What does Data Warehousing allow Organizations to Achieve

Hello Friends, Welcome to another article, and in this article, we will learn what is data warehousing and why it is important for your organization and What does Data Warehousing allow Organizations to Achieve? So without further ado, Let’s start our article.

What is a Data Warehouse?

What does Data Warehousing allow Organizations to Achieve
What does Data Warehousing allow Organizations to Achieve

A Data Warehouse is a computer system that stores and analyzes large amounts of data. It consolidates, formats, and organizes data from different places, such as transactional systems, relational databases, internal marketing, sales, and finance systems, as well as customer-facing applications and other sources, and serves as a central repository of information that can be analyzed to uncover relationships and trends.

Data engineers and scientists, business analysts, and decision-makers access this data through business intelligence tools and other analytics applications and use it to create reports and monitor dashboards.

While not every business needs a data warehouse, those that do can extract valuable business insights from their data to improve decision-making. The capabilities and ways to implement a data warehouse vary, but the best solutions are pre-built and cloud-based, allowing users to easily create and run their own analyses without relying on IT teams.

What does Data Warehousing allow Organizations to Achieve

There are several key goals Data Warehousing allows organizations to achieve, including :

  • An efficient data warehouse help in speeding up the process of accessing and analyzing a large amount of data from multiple sources, which helps organizations to gain insights that can be used to make better business decisions. As a result, BI (Business Intelligence) will improve.
  • Data warehouses stores a large amount of historical data. This helps organizations to analyze different time periods and trends to make future predictions.
  • It helps in determining many trends and patterns through the use of data mining.
  • It allows organizations to access critical data from a number of sources in a single place. So it saves a lot of time to access data from multiple sources, making it easier for users to access and analyze the data they need
  • With the help of other backup resources, it can also help in recovering from failures.
  • It restructures the data so that it makes sense for business users to gain access to any information from the data, which will allow the information to be analyzed well.
  • It restructures the data to deliver excellent performance, even for complex analytic queries, without impacting the operational systems.
  • It helps in improving data quality by providing consistent codes and descriptions and even fixing and cleaning any bad data before it is stored in the warehouse.
  • It helps improve data consistency because organizations generate data from multiple sources, including structured and unstructured data. The data warehouse converts this data into a consistent format, allowing a more efficient feed for analytics.

Characteristics of Data Warehouse

According to the definition of Bill Inmon, “Data Warehouse is a Subject-Oriented, Integrated, Non-Volatile and Time-Variant collection of data in support of management’s decision.

The Characteristics of a Data Warehouse are as follows :

Subject-Oriented Data

In Data Warehouse, data is organized around specific subjects such as sales, distribution, customers, etc., rather than specific applications or transactions. This helps organizations with decision-making and making more informed decisions for their business.

Integrated Data

Data in Data Warehouse comes from several operational systems. Integration in a data warehouse means having a common unit of measure for all similar data from different databases.

It helps remove inconsistencies from data like naming conventions, different coding structures, data attributes, etc. It also helps enable a more accurate and comprehensive analysis of the data and transformation into a unified view.

Time-Variant Data

It means Data Warehouse has to contain historical data, not just current values. Every data structure in the Data Warehouse contains the time element. It allows analysis of past data, relates information to the present, and makes predictions about future performance.

Non-Volatile Data

Data is not updated or deleted from the data warehouse in real-time, only added to. This allows the retention of historical data, which helps analyze the historical data and understand the trends and changes over time.

Components of Data Warehouse

There are mainly five components of a data warehouse, and they are as follows.

  • Data Warehouse Database
  • Extract-Transform-Load (ETL) Tools
  • Metadata
  • Data Warehouse Access Tools
  • Data Mart

Data Warehouse Database

  • Data Warehouse stores data of an organization for a particular period, like a period of 10 years or so on. So data warehouse maintains its own database.
  • The central component of a data warehousing architecture is a databank that stocks all enterprise data and makes it manageable for reporting. The Data warehouse database maintains all the data needed to capture in the data warehouse.
  • At its core, the data warehouse is a database that stores all enterprise data and makes it accessible for reporting in a simplified and optimized manner. Naturally, this means you need to decide which database you will use to store your data warehouse. There are four basic types of databases you can use for this purpose.
    • Typical Relational Databases
    • Analytical Databases
    • Data Warehouse Appliances
    • Cloud-hosted Databases

Extract-Transform-Load (ETL) Tools

Often considered the backbone of data warehousing, you will need an ETL tool to extract data from disparate source systems across the enterprise, transform this data to convert it into a format suited for your data warehouse, and load it into your data warehouse.


  • Metadata is data about data that defines the data warehouse. Metadata refers to data that defines the data warehouse and provides context to data.
  • A record in your customer database may look like this:
  • This data is not understandable unless you review the associated metadata.

Data Warehouse Access Tools

A data warehouse is a database or collection of databases that business users can interact with. In order to facilitate access to the data warehouse, you need to choose the right type of access tool. The access tool you choose will determine the level of access business users have to the data warehouse.

Data Mart

  • A data mart can be defined as the subset of an organization’s data warehouse that is limited to a specific business unit or group of users.
  • A data mart is a condensed version of a Data Warehouse designed for use by a specific department, unit, or set of users in an organization. E.g., Marketing, Sales, HR, or finance. It is often controlled by a single department in an organization.
  • Data Mart usually draws data from only a few sources compared to a Data warehouse. Data marts are small in size and are more flexible compared to a Data warehouse.

Data Warehouse Tools

With so many data warehousing tools on the market, it can be tough to figure out which ones are the best fit for your project. To help you out, we’ve compiled a list of the seven most popular data warehousing tools. You can learn more about their services by visiting the respective links below.

NameĀ  Link
Amazon Redshift
Hevo Data
Oracle Data Warehouse

To get more out of your data warehouse tools, you may opt for data warehouse consulting services at Data Sleek which will help your organization to effectively store, manage and analyze large amounts of data. They will help your organization maintain data continuity and accuracy to improve overall business performance.

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