BI Analyst and Data Scientist. What exactly is the difference? In this article, I’ll break down for you what the jobs entail, the skills you need, the tools you use and what type of person is best suited to the two roles. To help YOU decide which one is best for you.

So, the best place to start trying to understand the difference between BI analyst and Data Scientist is by explaining what Business Intelligence and Data Science are. Starting with Business Intelligence.

What is Business Intelligence?

Business Intelligence is the process of harnessing the data that a business generates in all of its various activities, then blending, analysing and visualising this data in interactive reports (also known as dashboards) that you share with stakeholders.

The goal of this process is to help businesses make smarter, data-driven decisions. This is done by analysing the data in the dashboards to monitor performance, and spot trends, correlations and anomalies in the data.

Because it’s Business Intelligence, the data is predominantly business data. So things like finance data in an Excel file and sales transactions data in a SQL database. And then you have data from web-based or SaaS solutions. So web traffic data from Google Analytics, Paid Advertising data from Google Ads, Facebook Ads etc, email marketing data, and so on and so forth.

Businesses today generate so much data that the challenge of Business Intelligence is to try and make sense of it all by joining the dots.

Finally, when talking about data in relation to BI, the focus is predominantly on current and historical data in order to analyse how a business is currently, and has been, performing to spot those trends, correlations and anomalies I just mentioned.

That’s not to say that no forecasting or predictive analysis is done in BI. I’ve been asked by clients in the past to calculate KPIs that focus on projected numbers. Here’s an example…

Example of Predictive Analytics For Business Intelligence

It’s the 10th of the month and there have been 2,500 visitors to a website. To project how many there will be by the end of the month just take 2,500, divide it by 10 and multiply by the number of days in the current month. Simple.

Then by incorporating the sales conversation rate and average order value of the previous period, you get a basic forecast of how much the website will generate in sales by the end of the month. You see, it’s fairly easy to calculate.

Some BI tools even include forecasting capabilities that can be applied to things like time series data.

The Intelligence part of Business Intelligence is all about monitoring and gathering information in order to better understand business performance and make smarter decisions to help a business grow.

So now you have an understanding of what BI is, let’s turn our attention to Data Science.

What is Data Science?

So, the first major difference to make clear between BI and Data Science is that Data Science isn’t predominantly focused on business data. It, of course, can and is used in business but that is just a part of all its possible applications.

The main role of Data Science is to use data in order to do things like test theories, find patterns and predict future behaviour. This is done by employing different methods like statistical analysis, multivariate analysis, algorithms, machine learning and artificial intelligence. So it’s really much more of a '“science”. Hence the name. And so, again, its scope is wider than Business Intelligence.

Examples of Data Science Applications

Let me give you some simple examples of applications for Data Science to add some context. A business example first.

So, a SaaS solution with a subscription-based model wants to improve customer retention by reducing churn, i.e. people who cancel their subscriptions. A way to do this is to try and predict accounts that are most likely to churn and then intervene in some way to try to prevent this from happening.

In order to do this they decide to use their subscriber data and analyse lots of different variables that could affect churn. Things like the total number of sessions, days since the last session, number of support tickets created, monthly subscription fee and so on.

Using Data Science tools and techniques, this multivariate analysis could potentially discover a pattern of subscriber activity that is most likely to result in a customer churning. If a pattern is discovered, it can then be applied to the existing customer base to identify accounts in danger of churning and reach out to them to see if they’re having difficulties and provide solutions and stop that churn!

Non-business examples would be things like using AI and machine learning for advanced image recognition that could then be used to help medical professionals better or more efficiently diagnose their patients.

Another example would be using a city’s traffic data to help improve traffic flow throughout the day or even to help make town planning decisions.

So, as you can see, Data Science goes above and beyond simple statistics. The possible applications are, essentially, without limit.

And, as you can also see, the roles of BI Analyst and Data Scientist, although having some crossover, are essentially two completely different jobs that require different skills.

Skills you need for BI and Data Science

Let’s start off by looking at the skills you need as a BI Analyst. I’ve already written a dedicated video on this topic that goes into more detail so please check that out if you want a deeper dive.

