Predictive Analytics in Action

Predictive Analytics, Marketing and Finance professional sharing thoughts on the technology - OR, how to tell the future to add value.

[businessanalytics]

maparkerfd20

ND being in the playoff is a friggin joke!

— M Parker (@maparkerfd20) December 29, 2018

from http://twitter.com/maparkerfd20 via IFTTT

[businessanalytics]

koopa_kinte

This the most Florida shit I’ve ever seen in my fucking life. And I’m saying this as a Floridian. pic.twitter.com/aw2wXyEypp

— koop (@koopa_kinte) December 29, 2018

from http://twitter.com/koopa_kinte via IFTTT

[businessanalytics]

The simple genius of a good graphic

In a talk that's part history lesson, part love letter to graphics, information designer Tommy McCall traces the centuries-long evolution of charts and diagrams -- and shows how complex data can be sculpted into beautiful shapes. "Graphics that help us think faster, or see a book's worth of information on a single page, are the key to unlocking new discoveries," McCall says.

[businessanalytics]

“The reason that God was able to create the world in seven days is that he didn’t have to worry about the installed base.” — Enzo Torresi. 1945–2016.

What I Learned Working for Steve Ballmer – Ben Fathi – Medium

[businessanalytics]

How the Houston Astros are winning through advanced analytics

In the first of a two-part interview, the architect of last year’s World Series champions shares how analytics, organization, and culture combine to create competitive advantage in a zero-sum industry.

[businessanalytics]

micheeaton

“Growth and comfort do not co-exist.” @GinnyRometty @mish2ne1 #CoSN16 #change #cpchat pic.twitter.com/hC7r9totCr

— Michele Eaton (@micheeaton) April 6, 2016

from http://twitter.com/micheeaton via IFTTT

[businessanalytics]

Breaking away: The secrets to scaling analytics

A handful of the world’s companies have cracked the code on embedding analytics into every layer of their organizations.

They got this one right....

[businessanalytics]

(via Why You Don’t Need Data Scientists – Kurt Cagle – Medium)

[businessanalytics]

We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten. Don’t let yourself be lulled into inaction.

Chatbots were the next big thing: what happened? – The Startup – Medium

[businessanalytics]

Chatbots were the next big thing: what happened?

A great bot can be about as useful as an average app. When it comes to rich, sophisticated, multi-layered apps, there's no competition. Today's most successful bot experiences take a hybrid approach, incorporating chat into a broader strategy that encompasses more traditional elements.

[businessanalytics]

fitz4cloud

Ginni Rometty - AI is the new industry inflection point. “Not an era of man vs machine, an era of man plus machine.” #Think18 pic.twitter.com/yUFOeowhBK

— Jim Fitzgerald (@fitz4cloud) March 20, 2018

from http://twitter.com/fitz4cloud via IFTTT

[businessanalytics]

cspenn

#smmw18 what to delegates: anything that falls in these three buckets. @ducttape pic.twitter.com/swjOEtMzs2

— Christopher Penn (@cspenn) February 28, 2018

from http://twitter.com/cspenn via IFTTT

[businessanalytics]

Cognitive insight. The second most common type of project in our study (38% of the total) used algorithms to detect patterns in vast volumes of data and interpret their meaning. Think of it as “analytics on steroids.” These machine-learning applications are being used to:

3 Things AI Can Already Do for Your Company

[businessanalytics]

The Science Behind IBM's Data Science Design - THINK Blog

Data science is a rapidly evolving discipline that leverages an ever-widening array of tools and capabilities to learn and exploit. Because of such inherent complexities surrounding adoption, integration and support, the work of the data scientist can be daunting. That complexity is one of the reasons IBM several years ago set out to bring clarity and uniformity to the otherwise disparate data discovery and analytics process. The goal: create a solution that leveraged the best capabilities available, in an integrated, collaborative platform that was easy to access and use. With it, everyone from data scientists to business analysts would be able to not only tackle the discipline, but conquer it. Along the way, we learned a lot about the role of data scientists; their challenges, their tools of choice, and how they valued certain processes and functionalities over others. But, first a little background. Up until the time IBM rolled out the popular Data Science Experience, if someone…

Great product and great article. One issue with the graphic... the “Deploy and share” arrow is pointing the wrong way. It needs to point back to the business. This isn’t about tweaking an image - its about the mentality of supporting the business through better science. The best analytics can be wasted with a poor deployment plan or without buy-in from the business.

[businessanalytics]

Mistaken extrapolations, limited imagination, and other common mistakes that distract us from thinking more productively about the future.

[businessanalytics]

(via Machine Learning – Can We Please Just Agree What This Means - Data Science Central)

[businessanalytics]

Skimming through Gartner Magic Quadrant for Data Science Platforms - Analytics Industry Highlights

In the latest round of 2017 reviews, Gartner released a Magic Quadrant for Data Science Platforms. Results this year were less surprising to me than the BI and Analytics Platforms. Even the ... Read More