Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Survey: Machine Learning Projects Still Routinely Fail to Deploy
 Originally published in KDnuggets. Eric Siegel highlights the chronic...
Three Best Practices for Unilever’s Global Analytics Initiatives
    This article from Morgan Vawter, Global Vice...
Getting Machine Learning Projects from Idea to Execution
 Originally published in Harvard Business Review Machine learning might...
Eric Siegel on Bloomberg Businessweek
  Listen to Eric Siegel, former Columbia University Professor,...

Industry News

Data Science – Found Your Unicorn Yet?

 You’ll find much banter in the data science ecosystem these days about all the skill-sets required to effectively function as a data scientist. Some have gone so far as to label the perfect melange of experience and knowledge as a “Unicorn.”  This a reference to the recent discussions in the press and blogosphere indicating that

Personalization Is Back: How to Drive Influence by Crunching Numbers

 Standard predictive analytics does not directly address what is the greatest challenge faced by marketing and healthcare: Across large numbers of individuals, deciding who to treat in a certain way. Yes, you heard me correctly. Predictive analytics...

Predictive Analytics Market to Reach USD 6,546.4 Million by 2019, Globally: Transparency Market Research

 According to a new market report “Predictive Analytics Market (Customer intelligence, Decision support systems, Data mining and management, Performance management, Fraud and security intelligence, Risk management, Financial intelligence, Operations and Campaign management) – Global Industry Analysis, Size,...

Data Scientist: Evolution of the Business Analyst

 The business/data analyst role is evolving into a new role due in part to the new technology of big data. The data scientist role has emerged because of the increase in breadth and depth of data being...

“Data Scientist” catches “Statistician”, surpasses “Data Miner”

 The rapidly rising term “Data Scientist” caught up with “Statistician” and surpassed “Data Miner” on Google Trends. However, Statistics remains a lot more popular than “Data Science”, which begs the question: What do Data Scientists do? Clearly,...

Searching for data scientists? They come in sets of 3!

 Data scientists have been called the most sought-after IT professionals of all for 2014. But that mission is distorted on two fronts, according to one current data scientist. Dr. Michael Wu, chief scientist at Lithium Technologies (aka,...

Perfect Information Doesn’t Equal Perfect Predictions

 Many organizations attempt to achieve “data nirvana” by having 100% complete information for any given business decision. In the customer analytics space, this is sometimes referred to as a “360 degree view of the customer.” However, we...

Why not now? The barriers to adopting true predictive analytics.

 While reading Jared Diamond’s excellent book on the rise and subsequent global dominance of Eurasian societies Guns, Germs and Steel, I was stopped in my tracks by his chapter on the evolution of technology entitled Necessity’s Mother. Diamond...

2014 Big Data Predictions from IDC and IIA

 Both IDC and The International Institute of Analytics (IIA) discussed their big data and analytics predictions for 2014 in separate webcasts last week. Here is my summary of their predictions plus a few nuggets from other sources. IDC predicts...

Hiring 1 Data Science unicorn is hard enough, a team is impossible. To scale means to specialise.

 The Data Scientists need a large set of skills, including business know-how, modelling and mathematics, plus programming. They are as hard to find as unicorns, or superheroes. I know this talent shortage first hand. Is the solution...

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