TripleTen grad Jennifer Foskett worked in the U.S. healthcare system before enrolling in the Data Analytics bootcamp. During her program, she learned to gather and process datasets, extract insights from this data, and present them in a way that benefits decision-makers. Now, as a data analyst, her responsibilities are to collect and analyze health-related data to make crucial predictions on the well-being of the sector like the shortage of doctors, medical equipment, or medical assistance in a specific state.
Data analysts are irreplaceable for medium and large businesses. They collect and interpret data to identify key business insights. Then, they present their findings to managers and shareholders, who use these insights to make business decisions in favor of the well-being of their organizations.
From this article, you’ll learn the typical daily routine and tasks of data analysts, their skills, and their tools. From there, you can decide whether this role sounds like the right fit for you!
Common data analyst responsibilities
There are three primary responsibilities that every data analyst carries out.
Developing well-knit systems for collecting and cleaning data. The result of your analysis depends on the input data, as results and ensuing conclusions will shift if you include inaccurate data in a dataset. And the following business solutions may turn out incorrect. That’s why one of the most critical skills of an experienced data analyst is understanding which data is appropriate for analysis and which isn’t.
Interpreting data the right way and with the right tools. Companies may have different goals. To decide how to reach those goals, they need inputs and insights. Data analysts work with the collected data in order to find those insights. Professionals distinguish four types of data analytics.
- Predictive analytics. Its goal is to determine the probability of a future outcome or situation. This type of analytics requires a good knowledge of statistical techniques. These include machine learning, data mining, and game theory.
Example: Using predictive analytics, we can forecast how much money an upcoming marketing campaign will bring in for the company.
- Descriptive analytics. It looks at data about past events to identify trends and relationships. In other words, we mine historical data to find the cause of success or failure.
Example: A data analyst can look into customer segments and clarify which brings the most value to the company.
- Prescriptive analytics. It allows us to predict future outcomes and define what we should do if the forecast is correct. Specifically, it suggests how to capitalize on future opportunities and mitigate future risks. It also tries to explain the reasons for the supposed future outcome.
Example: We determine which products our company should promote in the next month, quarter, or year.
- Diagnostic analytics. Its goal is to explain, using historical data, why some events have happened. Diagnostic analysis usually follows a descriptive one. It uses such techniques as data mining, data drilling, and correlation analysis.
Example: Using diagnostic analytics, a data analyst examines the characteristics of social media ads to see why their performance varies.
Although each type of analytics implies its advanced tools, the basic ones are common for all: SQL for querying databases, R or Python for coding, and Tableau or Microsoft Power BI for visualizing insights. If you’re going to analyze digital product data, you should know how to use specific digital instruments, for example, Google Tag Manager and Google Analytics for metrics on visits, sales, and average costs per action, or Google AdWords for analyzing the top-selling keywords and search queries.
Also, Microsoft Excel and SAS Software are must-have tools for professional data analysts. Some companies use them for data storage, management, and analytics.
Compiling the results and findings. Usually, they write a report enriched with graphs and diagrams, which they then present to managers and shareholders, explaining the key insights.
I've realized that analysts aren't just these nerds who live in Excel spreadsheets and SQL and look at computer screens all day and send off reports. I think that's traditionally what some people thought data analysts did, but I think there's a lot more translation and communication, stuff like that, that adds value. And there's a need: not just for people who pull data, but also for people who can translate that data — that's just as hard. Brad Stansbury, TripleTen grad and data analyst
Daily routine of a data analyst
While a data analyst’s schedule depends on many factors, there are some more or less common patterns. Here is an approximate model:
Communication (10–40% of the working day)
Data analysts often start their working day or week by discussing tasks or brainstorming within the analytics team. Sometimes it’s also necessary to have a call with other departments and to define their inquiries.
Examples of sales and marketing department inquiries in e-commerce:
- What social platform was the best for selling the company’s goods last year?
- Which customer segments promise to become less profitable next quarter?
- What social ads’ characteristics caused the difference in results between high-performing and poor-performing ones?
