What Is a Data Warehouse?

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People create an estimated 2.5 quintillion bytes of data daily. While companies traditionally don’t take in nearly that much data, they collect large sums in hopes of leveraging the information to strengthen their organizational positions, leading to smarter decision-making.

The issue is that many businesses don’t always have enough resources for managing their data effectively. As a result, many turn to data warehouses to make the process simpler.

Data Warehouse Definition

Data warehouses are centralized repositories designed to hold the information collected by organizations. Essentially, they’re data-management solutions that bring data together that’s been compiled from a variety of sources.

Typically, data warehouses contain vast amounts of historical data, and new information is added as necessary. The primary purpose of a warehouse is to support queries and data analytics, allowing companies to gather insights based on the information they collect over time.

In some cases, data warehouses have specific features. A relational database is one common element, along with various analytics tools to support data visualization, analysis, and retrieval.

How Do Data Warehouses Work?

Generally, data warehouses feature operational tiers. There’s a front-end client that gives analysts a pathway for accessing analysis, insights, and reports and a middle tier that handles the functional part of analytics activities. The third tier is where the data is held.

Within the data warehouse is a variety of databases that essentially use a rows-and-columns approach to keep various data organized. As information is shifted into the data warehouse, it’s placed in relevant tables based on the overall structure of the databases.

Pros and Cons of Using Data Warehouses

Data warehouses provide a variety of benefits. First, they support data analytics, allowing organizations to make data-driven decisions that can better position the company. Second, they create a primary location for data that’s collected from a wide range of sources, centralizing the information.

Often, data warehouses can contain massive amounts of information, making it possible to retain historical data that could be of use. They can also assist with ensuring data consistency and quality, and may improve accuracy. Finally, they can create some separation between transactional databases and analytics-processing solutions, resulting in an overall performance improvement.

When it comes to drawbacks, it’s easy to underestimate the time required to upload information into a data warehouse, causing the preparation of a new one to be more time-consuming than initially expected. Maintenance costs can be high, and there can be security challenges, particularly if some of the data is sensitive or confidential, restricting who can leverage the systems. There can also be issues created by data homogenization, causing organizations to lose track of details that could provide value.

Example of Using a Data Warehouse

In many cases, data warehouses are used for data analytics and business intelligence. For example, a retail business may acquire customer data to figure out which segments to target with specific marketing efforts. Purchase histories may reveal products that are worth highlighting in sales or that aren’t performing well enough to keep them listed.

Healthcare organizations could potentially use a data warehouse to analyze patient data to speed up diagnoses or determine which treatment options are more effective. Banks could review ATM traffic to outline a schedule for refilling the machines with cash. Ultimately, any scenario in which business insights are valuable could make using a data warehouse worthwhile.