EDUCBA

EDUCBA

MENUMENU
  • Free Tutorials
  • Free Courses
  • Certification Courses
  • 360+ Courses All in One Bundle
  • Login
Home Data Science Data Science Tutorials Head to Head Differences Tutorial Data Science vs Business Intelligence
Secondary Sidebar
Head to Head Differences Tutorial
  • Differences Tutorial
    • Scikit Learn vs TensorFlow
    • Azure Functions vs Logic Apps
    • Azure Data Factory vs Databricks
    • SHA1 vs MD5
    • Azure SQL Database vs Managed Instance
    • Azure SQL Database vs SQL Server
    • PostgreSQL vs MySQL
    • PostgreSQL vs MySQL Benchmark
    • ArangoDB vs MongoDB
    • Cloud Computing vs Big Data Analytics
    • T-SQL vs SQL
    • PostgreSQL vs MariaDB
    • Spark vs Impala
    • Datadog vs Splunk
    • Domo vs Tableau
    • Data Scientist vs Data Engineer vs Statistician
    • Big Data Vs Machine Learning
    • Predictive Analytics vs Business Intelligence
    • AI vs Machine Learning vs Deep Learning
    • Business Intelligence vs Data Warehouse
    • Apache Kafka vs Flume
    • Data Science vs Machine Learning
    • Business Analytics Vs Predictive Analytics
    • Data mining vs Web mining
    • Data Science Vs Data Mining
    • Data Science Vs Business Analytics
    • Analyst vs Associate
    • Apache Hive vs Apache Spark SQL
    • Apache Nifi vs Apache Spark
    • Apache Spark vs Apache Flink
    • Apache Storm vs Kafka
    • Artificial Intelligence vs Business Intelligence
    • Artificial Intelligence vs Human Intelligence
    • Al vs ML vs Deep Learning
    • SQL vs SQLite
    • Assembly Language vs Machine Language
    • AWS vs AZURE
    • AWS vs Azure vs Google Cloud
    • Big Data vs Data Mining
    • Big Data vs Data Science
    • Big Data vs Data Warehouse
    • Blu-Ray vs DVD
    • Business Intelligence vs Big Data
    • Business Intelligence vs Business Analytics
    • Business Intelligence vs Data analytics
    • Business Intelligence VS Data Mining
    • Business Intelligence vs Machine Learning
    • Business Process Re-Engineering vs CI
    • Cassandra vs Elasticsearch
    • Cassandra vs Redis
    • Cloud Computing Public vs Private
    • Cloud Computing vs Fog Computing
    • Cloud Computing vs Grid Computing
    • Cloud Computing vs Hadoop
    • Computer Network vs Data Communication
    • Computer Science vs Data Science
    • Computer Scientist vs Data Scientist
    • Customer Analytics vs Web Analytics
    • Data Analyst vs Data Scientist
    • Data Analytics vs Business Analytics
    • Data Analytics vs Data Analysis
    • Data Analytics Vs Predictive Analytics
    • Data Lake vs Data Warehouse
    • Data Mining Vs Data Visualization
    • Data mining vs Machine learning
    • Data Mining Vs Statistics
    • Data Mining vs Text Mining
    • Data Science vs Artificial Intelligence
    • Data science vs Business intelligence
    • Data Science Vs Data Engineering
    • Data Science vs Data Visualization
    • Data Science vs Software Engineering
    • Data Scientist vs Big Data
    • Data Scientist vs Business Analyst
    • Data Scientist vs Data Engineer
    • Data Scientist vs Data Mining
    • Data Scientist vs Machine Learning
    • Data Scientist vs Software Engineer
    • Data visualisation vs Data analytics
    • Data vs Information
    • Data Warehouse vs Data Mart
    • Data Warehouse vs Database
    • Data Warehouse vs Hadoop
    • Data Warehousing VS Data Mining
    • DBMS vs RDBMS
    • Deep Learning vs Machine learning
    • Digital Analytics vs Digital Marketing