As you’ll be analysing business data, you’ll need to know Excel and SQL to a fairly high standard because a lot of business data today is contained in those 2 formats. When it comes to web-based data, it’s normally accessed via the data source’s API. All BI tools have what are called “connectors” to different data sources built in that communicate with the data source and allow the tool to query the data where it is, without needing to extract or export it.

But if you do need to extract data from various sources, perhaps into a single place, like a data warehouse, you’ll also need to know how to set up data pipelines using ETL tools.

Once you’ve harnessed all the data, you then use dedicated BI tools to analyse and visualise it in interactive dashboards. The most commonly used tools and Power BI, Tableau, Qlik Sense and Looker (formerly Data) Studio. They all essentially do the same jobs but in different ways so once you’ve mastered one, the learning curve for any other will be a lot less steep.

When it comes to data science, even though there is some crossover with BI, the tools you’ll use are different. You may find yourself using a BI tool to visualise and present your analysis but that’s simply to take advantage of the data visualisation and sharing capabilities. Data Scientists also use Excel and SQL.

The main difference is this. BI tools are all built for the same general purpose, the tools used for a Data Science project will vary based on the types of analysis required. The main tools you’ll most likely be required to know are Python, R and SAS but there are many, many more.

BI Analyst or Data Scientist, which is best for you?

If at this stage you’re still not sure which career path you should opt for, the one that you’re best suited to perhaps, let me talk a little bit about the types of personality both BI and Data Science tend to suit best.

But I’ll start with a caveat. I am a BI professional and I’ve never worked as a Data Scientist so I’m really not best placed to accurately talk about the type of person best suited to that job. I’ll therefore talk in more detail about BI and in more general terms about Data Science.

As a BI Analyst, you need to have an interest in Business as a whole. Your work will, or at least should, contribute to helping the business you work for grow. Job satisfaction comes from the insights your work generates being used to achieve this. If you don’t understand or have little interest in the workings of a business then this won’t excite you as much as someone who has more of what you might call a “Business brain”. Besides, if you don’t understand business, how are you supposed to understand the data you’re working with.

As well as a BI Analyst, you’ll, of course, need to enjoy working with data and have a head for numbers. You’ll be writing calculations and formulas in Excel, SQL and the languages of BI tools in order to aggregate, join and manipulate data.

Next, you’ll also need to be creative. Not only in terms of problem-solving - you definitely need to be good at that - but also when it comes to designing your reports and dashboards. Visualising and organising data in the most efficient and effective way possible. Making it as easy to understand as possible for the stakeholders.

Finally, and this is by no means an exhaustive list, you’ll need to be a good communicator. You won’t be working in isolation. The data you work with comes from different areas of the business you work for. The “owners” of each data source will have their own specific requirements when it comes to analysing their data. You’ll need to be able to communicate with different colleagues to gather and understand those requirements. Being able to discuss potentially complicated subjects with those who don’t talk data.

So, here’s my take on becoming a Data Scientist. Data Science involves a lot more programming and coding than BI does and there are more languages you’ll potentially need to learn for your job so if languages aren’t your thing, BI would probably be preferable. Saying that, a lot of the time, languages can often turn out to be far less complicated than originally thought once you start learning some basics so don’t be too ready to dismiss Data Science because of the fear of programming and coding.

In fact, if you’d like to learn some basic SQL, check out my 3-part video course that I’ll put a link to in the description.

One big difference between Data Science and Business Intelligence is that whereas with BI the end goal is pretty much the same for every project: building interactive dashboards containing business data, Data Science projects are far more varied in nature. There are more techniques and methods that can be applied to different data to achieve different analyses and results. So this can potentially make for a more - dare I say it - interesting day-to-day experience.

There is obviously some crossover with BI in that you’ll need to be comfortable and enjoy working with data, have a head for numbers and be a logical thinker.

When it comes to the day-to-day of life as a data scientist, I’m afraid I can’t provide much insight as I explained previously so if you are interested, there are loads of dedicated YouTube channels out there dedicated to the subject.

If you want to learn more about Business Intelligence, please do take a look around the site. There are loads of interesting articles to help you better understand BI and level up your skills.

If you have any questions, why not ask them in the comments below or simply get in touch via our contact form.