Data analysts should know the company goals and the specific questions the management has to answer. This can help them collect the right data and analyze it correctly. For example, with the help of data analytics, real estate companies can predict the success of investments in new construction in a particular neighborhood for their clients. To do so, they analyze the occupancy rate and average price per square foot along with other valuable input.
Being able to ask specific questions of your employer, of what they want the data to tell, it's gonna be really helpful. You can know the tech side and know how the program works, but if you can't communicate that to an audience that's not tech-focused, it's pointless. Jennifer Foskett, TripleTen grad
Communication will take most of your time during the gathering and preparation of data. There will be meetings for status updates, 1:1 with the manager, and meetings with stakeholders to present insights or to negotiate what questions we could answer for them using data. On top of that, emailing or messaging within a company could take some time. You will be the one to communicate what crucial decisions can be taken according to the collected data.
Understanding and cleaning data (25–50% of the working day)
When meetings are over, it’s time to work on data. The first step is to gather and clean it. Usually, it takes quite some time to fix datasets and unload necessary properties. Data analysts with their teams need to arrange proper data extraction and preparation. Most of the time you will be using the SQL language to write automated queries instead of sorting loads of data manually in a spreadsheet. If data can be unloaded automatically, for example, from the Facebook Business account or the company’s CRM; this stage would not be time-consuming.
Building an impactful analysis starts with clean data, and not at all times will you have it clean right away. Cleaning data implies editing and eliminating information from a dataset that is wrong, redundant, or incomplete. A data analyst is responsible for turning messy data into clean databases.
TripleTen graduate and data analyst for Research Square, Dash Wieland shares some of his typical tasks to highlight the importance of understanding data:
“For instance, I took part in an analysis that looked at the length of scientific publications pre- and post-COVID. We took this big dataset and counted up all the words in each manuscript, and basically found that since the pandemic, scientific publications [...] have gotten much shorter. And so that’s work that we’ll publish and can share and talk about the ramifications that has for the industry.”
Analysis and visualization (25–50% of the working day)
When data analysts finish cleaning data, they can start processing it. At this stage, programming skills come into play. With the help of Python, data analysts start looking for meaningful patterns and insights in data.
At the final stage, data analysts visualize everything they consider significant. These visualizations can be done in the form of graphs or interactive dashboards. They should also answer specific business questions. Then they are presented to business departments and receive feedback.
Is it challenging to be a data analyst?
To become a successful data analyst today, you should be curious about the technical side of things. Without clear insights into data, this work can be frustrating at times. In these moments, your critical thinking skills should be working at full capacity.
Also, you should be able to combine technical knowledge with good communication and successfully retranslate your knowledge to non-tech people. This demands good visualization and storytelling skills. It could be challenging to focus on data intensely and piece the whole story together to detect meaningful patterns at the end. But once you begin to understand, the “Eureka” moment feels totally worth it.
Data analytics vs. Data science
Sometimes it may be difficult to distinguish the difference between the tasks of a data scientist and those of a data analyst. To simplify it, while the first harvests data in various ways, a data analyst figures out how to extract insights from it.
Their everyday tasks are also quite different. A data scientist’s responsibility is to ask the right questions and to be able to collect data. Whereas a data analyst usually processes data and turns it into valuable insights.
To become a prolific data scientist, you need to have an advanced level of programming skills and knowledge of calculus and statistics. By contrast, it may be enough for a data analyst to master basic programming knowledge.
How to become a data analyst
If this list of daily tasks sounds appealing, there is a great way to turn them into your daily job. TripleTen offers a 9-month Data Analytics Bootcamp! It’s a part-time online program that prepares students of all levels to become IT professionals. After completing the bootcamp, your future job title could be business analyst, IT analyst, growth analyst, data and insights analyst, or simply data analyst.
And even if at some point you will find the program challenging, you can always count on TripleTen support. “The first time I was holding back a little bit,” recalls TripleTen grad Melissa Raje. “But once I got in there and started just putting code in there, and just submitting it, even though I knew it wasn’t right, I got feedback. And that helped me break out of trying to be perfect.”