    • Digital Ocean vs AWS
    • DOS vs Windows
    • ETL vs ELT
    • Small Data Vs Big Data
    • Apache Hadoop vs Apache Storm
    • Hadoop vs HBase
    • Between Data Science vs Web Development
    • Hadoop vs MapReduce
    • Hadoop Vs SQL
    • Google Analytics vs Mixpanel
    • Google Analytics Vs Piwik
    • Google Cloud vs AWS
    • Hadoop vs Apache Spark
    • Hadoop vs Cassandra
    • Hadoop vs Elasticsearch
    • Hadoop vs Hive
    • Hadoop vs MongoDB
    • HADOOP vs RDBMS
    • Hadoop vs Spark
    • Hadoop vs Splunk
    • Hadoop vs SQL Performance
    • Hadoop vs Teradata
    • HBase vs HDFS
    • Hive VS HUE
    • Hive vs Impala
    • JDBC vs ODBC
    • Kafka vs Kinesis
    • Kafka vs Spark
    • Cloud Computing vs Data Analytics
    • Data Mining Vs Data Analysis
    • Data Science vs Statistics
    • Big Data Vs Predictive Analytics
    • MapReduce vs Yarn
    • Hadoop vs Redshift
    • Looker vs Tableau
    • Machine Learning vs Artificial Intelligence
    • Machine Learning vs Neural Network
    • Machine Learning vs Predictive Analytics
    • Machine Learning vs Predictive Modelling
    • Machine Learning vs Statistics
    • MariaDB vs MySQL
    • Mathematica vs Matlab
    • Matlab vs Octave
    • MATLAB vs R
    • MongoDB vs Cassandra
    • MongoDB vs DynamoDB
    • MongoDB vs HBase
    • MongoDB vs Oracle
    • MongoDB vs Postgres
    • MongoDB vs PostgreSQL
    • MongoDB vs SQL
    • MongoDB vs SQL server
    • MS SQL vs MYSQL
    • MySQL vs MongoDB
    • MySQL vs MySQLi
    • MySQL vs NoSQL
    • MySQL vs SQL Server
    • MySQL vs SQLite
    • Neural Networks vs Deep Learning
    • PIG vs MapReduce
    • Pig vs Spark
    • PL SQL vs SQL
    • Power BI Dashboard vs Report
    • Power BI vs Excel
    • Power BI vs QlikView
    • Power BI vs SSRS
    • Power BI vs Tableau
    • Power BI vs Tableau vs Qlik
    • PowerShell vs Bash
    • PowerShell vs CMD
    • PowerShell vs Command Prompt
    • PowerShell vs Python
    • Predictive Analysis vs Forecasting
    • Predictive Analytics vs Data Mining
    • Predictive Analytics vs Data Science
    • Predictive Analytics vs Descriptive Analytics
    • Predictive Analytics vs Statistics
    • Predictive Modeling vs Predictive Analytics
    • Private Cloud vs Public Cloud
    • Regression vs ANOVA
    • Regression vs Classification
    • ROLAP vs MOLAP
    • ROLAP vs MOLAP vs HOLAP
    • Spark SQL vs Presto
    • Splunk vs Elastic Search
    • Splunk vs Nagios
    • Splunk vs Spark
    • Splunk vs Tableau
    • Spring Cloud vs Spring Boot
    • Spring vs Hibernate
    • Spring vs Spring Boot
    • Spring vs Struts
    • SQL Server vs PostgreSQL
    • Sqoop vs Flume
    • Statistics vs Machine learning
    • Supervised Learning vs Deep Learning
    • Supervised Learning vs Reinforcement Learning
    • Supervised Learning vs Unsupervised Learning
    • Tableau vs Domo
    • Tableau vs Microstrategy
    • Tableau vs Power BI vs QlikView
    • Tableau vs QlikView
    • Tableau vs Spotfire
    • Talend Vs Informatica PowerCenter
    • Talend vs Mulesoft
    • Talend vs Pentaho
    • Talend vs SSIS
    • TensorFlow vs Caffe
    • Tensorflow vs Pytorch
    • TensorFlow vs Spark
    • TeraData vs Oracle
    • Text Mining vs Natural Language Processing
    • Text Mining vs Text Analytics
    • Cloud Computing vs Virtualization
    • Unit Test vs Integration Test?
    • Universal analytics vs Google Analytics
    • Visual Analytics vs Tableau
    • R vs Python
    • R vs SPSS
    • Star Schema vs Snowflake Schema
