These are the methods of moving horizontally with in the dimensional structure of Data-warehouse and OLAP. This term is more used in context with OLAP, because typically various End-user Business Intelligence tools sit on top of OLAP, which in turn sits over Data-Warehouse.
Drill-down Navigation
It is a method of exploring for more detailed data. It is done by revealing lower-level data than was previously displayed. For instance, you can drill down from State to City to offices. Available levels depend on the granularity of the data in OLAP and data warehouse.
Drill-link
A URL hyperlink to a destination, defining the parameters, such as the document name and prompt answers, for the drill. When the document is viewed in Web, a user can click the link to navigate to the link's destination.
Roll-up Navigation
A method of exploring for more widely summarized data. It’s an antonym to Drill Down. Typically you move up a dimension hierarchy. For example you have the office level break-up of sales revenue, and you can roll it up to city, zone, region and country level figures.
Cross-Dimensional (horizontal) analysis and Navigation
Cross-dimensional analysis, is an analysis across multiple dimensions- the key reason why OLAP and its multi-dimensional structure exists. Most of the business reporting and analysis goes across dimensions.
A single dimension analysis is, when you get measures for a single dimension. For example- when one looks for measures sales, headcount of employees, operating expenses etc. for 'location' dimension (office, city, state, region, country..)
A cross-dimension example will be to look for measures sales, gross profit etc. for 'location' dimension (office, city, state, region, country..) for a given set of products, for a given number of quarters. If you top this kind of example with other analysis types (max-min, exception, filtration), you come close to the real-life complexity of a business analysis query. One example can be:
Identifying top ten of the offices where, the sales for 'washing and cleaning' product range is more than the average sales for this product range across all offices, for those offices, which are open for more than 3 years and have an average growth of 5% per quarter over last 4 quarters.
Cross-dimensional analysis capability with an OLAP server is also manifested in the cross-dimensional navigation. For example- you are seeing a pie-chart of revenue share for different product-lines. By clicking on pie of a given product (general insurance- Vehicles), you may like to go for state-wise split for the revenue of that product. Going further, you may like to click on a given state (New-York, California..) and look for split across the channels (telemarketing, sales employees, tied agents, corporate agents, 3rd party brokers..).
In the above examples, you are able to seamlessly navigate and drill across due to the cross dimensional linkages.
Here is the list of cross-dimensional analysis you can perform:
Drill-across Dimensions
You drill across dimensions, when you move from one dimension to another.
For example you are looking at revenue break-up for the cities. However, now you want to have the break-up of revenue for various products (Say Fax machine, Telephone and copier) within that city (Say New York). With in the Fax Machine product in New York City, you want to find the break-up as per channels of telemarketing, mailers and direct sales.
In the above example you have drilled across the Dimensions of 'Location'->'Products'->'Channel'.. This is one of the most important and features And is fundamental capability expected out of an OLAP tool
Drill Across Measures It is similar to Drill-across dimensions.
For example, you are doing the sales revenue analysis and have been able to find out the best and least performing offices. However, to have a further understanding of the picture, you now move across measures to find out about the Sales transactions of these offices (a low revenue , but higher sales transaction point to a certain level of activity) and number of sales staff (the low performing offices could have lesser staff) and number of months since the office is set-up (the new offices being in gestation period could be performing lower)..
Drill Across Attributes This is by all means same as 'drill-across dimensions'. For example you have the data for revenue in US as per the customer relationship value bands (say USD 10K to USD 20K/USD 20K to USD 50 K/USD 50K and above.). For USD >50K band, you want to have the break-up as per the age bands (18 years to 25 Years/25 Years to 40 Years/>40 Years), and with in >40 years, you want to have the break-up for occupation (self employed, Practicing professional, employ ed..)
In this example we drill across the attributes of relationship value band-->Age Band-->Occupation All belong to the customer dimension. |
