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Difference Between Business Intelligence And Data Science
Though the terms data science and business intelligence are used interchangeably by organizations, they differ significantly. While data science incorporates methods and elements of advanced analytics, arithmetic, statistics, specialist programming, and machine learning to conclude the organizational data and predict future events, business intelligence involves analysis of past events through generating charts, graphs, and reports and making the data easy-to-understand for further decision making.
Key Takeaways
- Business intelligence denotes the technology used for data preparation, mining, management, and visualization.
- The software solution helps transform the data and presents it as graphs, charts, maps, and reports.
- The software solution helps transform the data and presents it as graphs, charts, maps, and reports.
- The software solution helps transform the data and presents it as graphs, charts, maps, and reports.
- Business intelligence can be viewed as a component of the big picture. The two concepts differ in substance. BI deals with unchanging or static, structured data, while data science uses structured and unstructured data.
Comparative Table
| Key Points | Business intelligence (BI) | Data science |
|---|---|---|
| 1. Concept | Business intelligence is software solution that provides historical business data and presents it readably in a user-friendly manner with the help of charts, graphs, and reports to assess business performance. | Data science includes specialized programming, math, statistics, advanced analytics, and machine learning and combinedly use them to deduce insights from the available organizational data to predict future events or patterns. |
| 2. Scope | The scope of business intelligence is usually limited to the business domain. | Data science, on the other hand, is a widely used method of analysis that does not restrict itself to the business domain. |
| 3. Data Used | BI deals with unchanging or static, structured data. It can understand only pre-formatted data. | It uses structured and unstructured data. There is no requirement for pre-formatted data. |
| 4. Analysis | BI provides more descriptive analysis through visualization for even non-technical people to understand. | Data science uses descriptive analysis but is more concerned with stating and analyzing facts. It is predictive and explicatory and analyzes data through hypothesis testing, analyzing common trends, etc. |
| 5. Focus | Past and present data, which can better be used to understand the situation and identify past trends for future decision-making. | Data analysis focuses on the future. It helps in making decisions that are more concerned with the future. |
| 6. Complexity | BI is not as complex when compared to data science | Data science is complex compared to business intelligence as it involves a multidisciplinary approach. |
| 7. Technical Aspects | It does not require much technical expertise in the analysis of data. This is because the results are published as visual representations in the form of reports and graphs. | It needs significant technical skill sets to analyze and interpret data. |
What Is Business Intelligence?
Business intelligence is a term that denotes the technology used for data preparation, mining, management, and visualization. The software uses past events, transforms the data, and presents it as graphs, charts, maps, and reports. It allows users to identify information that can be useful from the raw data. The analysis is then used to make important decisions. The data helps users understand the trends and extract useful and relevant information and insights to make better business decisions.
Business intelligence employs four essential procedures to convert unprocessed data into understandable insights. Collecting, analyzing, and visualizing data are the first three steps that prepare the ground for the ultimate decision-making, which forms the last step of the process. Before the evolution of BI, firms had to perform a large portion of their analysis manually, which was a great hassle for them. BI solutions automated many procedures and helped organizations save time and effort.
What Is Data Science?
Data science is a pool of technical methods and analyses that shape data and extract relevant information to predict future events. Its focus is on identifying patterns in data. Although it is a multidisciplinary field, the three most crucial elements that constitute the foundation of data science are programming, statistics, and mathematics. It employs various sophisticated statistical techniques and prediction models for reliable conclusions.
Analysts can obtain useful insights by using a variety of roles, tools, and processes throughout the data science lifecycle. A project typically includes the following steps: data ingestion, data storage and processing, data analysis, and communication. Data scientists can use this analysis to ask and respond to questions like what occurred, why it occurred, and what will occur. In addition, the data can also guide the user regarding how the data can be used concerning their outcomes. Furthermore, it can be used for real-time optimization, discovering previously unidentified transformative patterns, and helping develop innovative new products and business expansion.
For example, when an organization assesses the sales figures of the last five years and represents the data graphically to see the highs and lows to identify the peak seasons for product sales, it uses business intelligence techniques. On the contrary, when a company or organization conducts a survey to get customer feedback regarding their products to improve their quality, the data analysis done using the software or technological solutions becomes an instance of deployment of the data science solution.
Similarities
Data science and business intelligence are two terms that are frequently used in the digital age, and they share multiple similarities, which include the following:
- Both heavily emphasize data, which is their central point. Business intelligence can be viewed as a component of the big picture, even though Data Science is the larger pool containing more information.
- Business users can use data science and BI to make informed decisions.
- Both lead to data-driven decisions and insights.
- Both technologies are used to deliver positive results, which in the case of businesses may be profit margins, customer introduction and retention, new market penetration, etc.
- Both of these fields can interpret data. The technical professionals convert the outcomes of data-enhanced research into helpful insights.
- BI and data science provide dependable decision-support tools for users and data professionals to make data-driven business decisions.