    • DDL vs DML
    • R vs R Squared
    • ActiveMQ vs Kafka
    • TDM vs FDM
    • Linear Regression vs Logistic Regression
    • Slf4j vs Log4j
    • Redis vs Kafka
    • Travis vs Jenkins
    • Fact Table vs Dimension Table
    • OLTP vs OLAP
    • Openstack vs Virtualization
    • Cluster v/s Factor analysis
    • Informatica vs Datastage
    • CCBA vs CBAP
    • SPSS vs EXCEL
    • Excel vs Tableau
    • Cassandra vs MySQL
    • RabbitMQ vs Kafka
    • SAAS vs Cloud
    • RabbitMQ vs Redis
    • AMQP vs MQTT
    • Forward Chaining vs Backward Chaining
    • Google Data Studio vs Tableau
    • ActiveMQ vs RabbitMQ
    • Cloud vs Data Center
    • Cores vs Threads
    • Inner Join vs Outer Join
    • ZeroMQ vs Kafka
    • Mxnet vs TensorFlow
    • Redis vs Memcached
    • RDBMS vs NoSQL
    • AWS Direct Connect vs VPN
    • Cassandra vs Couchbase
    • Elegoo vs Arduino
    • Redis vs MongoDB
    • Chef vs Puppet
    • GSM vs GPRS
    • Keras vs TensorFlow vs PyTorch
    • Cloudflare vs CloudFront
    • Bitmap vs Vector
    • Left Join vs Right Join
    • IaaS vs PaaS
    • Blue Prism vs UiPath
    • GNSS vs GPS
    • Cloudflare vs Akamai
    • GCP vs AWS vs Azure
    • Arduino Mega vs Uno
    • Qualitative vs Quantitative Data
    • Arduino Micro vs Nano
    • PIC vs Arduino
    • PRTG vs Solarwinds
    • PostgreSQL vs SQLite
    • Metabase vs Tableau
    • Arduino Leonardo vs Uno
    • Arduino Due vs Mega
    • ETL Vs Database Testing
    • DBMS vs File System
    • CouchDB vs MongoDB
    • Arduino Nano vs Mini
    • IaaS vs PaaS vs SaaS
    • On-premise vs off-premise
    • Couchbase vs CouchDB
    • Tableau Dimension vs Measure
    • Cognos vs Tableau
    • Data vs Metadata
    • RethinkDB vs MongoDB
    • Cloudera vs Snowflake
    • HBase vs Cassandra
    • Business Analytics vs Business Intelligence
    • R Programming vs Python
    • MongoDB vs Hadoop
    • MySQL vs Oracle
    • OData vs GraphQL
    • Soft Computing vs Hard Computing
    • Binary Tree vs Binary Search Tree
    • Datadog vs CloudWatch
    • B tree vs Binary tree
    • Cloudera vs Hortonworks
    • DevSecOps vs DevOps
    • PostgreSQL Varchar vs Text
    • PostgreSQL Database vs schema
    • MapReduce vs spark
    • Hypervisor vs Docker
    • SciLab vs Octave
    • DocumentDB vs DynamoDB
    • PostgreSQL union vs union all
    • OrientDB vs Neo4j
    • Data visualization vs Business Intelligence
    • QlikView vs Qlik Sense
    • Neo4j vs MongoDB
    • Postgres Schema vs Database
    • Mxnet vs Pytorch
    • Naive Bayes vs Logistic Regression
    • Random Forest vs Decision Tree
    • Random Forest vs XGBoost
    • DynamoDB vs Cassandra
    • Looker vs Power BI
    • PostgreSQL vs RedShift
    • Presto vs Hive
    • Random forest vs Gradient boosting
    • Gradient boosting vs AdaBoost
    • Amazon rds vs Redshift
    • Bigquery vs Bigtable
    • Data Architect vs Data Engineer
    • DataSet vs DataTable
    • dataset vs dataframe
    • Dataset vs Database
    • New Relic vs Splunk
    • Data Architect and Management Designer
    • Data Engineer vs Data Analyst
    • Grafana vs Tableau
    • MySQL text vs Varchar
    • Relational Database vs Flat File
    • Datadog vs Prometheus
    • Neo4j vs Neptune
    • Data Mining vs Data warehousing
    • DocumentDB vs MongoDB
    • PostScript vs PCL
    • QRadar vs Splunk
    • Qlik Sense vs Tableau
    • DigitalOcean vs Google Cloud
    • PostgreSQL vs Elasticsearch
    • Redshift vs blueshift
    • Gitlab vs Azure DevOps

