Data Science  vs  Business Intelligence

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Index Of Stories

What is Data Science?

Objectives

What is Business Intelligence?

How is Business Intelligence Different from Data Science?

Skills requirements

Data collection and management

Complexity

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Data science involves extracting information from datasets and creating forecasts. It uses machine learning, descriptive analytics, and other sophisticated analytics tools. The process of data science starts from collecting and maintaining data. The second step is to process data through data mining, modeling, and summarization.

What is Data Science?

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Business intelligence is based on the concept of using data to drive actions. It aims to provide business leaders with actionable insights through data processing and analysis. For example, a business analyzes its KPIs (key performance indicators) to identify its strengths and weaknesses. Thus, the management team can decide in which area the company can improve its operating efficiency.

What is Business Intelligence?

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Both business intelligence and data science turn data into information that supports business decision-making. However, there are nuances between the two approaches.

How is Business Intelligence Different from Data Science?

Let's See Difference

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Focuses on identifying historical trends; answers questions such as what happened during the last period and what trends are developing

Objectives

Business Intelligence

Data Science

Extracts information from datasets and creating forecasts; answers the question of what will happen or which is the most likely outcome

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Basic statistics and business knowledge, as well as data transformation and visualization skills

Skills requirements

Business Intelligence

Data Science

More technical skillset like coding, data mining, as well as more advanced statistics and domain knowledge

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Designed to manage well-organized data

Data collection and management

Business Intelligence

Data Science

Designed to manage a large volume of dynamic and less structured data

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More practical in daily business management; less costly and requires fewer resources

Complexity

Business Intelligence

Data Science

More complex in terms of capacity for forecasting, ability to manage dynamic data, and requirements for more advanced skills