Each day, a data analyst must collect, clean, and validate data from a variety of sources, including running surveys or utilizing web scraping methods.

In the end, a data analyst’s job helps organizations make recommendations and decisions that are supported by data and contribute to overall business success.

The everyday duties of data analysts, necessary skills and equipment, job problems, and career chances will all be covered in this article.

Data Gurus: A Day in the Life of the Number Crunchers

What a data analyst does on a daily basis

As a data analyst, the day-to-day work will vary depending on the company you work for, your specific role within that company, and the type of projects you work on.

However, here are some of the real-world tasks that data analysts may have to do on a daily basis:

  1. Collecting and cleaning data: A core part of being a data analyst is gathering data. In fact, you can spend a lot of time collecting and cleaning data to remove duplicates and ensure it is accurate, consistent, and reliable. This may involve working with different data sources and using tools like Python to extract and manipulate data.
  2. Analyzing data: Now it’s time to analyze! Data analysts use various tools and techniques to analyze data and extract insights. This may involve creating pivot tables in Excel, building dashboards in Tableau, or running statistical analyses in R.
  3. Communicating insights: After analyzing data, you then need to communicate findings to other stakeholders in the company. This may involve creating reports, presenting findings to managers or executives, or working with other departments to implement data-driven solutions.
  4. Collaborating with team members: You often work as part of a team, collaborating with other data analysts, data scientists, and business stakeholders to achieve common goals. This may involve working on projects together, sharing knowledge and expertise, and providing feedback and support to other team members.
  5. Continuing education: Never stop learning. Data analysts need to stay up-to-date with the latest tools, technologies, and trends in the field. This may involve attending conferences, taking online courses, or reading industry publications to stay informed and enhance their skills.

These are just a few examples of the responsibilities of a data analyst. While the specifics of the job can vary, the core skills and tasks remain the same.

In the next section, we’re going to take a look at some of the core skills and tools data analysts use each day on the job.

Data Analyst Responsibilities

Data analyst using a laptop to create models.

As a data analyst, you need a variety of technical and soft skills to excel in your role. You also need to be proficient in using a range of tools and technologies to extract insights from data.

Let’s explore the essential skills, tools, and responsibilities that every data analyst should be aware of to succeed in their role. From SQL and Python to problem-solving and decision-making, we’ll cover everything you need to know.

Let’s look at the technical skills you need on the job.

Technical Skills for Data Analysts

A data analyst coding and programming.

Data analysts need to have a strong foundation in technical skills to be able to manipulate data and create meaningful insights.

Here are some of the technical skills expected of a data analyst today:

  1. Excel: Mastering Microsoft Excel is a must. The spreadsheet program is extremely popular for data analysis and manipulation. As a data analyst, you need to be proficient in Excel to perform basic data analysis tasks, such as filtering, sorting, and pivot tables.
  2. Programming: Python and R are programming languages that have become a staple in computer science. They are used for statistical analysis, data modeling, data collection, and machine learning. Data analysts are typically familiar with one or. Ideally, both of these programming languages to perform advanced data analysis tasks.
  3. Data visualization: You must be able to communicate your findings effectively to stakeholders so that they can make good business decisions. That means using tools like Tableau or Power BI to create effective charts, graphs, and other visual representations of different types of data.
  4. Database management: Data analysts should have a good understanding of how to work with databases, including how to manage, store, and retrieve data. They should be familiar with database concepts such as normalization, indexing, and data modeling.
  5. Big data technologies: With the growing volume of data, data analysts should be familiar with big data technologies such as Hadoop, Spark, and NoSQL databases. These technologies allow data analysts to work with large datasets that traditional relational databases cannot handle.

To stay relevant in the field, you must constantly update your technical skills and stay informed about the latest trends and technologies.

Make learning and upskilling a way of life!

Soft Skills for Data Analysts

A team of data analysts working on a project together

While technical skills are critical, soft skills are also essential for success in the field.

Soft skills can be looked over, but mastering these soft skills will give you an advantage in the workplace.

