Business Intelligence and Data Analytics: Whats the difference?

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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.

Furthermore, BI uses various methods and tools to gain business insights. For example:

1. Data and text mining
2. Real-time monitoring
3. Reporting
4. Implementation BI software
5. Benchmarking
6. Dashboard development
7. Performance management
8. 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:

1. Sales Analysis
2. Financial Reporting
3. Customer Analytics
4. Supply Chain Management
5. Predictive Analytics
6. Operational Performance Analysis
7. 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, Data Analytics allows businesses to build trends and patterns.
• It is based on both historical and real-time data.
• Also, 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

 Furthermore, 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:

1. Retail
2. Healthcare
3. Finance
4. Manufacturing
5. 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.

Career Opportunities provided by Henry Harvin in the field of Business Intelligence and Data Analytics 

Henry Harvin Data Science & Analytics Academy can indeed help you boost your career graph by providing a Business Analytics Course. You can also learn about Big Data Analytics from Henry Harvin. These courses have the following affiliations which make them stand out in the industry:

1. Nasscom
2. NSDC
3. MSME
4. AAEFL
5. ISO 29990:2010
6. Microsoft

Key features of the above-mentioned courses are:

• 24 Hours of Instructor-Led Sessions
• Along with, 8 Hours of Live Interactive Doubt Solving Sessions
• Furthermore, 4 Hours of Live Master Sessions by Industry Experts
• Job-Ready Portfolio of 2 Capstone Projects
• Also, get 2 Mock Interviews and 2 Hackathons
• Additionally, get 1-Year Gold Membership of Henry Harvin® Data Science & Analytics Academy
• Also, get a guaranteed Internship with Henry Harvin® and in top MNCs like J.P. Morgan, Accenture & many more via Forage
• Also, Get 3 in 1 Placement support through Placement Drives, Premium access to Job portal & Personalized Job Consulting

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.

Recommended Reads

1. Data Scientist vs Data Analyst vs Data Engineer: Role, Skills
2. Complete Guide To Business Intelligence Analyst: To Make You Successful
3. What Does A Data Analyst DO?
4. Data Types In Tableau: The Most Important Attributes
5. Top 10 Data Mining Tools You Should Know

FAQs

Ques 1. What is Business Intelligence?

Ans 1. Business Intelligence is a data analysis tool that assists in the organization’s business strategies and day-to-day decision-making.

Ques 2.  What do you mean by Data Analytics?

Ans 2. Data analytics is a tool to modify process data into a piece of meaningful information for business users.

Ques 3. How can you differentiate between Business Intelligence and Data Analytics?

Ans 3. Business Intelligence helps in data-driven decisions whereas data analytics is used to transform raw data into meaningful information.

Ques 4. What are common BI tools in the market?

Ans 4. In general, the common BI tools available in the market are Tableau and Microsoft Power BI

Ques 5. What are popular DA tools?

Ans 5. The two most popular DA tools are Sisense and Google Data Studio.

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