Business Intelligence and Data Warehouse

Definition of Business Intelligence and Data Warehouse

Business intelligence and data warehouse are the same concepts and they are operating in the same space. The BI and data warehouse are involved in data storage. BI is a collection methodology used for data analysis. The data warehouse provides storage for the process of BI. Maintaining and deploying a data warehouse is critical for the BI that is referred to collectively, both terms are important in information technology.

Difference Between Business Intelligence and Data Warehouse

Business intelligence and data warehousing differ in several important ways. Before going into the distinctions the data warehouse stores a large amount of data whereas business intelligence distinguishes the data as per requirement.

The main goals of BI are data analysis and providing practical insights to decision-makers, and Power BI is an example of a commonly used BI tool. On the other hand, well-known vendors of data warehouses include Azure and Amazon Redshift.

What is Business Intelligence?

Large commercial organizations frequently receive a large amount of data from various sources. This data can always be used to obtain a variety of information sets that help make better business decisions. This useful information could be descriptive. BI refers to the various approaches and tools used for gathering, integrating, analyzing, and visualizing corporate data.

Data from warehouses will be parsed for insights by BI software, which will then turn into information that is useful to decision-makers and easy to understand. Business intelligence serves as a link to a data warehouse.

What is Data Warehouse?

Data warehouses are a system and group of back-end technologies that assist in gathering huge volumes of disparate data from numerous sources and storing it for later use. Business logic that supports good data warehouses. Data warehouses are used by several applications, including business intelligence. Data is stored in Fact Tables and Dimensions in Data Warehouses, which typically adhere to a multidimensional paradigm.

Transactional databases are unable to handle complicated queries, whereas data warehouses can. Additionally, it can initiate the cleaning process by negotiating various data storage schemas according to the type of data. Data warehouses can only analyze past data.

Head to Head Comparison Between Business Intelligence and Data Warehouse (Infographics)

Below are the top 9 differences between Business Intelligence and Data Warehouse:

Key Differences between Business Intelligence and Data Warehouse

Let us look at the key differences between Business Intelligence and Data Warehouse:

• C-level executives are typically BI users who want to perform rapid data analysis for users. The data is stored in a data warehouse.
• Data for particular business activities of departments are the first aspect of data warehousing that has to be handled. This usually entails identifying the important stakeholders and the reports that must be directed toward the data warehouse.
• BI is primarily focused on producing company insights like product positioning and pricing to goals, even if they are interconnected and dependent on one another.
• After the BI solution has processed the data to generate the desired reports for them, the system must provide this information to end users in a way that allows them to take appropriate action. The first three steps in these backend operations are all concerned with making sure that the information is properly saved and prepared for use.

Business Intelligence Requirement

Business intelligence helps organizations identify opportunities in both recent and historical data, which improves performance and income. To find the best business intelligence software, the company can carefully weigh all of your options. The following are the requirements for business intelligence.

• Augmented analytics
• Security
• Mobile BI
• Reporting
• Deployment environment
• Pricing and plans
• Data analysis
• Data querying
• Data visualization
• Data management
• Scalability and availability
• Data connectivity

Data Warehouse Requirement

It serves as a data storage facility and a real warehouse. Many businesses have internal data warehouses where they keep track of lead generation statistics, sales quotas, performance indicators, and other data. The data warehouse requirements are listed below.

• Workload separation
• Maximize concurrency
• Data loading maximization
• Minimize latency
• Fast time value
• Handle semi-structured data
• Performance of business intelligence
• Inexpensive
• Elasticity
• Quickly scalable
• Consolidated
• Accessible to sharing of data
• Simplicity
• Hold the data

Comparison Table of Business Intelligence and Data Warehouse

The table below summarizes the comparisons between Business Intelligence and Data Warehouse:

Business Intelligence	Data Warehouse
It is a set of tools used to analyze the data.	It is used to store the data.
It is a decision support system.	It is a data storage system.
Business intelligence is used in the front end.	A Data warehouse is used in the backend.
Business intelligence’s purpose is to empower users to make defensible, data-driven decisions.	The primary goal of a data warehouse is to give business intelligence users a structured, thorough perspective of the data that a company has available to it.
Gathers information for analysis from the data warehouse.	Assembles data for effective BI analysis from a variety of unrelated sources.
Includes financial reports, graphs, and other visuals.	Consists of information stored in fact tables and dimensions.
Needs a lot of different, meaningful data.	There are various applications for data warehouses.
Work for or report to the executives and analysts in charge of handling and maintaining this data.	Handled comparatively higher up the hierarchy by executives and analysts.
Example – SAP	Example – Big Query

Purpose of Business Intelligence

To assist businesses in making more data-driven decisions, business intelligence integrates business analytics, data mining, and data visualization practices. Business intelligence is a very important term in every industry. Business intelligence and data warehouses both terms are important in the IT industry.

Business intelligence is used to distinguish the data as per business requirements. Business intelligence is a very important term in any industry or business where we use a large amount of data.

Purpose of Data Warehouse

A data warehouse is a data collection that can be examined to aid in the development of better judgments. For organizations to remain competitive, data and analytics are essential. To glean insights from their data, track corporate performance, and aid in decision-making tools.

Tiers make form the architecture of a data warehouse. The front-end client is the highest tier; it displays outcomes using tools for reporting and mining. The analytics engine used to access and evaluate the data is included in the middle tier. Data loads on the server, which is the lowest tier of the system.

Conclusion

The data warehouse is a storage facility that supports the BI process. Maintaining and deploying data warehouses is critical for business intelligence (BI), as both terms are important in information technology. SAP and Power BI are two examples of popular BI tools. Azure and Amazon Redshift, on the other hand, are well-known data warehouse vendors.

Recommended Articles

This is a guide to Business Intelligence and Data Warehouse. Here we discuss Business Intelligence and Data Warehouse key differences with infographics and a comparison table in detail. You can also go through our other suggested articles to learn more –

1. Small Data vs Big Data
2. Data Science vs Computer Science
3. Big Data vs Machine Learning
4. Data Science vs Machine Learning