Saturday, January 4, 2014

We continue our post on the data warehouses with a discussion on the end user community. The end users have a lot of say in how the data warehouse shapes. They have a lot of diversity so we recognize four types - the farmers, the explorers, the miners and the tourists. The farmer is the most predominant type of user found in the data warehouse environment. This is a predictable user in that the queries submitted by the user are short, go directly for the data, recur on the same time of the week and is usually successful on finding the data.
The explorer is the user community that does not know what he or she wants and hence takes more time and more volume of data to search. This user covers a lot of data and typically does not know what he or she wants before the exploration process begins. The exploration proceeds in a heuristic mode. In many cases, the exploration looks for something and never finds but there are also cases when the discoveries are specially interesting.
The miner is the user community that digs into piles of data to test assertions. Assertions are tested based on their strength from the data. Usually this user community uses statistical tools. The miner may work closely with the explorer. The explorer creates assertions and hypothesis and the miner may determine their strength. Usually this community has to have mathematical skills.
The tourist is the user community that knows what to find where. This user has a breadth of knowledge as opposed to the depth of the knowledge. This user is familiar with both formal and informal systems. He or she knows the metadata and the indexes, the structured data and the unstructured data, the source code and how to read and interpret it.
There are different types of data targeted by these end users. If data existed in different bands of probability of their use in the data warehouse, the farmers would be very predictable and target only the top small band of this data while the explorers would reach all over the data.
Cost justification and ROI analysis could be described for these user communities as follows:
The farmer's value and probability of success is very high. His queries are useful in decision support. The explorers success rate is not that high although his finds are much more valuable than the regular queries performed by the farmers. The warehouse therefore should present the ROI from farmers community instead of the explorers.


 

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