Business Intelligence vs Data Science

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Learn how Business Intelligence & Data Science both have the same goal of providing meaningful insight that is data-driven, but data science looks forward while business intelligence looks back

Business intelligence and data science are both essential topics when talking about business operations and management. These two topics are considered critical when business is the talk of the town as they focus on the ways to run a business based on its past performance. But they don’t mean the same and are also important to a business in their aspects. 

In this blog, we will be going through business intelligence and data science along with the difference between them.

Business Intelligence Definition

A set of processes, architectures, & technologies converting raw data into meaningful insights that can drive profitable business actions is known as BI (Business Intelligence).

Data Science Definition

The field of study combining domain expertise, programming skills, & knowledge of mathematics & statistics to extract meaningful information and insights from data is known as Data Science.

Difference between Business Intelligence and Data Science

Business Intelligence Data Science
An accumulation of processes & technologies taking raw data & deriving something meaningful out of itGathering raw data & analyze them with statistical techniques & algorithms to bring out meaningful insight & conclusions
Findings are presented in the form of dashboards, charts, graphs, summaries, etc.Findings are presented as statistical models & algorithms
The goal is to identify patterns & trends to turn raw data into insightsThe goal is to test the hypothesis using experimentation & iteration
It helps organizations to answer their questionsIt is used by data scientists
Data are stored in data warehousesData are distributed in real-time clusters
It focuses on descriptive analytics such as what happened, why did it happen, what are the learnings, etc.It focuses on predictive & prescriptive analytics such as what will happen in the future, how can we prepare ourselves for it, etc.
It handles structured & unstructured data sets static in nature It handles structured & unstructured data sets dynamic in nature 
Does it deal with what has happened?It deals with what will happen in the future & what if this happens
It opts for an analytical approach for decision-making applicationsIt takes a predictive & prescriptive analysis approach to generate insights into data & more complex in nature
It analyses historical data to discover patterns & trends so that businesses can make more informed decisionsIt makes use of historical & present data to create as much as impact possible on the business

Advantages and disadvantages of Business Intelligence

Advantages

  • Business folks can start analyzing data themselves. They don’t have to wait for IT specialists to run complex and custom reports.
  • Takes less time to prepare a dashboard of information than the time taken by a developer to prepare the same report.
  • Provides real-time metrics & reports for a better business plan.
  • Makes key performance indicators (KPIs) easy to understand.
  • Helps to increase team productivity by assisting with the relevant and required information.
  • Dashboard information can reveal and address ineffective activities that can help to increase profit.
  • Information can be isolated into small reports instead of getting all data at once.

Disadvantages

  • User resistance is one big barrier.
  • Sometimes it is necessary to analyze huge amounts of irrelevant & poor-quality data.
  • Often, the cleanup of such irrelevant data is time-consuming. 
  • Most organizations don’t understand their business processes enough to determine how to improve them. The entire business intelligence could disintegrate if the process does not have a direct impact.

Advantages and disadvantages of Data Science

Advantages

  • Helps organizations know how & when their products sell the best. That’s why the products are always delivered to the right place and at the right time.
  • Helps the marketing & sales team understand their efforts well by refining & identifying the target audience.
  • Faster & better decisions are taken by the business to improve efficiency & earn higher profits.

Disadvantages

  • Extracted information from structured and unstructured data for further use can be misused by some people.
  • Tools used for data science & analytics are quite expensive. These tools are also more complex to use, so people have to learn and be trained to use them.

Conclusion

Essential in business operations, Business Intelligence & Data Science helps in collecting raw data & analyzing them for future operations. 

Although these two concepts sound similar, there are major differences between them. Business Intelligence is about studying business growth patterns from achieving goals to decision-making processes based on the data collected from past operations. 

On the other hand, Data Science deals with transforming raw data into meaningful information which eventually helps to draw future trends on predictive grounds by questioning previous patterns & strategies. 

Lastly, these two concepts are highly distinct from each other but are intertwined in such a way that they cannot do without each other.

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