Valuable Predictive Analytics for the Non-Technical Online Lender
Auomation is the great equalizer when it comes to competing [...]
Time to Change the Corporate Paradigm
Dr. Eric Siegel Dr. Eric Siegel, founder of [...]
Are You Doing the Right Thing With Your Data? Infographic
Six insights automated predictive analytics can get you in a [...]
DMway Makes The Pitch Semifinals – Photos
DMway recently participated in Israel’s The Pitch, a competition to [...]
See You at Fintech Week – London
DMWay Analytics is looking forward to meeting with you at [...]
The Secret Sauce of Savvy Lenders: Automated Predictive Analytics
By Gil Nizri It doesn’t take a savvy businessman to [...]
3 Reasons Companies Need to Adopt Predictive Analytics
Data is growing at a rate of 2.5 quintillion bytes of data being created every year and organizations are drowning in numbers, facts and figures. In fact, most organizations do very little with data, yet wax confident that data is their greatest competitive advantage.
7 Questions with Dr. Eric Siegel
Everyone is talking about predictive analytics, artificial intelligence and machine learning. But these buzz words can be overwhelming to the average subject matter expert (SME), who before the world of big data just wanted to do his or her job. In the information age, SME’s have to be more strategic, effective and efficient.
DMWay and Integrant Analytics Announce Strategic Partnership
Integrant to leverage DMWay’s Predictive-Analytics platform to provide quicker and more effective predictive solutions for the US energy industry April 7, 2017 ------- Integrant Analytics recently announced it has selected DMWay predictive analytics platform and will harness the technology to provide exceptional accuracy in its predictive models for North American energy producers and utilities.
The Black Sheep of Data Science
My data scientist colleagues all get excited about the science of the analytical process. The goal is competing – sometimes with themselves! – to achieve the best model they can get. The process looks like this: Gather the best data set one can get that best fit the business problem. Often, this involves merging dozens of pre-existing data sets, making the process take weeks … if not months and, in some cases, years. Use this data to create the first model. But my data scientist friends are never satisfied. Instead, they are constantly trying to improve the model using machine learning algorithms that no business person understands.