Using Data Virtualization to Maximize Return on Data Warehousing Investments
InfoManagement Direct, May 16, 2008
Today’s enterprises rely on the information in their data warehouses more than ever for making informed, business-critical decisions and complying with a myriad of ever-increasing regulations and compliance mandates. Data warehouses integrate and transform the complex, disparate and globally distributed data from back-office transaction systems and other sources into the rock-solid information stores that support a range of financial, customer and supply chain performance management analytics - reporting that is critical to helping enterprises increase revenues, decrease costs and reduce risk.
But are these enterprises really maximizing the return on their data warehousing investments? Might complementary technologies provide additional performance management insights and therefore valuable returns? In particular, how are enterprises leveraging new advancements in data virtualization required to achieve even greater revenues, larger cost decreases and better risk reduction today?
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Data Warehouses - Cornerstone For Business Intelligence
The data warehouse is the cornerstone of any business intelligence (BI) strategy and architecture. The warehouse is the repository of nonvolatile, historical enterprise data snapshots used across a range of analytical and reporting purposes. Securely storing the data needed for analytical purposes, the data warehouse provides stability, reliability, quality and consolidation. After so many years building and enhancing their data warehouses, few enterprises question their business value and return on investment.
However, data warehouse practitioners also understand that data warehouses have drawbacks. These include:
- Time-to-solution - Integrating disparate data, even for a single project, can be a difficult and time-consuming task. Typically, the data warehouse is built project-by-project, with each project contributing another useful set of data to the overall environment. It takes time, but eventually a deep wealth of analytical data is created.
- Generic data schema - Because the data warehouse serves many analytic purposes and departments, its design cannot be optimized for any one form of analysis over another (e.g., star schema versus cube versus flat files).
- Data latency - To achieve quality and consolidation goals, as mentioned, data loaded in the warehouse often undergoes significant physical transformation, cleansing and integration processing, which delays its availability. As a result, data in the data warehouse has built-in time latency (a few minutes to several hours to even a day).
- Deep resources/total cost of ownership - It takes a village to design, build and maintain a data warehouse. Vendors and IT departments have adopted a range of strategies to mitigate these costs from data warehouse appliances on the tools side to integration competency centers on the people and process side. But significant inefficiencies and limitations remain.
Data Marts and Operational Data Stores Extend Value
Data marts and operational data stores have evolved as complementary repositories designed to address the schema and latency drawbacks identified above. Data marts can provide optimized schemas in support of specific analyses, for example, a cube for market segmentation or a relational database for departmental reporting. Operational data stores can combine historical warehouse data with up-to-the-minute operations data to overcome the latency challenge when performing operational BI activities such as supply chain planning or equipment dispatching. However, time-to-solution and total cost of ownership remain as lost value opportunities.
Physical data warehouses, data marts and operational data stores all leverage a common data integration middleware toolset, extract, transform and load (ETL). This combination can be seen in the physical data integration landscape in Figure 1.
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