Culling meaningful, usable data with predictive power out of the chaff heap is challenging enough; feeding enough of it to AI and machine-learning algorithms to produce intelligence is proving to be even more challenging. Are there any ways we can make data tools smarter about sorting data so we don't have to? Dr. Sarah Cooper, chief operating officer at M2Mi Corp., spoke about the difficulty of figuring out the unknown processes that need to be automated in data tools. "You've got a ton of data coming in -- most of it is very low value," she told John Furrier (@furrier), cohost of theCUBE, from the SiliconANGLE Media team, during the IBM Open Cloud Architecture Summit. "There's an incredible amount of intelligence that goes into filtering and determining what is actually valuable data and getting that up into the decision and interpretation tools like the Big Data, the machine learning, Spark, some of the streaming analytics as well," Cooper explained.