{"id":55321,"date":"2019-04-30T18:05:03","date_gmt":"2019-04-30T12:35:03","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=55321"},"modified":"2025-07-31T21:41:29","modified_gmt":"2025-07-31T16:11:29","slug":"business-intelligence-vs-data-science","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/business-intelligence-vs-data-science\/","title":{"rendered":"Business Intelligence vs Data Science – 4 Ways to Tell Them Apart"},"content":{"rendered":"

Business Intelligence and Data Science are two of the most recurring terms in the digital era<\/em>. While both of them involve the use of data, they are totally different from one another.<\/p>\n

Data Science is the bigger pool containing greater information, BI can be thought of as a part of the bigger picture.\u00a0By the end of this Business Intelligence vs Data Science article, you will have a clear understanding of their differences.<\/p>\n

Analyze tools, roles, and responsibilities of these two professionals to pick out your precise future.<\/em><\/p>\n

\"Difference<\/a><\/p>\n

Data is omnipresent. It is utilized in every field of the world today. However, data science is like a vast ocean of several data operations. Data is constantly evolving and has several applications in various industry.<\/p>\n

One such application, known as Business Intelligence, is in the business industry where data is utilized to make careful business decisions. Study Business Intelligence vs Data Science to compare them against each other to have a better understanding of these topics.<\/em><\/p>\n

What is Business Intelligence?<\/h3>\n

Business Intelligence is a process of collecting, integrating, analyzing and presenting the data. With Business Intelligence, executives and managers can have a better understanding of decision-making. This process is carried out through software services and tools.<\/em><\/p>\n

Using Business Intelligence, organizations are able to several strategic and operational business decisions. Furthermore, BI tools<\/strong><\/a> are used for analysis and creation of reports.<\/p>\n

They are also used for producing graphs, dashboards, summaries, and charts to help the business executives to make better decisions.<\/p>\n

Business Intelligence makes use of the data that is stored in the form of business warehouses. Furthermore, it also supports real-time data that is generated from the services. As a result, Business Intelligence is used for strategic decision making.<\/p>\n

\"Introduction<\/a><\/p>\n

Moreover, business intelligence is used for optimizing the business processes, increase the efficiency of operations and gain insights about the market, giving an edge over the competitors.<\/p>\n

Using BI tools, businesses can monitor the growing trends in the market and address business problems as well as client queries.<\/p>\n

Some of the important uses of Business Intelligence are –<\/p>\n

1. Measuring Performance and quantifying the progress towards reaching the business goal.<\/p>\n

2. Performing quantitative analysis through predictive analytics<\/strong><\/a> and modelling.<\/p>\n

3. Visualizing data and storing data in data warehouses and its further processing in OLAP<\/p>\n

4. Using knowledge management programs to develop effective strategies in order to gain insights about learning management and raise compliance issues.<\/p>\n

What is Data Science?<\/h3>\n

Data Science is the most trending buzzword in the world today. Harvard Business Review dubbed it as the \u201csexiest field of the 21st century\u201d. However, very few people know the actual meaning behind the term Data Science.<\/p>\n

It is an umbrella term that is used to represent all the underlying data operations. Data Science is like a pool of many tools<\/a><\/strong> that are used to shape data.<\/p>\n

A Data Scientist, in general, is about finding patterns within data. It is a multi-disciplinary field, meaning that data science is a combination of several disciplines<\/em>.<\/p>\n

Three most important fields are – Mathematics, Statistics and Programming form the backbone of data science. Other than this, data scientists need to have domain knowledge in order to find out patterns in the data.<\/p>\n

\"Data<\/a><\/p>\n

Data Science is a process of extracting, manipulating, visualizing, maintaining data as well as generating predictions<\/em>.<\/p>\n

A Data Scientist is supposed to have knowledge of various data operations as well as machine learning algorithms. Using Data Science, industries are able to extract insights and forecast their performance.<\/p>\n

This is the right time to explore the<\/strong> Latest Career Opportunities in Data Science<\/a><\/strong><\/p>\n

Business Intelligence vs Data Science – Definition<\/h3>\n

Both Data Science and Business Intelligence revolve around data. However, while Data Science is the bigger pool containing greater information, Business Intelligence can be thought of as a part of the bigger picture.<\/p>\n

Furthermore, Business Intelligence is limited in the scope of the business domain<\/em>. BI is about developing dashboards, creating business insights, organizing data and extracting information that would help the businesses to grow.<\/p>\n

However, Data Science, on the other hand, acquires a much larger picture. Data Science makes use of a wide array of complex statistical algorithms and predictive models<\/em>. Data Science is much more complex compared with Business Intelligence.<\/p>\n

