Date and time-stamped dimensions are dimensions that track the history of changes in the dimension tables themselves. (Star Schema, 2012) The easiest way to think about these type of dimensions are regular data dimensions that have the added columns necessary to show each record by a specific date or [...]
As we slowly move towards a more data focused society as well as with companies well entrenched in the usage of data, analytics, and business intelligence we see that analysts that are hyper-focused in their area of expertise and job function are increasingly more capable with the nitty-gritty details. However, you’ll see that the [...]
Sometimes it is incredibly hard for me to understand the amount of money being spent on Business Intelligence software knowing that within a year and sometimes even less the purchasers of Business Intelligence software will scrap it, buy something else, and/or just not even use it at all.
For some this will be a no-brainer and for those I apologize, however for a good majority of you I think this is going to be a good post to remind you that any report you are building whether in Cognos, SAP, MicroStrategy, Microsoft, or what have you, strive to always do [...]
I have seen a lot of posts and forums inquiring about CSIM (Cognos Solution Implementation Method) and Business Analytics Solutions Implementation Method (BASIM). Here is some key details and a great tool below you can download to walk you through the method step by step.
A Data warehouse is a repository of an organization’s electronically stored data. Data warehouses are designed to manage and store the data whereas the Business Intelligence (BI) focuses on the usage of data to facilitate reporting and analysis. The purpose of a data warehouse is to house standardized, structured,
There are three types of slowly changing dimensions: Type 1, Type 2, and Type 3. Each of these types tries to help the designer of the star schema eliminate paradox from their dimensional model (just as the three interpretations of the Schrödinger’s Cat thought experiment tries to eliminate the paradox of the living dead).
An operational data store (or “ODS”) is a database designed to integrate data from multiple sources for additional operations on the data. The data is then passed back to operational systems for further operations and to the data warehouse for reporting. Because the data originates from multiple sources, the
Operational Source Systems are the systems of record that capture the transactions of the business. The source systems are usually different than the data warehouse because presumably we have little to no control over the content and format of the data in these operational legacy systems.
The reporting, analysis, and interpretation of business data is of central importance to a company in guaranteeing its competitive edge, optimizing processes, and enabling it to react quickly and in line with the market.
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