Here are some of the key soft skills that data analysts should develop:

  1. Communication skills: You must be able to communicate your findings clearly and effectively. This includes explaining complex data concepts in simple terms and creating compelling data visualizations that convey your insights.
  2. Critical thinking: You must be able to think critically and analytically. You should be able to identify patterns and trends in data, recognize potential biases or errors, and make decisions based on, you guessed it, data.
  3. Problem-solving: You should be able to solve problems using data. Ideally, you should be able to identify areas of opportunity and develop strategies to address them.
  4. Time management: Depending on the job, you may often work on multiple projects simultaneously, so you must be able to manage your time effectively. You should be able to prioritize tasks and meet/exceed deadlines while maintaining the quality of your work.
  5. Attention to detail: A strong attention to detail is crucial to ensure that your findings are accurate. You should be able to identify errors or inconsistencies in data and take corrective action.
  6. Business acumen: You don’t need to have a bachelor’s degree in business, but you should have a good understanding of the business context in which you work. You should be able to identify business problems as well as business needs and develop data-driven solutions that align with your company’s goals.
  7. Collaboration: You’ll often find yourself working as part of a team, so you should be able to collaborate effectively with others. This includes working with stakeholders to help them understand their needs and develop solutions that meet their requirements.

The exact skills you need will depend on the specific data analyst job description. However, developing these soft skills can lead to increased career success.

In the next section, we’re going to cover some of the tools every data analyst should be familiar with.

Data Analysis Tools

Some of the tools of the trade for data analysts.

Data analysts work with a variety of tools and technologies to collect, store, and analyze data. Knowing how to use these tools is essential for success in the field.

Here are some of the most important tools that data analysts should be familiar with:

ToolDescription
Microsoft ExcelAs mentioned above, Microsoft Excel is a popular spreadsheet program that analysts and statisticians use to organize and analyze data. It includes a wide range of functions and features for data manipulation, analysis, and visualization. We can’t stress the important of getting used to using excel.
TableauTableau is a data visualization tool that data analysts use to create interactive dashboards and visualizations. It includes a wide range of features for data analysis and visualization, such as drag-and-drop functionality and real-time collaboration.
Power BIPower BI is a business analytics tool and, as Im sure you’ve gathered, one of our favorite tools that data analysts use to create interactive dashboards and reports. It includes a wide range of features for data analysis and visualization, such as data modeling, data transformation, and artificial intelligence.
RR is a programming language that data analysts use for automation, statistical computing, and graphics. It includes a wide range of packages for data manipulation, analysis, and visualization, such as dplyr, ggplot2, and tidyr.
PythonPython is a powerful programming language that data analysts use for data mining, manipulation, analysis, and visualization. It includes a wide range of libraries and packages for data analysis, such as NumPy, Pandas, and Matplotlib.

These are just a few examples of tools that can be used on a daily basis. By learning how to use these tools effectively, data analysts can enhance their technical skills and their chance of moving up the corporate ladder.

If you’re not sure where to begin, check out the video below to learn more about the power of data.

Now that you know what skills and tools are needed by data analysts, let’s take a look at some of the challenges of working this crazy world of data.

Challenges of Working with Big Data

A data analyst experiencing challenges

Data analysis is a challenging field that requires a combination of technical skills, business acumen, and analytical thinking.

While the rewards of the job can be significant, data analysts also face a number of challenges that can make their work difficult.

Here are some of the challenges that data analysts are likely to face:

  1. Managing big data: With the increasing amount of data generated by businesses, data analysts must be able to manage large(very large) volumes of data effectively. This requires a combination of technical skills and knowledge of big data technologies. Constantly upskilling and practicing your skills will keep you ahead of the trend.
  2. Data security: As data becomes more valuable, the risk of data breaches and cyber-attacks increases. Familiarity with best security practices is necessary if you are to ensure that data is kept secure and that appropriate measures are taken to protect it.
  3. Data quality: Ensuring that data is accurate and reliable is a critical aspect of data analysis. Data analysts must be able to identify and address data quality issues to ensure that their findings are accurate.
  4. Data visualization: Communicating complex data to stakeholders in an understandable way can be a challenge. Data analysts must be able to create effective data visualizations that communicate their findings clearly and accurately.
  5. Keeping up with emerging technologies: Data analysis is a rapidly evolving field, and data analysts must stay up-to-date with the latest trends and technologies to remain competitive.

To overcome these challenges, data analysts should focus on developing their technical skills, staying up-to-date with the latest trends and technologies, and building strong relationships with stakeholders to better understand their needs.

Next, we’re going to take a look at typical career path and what growth opportunities are available in the industry.

Charting Your Path: Navigating Career Growth Opportunities

A sapling in a data analyst's hand.

