How is Business Intelligence Different from Data Science?
Skills requirements
Data collection and management
Complexity
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?
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?
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
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
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
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
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