Why Business Intelligence Analysts should learn Python
We produce an enormous amount of data every day. In 2020, Statista estimated that the total amount of data produced worldwide reached 64.2 zettabytes (one zettabyte is equal to one billion terabytes) and that during the following five years, it will increase to more than 180 zettabytes.
Data science and business intelligence are playing crucial roles in helping firms make sense of this boom in data volumes.
Business intelligence analysts use high-end GUI-based tools like Tableau or Power BI, whereas data scientists mostly use coding-based tools like Python, R, command-line tools, and others.
In this regard, business intelligence analysts are crucial to every organisation’s ability to power its insight layer. Because of this, the demand for business intelligence positions is rising. For instance, a business intelligence analyst in the United States makes an average of $84,019 a year.
The Evolution of the BI Analyst.
The job of a business intelligence analyst has changed significantly during the last 20 years. Big data’s emergence and ever-expanding data quantities have increased the need for sophisticated BI platforms and tools to aid analysts in making sense of their organisation’s data.
Analysts used Excel, SQL, or basic BI tools in the late 1990s and early 2000s to analyse and visualise data for their firms. But with the introduction of technologies like Power BI, Tableau, and Qlik, current analysts are expected to be proficient in a variety of tools in their daily work.
Modern analysts are expected to be professionals in reaching out, in addition to technical proficiency in specialised instruments. This indicates that for the modern analyst, having good project management abilities, subject expertise, storytelling abilities, and communication abilities are prerequisites.
Now that we have that in mind, let’s go back to the original opening question: Should business intelligence analysts learn to code? Yes is the answer.
Although mastering programming skills can assist analysts expedite the value they bring firms and help them stand out from the competition in the job market, many business intelligence professions may not require them. The five main justifications for business intelligence analysts learning how to code are listed below. For more information, check out the online Python certification course.
The Benefits of Learning Python as a Business Intelligence Analyst
- Improved Problem-solving skills.
Business intelligence analysts address issues every day, whether it’s figuring out how an A/B test affects crucial KPIs, creating dashboards for stakeholders, or making suggestions based on data insights. These abilities can be developed more quickly by learning to program because it provides a fresh perspective on technical issues.
Programming teaches users to consider a broad range of solutions to potential problems, which has been linked to enhanced problem-solving abilities in numerous studies.
- Improve collaboration with other data team members.
Business intelligence analysts frequently work as part of larger data or analytics teams. This implies that they will work along with professionals who prioritise codings, such as data scientists, engineers, and analysts. Better empathy and teamwork are important outcomes of learning how to code for business intelligence analysts.
When business intelligence analysts learn to code, they will be better able to communicate their needs to other team members, develop ad hoc solutions that include coding when other team members’ time is restricted, and comprehend the workflows of their coworkers. An organisation as a whole can only benefit from improved team cohesion.
- Create data visualisations and workflows that are more complex.
For contemporary business analysts, mastering BI solutions like Tableau and Power BI is crucial. But there are other ways that becoming a coder can hasten the work of a business intelligence analyst.
First, R and Python connections are offered by Tableau and Power BI. As a result, analysts can use scripts in their preferred business intelligence application to automate their activities. Running Python in Power BI to automate a data manipulation step on a widely used dataset is a prime example of this.
Additionally, you can create more advanced data analysis and visualisations using programming languages like R and Python rather than BI tools. Using programs like R’s ggplot2 and Python’s Matplotlib, for instance, professionals can create incredibly unique visualisations and reports.
- Join the community of open-source python programmers.
Programming languages have the unusual characteristic that practically all of the tools used by top researchers and practitioners are open-source. This implies that there are no restrictions on who can get the most advanced, powerful tools at any time for free.
Additionally, this implies that professionals can collaborate directly with the designers of these tools to suggest changes to the source code. Being a part of an open-source community can benefit analysts’ jobs, education, and reputation. Future professional chances will be better as a result, and the data world will have more robust linkages.
- Opportunities for Career Growth.
The ability to broaden your employment options and even change careers is a significant benefit of studying programming. There are many different job pathways in the vast field of business intelligence, including those for data analysts, product analysts, BI developers, and business intelligence architects. According to Glassdoor statistics, coding positions are highly sought after and offer excellent pay and job satisfaction.
Such occupations include those for data analysts, which are similar to those in business intelligence but demand a greater level of programming and statistical expertise. The average salary for data analysts in the US is $94,687 a year, with a range of $391K.
Additionally, there is a huge demand for data scientists and engineers. Large amounts of data need to be consolidated, which is where data engineers come in. Data scientists will use this data to mine insights and create machine-learning models. The median annual salary for data scientists and engineers is $117,212 and $112,493, respectively.
Conclusion
The modern business intelligence analyst will need to change as long as business intelligence is still evolving. Developing Python coding skills can lead to improved problem-solving abilities, improved teamwork, complicated and efficient processes, job advancement chances, and more. For additional information, check out the online Python training.
