Do you know when you need to keep certain data, or when you’re clear to send it on its merry way to be deleted? Storing everything for an indeterminate amount of time is hardly practical, but knowing what’s safe to get rid of and what you should keep is an issue many organizations struggle with when dealing with the growth of unstructured data.
The problems associated with the growth of unstructured data aren’t new to many people, and some may even convince themselves that it’s not an issue. But that’s likely not the case, according to Randy Kerns, senior strategist and analyst at Evaluator Group.
“Almost everybody has some percentage of growth,” Kerns explained. “What we typically see is somewhere between 20% and 35% growth rate, organically. Then we also see a significant number of our clients that have [the] introduction of some big data projects that came from outside. IT wasn’t part of it — that just got dumped on them — and so they end up with some huge spike they have to deal with.”
In his Storage Decisions session, “Scale-up, scale-out: A survival guide for coping with capacity growth,” Kerns discussed the growth of unstructured data, and how those who have to deal with it are probably not completely prepared.
So, how do you deal with unstructured data? Defensible deletioncan be effective, but it’s not always an option. Many in IT are reluctant to delete data because they don’t know who it belongs to, or if it will be needed down the line.
“They keep it, and, primarily because they’re not sure, or who owns the data hasn’t authorized it, deleting data isn’t really an option for most,” Kerns explained. “And so, we get all this data in, and we just keep it, and it costs us.”
If you can’t delete data, and it keeps growing consistently, the situation might seem helpless. However, Kerns outlined a few best practices. First, deal with it early. As capacity grows, problems will develop, so face issues from the start.
Having a strategy in place will help you deal with concerns that may arise from the growth of unstructured data, and protect you from possible surprises. A scale-up or scale-out approach as more capacity is needed, and taking advantage of practices like external tiering, can help with capacity issues and performance.