Related Courses

Online Data Science Course

Online Tableau Training

Azure Training Course

Hadoop Certification Course

Data Visualization Courses

All in One Data Science Course

Data Science vs Business Intelligence

By Priya PedamkarPriya Pedamkar

Data science vs Business intelligence

Difference Between Data Science vs Business Intelligence

As Information Technology is getting more matured in all organization, there come more jargons. And no wonder, why people get confused over it. This usually leads towards, using the words interchangeably and overlap of concepts. But then it becomes a necessity, to understand the concept behind it so that it becomes easy to apply it practically and one can do justice with the business.

All in One Data Science Bundle(360+ Courses, 50+ projects)
Python TutorialMachine LearningAWSArtificial Intelligence
TableauR ProgrammingPowerBIDeep Learning
Price
View Courses
360+ Online Courses | 50+ projects | 1500+ Hours | Verifiable Certificates | Lifetime Access
4.7 (85,992 ratings)

In past years, purchasing and deploying analytical software’s were expensive. Over the time, it has become less expensive and hence easier way of gathering industry information to correlate various datasets, that can give useful information about the business.

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

However, as the data size is becoming huge day by day, not only in terms of volume but variety and velocity as well. Business needs data science which can transform the big sized data into actionable insights. The faster pace of innovation, finding opportunities are highly in focus.  Data science is not limited till extractions of insights and finding opportunities. It ends when everything can be brought into a story, which can influence people’s thought working in that field. It should enable business leaders for taking actions. So let us understand the simple difference between Data science and Business intelligence in detail.

Head to Head comparison Between Data Science and Business intelligence (Infographics)

Below is the Top 20 Comparision between Data Science and Business Intelligence:

Data science vs Business Intelligence InfographicsKey Differences Between Data Science and Business Intelligence

Below is the difference between Data Science and Business Intelligence are as follows

Considering all the above comparison, it can be said that both Data Science and Business intelligence streams are analytical & information-centric, but the levels of insight value make a difference. Data science provides matured & futuristic insights. That’s the reason data science is said as an evolution from Business intelligence.

Generic steps followed in business intelligence stream:

  1. Set a business outcome to improve.
  2. Decide from various datasets, which will be the most relevant one.
  3. Bring data into a good shape.
  4. Design KPI’s, reports, dashboards to give a nice visualization.

Generic steps followed in data science stream:

  1. Set a business outcome to improve or to predict.
  2. Gather all possible and relevant datasets.
  3. Choose an appropriate algorithm to prepare a model.
  4. Evaluate the model for good accuracy
  5. Operationalize the model

Data Science and Business Intelligence Comparison Table

Below is the comparison table between Data Science and Business Intelligence.

Basis Of Comparison Data Science Business Intelligence
Complexity Higher Simpler
Data Distributed and real-time Siloed, Warehoused
Role Using Statistics & Mathematics on a dataset to uncover hidden patterns, analyze and forecast the upcoming situation. BI is about arranging the dataset, extracting useful information and visualizing it to a dashboard.
Technology With cut-throat competition in today’s IT market, companies are striving for innovation and easier solutions for complex business problems. Hence more focus is on data science rather than business intelligence. BI is about answering questions through dashboarding, which could be difficult answering it through excel. BI helps to find a relationship between various variables and time periods. It enables executives to make business decisions.

Prediction is not included in BI.

Usage Data science helps companies to foresee the upcoming situation. Companies can use their potential to mitigate the risk and to enhance the revenue. BI helps companies to do root cause analysis on some failure or to know its present situation.

 

Focus It focuses on the future. BI focuses past and present.
Career Skill Data science skills are more advanced. It requires Data modeling, familiarity with predictive algorithms, good knowledge of languages like R, Python, Scala. Data science is the combination of three fields: Statistics, Machine Learning and Programming. BI requires less qualification as compared to data scientists. The basic skills required are data extraction tools and visualization tools like Tableau, QlikView, Watson Analytics, etc. knowledge.

Till now, many reporting tasks and BI happens through excel.

