Summary: Management values the self-starting, data-driven, curious, and urgent characteristics that define the Citizen Data Scientist. But the path to encouraging these individuals also requires setting limits and risk procedures of a wholly new type. Procedures that will protect the organization so that bad analytic conclusions don’t become bad financial outcomes for the company.
Added by William Vorhies on March 29, 2016 at 8:30am — No Comments
Summary: Which is the most critical element in data exploration, statistics or data visualization? The answer is a little like the lyric ‘love and marriage, you can’t have one without the other’. It can be tempting to skip the data visualization but it’s frequently the key to making sure we aren’t heading down the completely wrong path.
Added by William Vorhies on March 23, 2016 at 8:35am — No Comments
Summary: This is my favorite IoT story. We are so used to IoT platforms being physical objects that we forget about the potential for biologics. In terms of direct economic reward little will compare to this story about the IoT and cows.
This is my favorite IoT story which I first heard from Joseph Sirosh, CVP of Machine Learning for Microsoft at the spring Strata convention in San Jose. We are so used to IoT platforms being physical objects like cars…Continue
Summary: The premise of this new Key Object architecture is that search is broken, at least as it applies to complex merchandise like computers, printers, and cameras. An innovative and workable solution is described. The question remains, is the pain sufficient to justify a switch?
Added by William Vorhies on March 15, 2016 at 9:39am — No Comments
Summary: It’s become almost part of our culture to believe that more data, particularly Big Data quantities of data will result in better models and therefore better business value. The problem is it’s just not always true. Here are 7 cases that make the point.
Summary: Self Service Data Prep Platforms (SSDPPs) may offer some relief for BI and data workers who must deal with IT bottlenecks in getting data. But watch out for widely varying capabilities and the assumptions underlying some of their automated features.
Added by William Vorhies on March 2, 2016 at 3:32pm — No Comments