An enterprise data warehouse (EDW) is a relational data warehouse containing a company’s business data, including information about its customers. An EDW enables data analytics, which can inform actionable insights. Like all data warehouses, EDWs collect and aggregate data from multiple sources, acting as a repository for most or all organizational data to facilitate broad access and analysis.
Data Warehouse versus Enterprise Data Warehouse
The difference between an EDW and a data warehouse is semantic. An EDW is a data warehouse that encompasses and stores all of an organization’s data from sources across the entire business. A smaller data warehouse may be specific to a business department or line of business (like a data mart). In contrast, an EDW is intended to be a single repository for all of an organization’s data.
Components of an Enterprise Data Warehouse
A typical EDW consists of:
- Data sources from operational and transactional systems (ERP, CRM, finance apps, IoT devices, mobile and online systems)
- A staging area for data aggregation and cleaning
- A presentation or access space where data is made accessible for analytics (querying, reporting) and sharing
- A range of data tool integrations or APIs (BI software, ingestion and ETL tools, etc.)
Benefits of an Enterprise Data Warehouse
As a centralized repository of all of an organization’s data, an EDW provides enhanced availability and accessibility to meaningful, contextual cross-organizational information, enabling keener holistic insights and smarter decision-making. That means faster-to-market action with improved return-on-investment (ROI) and greater company growth.
5 Business Needs that Require an Enterprise Data Warehouse
Nearly every department within a business can benefit from data-driven insights. Here are a few business needs that EDWs address.
1. Real-time access to data for action
EDWs make data viewable and actionable in real-time by favoring an extract-load-transform (ELT) approach over the once common extract-transform-load (ETL) paradigm, in which data was cleansed, transformed, or enriched on an external server prior to being loaded into the data warehouse. With an ELT approach, raw data is extracted from its source and loaded, relatively unchanged, into the data warehouse, making it much faster to access and analyze.
2. Holistic understanding of customer
EDWs enable a complete view of a business’s customer, helping improve campaign performance, minimize churn, and ultimately grow revenue. An EDW also facilitates predictive analytics, where teams use scenario modeling and data-driven forecasting to inform business and marketing decisions.
3. Tracking and ensuring data compliance
EDWs enable data customers to audit and vet data sources directly and find errors quickly. A modern EDW can also enable compliance with the EU’s General Data Protection Regulation (GDPR) without implementing an involved process to check multiple data locations.
4. Empowering users with limited technical knowledge
An EDW benefits non-technical employees in job functions beyond marketing, finance, and the supply chain. For example, architects and store designers can improve the customer experience within new stores by tapping into data from IoT devices placed in existing locations to understand which parts of the retail footprint are most or least engaging.
5. Consolidating data to a single, reliable repository
Modern data warehousing technology enables companies to store data across different regions and cloud providers. Users can query an EDW as though it were a global unified data set.
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