What Is Enterprise Data Warehouse And Why Is It Important?

June 30, 2022

As data warehouses move to the cloud, organizations must adapt their strategies to ensure success. This blog post explains what is enterprise data warehouse and how it can help your organization stay ahead of the curve.

What Is Enterprise Data Warehouse?

What is enterprise data warehouse? An EDW is a centralized database that stores all company data for reporting and analysis.

An EDW is a type of data warehousing that is typically run on a robust, powerful, and scalable platform that can handle massive amounts of data.

An EDW is a centralized repository for an organization’s information. It serves as a central resource for all business functions.

EDW gathers data from multiple resources and then normalizes it to provide useful insights for our products and services.

Data is turned into information, then analyzed, and the results of that analysis are used to make business decisions.

The EDW can provide your organization with valuable insights into your business and customers.

An EDW uses an ETL (Extract, Transfer, and Load) process for ingesting, consolidating, and normalizing data.

The data is formatted in a way that is meaningful to the stakeholders and is integrated into other software.

The EDW can be used by the organization’s sales and marketing teams to gain a complete view of the organization and the clients being serviced by it.

The EDW being cloud-based makes data access easy because all you need is an internet connection.

By integrating all your data into one system, you can unlock valuable business insights.

EDWs can quickly adapt and integrate new data into their systems.

As an organization becomes more comfortable using the data and begins giving feedback on what’s working and what isn’t, the EDW can evolve quickly to meet those needs. As the company evolves, so can the EDW.

An organization can have strategies, tactics, and operational activities that are performed to meet the overall vision and mission of the organization for service to its customers.

To be high-performing, you need to use the data available to you and innovate with technology.

Business decisions are being made every day by advanced technology. These decisions are based on the data.

Benefits of an EDW

Here are a few of the advantages that EDWs offer:

  1. Integration with analytic software: Salesforce.com reports that 37% of businesses believe data analytics processes help them grow their business. EDWs facilitate more robust analytics through integration with analytics software. A full range of data makes it easier to report and visualize company-wide KPIs.
  2. Contextual analysis: An EDW shows and describes relationships between data points. It provides context and information when analyzing the entire business. An EDW allows you to better predict how minor changes can impact the company as a whole.
  3. Storage and standardization: EDWs can store and retrieve large datasets from all business areas. EDWs can not only store large amounts of data but also transform and translate data to allow for precise comparison. Although data sources may seem disconnected, standardizing and storing data can help to identify key connections between projects. This can have a significant impact on business success.
  4. Flexibility: The structure of enterprise data warehouses can be modified. Users may notice that the data model has changed or that data needs to be added/removed. These adjustments don’t require a complete overhaul of the system. EDW quality improvements can be made easily. EDWs evolve with a company and become more refined over time.

Clearly, an EDW offers more than a “normal” data warehouse.

What EDW is Good For … And What It Is Not

A system of “extract, transform, load” or ETL is necessary to build a high-quality EDW.

ETL is popular because it allows organizations to develop and manage a successful enterprise data warehouse. As data volumes grew in the 2000s, there was a trend of using databases to facilitate data integration. This led to “ELT” – where data was extracted (from the source apps), loaded (into EDW), and then transformed (within EDW).

When leveraging your EDW, it’s important to remember that doing so can result in unexpected costs, slower processing time, and longer wait times for report delivery.

Companies are now utilizing big data frameworks that are optimized for their data warehouses, such as Hadoop MapReduce or Apache Spark. This allows them to provide faster, more insightful analytics, and cut costs.

Precisely, a leader in big data software solutions provides fast, scalable, and easy-to-use tools for integrating and transforming data from any source.

Their products and services have been used by many of the largest companies in the world to greatly improve the performance of their data warehouses by switching the ETL/ELT processes to big data platforms.

The Future of Cloud Solutions and Data Warehousing

The demand for CRM and marketing automation systems integrated with customer support, invoicing, and product information is quickly growing. This is making on-premises software obsolete.

Since cloud-based EDWs are connected to other SaaS platforms, they can store more information than on-premise options. This is because their “uptime” is much higher. Also, because EDWs are in the cloud, they are more up-to-date on the latest security measures.

The rise of the cloud has brought with it an influx of data sources such as mobile data, IoT, and social media posts. Enterprises need to capture and make sense of this data as customers interact with their products.

Cloud-based tools will be able to fully support predictive analytics and artificial intelligence in the future. This will encourage more agile, real-time decision-making.


So what is enterprise data warehouse? An Enterprise Data Warehouse (or EDW) is a centralized repository that stores an organization’s data.  EDWs are a valuable resource for many businesses. Not only do they make decision-making easier, but EDWs are also great for organizing and analyzing large amounts of data.

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