Business Intelligence vs. Data Science


 

Dollar, Gold, the stock market, oil, and water now became old. It's all about data now. Today, data is the most precious commodity, and businesses are gathering it to enhance their performances, target customers, and raise income. Whether it is large or small; businesses now extract data to gain useful insights for their businesses. If we go into deep-dive one of the hottest topics in today’s era is "Data Science vs. Business Intelligence ", which is a point of dispute among data experts.

 

 “Data” plays a vital part in both Data Science and Business Intelligence. While Data Science is the broader pool with more data, on the other hand, Business Intelligence is also a part of it. We can forecast future growth using Data Science by studying historical data. Both are two topics that are associated with each other. There are various similarities between the two, what differentiates them? Basically in this blog, our focus will be to understand Data Science and Business Intelligence, the differences between the two fields, and the skills required for each. So let’s get started.

 

Table of Content 

  • Data Science vs. Business Intelligence: Introduction
  • Data Science vs. Business Intelligence: Differences
  • Data Science vs. Business Intelligence: The kind of Analysis
  • Data Science vs. Business Intelligence: Scope of work
  • Data Science vs. Business Intelligence: Skillset
  • Frequently Ask Questions

·        Courses to take in each field

  • Conclusion

 

Data science is an interdisciplinary field that combines multiple fields, including statistics, scientific methods, artificial intelligence (AI), and data analysis, to extract value from data. In Data Science data is mined for information and knowledge using various scientific methods, algorithms, and processes. It is characterized as a collection of mathematical tools, algorithms, statistics, and machine learning techniques that are used to uncover hidden patterns and insights in data to aid decision-making. 

 

Business Intelligence Business intelligence is the process by which businesses analyze current and historical data using methods and technology to improve strategic decision-making and gain a competitive advantage. Its goal is to give actionable insights to company leaders through data processing and analysis. A company, for example, examines its KPIs (key performance indicators) to determine its strengths and weaknesses. As a result, the management team can determine which areas of the business may be improved.

 

Data Science vs. Business Intelligence: Differences

 

Till now we have been familiar with Data Science and Business Intelligence now let’s go further and understand Data Science vs. Business Intelligence. Knowing the difference between the two will assist you in choosing the best option. In simple words, Data Science is the future and Business Intelligence is present. Data Science deals with perspective and predictive analysis while on the other hand, Business Intelligence deals with descriptive analysis. Let us understand it in detail.

                            

1.     Data Science vs. Business Intelligence: The kind of Analysis

                                           

The field of data science is concerned with the probability of future conditions and events. The predictive analysis makes use of any past data that can be utilized to forecast businesses, customer behavior, their purchases, and product success. Data science aims to provide a solution to the question of what might occur in the future

 

What has happened is seen by business intelligence. It uses descriptive analysis to give historical data to the organization in a way that is simple to comprehend and display. Business intelligence is utilized to develop reports that help explain the current situation of the business precisely and correctly.

 

2.     Data Science vs. Business Intelligence: Scope of work

 

Data science is used to forecast circumstances and events using a specific hypothesis or concept. Data science determines whether the hypothesis is correct or not. Then the hypothesis is next subjected to predictive analysis.

 

 On the other hand, Business Intelligence uses descriptive analysis and allows any of the business units to create and maintain reports and present those reports in a way that anyone can understand. The product manager might use the information to assess the success of the most recent project. The information may be sent to a sales director who is interested in reviewing his quarterly results.

                                    

3.     Data Science vs. Business Intelligence: Skillset

 

The domain of the data scientist is data science. The data scientist will require some specific talents and required experience to reach that level, but they will also require assistance from the operations, IT, finance, and business areas. Here are some skills that are required to become a Data Scientist.

 

Business intelligence is related to Data Analyst and they both required and accessed some skills set. Business users are the ones who gain and require the most from business intelligence

 

  • Data science uses previous data to create future predictions, whereas business intelligence analyses past data.
  • Data science uses both structured and unstructured dynamic data, whereas business intelligence deals with structured and statistical data.
  • In business intelligence, data is stored in a warehouse, whereas in data science, data is dispersed in real-time across the cluster.
  • Data science curates as well as answers queries, whereas business intelligence assists companies in solving challenges.
  • MS Excel, Micro Strategy, Sisene, and SAS BI are used in business intelligence, whereas Python, Spark, Hadoop, and TensorFlow are used in data science.

 

Frequently Ask Questions:

 

Ø  Can business intelligence become a data scientist?

Both Data Science and Business Intelligence have many differences; despite this, these two fields have one thing in common: they both use data to provide significant insights to the organization. As a result, business intelligence experts with a programming background and a statistical analysis are more likely to make a successful job shift. To achieve their goals, all they need is a good plan and proper guidance and supervision.

 

Ø  Is business intelligence the same as data science?

Business intelligence is concerned with the present, whereas data science is concerned with the future and forecasting what may occur. Data science produces predictive models that anticipate future opportunities, whereas BI works with historical data to decide a responsive course of action. So we cannot say that Business intelligence same as Data Science.

 

Ø  Who is a business intelligence analyst?

A business intelligence analyst (BI analyst) analyses data and other information to assist firms in making effective business choices. In addition, BI analysts may be requested to create tools and data models to aid in the visualization and monitoring of data.

 

Courses to take in each field

For Data Science Coursera, Udacity, Data Camp and Khan Academy are great options. The CS Master class on Data Science is also a great resource for someone who has never coded before.

 

For Business Intelligence, there are many free and low-cost ways to learn business intelligence skills (including Excel), but if you want a paid course, there are quite a few available on Udemy.

 

Alternatively, you can search YouTube to find a video that teaches you how to use Excel in order to perform data analysis. There's no better way than hands-on experience! By doing practice you can learn more.

 

Conclusion:

The debate between data science vs. business intelligence has raged for years, knowing when to turn to data science and business intelligence can be confusing, but it all comes down to identifying your goals. Do you need a system that creates metrics and reports, like in business intelligence? Or do you need a solution that can support an innovative data-driven culture, as in data science? These questions will help you decide which path to take. Once you’ve made your decision and then work according to the particular skills and requirements.

 

 

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