Beyond Technical Skills: Essential Aspects of Data Analysis

During my initial days of transitioning to the data analysis field, I used to think that the most crucial part was the ability to use all kinds of tools and software. I spent most of my time learning all sorts of tools like MS Excel, SQL, Power BI, Tableau, Python, and others. However, as I started to work on projects, I realized that there is much more compared to the tools and software.

Here are 4 aspects which I thought are crucial when working in projects as Data Analyst

1. Dataset information/ Detail and Meaning of the Dataset As a data analyst, you will have to understand the data you're working with, such as the meaning of headers, the unit used in each column, the relationship among tables, columns and each records, how and where the data were collected, and so on. With an understanding of the data you're working with, you can produce meaningful reports or dashboards for your readers. Most of the time and effort I spend dealing with data and creating reports or dashboards is spent examining each piece of data I'm working with. There are a few methods to understand a dataset:
   

   • Google is one of the best tool where you can search anything you need without troubling other people around you.

   • Use the "help" provided in the system where the raw data is extracted. It should explain on the meaning of each header and the unit used.

   • Ask your colleagues and project member; however, always do your research before asking any question as everyone's time is precious.
     

2. Communication among Different Teams Excellent communication skills help you get what you require for your work. Communicate effectively by providing clear and precise instructions or help that you need from your teammates and managers such as specific date or time and the exact file or information. Using the right words and tone or even the platform is also important when interacting and communicating with your colleagues. For more tips on effective communication, please also read this article: https://www.coursera.org/articles/communication-effectiveness. Last but not least, always have empathy with the people you're working with. This may be challenging, but it's important in building a healthy workplace.
   

3. Documentation of Work Flow Documentation allows you to save a lot of time and energy as you can always refer back to your documentation for your work and the information provided by other people. Documentation is basically where you take note and record down the steps and important notes of your workflow or some information you have asked from your colleagues and clients so that you don't need to ask the same question again or you don't need to go through the same thought process when you're developing the new report or dashboard. Besides, documentation of your work also allows a smoother work transition when you're handing over the work to other colleagues. PS: Asking the same question over and over again is really going to consume patience of others.
   

4. Work/Project Review Always review and document the work done for each report or dashboard, especially when it comes to new reports or dashboards created. New reports and dashboards may require new dataset, new system for data extraction or new way of presenting the data. Review and documentation help you to identify more efficient ways to complete the work and avoid the same mistake recurring in the future. Review your work can be done by recording how much time you spent producing the report or completing the work, what formulas or tools you used to complete the work, and then research whether there are better ways to complete the job. Research can be done by searching similar solutions online or discussing it with your colleagues.

Conclusion

To become a successful data analyst, you not only have to master technical skills, but also deal with other problems that require your soft skills, the right attitude, and a growth mindset. So, consider the above aspects as you strive for excellence as a data analyst.