Data is knowledge, new oil, powerful weapon. Data is highly valued nowadays. Good data can give you keen insights, convincing evidence to make informed decisions. By observing and analyzing data, we can develop more accurate theories and formulate more effective solutions. For this reason, data science and/vs. business intelligence has become two buzzwords that represent some new trends in the scientific and business area. 

Although these terms are interconnected, failing to grasp the distinction behind them can have significant consequences. If you are curious about the difference and similarities between them, this article will unveil the mystery of business intelligence vs. data science vs. data analytics.

Definition: BI vs Data Science vs Data Analytics

What is Business Intelligence?

Business Intelligence describes the process of using modern data warehouse technology, data analysis and processing technology, data mining, and data display technology for visualizing, analyzing data, and delivering insightful information. It helps executives, managers, and employees make informed business decisions. 

It meets the analysis needs of structured and sometimes unstructured data, paving the way for new and profitable business opportunities.

Typical tools for BI: Power BI, Tableau, FineReport

What is Data Science?

Generally speaking, data science is an interdisciplinary field to study how information and knowledge are extracted from data with multiple scientific and mathematics methods. It can be defined as a combination of statistics, math, and computer science techniques employed to discover the patterns behind data and thus help the decision-making process.

Typical tools for data science: SAS, Python, R

What is Data Analytics?

Data science generally refers to all the knowledge, techniques, and methods used for data analysis, while data analytics is the manner of analyzing massive data. There are four primary types of data analytics: descriptive, diagnostic, predictive, and prescriptive analytics. 

Difference between Business Intelligence vs. Data Science

Basically, business intelligence and data science all refer to the extraction of actionable insights from raw data. Both Data Science and BI focus on “data,” intending to provide favorable outcomes, which in the case of business may be profit margins, customer retention, new market capture, and so on. In other words, these two terms refer to the ability to transform and interpret data, and there are usually experts involved.

But Business Intelligence is an umbrella term that describes concepts and methods to improve business decision-making by using enterprise BI systems. Business Intelligence helps direct the current state of business data, tells you where your business is standing now, and suggests where to go.

On the other hand, data science is typically more extensive and complex. Perhaps most importantly, it enables machine learning (ML) models to learn from the vast amounts of data being fed to them and put more responsibility on scientific techniques rather than human’s eyes and minds. Data science can get involved in business and create profits, but it can go beyond this scope and find a foothold in healthcare, academic research, public affairs, etc.

As a result, it is not a matter of choosing one or the other. In some large corporations, you can also find that a team contains both business analysts and data scientists. Business analysts usually focus on discovering data trends and developing ways to use that information to improve organization operations, while data scientists tend to dig more into the factors behind these trends and work on optimizing algorithms.

BI Tools vs. Data Science Tool

Let me take two software examples to compare the difference between BI tools and data science tools. 

BI tool

Basically, BI meets the two primary needs of enterprises in reporting and analytics-yes, reports vs. analytics are different. For example, FineReport supports the realization of excellent data visualization in more than 50 chart styles, and publishes reports to the report server, browses and analyzes the reports on PCs, mobile devices, and large screens. 

The advantage of FineReport is the rapid design and development of reports and dashboards, with 3 design modes. Users can combine data from multiple data sources and enterprise systems for comprehensive analysis with one click. 

Programming skills are minimally required to use BI tools. Generally speaking, you only need to know some simple SQL statements to get started using FineReport. For novices, the Excel-like interface and drag-and-drop operation mode are very friendly. If your goal is to visualize data quickly and develop dashboards, then BI tools are undoubtedly the best choice.

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Data Science tool

Python is very common, but in academic environments, R and SAS are used more. I think that Python is becoming more and more popular as a programming language.

Python provides us with an easy-to-code, object-oriented language and provides different libraries to implement data analysis, such as mathematics, data mining, data exploration, and visualization. Compared with BI tools, Python or R will have specific requirements for users’ programming skills. Therefore, the learning curve will be steeper.

Another weakness of Python is sharing. Because sending Jupyter Notebook or Python scripts to non-technical people will undoubtedly cause some problems, which prevents them from being widely promoted in the traditional business of large enterprises.

Conclusion

If we go back to a few years ago, the company has no data-related positions but is still engaged in analysis. Now with the headway of technology and the increase of data volume, the differentiation of data science and business intelligence has gradually appeared, but they are still analysis in nature. Through this article, you will gain a more solid understanding of the differences in definitions, tools, and focus between data science and BI.

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