In business intelligence, past data is analyzed to understand the current trends of the business. However, in Data Science, we use data to make future predictions and forecast growth of the business.<\/p>\n

The tools of business intelligence are also limited to the analysis of management information and curation of business strategies.<\/em> However, the tools of a data scientist involve complex algorithmic models, data processing and even big data tools.<\/p>\n

While BI focuses on generating reports based on the internal structured data, Data Science focuses on generating insights out of the data<\/em>. These insights are generated as a result of complex predictive analytics and the output presented is not a report but a data model.<\/p>\n

This data model is a predictive platform that uses Machine Learning to gain future insights and capture trends in the data.<\/p>\n

Business Intelligence vs Data Science – Skills<\/h3>\n

Some of the important skills required for Business Intelligence are –<\/p>\n

    \n
  1. Possession of creative thinking and strong business acumen.<\/li>\n
  2. Ability to perform problem-solving.<\/li>\n
  3. Knowledge of data analysis to make business decisions.<\/li>\n
  4. Excellent communication and presentation skills.<\/li>\n
  5. Ability to extract data using SQL<\/strong>.<\/li>\n
  6. Well versed with various ETL (Extract, Transform, Load) tools.<\/li>\n<\/ol>\n

    Following are the skills required for Data Science –<\/p>\n

      \n
    1. Well versed with tools like Python, R<\/a>, SAS etc.<\/li>\n
    2. Able to perform complex statistical analysis of data.<\/li>\n
    3. Ability to visualize data through tools like Tableau, Matplotlib, ggplot2 etc.<\/li>\n
    4. Should be able to deal with both structured and unstructured data.<\/li>\n
    5. Proficiency in both SQL and NoSQL.<\/li>\n
    6. Knowledge of Machine Learning algorithms<\/li>\n
    7. Familiarity with tools of big data like Hadoop<\/a><\/strong> and Spark.<\/li>\n<\/ol>\n

      Business Intelligence vs Data Science – Responsibilities<\/h3>\n

      Some of the key responsibilities of working in business intelligence are –<\/p>\n

        \n
      1. Identification of the source system and engagement in business connectivity<\/li>\n
      2. Focus on key business areas and resolution strategies.<\/li>\n
      3. Working with the project managers and clients to define business requirements.<\/li>\n
      4. Performing validation on the data.<\/li>\n
      5. Implementing approved projects and delivering strategic results.<\/li>\n
      6. Reporting progress of the BI program.<\/li>\n<\/ol>\n

        A Data Scientist is responsible for the following –<\/p>\n

          \n
        1. Preprocessing and transforming the data.<\/li>\n
        2. Development of predictive models that forecast future events.<\/li>\n
        3. Fine-tuning the machine learning models and optimizing their performances.<\/li>\n
        4. Assisting the industries to identify questions required to be solved.<\/li>\n
        5. Using story-telling for visual communication of results.<\/li>\n<\/ol>\n

          Data Science vs Business Intelligence – Salary<\/h3>\n

          According to Glassdoor, a Business Intelligence analyst earns an average of $80,154 per year<\/strong>. A Data Scientist, on the other hand, earns an average of $117,345 per year<\/strong>.<\/p>\n

          Summary<\/h3>\n

          Business Intelligence (BI) focuses on reporting and dashboard tools. It uses past data to help managers make smart decisions. BI tools like Tableau, Power BI, and Excel create easy-to-read charts and reports. It helps in tracking sales, profits, and performance in real time.<\/p>\n

          Data Science is more advanced. It builds models to predict the future. It uses coding, machine learning, and big data tools to go deeper than BI. A Data Scientist may create a system that predicts customer behavior, while a BI user will only see what happened last month.<\/p>\n

          You got all the relevant information about\u00a0Data Science vs Business Intelligence. Now, it’s easy to\u00a0decide your career. Data Science is a bigger term and Business Intelligence is a concept used in it.<\/p>\n

          Today, Data Science is offering many jobs, now it’s your turn to grab it. Get this complete details of Data Science Certificates<\/a><\/strong> and kickstart your career in it.

          Business Intelligence and Data Science are two of the most recurring terms in the digital era. While both of them involve the use of data, they are totally different from one another. Data Science...<\/p>\n","protected":false},"author":7,"featured_media":55394,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[19],"tags":[19630,19631],"class_list":["post-55321","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science","tag-data-science-and-business-intelligence","tag-difference-between-data-science-and-business-intelligence"],"yoast_head":"\nBusiness Intelligence vs Data Science - 4 Ways to Tell Them Apart - DataFlair<\/title>\n<meta name=\"description\" content=\"Data Science vs Business Intelligence - See the differences on the basis of roles responsibilities, tools used, salaries etc.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/business-intelligence-vs-data-science\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Business Intelligence vs Data Science - 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