In its essence, a data warehouse is a centralized repository or database that is able to integrate information, queries, and analytics from different systems and applications. For most enterprises, the data warehouse is a separate environment from a brand’s operational systems. It may be a physical data warehouse or a logical data warehouse, each of which with a variety of applications.
Since the data warehouse is dependent upon its isolated environment, real-time big data queries, data mining, and data integration can take place without any impact on business context operations. For business users, it’s important to know the role of a logical data warehouse and how it can impact your data model.
Data Warehouse Basics
So, what is a logical data warehouse? Ultimately, an enterprise data warehouse solves some key data integration processes. Your logical data warehouse allows you to pull data from a variety of sources and operational systems. The logical data warehouse lets you do so quickly and efficiently so you’re not disrupting any time-sensitive business processes. As a result, you’re able to use database queries to make more impactful decisions with a greater degree of confidence and reliability. Naturally, for brands that have a consistent store of data and several data structures, this is critical.
When you need to transform data from various data sources, a logical data warehouse can prove incredibly useful. The logical layer on top of a traditional data warehouse ensures that you have access to different data sources. A data warehouse can also provide higher levels of data virtualization scenarios. When you have the right data in the right context, it’s much easier for the end-user to both parse and interpret the information they’re being presented. These higher levels of data integration take advantage of the logical layer for each data set. This develops a “single source of truth” in your business intelligence and data model forecasting.
Data Warehouse Use Cases
The logical architectural layer on top of a traditional data warehouse can be applied to data sets in several critical ways. Firstly, you’re able to access data from multiple sources. This access strategy helps you correlate viewing data, historical data, and metadata within your system architecture. This is a key use case across North America as well as worldwide. When this data is leveraged as a single source of truth, it enhances your business intelligence in the support of management and insights. Data scientists and analysts rely on this new data management architecture for business success.
Data warehouse architecture also allows for greater insights into data quality and consistency. Even though you’re able to access a batch of data with relative speed and ease, this alternative data management doesn’t negatively impact your data. You can rely on the insights of a data cache without worrying about the quality of a new data source. Big data is a critical component of business growth and development and should be treated as such.
One last major component of a warehouse store and similar data platforms includes timely access to data. Some businesses operate in a reactive manner, as opposed to a proactive manner. With predictive analytics and forecasting, it’s much easier for a business to anticipate status changes and make appropriate adjustments.
A Powerful Tool
If you’re uncertain of how to better leverage your current data, it may be time to switch from your usual data warehouse model to a logical data warehouse. You can cache and warehouse data more effectively compared to a traditional data warehouse and make smarter adjustments to your business models. A brand’s business intelligence can benefit heavily from a logical data warehouse and should consider incorporating the data model into its operations.