Data Scientist vs Business Intelligence Analyst. Here’s the Difference. | Towards Data Science

IntroductionData ScientistBusiness Intelligence AnalystDifferencesSimilaritiesSummaryReferences
Introduction
Data Scientist
Business Intelligence Analyst
Differences
Similarities
Summary
References
I have compared several other similar jobs to Data Science in past articles, and now will be comparing the role of Business Intelligence Analyst or Developer, oftentimes called a BI Developer. While at first glance you may question how similar these roles really are, but after analyzing the key components of these roles, you will see that they share quite a bit of skills, tools, and goals. Similarly, the same could be said about the differences. There are important differences between these roles that are associated with the processes and methods that each employee will take to achieve their end goal. Ultimately, both roles are essential and companies look to have either one or both a part of their business to increase awareness of data and insights, metric tracking, automation, and prediction.
Keep on reading if you would like to learn with more granularity between what makes these roles unique and what makes them similar.
A Data Scientist helps a business become more efficient by utilizing Machine Learning algorithms with company data, to predict and classify target variables. A Data Scientist can also expect to perform some of the tasks that a Business Intelligence Analyst will employ as well. It is crucial to work with data, query it, and analyze trends and key metrics associated with a business problem. An example of this process would be: isolating a problem that of ‘it is difficult to predict if a customer will churn’, creating and querying a dataset that has churn as the target variable with its respective, associated features, then predicting and assigning a likelihood score if a new customer will churn. The best part of Data Science is isolating these complex problems and using preset or packaged Machine Learning libraries to speed up the process that a human would typically do. Sometimes, these algorithms are not worth their time in implementing, and that one of the reasons there are Business Intelligence Analysts — as well as the difference in goals between the roles.
The main skills for a Data Scientist include, but are not limited to:
Skills:
Python or R
SAS
SQL
Statistics
Visualization tool (Tableau)
Jupyter Notebook
Git and GitHub
These skills revolve around data, statistics, and programming (OOP — Object Oriented Programming).
The main goals for a Data Scientist include, but are not limited to:
Goals:
Business understanding
Use of Machine Learning algorithms
Data preprocessing
Automation
Deployment of the model (sometimes this can be a Machine Learning position specifically, depending on the company)
The goals of a Data Scientist are associated with the understanding of the business and efficiency of the business. The goals included act as parts of the process you should be focusing on in your everyday work as a Data Scientist.
The role of a Business Intelligence Analyst is to use data to find out where a company can ultimately improve upon its current processes. From the data, you will be expected to come up with metrics or KPI’s (Key Performance Indicators). These metrics help a business not only keep track of how their product is performing, but also where they are lacking — which can, in turn, help the business find out about its weaknesses. You will use tools that keep data and be expected to query that same data.
You will also need to be proficient at visualizing that same data so that it is easily digestible to stakeholders — people in the company in charge of the project or decisions.
The main skills for a Business Intelligence Analyst include, but are not limited to the following:
Skills:
Tableau
R (sometimes)
SQL
Communication
Presentation
Business understanding
Reporting
These skills represent some of the whole entire amount of skills, tools, and languages that you will possibly need to know. They are some of the most popular skills I have seen in job descriptions over the years, as well as working with Business Intelligence Analysts myself.
The main goals for a Business Intelligence Analyst include, but are not limited to:
Goals:
alignment with the business
use of data to create important metrics
bring up insights that can provide action
communicate and collaborate with stakeholders to inform decisions
As you can see, both the skills and goals associated with this role early relate to the job title itself, including business intelligence, and analyst. Ultimately, you will be analyzing business intelligence within the company using data.
Data Scientists and Business Intelligence Analysts share a lot of the same skills and goals, but the method in which they approach problems and projects is different. While what I include before might be true for both positions, there can be a difference in how much importance is placed on that point in that respective role.
Here is a summary of the differences you can expect to find between these positions.
Differences:
Data Scientists use Machine Learning algorithms
Business Intelligence Analysts focus more on Tableau
Business Intelligence Analysts focus more on reporting
Business Intelligence Analysts work more with stakeholders and present findings more often
Data Scientists focus more on programming
Data Scientists focus more on automation
Business Intelligence Analysts focus more on statistics and ad-hoc analytics
Data Scientists focus more on prediction
Above are just some of the differences, but I think they represent the general discrepancies you would expect to see between these two roles.
While the concepts of Data Science and Business Intelligence Analysis work may differ the most, there are common tools, skills, and goals shared between them. Both positions require you to have communication skills, data wrangling, collaboration between teams, as well as metric generation and analysis.
Here is a summary of the similarities you can expect to find between these positions.
Similarities
Both work with databases
Both visualize with tools like Tableau
Need an understanding of the business not just technical
Both use SQL
Both need to either understand or create metrics (e.g., avg click rate per week) — one can serve as the metric to analyze and track as a Business Intelligence Analyst, and as a Data Scientist, you would use that feature in your model
Both of these roles share similarities and have some key differences as well. We have discussed the main skills, tools, and goals respective to Data Scientists and Business Intelligence Analysts.
Which would you rather be and why?
Data Scientist: statistics, Machine Learning, and automationBusiness Intelligence Analyst: metrics, visualization, and business insights
Feel free to comment down below, as well as discuss some of the experiences you have had in either of these roles — what skills, tools, and goals would you place in either or both of these positions? It may be very different depending on where you work for what the company goals are.
Thank you for reading, I appreciate it!
If you would like to learn more about the differences and similarities between Data Scientists and Data Engineers, please see my other article here [6]:
towardsdatascience.com
[1] Photo by Chris Mok || @cr.mok on Unsplash, (2019)
[2] Photo by Possessed Photography on Unsplash, (2018)
[3] Photo by bruce mars on Unsplash, (2018)
[4] Photo by Thomas Kinto on Unsplash, (2019)
[5] Photo by Ambitious Creative Co. — Rick Barrett on Unsplash, (2016)
[6] M.Przybyla, Data Scientist vs Data Engineer. Here’s the Difference, (2020)