Evolution It will not be wrong saying; Data science has evolved from Business intelligence. Business intelligence is there for a long time, but previously with only excel. Now in a market, ample of tools available to give a better view of the same with better capabilities.
Process Data science is more towards experimentation and doing something new. Hence it is dynamic and iterative in nature. Business Intelligence is static in nature. Experimentation has less scope in this field. Extraction of data, slight munging of data and finally dashboarding it.
Flexibility Flexibility is very much in Data Science. Data Sources can be added as per the need going ahead in the future. Flexibility is very less in business intelligence. Data sources estimate needs to be pre-planned. And in case of need is to add more data source, it’s slow.
Business Value Data science brings out much better business value than business intelligence, as it focuses on the future scope of the business. Business intelligence has a static process of extracting out the business value by plotting charts and KPI’s. Hence, it tends to show lesser business value than Data science
Thought Process Data science helps someone to come out with questions, which encourages a company to run in a strategic and efficient manner. Business intelligence helps someone to answer the question which already exists.
Data Quality Data science brings in, a fact of data with other parameters like accuracy, precision, recall value and probabilities. It enables decision-makers by giving them confidence levels. Business Intelligence offers good dashboarding with good quality of data only. Good in terms, it should be enough to take out the insights out of the dataset.
Method Analytic & scientific Only analytic
Questions What will happen?

What If?

What happened?

What is happening?

Approach Proactive Reactive
Expertise Role Data scientist Business user
Data Size Hadoop like technologies has evolved and many like these are evolving which can easily handle big size datasets (e.g.=> terabytes of data) Here the tools and technologies are not enough to handle big datasets.
Use cases Not a periodic task. Many of the use cases of BI is around generating and refreshing the standardize dashboards.
Consumption Data science insights are consumed from the enterprise level until the executive level. Business intelligence insights are consumed at the enterprise or department level.

Conclusion

Business intelligence is no doubt really a good thing for an industry to start with. But in the long run, adding a layer of data science is ultimately going to make it stand differently. Planning the future by making a prediction today is one of the wonders of data science.  Hence Data science plays a pivotal and better role than business intelligence. Looks like, Data science in amalgamation with automation, is going to redefine the future.

Recommended Articles

This has been a guide to Data Science vs Business Intelligence. Here we have discussed head to head comparison, key difference along with infographics and comparison table. You may also look at the following articles to learn more –

  1. 5 Best Thing You Must Know About Business Intelligence vs Data Warehouse
  2. Predictive Analytics vs Data Science – Learn The 8 Useful Comparison
  3. 5 Best Thing You Must Know About Business Intelligence vs Data Warehouse
  4. Data Science and Its Growing Importance
Popular Course in this category
Business Intelligence Training (12 Courses, 6+ Projects)
  12 Online Courses |  6 Hands-on Projects |  121+ Hours |  Verifiable Certificate of Completion
4.5
Price

View Course

Related Courses

Data Scientist Training (85 Courses, 67+ Projects)4.9
Tableau Training (8 Courses, 8+ Projects)4.8
Azure Training (6 Courses, 5 Projects, 4 Quizzes)4.7
Hadoop Training Program (20 Courses, 14+ Projects, 4 Quizzes)4.7
Data Visualization Training (15 Courses, 5+ Projects)4.7
All in One Data Science Bundle (360+ Courses, 50+ projects)4.7
8 Shares
Share
Tweet
Share
Primary Sidebar
Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Live Classes
  • Corporate Training
  • Certificate from Top Institutions
  • Contact Us
  • Verifiable Certificate
  • Reviews
  • Terms and Conditions
  • Privacy Policy
  •  
Apps
  • iPhone & iPad
  • Android
Resources
  • Free Courses
  • Database Management
  • Machine Learning
  • All Tutorials
Certification Courses
  • All Courses
  • Data Science Course - All in One Bundle
  • Machine Learning Course
  • Hadoop Certification Training
  • Cloud Computing Training Course
  • R Programming Course
  • AWS Training Course
  • SAS Training Course

ISO 10004:2018 & ISO 9001:2015 Certified

© 2022 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA
Free Data Science Course

SPSS, Data visualization with Python, Matplotlib Library, Seaborn Package

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA Login

Forgot Password?

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

Let’s Get Started

By signing up, you agree to our Terms of Use and Privacy Policy.

This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy

Loading . . .
Quiz
Question:

Answer:

Quiz Result
Total QuestionsCorrect AnswersWrong AnswersPercentage

Explore 1000+ varieties of Mock tests View more