Editor's Note: Take a look at our featured best practice, Data Analytics Strategy (205-slide PowerPoint presentation). Curated by McKinsey-trained Executives Unlocking Business Potential with a Comprehensive Data Analytics Strategy Business Toolkit In the age of information, data is the lifeblood of any organization. The ability to harness the power of data is a critical factor in driving success and [read more]
Four Tips for a Successful Career as a Data Analyst
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Data analysts are in demand and can command lucrative salaries, so there has never been a better time to begin a career in this field.
If you are unaware, a data analyst gathers and processes data and then performs statistical analysis. The findings can then be used by an organization to come up with effective solutions and decisions.
So, if you are passionate about numbers and things like algebraic functions, and you enjoy problem-solving, you should enjoy working as a data analyst.
But how do you attain a successful career in this role? Here are four tips to help you get started.
1. Make Sure it Is the Right Role for You
You are sure to research precisely what a data analyst does before you embark on a career in data analysis, but you should also look at other similar roles to ensure the job is actually the position that is best suited to you.
For instance, data analysts and data scientists work in similar areas but are not the same, so you may want to explore data science roles as well as data analysis positions. Both jobs involve collecting data and using it to make smart decisions.
The main difference is a data scientist gathers raw data from a range of sources and nurtures it in order to develop something valuable from it while a data analyst works with structured data that has already been collected, cleaned, and organized, and identifies patterns that can be of value to an organization or business.
Check out this helpful article to learn more about data scientist vs data analyst.
2. Learn the Right Skills
Data analysts need to learn the right skills if they are to start out on their journey toward a successful career.
Data analysis overlaps with things like statistics and higher mathematics as well as things like programming and software development, so you need to gain expertise in a number of different areas.
Some of the key skills you will need to master before you can embark on a successful career as a data analyst include Microsoft Excel skills, basic SQL skills, basic web development skills, data mapping skills, and the ability to find patterns in large data sets and derive insights from the processed data.
There are various courses available to learn the skills you need to become a data analyst.
3. Develop Excellent Communication Skills and Connect with Your Colleagues
Once you have the skills and qualifications and you have landed your first job, you can focus on ways of making your career a successful and lucrative one.
Communication is key. In addition to communicating well with your colleagues when you begin a new job, you should spend time connecting with relevant people in the organization.
The more you create solid relationships with managers, system administrators, stakeholders, and people who are well-connected both inside and outside the organization, the easier it will be to climb the career ladder.
4. Continually Learn New Skills and Get to Know Your Industry Well
The world of data analysis is always evolving, so you should continually learn new skills and stay up-to-date with the latest advancements in technology and processes if you want to build a successful career.
You should also get to know the industry in which you are working. Data analysts can work in a broad range of sectors, from finance to retail.
The sooner you get a good understanding of your specific industry, the more easily you will be able to forge an exciting career path.
It can be a good idea to attend trade shows and speak with experts in other organizations to open up more opportunities.
And spend time familiarizing yourself with industry-specific tools. For example, PyHealth is commonly used in the healthcare industry while PyAlgoTrade is a Python library that is commonly used in Fintech.
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About Shane Avron
Shane Avron is a freelance writer, specializing in business, general management, enterprise software, and digital technologies. In addition to Flevy, Shane's articles have appeared in Huffington Post, Forbes Magazine, among other business journals.
