According to the US Bureau of Labor Statistics, demand for qualified Business Intelligence analysts and managers is expected to soar to 14% while demand for data scientists is expected to rise 16 % by 2028.
Given the average of the growth rate for all occupations is about 5%, these two different fields seem to have a better outlook. However, a business intelligence analyst does not seem to be as popular as a data scientist given that both the future outlook is quite similar.
If you are looking to transit from business intelligence analyst to data scientist, but not sure which kind of skills are transferable.
If you are bored with building machine learning models and start to wonder whether a business intelligence analyst would be a better option for you?
This article is for you.
I have worked in these two fields before. Although these two fields seem very different, they are actually quite relatable.
In this article, I am going to share with you the top 3 similarities between these two fields.
1. Analyze and understand data
Nowadays, I find a lot of people called themselves as a data analyst. I think it is because it sounds cool. However, it is not difficult for you to figure out that most of them are just reporting the numbers without understanding why it happens.
For instance, they reported that the sales increase by 30% compares to the previous week. However, they do not try to understand what is the reason which contributes to the increase in sales.
If you want to do well in either field, you must understand the data. Don’t just report for the sake of reporting, but instead try to figure out the fact that data wanted to tell you.
In Data Science, your understanding of data will determine how successful your model does. For instance, you are building a model to forecast the sale. If you know what is the most important or combination of factors that affect sales, then you can input those factors into your model. Therefore, your model will be able to give reasonable predictions.
In Business intelligence, this is also very important. Every day, you will be flooded with business requests. No matter you are trying to answer business questions or provide insights, you need to have a concrete understanding of data. For example, you can’t give insight into which kind of customers convert more without knowing the customer journey.
2. Using data to solve business problems
The software or technique use varies quite different between these two fields, but they are all used to tackle problems.
For instance, the business intelligence department is required to forecast the sales to allocate budget for each sales team to spend. By performing simple regression on historical data in the excel sheet, and also manually tweak some variables to comprehend future events, it can just be done using some formulas in an excel sheet.
If this problem is required to tackle by the data science team, base on what I observed, the tool used will be very different. For instance, they will try out with a sophisticated Machine Learning model or a deep learning model that cannot be achieved by just using excel.
Although the tool is different, both of them are just trying to forecast the sales as accurately as possible. A combination of both teams will actually lead to a more efficient result.
Why do I say so?
This is because the tasks of the business intelligence team are answering questions or provide insights. They will understand the data more and therefore if both teams work together, the data science team can save time to find out those important variables to be used to train the model.
3. Storytelling and Visualization
Imagine you are able to find out some valuable insights, but you ended up plotting a graph that is not able to clearly show your insights.
Or maybe you plot the right graph, but you are not able to communicate with your customers or clients for them to understand your insights clearly.
After all the hard work of cleaning and analyzing the data, you need to also make sure that you can present your results in a clear manner. However, the tool to plot the graph could be different. For instance, the business intelligence team uses Tableau, Excel or inhouse built’s business intelligence dashboard to plot the graph.
On the other hand, the data science team will utilize Programming languages such as Python or R to visualize by coding.
No matter what tools both teams are using, the end result will be to do PowerPoint slides to show their work.
Final Thought
By now, you will have an overall picture of the similarities between these two professionals. Some of the examples stated above might vary across companies.
If you want to transit from one field to another, you will know what kind of skills are actually transferable.
Therefore, you don’t need to worry so much about the different skillsets between these two jobs, you can either foster transferable skills or work on skills that you think you currently lack.
Don’t be afraid to fail. Be afraid not to try – Michael Jordan
Thank you for reading until the end, and hope to see you in the next article.
About the Author
Low Wei Hong is a Data Scientist at Shopee. His experiences involved more on crawling websites, creating data pipeline and also implementing machine learning models on solving business problems.
He provides crawling services that can provide you with the accurate and cleaned data which you need. You can visit this website to view his portfolio and also to contact him for crawling services.
You can connect with him on LinkedIn and Medium.
