I’ve spoken in some detail about what Sift Science does in my previous post. As a quick reminder, Sift Science helps e-commerce businesses fight fraud using the power of machine learning and predictive analytics. They collect diverse data, glean signals from it, and assign a ‘Sift score’ to every user which predicts how likely it is that a particular user is a fraudster.
We're back with our round-up of the biggest news on big data, business intelligence, and data visualization. Going forward, we'll be doing this on a bi-weekly schedule, which we think would be the ideal frequency. 1. Explaining machine learning to non-computer-science people Quora is a great place to find nuggets of interesting conversations between prominent… Read the rest of this entry »
Over the past few weeks we’ve been discussing predictive analytics at length. We started with an overview, and then went on a tour of 9 unique businesses that are leading innovation in predictive analytics. So far, we’ve just set the stage for this and the next post, where we take a focused look at the visualization methods and concepts used in predictive analytics products today.
Last week, we took at look at 5 startups doing cutting edge work in predictive analytics. But there really are so many examples of innovation in predictive analytics, that we couldn’t include them all in a single post, and have spread them out over to this post too. Here are 4 more startups that are paving the way for a future in predictive analytics.
This first post starts with a disclaimer that it's a contrary to popular opinion on business analysis. Tim Wilson begins stating that dashboards are primarily for performance measurement, and not for recommendations. Then, he goes on to argue that business executives should expect dashboards to simply alert them to the state of their business, after which there should be a joint effort between the analyst and the recipient of the dashboard to analyze the data and decide necessary action. This way the time to take action is reduced drastically, and the outcomes are more in line with business goals.
In part 1 of this series on predictive analytics, we discussed the variety of data, particularly unstructured and semi-structured data, and the three types of predictive analytics. We also touched upon the approach of continual optimization of a predictive analytics system for it to become more and more accurate. With those basic concepts in place, we’re ready to take a tour of the various applications of predictive analytics as it’s used in businesses today. You can think of this as the hall of fame of predictive analytics. These businesses are playing “where the puck is going to be”
This week’s data digest takes a look at how data can transform CRM systems in the future, how it’s changing the way news is consumed today, and finally how visualization helps us comprehend complex patterns in data.