More organizations are recognizing that their data assets are actually a key part of a better business strategy. Within this litany of datasets, there is a plethora of information that can forward a company to complete goals never once thought imaginable. By handing over the reins of analytics to machine learning capabilities, businesses save time, effort, and cash for the chance at making more of that green. Let’s take a look at one of the capabilities of data virtualization: the logical data warehouse.
What is a logical data warehouse?
A logical data warehouse, or LDW, is a modern data structure in which an architectural layer sits on top of a traditional data warehouse. This enables access across multiple data sources while appearing as just one unified piece of viewing data to users. It’s basically an analytical data architecture that optimizes traditional data sources like databases and data lakes, and other applications like web services and cloud services. This is done with the greatest emphasis on security and proper data governance in a business context.
The logical data warehouse is touted as the next generation of data warehousing, meeting companies’ growing data management needs. By combining multiple engines and various data sources across the enterprise, LDW components are able to be combined in one logical design rather than a physical data warehouse. This modern system architecture has advanced to support a variety of datasets, data platforms, and other business use cases. With a proper LDW system, organizations are able to digitally reinvent themselves, enabling real-time analytics and promoting better data-driven decision-making.
Benefits of a Logical Data Warehouse
Data scientists are finding plenty of benefits from modern data warehousing. The logical data warehouse approach allows companies to meet evolving requirements while taking advantage of physical data already at their disposal. Pre-existing data warehouses and data lakes can still come under evaluation for analytical assessment. This is a significant volume of data that should not be cast aside. With logical data warehouse architecture, there is a more streamlined effort in place to make sure a company is agile and flexible for new data demands.
A logical data warehouse deploys common management architecture across data types based on their business requirements. An LDW structure enables a company to answer queries about business, past performance, and future outcomes through the power of this analytics architecture. LDW technology also empowers data consumers across the platform to deal with different types of data to find information quicker and understand it easier. With an increasingly diverse variety of data, the logical data warehouse has become even more necessary since its creation. This allows for self-service analytics in a consistent and accurate fashion.
Data Warehouse Uses
There are common logical data warehouse use cases across various sectors that are allowing for analytics to forward businesses into the future. One of the common uses of this data modeling is in risk management. By using past data volumes to determine historical data, advances can be made to understand areas where greater protection and coverage are needed. In a matter of data mining, proper analyses can be put forth from those different sources to then be turned into how analysts deal with fresh data in a real-time fashion.
Predictive analytics through a logical data warehouse has become an asset in the manufacturing industry. This allows manufacturers to better understand all elements of the supply chain from each of their vendors. A proper LDW platform allows for better governance of data requirements through all of these indexes. By better understanding this logical structure, manufacturing companies are able to better address issues with immediacy and a completeness that backs up their organization’s decisions. Simply put, a logical data warehouse is a logical decision.
