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July 09, 2021

Business Intelligence vs. Data Science. The transition from BI to DS


Artur Haponik

CEO & Co-Founder

Reading time:

13 minutes

As the world shifts to the big data era, the need for data collection, processing, and interpretation techniques continues to grow. Businesses are in the race to becoming data empowered, demanding insights from data science-powered algorithms. Therefore business intelligence and data science alike play a very imperative role in the success of many organizations. What do you need to know about both these fields? And why should we be interested in the transition from business intelligence to data science?

In 2019, LinkedIn co-founder Allen Blue predicted that the demand for data science-related jobs in the marketing, education, and manufacturing sectors alone would go up by 15 to 20 times by the end of 2022. And according to Glassdoor, data scientists remained one of the top professions as of 2020, with an average base pay of over 115,000 USD[1].

The global shortage in the data science talent supply pool has created a golden opportunity for BI analysts to make a career transition into data science. Business intelligence and data science are quite similar, and the relationship between the two has long been a subject of discussion. This article will help you understand the comparison of business intelligence vs data science and show you how to climb up the ladder and transition from a business intelligence role to a data science role.

What is Data Science?

Data science is a domain of study that blends multiple fields such as statistics, artificial intelligence, deep learning, and data analysis to unmask hidden patterns from raw data. It also derives meaningful information from complex data to help businesses make better decisions.

Let’s have a look at the different professionals that fall under the data science umbrella.

data scientist
1.Data Scientists: Data scientists practice data science. They are well-rounded professionals with a high level of technical skill sets such as programming, statistics, and machine learning that enable them to build quantitative algorithms to mine, clean, and present complex data. They have become necessary assets in almost every organization.

2.Data Analysts: They bridge the gap between data scientists and business analysts. Data analysts typically use data analysis tools to retrieve, gather, organize and analyze data to help their clients make better business decisions. Today, data analysts utilize variegated techniques and fields of IT knowledge, including statistics, programming, and visualization into their expertise to communicate their findings to their stakeholders effectively.

3.Data Engineers: They play the engineering role within the data science team, which involves developing, deploying, and optimizing data infrastructure focused on collecting and preparing data to be used by analysts and scientists.

What is Business Intelligence?

Business intelligence is a technological process that leverages data mining, data visualization, and business analytics to help businesses make better data-driven decisions. You can call it an umbrella term that’s covering the entire process of data storage and analysis. BI tools help companies create insightful reports, summaries, maps, graphs, and charts that are easy to digest, helping professionals develop detailed intelligence about an organization’s nature of business based on available data.

Here are examples of how different organizations can manage their complexities by the use of BI systems:

  • A fashion company can use BI to process retail data of its stores, streamlining its customer experience.
  • A global beer manufacturer uses BI to produce more accurate demand forecasts, facilitating the manufacturing planning process.
  • A hospital uses BI to improve data quality and increase patient retention.

business intelligence

Business Intelligence vs. Data Science

It is important to understand the difference between data science and business intelligence and how they work together to develop a solid plan for your transition. The easiest way to differentiate between them is in terms of scope, skillset, data integration, and type of analysis, as discussed below:

1. Data Integration: BI uses the extract, transform, load (ETL) process of data integration which transforms data before loading it into data warehouses. In contrast, data science uses the extract, load, transform (ELT) process, which loads data into warehouses and transforms it at the time of querying.

2. Type of analysis: Data science uses predictive analysis, which utilizes historical data to foretell customer behavior, business trends, and product success. BI, on the other hand, uses descriptive analysis, which looks at what has already happened. It presents historical data in a way that makes it easy to visualize and understand.

3. Skillset: Data scientists can be called links that connect IT, business, and in some cases even finance. Therefore, their knowledge and skillset has to be extensive. They should be well versed with tools such as python and R and be able to perform complex statistical analysis of data. BI professionals’ skill sets, on the other hand, lie more on the soft side. They are good communicators and problem solvers and possess strong business acumen. Most BI tools have effective self-service capabilities and therefore do not require intense technical knowledge.

