How Business Intelligence Is Different From Data Science

How Business Intelligence is different from Data Science

Data Science contains both structured and unstructured data, it’s essential in dealing with the data volumes of modern times. Mainly it is used in technology, finance, and internet-based businesses. It is not useful in business decisions because of its inability to apply the outcomes of applied algorithms. Data Science has a complex and stringent approach to uncover the hidden patterns and trends prevailing in data. It is upgraded business intelligence with refined statistical tools to analyze data and predict better for wiser use of predictions.

Business Intelligence is mandatory with rising data size and complexities. BI is widely implemented in data management solutions. It collects and analyzes data, the purpose to provide insights and to streamline business operations. BI refers to data collection methods, opting technologies, using it in applications using data points to populate business analysis and for data presentation. Business analytics is part of business intelligence that analyzes historical data to predict business trends and generates actionable data. It can minimize operational costs and increase revenues.

BI and Data Science

Differences & Features:

Sr.No.Data Science:Business Intelligence:
1It deals with a variety of structured, semi-structured, and unstructured data.It requires an adequately structured data for accurate predictions.
2Gathers data and has multiple supersets.Gathered data is used for analysis.
3Data input sources are multiple.Limited data input sources as it is of past performance.
4Largely it is data-dependent. Involves data but is not data-dependent.
5Data Science blends data and algorithms to build technology that can respond to a set of questions. It encourages you to discover new questions that can change your outlook. Business Intelligence can draw interpretations based on business requirements. It answers the questions you put forth.  
6Data Science analyzes past data trends or patterns for predictive analysis. BI helps to interpret past data for illustrative analysis.
7Data Science answers queries like geographical influence on business, seasonal factors that affect business and customer preferences. Business Intelligence can respond to the financial aspect of factors affecting business.
8It involves the use of statistics and coding for algorithms and the development of software. Uses statistics but no coding involved.
9Programming languages used are  C, C++, C#, Java, Julia, Matlab, Python, R, SAS, Scala, SQL, Stata, Haskell, Programming languages used in Business Analytics are  C, C++, C#, Objective C, Java, Javascript, PHP, Python, R, SQL, Ruby.
10Data Science findings are not used by decision-makers in business due to the lack of clarity in data sets. BI accesses your organizational data to understand current business performance and improving it.
11Data Scientists use various methods, algorithms, and processes for insights from structured and unstructured data.Business Intelligence is knowledge acquired over a period, its statistical interpretation, and continuous upgrades in the sector.
12Investment costs for data science is higher.Lesser investments for Business Intelligence as data is historic.
13Used in Machine Learning and Artificial IntelligenceUsed in Business Analytics
14Data Science is not useful in day-to-day business decisions.Business Intelligence is useful in day-to-day business decisions.
15Data Science can tell you why things are happening the way they are happening in the business.Business Intelligence can tell you what and why things are happening in the business. 
16It is a predictive and proactive analysis of data. It is more of a retrospective and reactive analysis of data.
17It is a modern and flexible approach to handling business data. It is the traditional and inflexible approach to handling business data.
18Data scientists acquire skills to interpret data sets.Business experts interpret data based on their intelligence and experience.
19Machine analytical can maintain the quality of the analysis.Manual intervention can impact the analytical quality.
20Data Science is also known as AI-enables Data Science.Business Intelligence is not the same as Business Analytics.
21It requires the technical team to extract insights; ordinary businesses are forced to rely on expertise.Non-technical people can easily draw powerful insights if they are trained.
22Data scientists continuously refine algorithms for efficient predictions.These are set processes and based on statistical calculations, the change in formulas will change the outcome.
23Data Science focuses on experimentation.Traditional Business Intelligence systems have no room for experimentation.
24Widely used in healthcare, banking, e-commerce, etc.Widely used in retail, food, oil, fashion, pharma, etc.

Similarities of Data Science and Business Intelligence:

The focus is on data collection, formatting and interpretation in Data Science and Business Intelligence. Business insights can give a competitive edge to decide on the actions. Both provide a high level of support based on a detailed study of data points and help in taking accurate decisions.

Data Science reinforces Business Intelligence with the analysis that gives power to the assessors and decision-makers. Business Experts can work with technology and enhance their work patterns instead of just relying on their knowledge.

The perspective of DS and BI real data and its predictions, to improve processes, transform data interpretation and adds business value with best business decisions.

Benefits of Data Science:

  • Automate redundant tasks and business processes
  • Increased productivity
  • Identifying the target audience
  • Personalized insights, purchases, and customer experience
  • Employee Training
  • Trend based actions
  • Adopting best practices
  • Analyze purchasing patterns
  • Predictive Analysis
  • Assessing Business decisions
  • Better decision making

Benefits of Business Intelligence:

  • Quicker reporting, analysis or planning
  • Precise reporting, analysis or planning
  • Better data quality
  • Improved employee and customer satisfaction
  • Enhanced operational efficiency
  • Increased competitive advantage
  • Reduced costs and expenses
  • Increased business revenues
  • Standardization of business processes
  • Lesser Workforce needed
  • Better business decisions

A survey of 2600 business intelligence users by BI-Survey received a detailed opinion on various comparatives.

