It would take a long time to discuss the challenges of data disposal. It is not only not easy, it can be nearly intractable, and no high-tech Alexander the Great is likely to be able to cut the Gordian Knot of storage. Plus, there are logical and technical challenges to address. A data governance function has to set and establish policies. A methodology has to be found to separate the data wheat from the data chaff (which is a major entanglement and comingling problem). The process likely can't be done manually, so software tools to aid in the automation of the process are likely required.
Realistically, a triage concept to get the most reduction with the least investment may have to be employed. Even in the best circumstances, you will likely have to accept the fact that you can't get it all. Moreover, you not only have to tackle the current data mountain, but you have to put processes and procedures in place to try to prevent it from happening again. Cleaning up after the data elephant is not easy, but organizations have to acquire the shovels and discipline to make it happen.
Mesabi Musings
News of continued exponential storage growth is a continuing circus, with big data being one of the most prominent acrobats. But while it is less glamorous to talk about what is left after the parade of data elephants has gone by, that cleanup process has to take place.
If storage is 20% of an IT budget and 70% of the current data mountain has no value, roughly 14% of the annual IT budget may simply provide no value whatsoever. Facing up to the problem is the first thing that enterprises need to do. Figuring out how to solve the problem will require time, mental discipline and hard work, but the prize would seem to be worth the effort. How to address the problem so everybody benefits is a subject for another day.
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