Beginners in the field often start their careers in entry-level positions where they are responsible for collecting and analyzing raw data.

However, with time and experience, junior data analysts can progress to more advanced roles and take on additional responsibilities.

Here are some common career growth opportunities to aim for:

  1. Senior Data Analyst: A senior data analyst is a more experienced version of a data analyst. Senior data analysts are generally responsible for overseeing the work of junior data analysts, providing guidance and support, and making recommendations to senior management based on their findings. Typically, it takes around 3-5 years of experience to move into a senior data analyst role.
  2. Data Scientist: Data scientists are responsible for building predictive models and algorithms that can help businesses forecast trends and make informed decisions. Data scientists typically have more advanced statistical and programming skills than data analysts. It can take around 5-7 years of experience to move into a data scientist role.
  3. Data Engineer: Data engineers are responsible for building and maintaining the infrastructure that allows data analysts and data scientists to access and analyze their data. They work closely with data analysts and scientists to ensure that data is properly organized and accessible. It can take around 5-7 years of experience to move into a data engineer role if you wanted to go down that path.
  4. Business Intelligence Analyst: Business analysts are responsible for developing dashboards, reports, and other tools that can help businesses make data-driven decisions. They generally work closely with stakeholders and key business personnel to understand their needs and develop solutions that meet their requirements. It can take around 2-3 years of experience to move into a business intelligence analyst role.

To progress in these roles, junior data analysts should focus on developing their technical skills, staying up-to-date with the latest trends in data science and the latest tech.

As a beginner, you should consider getting advanced certification from reputable organizations to further boost your prospects. In the next section, we cover some popular advanced certifications for data analysts.

Advanced Certifications Worth Considering

Advanced certificate for experienced data analysts.

A key aspect of career development in data analytics is obtaining relevant certifications and continuing education beyond a bachelor’s or master’s degree level.

Some popular certifications worth investing in include:

CertificationDescription
Certified Analytics Professional (CAP)A vendor-neutral certification that demonstrates expertise in analytics and requires passing a rigorous exam covering seven areas of knowledge.
Microsoft Certified: Azure Data Scientist AssociateA certification that validates skills in designing and implementing machine learning models on Azure, as well as data preparation, feature engineering, and model deployment.
SAS Certified Data ScientistA certification that validates skills in advanced analytics, including machine learning, predictive analytics, statistical analysis, and big data technologies, and requires passing multiple exams.
Tableau CertificationA series of certifications that demonstrate proficiency in using Tableau for data visualization, including Desktop Specialist, Desktop Certified Associate, Desktop Certified Professional, and Server Certified Professional.
Google Certified Data EngineerA certification that validates skills in designing, building, and maintaining data processing systems using Google Cloud Platform (GCP) technologies, including data warehousing, data pipeline, and data analysis.

In addition to obtaining certification, we believe the best way to start your career is by interacting with industry experts and data professionals in our international forum.

That asides, by using social media, attending industry conferences, joining conversations on platforms like LinkedIn, and participating in professional organizations, you can make valuable connections and learn about the latest tools, techniques, and best practices in the industry.

As humans, we tend to achieve more success if we are fundamentally interested in a subject. The great thing about data is that every industry has it.

You will find data jobs in retail, healthcare, manufacturing, hospitality, and shipping, to name a few.

So, your best bet is to start looking for work in an industry that interests you.

So, what’s next?

Final Thoughts

 Big data models on a large monitor.

A typical day in the life of a data analyst involves a wide range of tasks, from data cleaning and preparation to data visualization and presentation.

However, your specific tasks and responsibilities will depend on the organization, industry, and project you’re working on.

As we’ve seen, the role of a data analyst is both challenging and rewarding. From gathering and analyzing data to presenting insights and recommendations to stakeholders, data analysts play a critical role in helping organizations make informed decisions.

To excel in this role, you must have a strong foundation of technical and soft skills necessary to effectively communicate insights and collaborate with team members.

With the right combination of skills and experience, as well as a commitment to ongoing learning and growth, anyone can build a successful career as a data professional and make meaningful contributions to the field.

Ready to start learning and taking the first step in your data career? Start learning data skills for free.

author avatar
Sam McKay, CFA
Sam is Enterprise DNA's CEO & Founder. He helps individuals and organizations develop data driven cultures and create enterprise value by delivering business intelligence training and education.

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