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Business Intelligence vs Data Science: Learning the Differences

Business Intelligence vs Data Science

In the modern IT world, some words often overlap with each other, which results in creating confusion between the concepts like analytics vs big data, or machine learning vs artificial intelligence or cognitive intelligence.

These are some common overlay words used many times, Data Science vs BI is also one among them. In this blog, we are going to have a clear idea of what is data science, and Business intelligence is all about.  

In earlier years, there were only a few Business Intelligence (BI) companies listed in the big blue-chip. Mostly because utilization of analytics software is expensive, and it requires IT specialist and building data centres those are also expensive.

After a while, BI Tools entered into the market that comes at low cost, and Tableau is the market leader and the tableau certification developers have high demand in the market. 

In terms of volume and variety, Data plays a vital role in everyday life. Because of this, businesses need a data scientist, Data science and BI are distinctively different.

While BI delivers the answer to the question, you know you need to ask. Usually, BI systems didn’t help you predict anything.

The way they can help you to view the connections of various variables. On the other hand, Data science has driven the Big data developers with the analytics we can push the present and future models. As a result, they allow them to react according to customer behaviour.

Now, let’s explore the differences between Data Science and BI.

By using Data science, we can allow the organizations to pause reactive and retrospective in their analysis of data, and start being proactive, empirical and predictive.

While we are adopting the Data scientist instead of BI, then the organization becomes a data-driven, and there is a significant shift there.

In the empirical view of data, the implementing tools like NoSQL and Hadoop can convert a public sector organization and its activities accurately in no time.

Though you survive and succeed in the competitive market, innovative organizations should resolve the complicated business problems and shift their focus towards Data science from Business Intelligence.

When we used in conjugating with predictive analytics, it permits to achieve real-time insights and provide future predictions according to customer exemptions, and it also improves the response to the customer. Here we can discuss the essential difference between Data Science and BI.

Business value

The data analysis of the company should appraise the business decisions; it means it shows the values of past, present, and it will predict the future value also. In this scenario, Data science is the best than BI.


BI systems will perform based on real-time data from real-time events. But data science looks forward, construing the data and predicts what happens shortly.

Data sources

By its static nature, Business intelligence information source has to be pre-planned and slowly added. Another side Data science offers a more flexible approach, i.e. we can add the required data source as we need.


BI systems are using data warehouse and soiled structure for Data Utilization. It implies it is difficult to deploy throughout the business on the other face Data science can distribute the data in real-time.


Business Intelligence follows prescriptive and retrospective systems, which is much not as likely than Data Science Predictive system.

IT-owned vs Business owned.

Before, BI systems are frequently operated and owned by the IT section and send analytics to intelligence who interpreted it. With Data Science, the phase of analytics is changed.

The new big data designed solutions are owned by analytics, and they spend little time on IT housekeeping, and most of their time is used to analyze the data and make predictions of business decisions.

Also Read: 8 Information Technology (IT) Trends for Upcoming Year 2020

Data quality  

Data science offers accuracy, much more probabilities and confidence level. While working with BI, a data analyst has a limit to provide a single version of fact with their findings.


The BI systems follow comparative and static, and they don’t offer experimentation and exploration in terms of how information gathered and succeeded.


BI system delivered detailed reports, trends, and KPIs. However, it doesn’t say how data will look like in the future, a prediction analysis is the biggest advantage to Data science, and it allows data in the form of experimentation and patterns.


Business intelligence will help you answer the questions as you know. In business data delivery variation is essential. Data science will help to discover the new inquiries as long as the way it promotes companies to apply penetrations to new data.

The below table represents the Key difference between Data Science and BI Analyst characteristics: 

AreaData scientistBI Analyst
ProcessVisual, Experimentation, ExploratoryComparative, Static
FocusCorrelations, patterns and modelsKPIs, Reports, Trends
Data ModelSchema on querySchema on load
TransformOn-demanded, enrichment, In-databaseUpfront, carefully planned
Data QualityProbabilities, Good enoughThe single version of the truth
Data sourceOn-the-fly, as neededAdd slowly, pre-planned 
AnalyticsPrescriptive, Predictive, Preventative  Descriptive, Retrospective 
Use of MLHighVery low or None
ResourcesFinding resources is more difficultResources are well planned
CodeUsually high in codeLow code

For that reason, the typical Data Scientist’s practices and toolbox are made for agility and flexibility. Open-source libraries, containers, programming languages, APIs and agile are Data Scientist to find solutions and skim over the ideas quickly

All these tools, work best in an agile, fluid, and open environment, where the usage of these tools are not restricted by any external factors. This kind of environment can generally find in start-ups and tech companies.


Business intelligence is too good for newly established industries. But for in long period Data Science will going to place your business into the next level, future scheduling by making predictions now is one of the marvels in Data Science.

Therefore, Data science plays a crucial and vital role than Business intelligence. The combination of Data Science and Automation is going to redefine the future.

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