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Business Intelligence vs Data Analytics


The Big Data analytics market is gaining ground the world over. “Big data” refers to huge data sets that are generated from several sources. This data is complex to process and analyze for traditional data processing applications. Therefore, multiple data analytics techniques are used to make it understandable for data-driven organizations. According to Wikibon, the big data market is expected to grow with a compounded annual growth rate (CAGR) of 11%. The forecast shows that the market will be worth a staggering $103 billion by 2023.

Enterprises are harnessing the power of big data through Business Intelligence (BI) and data analytics. They rely on raw data storage and employ strong analytics capabilities to extract meaningful insights. Data traffic is rapidly growing with the advent of new technologies, for example, the Internet of Things (IoT). According to Statista, IoT connections have already reached 13.6 zettabytes in 2019 and are expected to reach more than 79 zettabytes by 2025. The major providers of big data analytics include SAP, IBM, Oracle, and Microsoft.

Business Intelligence (BI) and Data Analytics are often used interchangeably due to their subtle distinction. However, they are not the same. This article highlights the difference between them in a comprehensive manner.

What is Business Intelligence (BI)?

Business Intelligence is the use of raw data to make effective decision-making for a business. Broadly, it is used to make predictions from the data. BI provides an array of business metrics based on which the performance and growth of a business are analyzed. Business analysts learn from the various patterns and results of the data. Moreover, BI is used to make strategies and plans to improve business revenue.

Process/Phases of Business Intelligence

The general process of BI is divided into four processes/stages. These are:

1. Data collection from various data sources.

2. Data standardization and cleaning.

3. Data analysis to extract meaningful information.

4. Actionable insights from data visualization and reporting

Different tools and high-end technologies are used in each phase of BI. From data collection to reporting, the IT industry uses various techniques that help simplify the complex data patterns of raw data.

Components of Business Intelligence

The components of BI include:

Data Mining: Involves the extraction of hidden information from the data. Data Mining  analyzes huge datasets to determine emerging trends.

Data Analysis: Establishes the relationship between the groups of data.

Data Quality: The quality of data is determined to identify the correlation between real-world objects and data.

Predictive Analysis: Predicts the probability of certain outcomes. The forecast reports are generated from BI results that help make effective decisions and strategies.

Reporting: Involves data processing, formatting, and representation in a visual form.

BI software defines all benchmarks, market trends, and customer interests. It incorporates the key performance indicators (KPIs) that vary with respect to the nature of the business. For instance, if you run an online marketplace, you would be interested in the website traffic, the number of leads, customer retention score, responsive geographical regions, business revenue, etc.

Data Analytics

Data Analytics deals with the prediction of events to take effective decisions beforehand. It involves the detailed analysis of raw datasets to derive conclusions. The processes of data analytics use cutting-edge machine learning algorithms to automate the various modules of the system. It helps organizations know the interest of their clients, determine promising marketing campaigns and strategies, and transform the product/service accordingly.

Types of Data Analytics

There are four types of data analytics.

Predictive Analysis: Focuses on the results that are expected in the future. For example, it helps you determine how much revenue your business is expected to generate in the next 12 months.

Diagnostic Analysis: Identifies the reasons for the occurrence of some event. For instance, if the number of sales has increased, it would tell you if there is some campaign that worked splendidly or some marketing strategy that performed better.

Descriptive Analysis: Describes the current performance of your business. It helps you determine the current number of sales, the traffic on your website, or the current business revenue.

Prescriptive Analysis: Focuses on taking action against certain predictions. For instance, if your recently activated marketing strategy or campaign works out, you would invest more to increase the number of deals.

Business Intelligence vs Data Analytics

Business Intelligence employs complex technologies and techniques to make data meaningful for the end-user such that the process of decision-making can be streamlined.
On the other hand, data analytics convert unstructured data into structured form. The transformed data is then used to support decisions.

Some other differences are:


BI focuses on identifying information that could help in making effective decisions for the business. Whereas data analytics refers to transforming unstructured data into a meaningful form.


BI is a decision-making phase that extracts actionable information from the raw data and gives insights to businesses to grow their business. Data Analytics and Data Science take into consideration the processes of modeling, cleaning, and transforming the data according to business needs.


Business Intelligence software and tools are available today. Businesses integrate the system with all their historical data to predict and make decisions. Data analytics is implemented using data processing and storage tools. It critically depends on the requirements of a business.


Data analytics involves mathematical expertise to process and apply algorithms to data sets. The predictive reports are created through multiple simulations and qualitative analysis techniques. However, BI does not involve any complex processes.

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