A few days ago, my friend told me about their motorcycle store company. They are thinking about data, and my friend mentioned they have five years of sales data. They believe their data needs to be analyzed to make better offers for their regular customers.
Last month, my friend’s company launched a new product and sold it to their established customers, resulting in a 10% increase in revenue for that month. You believe this is possible only through data analysis, highlighting the growing importance of data for every business. Today, every business has raw data, but this data can be a great opportunity if analyzed correctly.
As you know, data is a valuable asset for every business. So, if you’re considering a career focused on data, you’re making a smart choice. In this technological age, data analytics is a great career option.
I believe you might be interested in pursuing a career as a data analyst. Based on my practical experience, I’ll share some tips on how to become a good one. While some rules are fixed, other valuable tools can help you excel in this field.
Understand data patterns.
As a good data analyst, you should be able to understand data patterns. This requires specific domain knowledge. When you understand the data patterns of an industry, you need to have expertise in that industry. For example, if you know everything about the medical industry, you have domain knowledge in that field. My domain knowledge lies in digital data. Understanding data patterns can help answer various questions and achieve different goals.
Understand the analytical game.
A few months ago, I analyzed a company’s data. I excelled at extracting insights from their data, and based on my findings, the company launched a marketing campaign. However, the campaign ultimately failed, resulting in a loss. I then re-analyzed their data and discovered they had many competitors and their product was more expensive. I shared this entire analysis with them. This highlights the importance of understanding the “analytical game” as a good data analyst.
Know the basic math.
Basic math helps you quickly understand data patterns. When you see clean data, you might think about how you can work with it. I always start calculating whenever I see a dataset because it helps me understand data patterns.
I believe you’re familiar with basic math operations like addition, subtraction, multiplication, and division. These basic math concepts help you work with various shapes, patterns, and fractions.
Many analysts ignore this, but these skills are important for good data analysis.
Tell a good data story.
“You’re never going to kill storytelling, because it’s built in the human plan. We come with it.” – Margaret Atwood.
This concept is truly helpful for your data analysis career. If you tell a good story using your data, you can gain better insights from it. During my last data analyst project, I designed two different dashboards and created two stories based on the data. I believe these stories were helpful for the business.
Therefore, knowing how to tell a good data story is essential for being a good data analyst.
Work with real data.
Every beginner starts learning data analysis with dummy data. However, all experts recommend working with real data to gain real-world experience and improve your learning. You can practice with real data and create small data analysis projects to gain valuable insights. As I began teaching some students about data analysis, I started them with real data like Google Ads data and Facebook Ads data. They easily understood this data, and I believe this process is quite beneficial for learners.
Therefore, working with real data is essential for becoming a good data analyst.
Practice and Exercise
I remember when I was new in this data field, I practiced a lot. Because I knew that practice is more important to understanding data concepts. I think data can be messy. When you can work with it repeatedly, then you understand the whole nature of data.
Practice, practice, practice is more important to be a good data analyst. When you work with different types of data, it leads to a great understanding.
I think these steps will make a good data analyst. At this stage, what data stage are you in? Tell me something in the comments.