| Data warehousing is the process whereby your | | | | you to take data from disk, filter it and present it |
| business collates its data to help you make better | | | | out to build queries from terabyte databases |
| business decisions and improves your data | | | | (which means it's more suited to larger databases |
| analysis capability. A lot of businesses have their | | | | and solutions). Even if you're using a data |
| data in separate source systems, which makes it | | | | warehouse appliance, you still need to make sure |
| difficult to report across the enterprise, the data | | | | it's designed, built and delivered correctly, and |
| quality may not be clean enough to support | | | | even after it's completed, your user requirements |
| decision making which is an issue you may need | | | | may still be changing. In the interest of planning |
| to address. You may also have to look at the | | | | for the future, it's best to optimise the design the |
| performance of reporting too as we all need our | | | | best you can then use the appliance to give you |
| data as quickly as possible. Due to these different | | | | the performance and scalability you may need at |
| factors a lot of businesses can struggle with data | | | | a later date, but it does allow you to process |
| warehousing because the thought processes in | | | | your data much more efficiently |
| building data warehousing is different to other | | | | It's not uncommon to struggle with designing a |
| types of systems but when you get it right it can | | | | data warehouse, one of the most popular ways |
| have a massive positive impact on your business. | | | | for implementing data warehouses now is a star |
| Although you may need a data warehouse to | | | | schema (there's debate out in the industry as to |
| deploy business intelligence, the modern business | | | | whether that's right or wrong). This is mainly |
| intelligence tool actually mask the complexities of | | | | because it's assumed that our transactions, our |
| the underlying data very well for the end users, | | | | facts, our data is so vast and we need to make |
| this means that you've got one primary source of | | | | it narrow as possible. |
| information and this is where you should begin | | | | Slowly changing dimensions are a mechanism by |
| with your business intelligence. Your IT | | | | which we store history in the dimension data that |
| department may object to running queries directly | | | | we record. So for example, your operational |
| off their source systems, but modern technology | | | | systems is the data changes we normally |
| will not struggle with this task, and because disk | | | | overwrite it because we're not really interested in |
| space is so cheap, you can replicate data very | | | | the previous version of that record, but for |
| easily. | | | | historical reporting purposes we want to know |
| A data warehouse appliance is an all in one solution | | | | what that record was previously, in the data |
| of hardware, database software and networking | | | | warehouse we version those records and they're |
| software, it's a very cleaver bit of kit that allows | | | | called slowly changing dimensions. |