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Is an Enterprise Data Warehouse Still Required for Business Intelligence? | TheVirtualCircle
Colin White, questioning whether ‘the center holds’.
“Why is this discussion important? The main reason is at present business intelligence is synonymous with data warehousing. This thinking is wrong and needs to be changed. Data warehousing is a component of BI, but BI may employ data in other data stores. In some cases a BI application may not even use data managed in a data warehouse. The tight connection between BI and data warehousing is causing terms such as virtual data warehousing to be used to describe other types of BI processing. These terms are unnecessary and just confuse everybody.
Another issue is that people have forgotten that data warehousing was created to overcome deficiencies in business transaction systems. Many of these issues are now solvable. My concern is that data warehousing has become a system in its own right and companies are now extending the data warehouse into other application areas such as master data management and content management. This is completely the wrong direction and must be argued against.
The bottom line is that data warehousing is still an important component of business intelligence, but it is no longer the foundation on which all BI projects have to be built.”
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Teradata Magazine Online | Article | Time Travel
Teradata 13.10 has option to incorporate temporal processing capabilities into the data warehouse.
“Typically, data analysis has been limited to how two data elements relate to each other, such as projected revenue change based on product sales assumptions. Time-based analysis was limited to comparing events that occur at different points in time, like sales today versus sales a year ago. Systems that enable such analysis are called decision support systems (DSSs). DSSs have been limited to understanding the relationship between data values that represent the current state of the data.
Then there is active data warehousing, which allows you to do such analysis in a timely fashion across more data areas.
Temporal is the next step in analytic capability. It analyzes how a data element evolves or how it relates to other data over time—for example, which products were in a category last year, which are in that category this year and how that change will affect sales. This argues for a much deeper understanding of the time dimension.”
