Enterprise Data Warehouse Definition

An Enterprise Data Warehouse or Data Warehouse is a broad collection of business data that helps an organization make decisions. The concept of data warehouse existed since the 1980s. When it was developed to aid in the transition of data from operations merely from food to support decision support systems that allow business intelligence to be seen.

A large amount of data found in data stores comes from different places. Such as internal marketing, sales or finance applications, customer-facing applications, or external partner systems, to give just a few examples.

From a technical point of view, a data warehouse periodically extracts data from these applications and systems. And then the data goes through formatting and import processes to match the same warehouse.

The data store saves this processed data so that, when making decisions, they can be consulted. The frequency at which they extract or how they format, for example, will vary depending on the needs of the company.

What are the Advantages of an Enterprise Data Warehouse?

Organizations that use a data warehouse to get help with their analytics and business intelligence know many substantial benefits:

Better data:

By adding data sources to a data warehouse, organizations can ensure that they collect uniform and relevant data from that source. No need to doubt whether the data will be accessible or inconsistent when entering the system. This ensures greater quality and integrity of the data to make logical decisions.

Faster decisions:

Data from a warehouse have such uniform formats that they are ready for analysis. It also offers analytical power and a complete set of data to base decisions on proven facts. Therefore, when making decisions, it is no longer necessary to pull hints, incomplete or poor quality data, and risk providing slow and inaccurate results.

Enterprise Data Warehouse Architecture

The specific needs of the organization determine the architecture of the Enterprise Data Warehouse. Some common architectures are the following:

Simple: All data stores share a basic design in which it stores metadata, summary data, and raw data in the central repository of the warehouse. The repository feeds on data sources at one end, and end-users access it for analysis, report preparation, and extraction at the other end.

Simple with a temporary storage area: Operational data should be cleaned and processed before being placed in the warehouse. Though this can be done programmatically. Many data stores incorporate a temporary storage area for the data before they enter the warehouse, to simplify data preparation.

Hub and spoke: The incorporation of data marts between the central repository and the end-users allows organizations to personalize their data warehouse to serve several lines of business. When the data is ready for use, they move to the appropriate data mart.

Isolated spaces: Isolated spaces are private and secure areas that allow companies to quickly and informally explore new data sets. Or ways to analyze data without having to comply with the formal rules and data warehouse protocol.

Example of a data warehouse

Beachbody, which is a leading provider of fitness, nutrition, and weight loss programs. Needed to adapt better and customize the offers it made to its clients to produce better health outcomes for its clients and, ultimately, improve the performance of its company.

The company modernized its analytical architecture by incorporating a Hadoop cloud data lake in AWS, powered by Talend Real-Time Big Data. This new architecture has allowed Beachbody to reduce its data acquisition time to a fifth. And also improve the accuracy of the database for its marketing campaigns.