Before I graduated from my university, I was already very passionate about data. I was fascinated by how data could be utilized to improve the efficiency of current society. So, I knew for sure, I am going to take up a job on analyzing, cleaning or modeling data. Below is a quote that I believe why I am in love with data.
Data really powers everything that we do. – Jeff Weine
Data is everywhere, especially in our daily life. For instance, when we are deciding what kind of apple to buy. I believe if you are a price-sensitive person, your final decision will base on the price. However, if you were a more quality-seeking person, you would buy an apple that is imported from a country that is famous for producing apples. From this situation, we can see that we are making decisions base on data unconsciously in daily life.
After I graduated, I ended up accepting a Business Intelligence, as my first job. During that time, I thought maybe Business Intelligence, and Data Science‘s job does not make much difference.
"They are both using data to provide value at the end of the day, right?" I told myself.
But after 3 months working as business intelligence, I found out that I was WRONG!!!
Why?
Bear with me and you will figure out the reasons!
Working Journey: Business Intelligence

After working as a business intelligence for 10 months, here are some experiences I would like to share. If you are still an undergraduate, or just graduated, or still considering to switch your job career to business intelligence, I hope you will make use of this "past data" before making your final decision.
Before I started, just to provide some background of mine, I was working at Shopee as local business intelligence. Thus, I was responsible for providing reports only on the local market. Besides, my main responsible not only included crawling a large scale of websites to perform competitor analysis but also build Machine Learning models to help the company in saving operating costs.
From what you notice above, this position is all about business. Every day, I will receive multiple requests from other internal teams to perform certain analyses. I will need to communicate with them, giving them advice on how this kind of analysis should be performed so that it will not be simply wasted. To retrieve the data I need, I would need to write a lot of SQL queries.
Here are three points I would like to point out so far.
Do anticipate that your daily working life will be flooded with requests, whether it is a do-able or a not do-able request. Don’t be shocked that your working life will be filling with lots of ad-hoc requests.
Besides, there will be lots of communication required for this position. If you are a person who wants to focus only on the technical part of business intelligence, do ask the interviewer more detail about what you will be doing in day to day basis and also bombard them with technical questions. This will at least make sure you will be able to know the environment before accepting the job.
Other than that, proficient in SQL is a plus. No matter what requests are coming in, the only way to extract data internally is through SQL queries.
Back in my working life, reports occupied most of my time as business intelligence. They could come in different forms, daily, weekly, monthly, quarterly or even ad-hoc basis. Therefore having skills like VBA or Python would be very beneficial, so that you will be able to automate them.
Sharing on more technical parts of this position composes of web crawling and build some machine learning models. These two components are also essential in an e-commerce business, one reason is to gain a competitive edge and the other one is to reduce the cost for the company.
For web crawling, from what I have experienced, the most difficult part is to maintain it. Building a web crawler is simple, but to make sure your web crawler will not be blocked by websites is a whole new different story. Besides, building machine learning models, it is just less than a small piece of the pie in **** my business intelligence working life.
This is the last point I want to make. Tech Skill required in this position is minimal, business is the most important aspect. Even though you are not good at tech, if you are interested in business, then this position is very suitable for you.
Working Journey: Data Science
After switching my position to a data scientist, I could say the experience is very different. Let me share it with you.
My tasks are still based on internal requests, but in a project form. Thus, I will have more time to analyze data and build models. So, you might think projects are better, as you have more time to focus and to produce the best work. However, this is not completely correct. Occasionally, the projects which you have done halfway will be shut down due to various reason, or become less prioritize, which is quite normal.
One of the problems I faced in building models during my business intelligence working life is – the resources. However, here, there are far more resources for me to try out my ideas and thoughts. Remember, resources also come with conditions, you need to be able to produce quality work in a limited time.
Self-improvement is a must in data science. I will need to constantly read up the latest papers to be able to follow the latest trend in this field. Not only that, pick up more technologies knowledge, coding skills, and Programming languages so that you can use it when necessary.
Besides, UNIX commands are the fundamental skills in data science. To be able to SSH to servers, vi commands when using servers, etc are the command I am using every day in my job. However, in business intelligence, this skill may require, but is not a must.
Code efficiency is prioritized in data science. I would need to make sure my code is efficient and at the same time, check whether the resource of the server is enough so that the server will be able to handle it. There will be lots of people sharing the server, thus by communicating on how to share the resources is also important.
Communication skill is still very important. Let’s picture this if you have a brilliant idea or you are making a vast improvement in the model performances, but you accidentally screw it up while presenting to others. Or maybe when you are communicating with internal teams about setting the requirements of the project, and you are not able to express your views clearly. You would have made your life way harder or lose your chance to showcase the value of your work.
Understand the pros and cons of various kinds of machine learning models. This is important which you should think of before you choose to try out any model. So that you will not waste your time in implementing a model that’s clearly won’t be performing great in that particular task.
Last but not least, coding and query languages are two of the most essential skills in data science. Able to pull the right data and try out different models in a short period is one of the sought out skills in the current market. Besides, having the skill to understand other peoples’ codes quickly is also very important as your projects would be handed over by your colleagues.
Final Thoughts

Thank you so much for reading until the end. I appreciate that! However, these are just my views on how different the job scopes of these two jobs are. As I am sure that in some other companies, the tasks being assigned to the business intelligence might be very different from what I describe above.
Business Intelligence deals with known unknowns, while Data Science deals with unknown unknowns – Maxim Scherbak
I hope you will be able to understand how different these two jobs are by now so that you could make a well-informed decision.
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.