4. Scope: Data science aims to perform predictive analysis on a specific idea or hypothesis, while BI performs descriptive analysis on a general scope.

These are the main differences that come up when analyzing business intelligence vs data science. But what do they have in common?

How Business Intelligence and Data Science Reinforce each other

Companies can gain useful insight from either BI or data science, but using the two together gives greater insights that can be useful in driving strategic decisions. For instance, an organization can use BI to examine past RPF results and compile projects with high success rates, then use data science to create a wide range of scenarios and hypotheses to predict the likelihood of making future projects successful.

The transition from Business Intelligence to Data Science

BI professionals have a great advantage over anyone else trying to shift into the data science domain. This is because they work with data scientists on various projects and already have the knowledge on how to handle data. In other words, they work in the first half of data science projects.

If you are a transitioner looking to jump into data science, the following points will help you create a learning path that you can follow to achieve your goal.

Polish your math skills

It is accurate to conclude that math is the bedrock of any science discipline. Almost every artificial intelligence algorithm has some form of mathematical structure that needs to be understood and, coming from a business intelligence background, you need to strengthen your math game to put weight on your insights. Below are suggestions on some of the topics you need to absorb to be on top of your game in data science:

1. Statistics: Statistics is a must-know domain to grow as a data scientist. Having a strong grasp of the essential concepts of statistics adds flavor to your insights and makes them look more convincing to senior-level executives. The subject is quite broad, and therefore focused planning of the essential topics to cover is crucial. You will need linear regression, hypothesis testing, probability distribution functions, and basic probability in your everyday tasks and in interviews to impress the other side of the table.

2. Linear Algebra: Data science leans on different applications of linear algebra. It is an essential branch of mathematics that you should grasp to enhance your understanding of machine learning algorithms and other data science concepts. Singular Value Decompositions (SVD), for example, is an important linear algebra concept that is applicable in image compression and dimensionality reduction. When it comes to the representation of large amounts of information, vectors and matrices come in handy.

3. Calculus: It’s a valuable concept to add to your repertoire. Usually, you don’t need to know how to minimize sums or cluster algorithms from scratch because there are calculators meant for that. But having a general knowledge of what is happening in the background is important to tell when results are not making sense.

It is always wise to know the machinery under the bonnet rather than just being the person behind the wheel. As a data scientist, having knowledge about the ins and outs of math in every concept gives you an edge over your peers.

Step your programming game

Math gives concepts, while programming reinforces those concepts, making them tangible. Business intelligence requires technical programming skill sets in some stages of the project lifecycle, and coming from this background, you are already conversant with basic programming languages and tools.

Programming in data science is driven by a set of thriving technologies that are gaining mainstream adoption. Therefore, making a transition demands mastery of several languages to help you adapt to the latest trends and developments in the rapidly evolving industry. Your choice of languages boils down to the nature of tasks you anticipate handling in the near future.

Here is what you need to consider when choosing the best programming languages to learn:

1. The kind of data science role that you are eyeing.
2. The programming languages that you are already conversant with.
3. The level of difficulty that you are capable of handling.

Now that you already know the direction you are making strides towards and what you are willing to challenge yourself to, it is now time to select the right languages to learn.

Examples of essential programming tools

Here are examples of essential programming tools that you need to learn to stay in demand as a data scientist:

1. Python: One of the factors that make python stand out is its flexibility. When combined with quantitative reasoning, strong aptitude, and experimental analysis, it enables you to build solutions for a wide range of cases. It is mostly used in data mining, classification and categorization, development of ML algorithms, and creation of web services.

2. R: This one has outdone other programming languages and is now among the most prominent tools in the data science sphere. It is useful in data visualization and statistical model design.

3. Java: Java remains a favorite programming tool for data scientists due to its degree of scalability. It is a suitable choice when it comes to the creation of machine learning systems. Some of the common Java libraries used in machine learning include ADAMS (performs data mining), Java ML (implements machine learning algorithms), and Stanford CoreNPL (executes Natural Language Processing duties).

