November 16, 2005
Useless Information - or is it?
Have you ever had that friend who always comes up with what is seemingly ‘Useless Information?’ If however, this information came up in a topic of conversation and it had some use or value, it would therefore render the useless argument - well useless. My main problem with people who categorize this information or any other information as useless, is the fact that they are only taking the data at face value. Where am I going with this you might ask? And if you aren’t asking that - you may be asking how this could be applicable to what we do at J&R?
There is an old adage about turning data into information and finally turning that information into knowledge. The whole starting point behind that adage is based around taking what could seemingly be useless information to some, and turning that information into some sort of advantage for another.
An example I will use throughout this post is LIMS data, as this data is mostly regarded as transactional data rather than anything that can provide strategic advantages. LIMS applications are classified as OLTP - which facilitates data entry and data retrieval. The key component missing in OLTP systems is data analysis or reporting of data.
These data structures are designed in a format to provide the fastest mechanism for single record entry and single record retrieval. The databases are not setup in a fashion that would allow for more complex analysis to be performed in a simple manner.
This is where the idea of housing transactional data in a data warehouse comes into play. The new structure of the data provides new meaning to the data and turns it into information. What used to be a set of results that were either in or out of specification, is now turned into a trend of information that could include variations of the raw material providers, analysts performing tests, the time in relevance to the analysts shift that the tests were performed, instruments that were used for the tests, etc. All of a sudden this seemingly useless data could have shown you that when tests of Methyl-Ethyl-Ketone are provided by ABC Company and tested by Joe Analyst while working on the 3rd shift, the tests fail at a 70% rate. Now we are able to reduce the costs associated with testing by no longer having Joe test this product as he has an accuracy rating of 100% on all other products that he tests on 3rd shift.
This is a basic concept of data warehousing and data mining and you will see be seeing a series of blogs in the upcoming months in which I will discuss the benefits that can be achieved by having a separate environment that is designed for reporting purposes.
Posted by Bryan J. Holmes at November 16, 2005 09:38 AM
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