About 64% found BI as faster reporting, analysis or planning and 56% voted for its accuracy and 49% could make better business decisions.

  • Target is a company with business intelligence and analytics software is the world’s largest Business Intelligence provider and serves Microsoft too.
  • Kognito offers solutions to companies that need to analyze large and complex data for data migration, a fast and scalable analytical database for telecom, finance, and retail sectors.
  • Host Analytics a leader in cloud-based financial applications helps in planning, consolidation, reporting, and analytics. Businesses benefit from the improved business agility and lowered costs of improved security.

Top 9 Business Intelligence Companies:

  1. Microsoft
  2. Tableau Software
  3. Sisense
  4. IBM
  5. SAS
  6. Tibco Software
  7. SAP
  8. Oracle
  9. Pentaho

Shell a giant oil company used data science to forestall machine failure in facilities globally.

Qubole uses ML and AI to analyze data, integrates with lots of coding languages and open-source tools to automate data processing for data science.

Sumo Logic believes that businesses are incessantly generating data online and the analysis of this data should be done simultaneously. It can give better insights in real-time and for efficient processing, it uses the cloud.

Top 9 Data Science Companies:

  1. Numerator
  2. Cloudera
  3. Splunk
  4. SPINS
  5. Alteryx
  6. Civis Analytics
  7. Sisense
  8. Teradata
  9. Oracle
Data Science

Future of Data Science and Business Intelligence:

Business Intelligence is progressing towards Data Science for real-time insights and profitable business outcomes. Wipro has over 1000 data scientists working across various domains this points towards the current status and rising demand because of changing business needs and the increase in data-driven organizations.

By the year 2020 data science will automate over 40% of tasks, 90% of large enterprises will generate revenues from data as service.

Ventana Research Assertions predicts that by 2021, 66% of analytics processes to go beyond what happened and why and share what should be done and 33% of organizations would want NLP as a capability of Business Intelligence systems.

Business Intelligence incorporated in real-time to improve business decisions by nearly 50% of the companies will change the scenario by 2022. Around 60% of companies that have 20 data scientists will need a professional code of conduct for the ethical use of data by the year 2023.

The year 2025 will make mark forever increasing data-related activities. We will see 150 billion device users, the digital data will rise from 40 zettabytes to 175 zettabytes and IoT devices will generate more than 90 zettabytes and almost 6 billion consumers will interact with the data.

Trends in Business Intelligence include integrated content and capabilities; automation, generate actionable insights, data collaborative to leverage usage, data governance, adoption of AI, machine learning as service, and overall efficiency.                             

Trends in Data Science include data quality management, predictive and prescriptive analytics, data as a service, work towards tight privacy and better personalization.

Business Intelligence Process:

  • Define levels of questions to be answered.
  • Select BI tools you will use.
  • Achieve anything specific and plan in comparison with past performance.
  • Define the reports you need.

Data Science Process:

  • Identify sources to collect data
  • Collect data from multiple sources
  • Integrate different data sources
  • Visualize the data

Whether to Choose Business Intelligence or Data Science:

Define your needs and the approach that suits your business. Business Intelligence earlier meant for large enterprises but not this is available for small and mid-sized organizations. Newer techniques like self-service business intelligence enable users to work on data with no technical knowledge and are user-friendly. BI lets you target the weaker areas by providing actionable insights for the problems. Business Intelligence tools improve productivity, processes and are excellent solutions for less complicated businesses.

Data Science lets you achieve a generous understanding of customer behavior, real-time insights, and predictive analytics for the business to take a competitive advantage. More the business expands and you have to deal with complex and huge datasets data science is the ultimate reliable technology for accurate business decisions.


New technologies like Business Intelligence and Data Science have immense capabilities that depend on implementation to bring transformation in business. Data and its value are known to all business enterprises but the pain points should be identified to extract maximum benefits by applying technology.

Questions that arise while applying new technology should make you unstoppable as the same technology or in combination with other technology; opens the realm of possibilities for your business.

The recent developments of Data Science and Business Intelligence are bringing major change to the way data is analyzed and the results are utilized. There is a variety of data that gets generated with increased accessibility and internet usage. This data is useful for business growth and represented in simple formats for the management and other decision-makers. They can rapidly change the current business processes and visible future from the forecasts.

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Bridged is striving to improve the efficiency of clients in the artificial intelligence sector through the use of training data powered by human intel. Since 2018, Bridged has delivered 50M+ datasets by deploying its 13,000+ Bridged-qualified crowd-force.

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