4. SAS (Statistical Analysis System): SAS is a big name in the analytics industry. It is majorly used to execute statistical modeling for data management, predictive analytics, and multivariate analytics. Data scientists use it to access data in different formats, split and merge data sets, and perform data analysis.

5. Scala: It is one of the most popular functional languages that run on JVM, and can be used alongside Java in data science. It is an ideal option if you want to work with high volumes of data sets.

Of course, you have to remember you are not limited to the programming tools mentioned above. Take time and select the most relevant programming tools for a smooth transition to a data science role that you are eyeing. Checking job postings also helps you know what employers are looking for to keep improving your skills and make yourself more desirable.

Dive into machine learning

Data science draws most of its popular methodologies from machine learning. A good number of data scientists are not proficient in machine learning techniques, and mastering them, therefore, makes you stand out from your counterparts. Its prime goal is to use data for self-learning, enabling machines to process inputs based on learned patterns, eliminating the need for explicit programming. What makes it different from other computer-guided decision processes is its ability to use data to build prediction algorithms.

As a data scientist, you need to know different machine learning methods to solve a wide range of data science problems. Let us explore some of the critical areas that you need to be familiar with.

Business Intelligence to Data Science
1. Supervised Learning: In simple terms, supervised learning involves the training of machines to find solutions to given problems with the help of already collected and labeled data. It uses classification and regression techniques to predict discrete and continuous responses. An example of its application is in the financial industry to analyze historical data and identify financial risks.

2. Unsupervised learning: It enables machines to recognize patterns from a set of data without supervision from users. An example of its application is in the cybersecurity industry to identify anomalous activities on computer networks.

3. Semi-supervised learning: It combines techniques from supervised and unsupervised learning. Models use small volumes of labeled data and large volumes of unlabeled data. It is used in business to detect identity fraud.

Machine learning is quite expansive and overlaps with other disciplines such as artificial intelligence and deep learning. Deep learning is useful in providing solutions to problems that come with massive volumes of input data and is a valuable asset in data science services.

Be a problem-solver

Now that you have acquired basic knowledge in programming, machine learning, and math, it is time to solve real-world problems. But where do you find a dataset for your domain? Perhaps you can talk to one of your team managers and ask if you could take one of their business challenges as your problem. One of the advantages you have as a business intelligence professional is that you already know all the variables in the dataset.

Start off by hopping on to tasks such as hypothesis generation, data collection, and model building. Websites such as Kaggle and GeeksforGeeks are a great starting point for learning how to solve data science problems. Kaggle, for example, offers data science competitions that can help improve your knowledge and profile. Participating in such competitions is a wonderful way to gauge where you stand amongst other data scientists. Remember, the best way to improve your problem-solving skills is to keep practicing.

Join collaborative projects and focus on building your network

Learning data science never stops. After working on algorithms and practicing on a few projects, you are ready to move on to advanced tasks. This is where collaborative data science programs come in.

Examples of collaborative initiatives include Data Science for Social Good, Science to Data Science, and DataKind. Joining different forums and websites also helps you stay on top of new trends and ideas, while data science-focused events such as conferences and meet-ups are gateways to meeting like-minded individuals and experienced professionals who can guide you through your career transition.

data science project

Key takeaways

So, business intelligence vs data science. Despite the two disciplines being different from each other, they still share a common ground: both use data to give meaningful insights to organizations. For this reason, business intelligence professionals with a background in programming and a knack for statistical analysis have a greater chance of attaining a successful career transition. They only need a good plan and proper guidance to push through with their ambition.

And remember, if you’re looking for an experienced and reliable AI consulting and data science team–Addepto is at your disposal. Every day, we deal with business intelligence, data science, data engineering, and other AI-related technologies. We will gladly help you find the best solution for your company and then implement it! Drop us a line!


[1] Data science salaries. URL:,14.htm. Accessed July 5, 2021.


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