Everything around us is indeed managed and governed by data. Hence, in modern-day business paraphernalia, “Business Intelligence” and “Data Analytics” are often used as interconvertible approaches. However, the objective of both BI and DA is better decision-making. Eventually, their approach is from different perspectives. Business Intelligence analyzes and presents data in a manner that helps organizations in strategic decision-making. On the other hand, Data Analytics uses statistical and computational methods. Above all these methods help to extract meaningful insights from the data.
Business Intelligence(BI)
BI is a tool that certainly helps transform data into meaningful insight. Eventually, it is used in an efficient manner which in turn helps businesses make data-driven decisions. Additionally, it gives the tools, technologies, and strategies using data. Thus, these tools in turn assist in the organization’s business strategies and day-to-day decision-making in a big way. BI tools provide businesses with user-friendly charts, graphs, reports, maps, dashboards, etc. One can learn Business intelligence by enrolling in the Business Analytics Course in India.
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Furthermore, BI uses various methods and tools to gain business insights. For example:
Data and text mining
Real-time monitoring
Reporting
Implementation BI software
Benchmarking
Dashboard development
Performance management
Data Analytics
Purpose of Business Intelligence
BI can help businesses in the following areas:
• Improved strategies
• Identify trends and patterns
• Decision- making
• Monitor performance
• Make informed decisions
Business Intelligence Categories
BI can be broadly divided into the following 2 types:
1. Traditional Business Intelligence:
Traditional BI uses structured data. In particular, this data can be derived from different internal sources of the company. Chiefly these sources could be sales data, marketing data, finance data, inventory data, etc. Furthermore, the data is stored in data warehouses. Subsequently, it is analyzed with the help of SQL-based tools. Following are the areas where traditional BI is used are:
• Sales Analysis
• Financial Reporting
• Operational performance analysis.
2. Modern Business Intelligence:
As the name suggests modern BI uses modern-day technologies explicitly in BI. Thus, it facilitates real-time access to data. Also, it enables the user to acquire data from different sources on its own. Modern BI further assists users in exploring and analyzing data. In short, this can be done in various interactive methods like visualization, dashboards, and queries in natural language. Following are examples of Modern Business Intelligence:
• Data Discovery
• Self-Service Analytics
• Real-Time Reporting
• Predictive Analytics.
Examples of Business Intelligence
Following are a few examples of BI:
Sales Analysis
Financial Reporting
Customer Analytics
Supply Chain Management
Predictive Analytics
Operational Performance Analysis
Social Media Analytics
Data Analytics(DA)
The overall process of collecting, cleaning, and inspecting data is often termed Data Analytics. Also, it involves storing, analyzing, and transforming large sets of data. Subsequently, this helps to extract patterns and meaningful insights. These patterns and insights indeed make day-to-day business decisions easier. Moreover, the statistical and computational techniques used in business analytics assist businesses in identifying trends and patterns.
DA often makes use of specialized tools and techniques such as:
• Data visualization software
• Big data platforms
• Machine learning algorithms
Advantages of Data Analytics
DA can be used in different areas and disciplines, especially ranging from big corporations to small businesses to government to science. Furthermore, Data Analytics has the following advantages:
• Firstly, DA allows businesses to build trends and patterns.
• It is based on both historical and real-time data.
• Also, DA provides organizations with insights that may not be available with other BI techniques.
• Lastly, DA can help enterprises analyze data and identify improvement areas
Types of Data Analytics
DA can be categorized into the following 4 types:
1. Descriptive Analytics
Specifically, this type of DA involves analyzing historical data. Additionally, it helps to gain insights into the past. E.g., of descriptive analytics can be customer sales reports, customer satisfaction scores, and website traffic analysis.
2. Diagnostic Analytics
This type of DA focuses on “why” something happened in the past. Thereafter, it helps investigate the root cause of a problem. It also analyzes patterns and relationships within the data. E.g., product defect analysis, and employee turnover analysis.
3. Predictive Analytics
This method uses statistical models. Also, it employs machine learning algorithms that predict future outcomes. Besides, these outcomes are often based on historical data. E.g., demand forecasting, fraud detection, and customer value prediction.
4. Prescriptive analytics
Moreover, It offers techniques that use data and analytics for decision-making. It describes “what” actions should be taken by the organization to achieve a specific target. Accordingly, it aids businesses to make informed decisions. E.g., pricing and marketing optimization, and supply chain optimization.
Examples of Data Analytics
Following are a few examples of DA:
Retail
Healthcare
Finance
Manufacturing
Transportation
Differences Between Business Intelligence and Data Analytics
Undeniably, Business Intelligence and Data Analytics are two sides of the same coin- data analysis. Consequently, Data Analytics is an important Data Analysis Tool for decision-making in Business Intelligence.
S.No.
Business Intelligence
Data Analytics
1
Monitoring business performance
Discovering patterns and trends
2
Emphasizes specific information for decision-making
Focused on broad data analysis, and advanced statistical techniques
3
Dashboards, reports, visualizations
Data mining, and advanced statistical techniques
4
Short-term, tactical view
Long-term, strategic view
5
Managers, executives, front-line employees, business users
Analyst and data scientist
6
Structured data from databases or other systems
Unstructured or semi-structured data
7
Accessible to a wider audience
Deals with more complex data sets
8
Focuses on data from a single source
Pulls data from multiple sources
9
Works at a higher level, aggregating data
Works on a more granular level
10
Answers specific business questions, provides insights for decision-making
Answers open-ended questions, finds patterns and insights
11
Deals with smaller volumes of data
Deals with large volumes of data.
12
Tools such as Tableau, Power BI, Excel
Tools such as R, Python, SQL
Difference between Business Intelligence(BI) and Data Analytics(DA)
Career Opportunities provided by Henry Harvin in the field of Business Intelligence and Data Analytics
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Conclusion
Indeed, we have discussed above the differences between Business Intelligence and Business Analytics. However, in reality, the difference between BI and DA is not distinct. Consequently, the decision to choose between BI and DA will depend on the business’s needs. Both BI and DA have their pros and cons. Therefore, which one should be the best fit for a business, will depend on various parameters. By and large these factors could be the size of the business, business goals, quantity of data to be managed, etc. Finally, the enterprises that can effectively use both approaches are often better positioned. Eventually, it will assist in data-driven decisions and also improve the overall performance of the business.
The Data Science Course from Henry Harvin equips students and Data Analysts with the most essential skills needed to apply data science in any number of real-world contexts. It blends theory, computation, and application in a most easy-to-understand and practical way.
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