What is Business Intelligence vs. Data Science?
Business Intelligence (BI) and data science are both data-focused processes, but there are some key differences between the two. In general, business intelligence focuses on analyzing past events, while data science aims to predict future trends. Data science requires a more technical skillset compared to business intelligence.
- Business intelligence converts data into information that can support business leaders in decision-making.
- Data science involves creating forecasts by analyzing the patterns behind the raw data.
- Business intelligence is backward-looking that discovers the previous and current trends, while data science is forward-looking and forecasts future trends.
- Compared to business intelligence, data science is able to manage more dynamic and less organized data. Yet, it also requires more technical skills and resources.
What is Business Intelligence?
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.
It is not a new practice to support decision-making with data. However, dramatic improvements in BI technology also mean significant improvements in speed, efficiency, and effectiveness. Automation and data visualization are two examples, which both are transforming the process of business intelligence.
What is Data Science?
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.
The next step is data analysis, which can be conducted through text mining, regression, descriptive and predictive analytics, and so on. By analyzing the data, the patterns behind the raw data can be discovered to forecast future trends.
Data science is broadly used in many industries. Businesses can use such an approach to develop new products, study customer preferences, and predict market trends. For example, auto-driving developers collect extensive amounts of data for statistical analysis. The developers work to improve the auto-driving system so that it can be responsive to different situations through machine learning.
Data science is also an essential tool in the healthcare industry. High volumes of data can be collected from electronic medical records and individuals’ fitness trackers. Professionals can better understand diseases and develop more effective treatments by applying data science tools to the collected data.
Roles in Data Science
Data engineers support the processes of business intelligence by creating data warehouses and managing data ETL (extract, transform, and load). They also ensure the integrity and security of data. Data analysts are responsible for data modeling and analysis.
Then, data visualization specialists use clean data to create visuals and dashboards. These can communicate the key metrics, trends, and results to their primary audience, a company’s decision-makers or business leaders.
The above step makes data analysis results more understandable. The roles of data engineer, data analyst, and data visualization specialist are not completely separate in the real world. Some of the responsibilities and skill sets are shared among these roles.
How is Business Intelligence Different from Data Science?
Both business intelligence and data science turn data into information that supports business decision-making. However, there are nuances between the two approaches.
|Objectives||Focuses on identifying historical trends; answers questions such as what happened during the last period and what trends are developing||Extracts information from datasets and creating forecasts; answers the question of what will happen or which is the most likely outcome|
|Skills requirements||Basic statistics and business knowledge, as well as data transformation and visualization skills||More technical skillset like coding, data mining, as well as more advanced statistics and domain knowledge|
|Data collection and management||Designed to manage well-organized data||Designed to manage a large volume of dynamic and less structured data|
|Complexity||More practical in daily business management; less costly and requires fewer resources||More complex in terms of capacity for forecasting, ability to manage dynamic data, and requirements for more advanced skills|
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