This week’s roundup looks into Google’s AI guide designed by their Chief Decision Intelligence Engineer. We notice new use-cases for predictive analytics, the implementation of prescriptive analytics over predictive, and bringing back the basics to data science. Lastly, there is a growing demand in Silicon Valley for data scientists, and Lityx’s VP of Analytics discusses how third-party data can help you understand and target customers better.
The Ultimate Guide to Starting AI
by Cassie Kozyrkov, Chief Decision Intelligence Engineer, Google featured on TowardsDataScience.com
Google’s Chief Decision Intelligence Engineer dives into the first steps for implementing ML/AI into your firm, along with all its little sub-steps you will find along the way.
Starting Lineups, Crime, Elections: The Unusual Use Cases For Predictive Analytics
By Greg Petro, First Insights Contributor for Forbes.com
Predictive analytics aren’t just being used to predict business outcomes. Their usage has spread into predicting elements that touch the everyday lives of consumers—from a team’s starting lineup to deadly health issues, to even identifying where a crime is most likely to take place.
The Path from Predictive to Prescriptive Analytics
by David Judge, Vice President of Leonardo, SAP featured on TechRadar.com
Businesses are becoming more competitive and realized they need to tap into more in-depth insights before beginning to look at the next steps to take. By going beyond predictive analytics to leverage more advanced tools, such as prescriptive analytics, enterprises can leverage recommended actions that address uncovered insights.
The Cart Before The Horse In Data-Science Projects: Back To Basics
by Pedro Parraguez, Postdoc at the Technical University of Denmark, Engineering Systems Division and co-founder at Dataverz, featured on Dataeconomy.com
Vendors, consultants and a long list of articles put the focus on the strengths and untapped potential of new analytics, data visualizations, and the increasing volume and quality of data. All this creates excitement and an urge to act. However, among all the rush to act on insights, we miss clarity about the actual problem that we are trying to solve.
Data science, the ‘new Latin’ for students, in demand in Silicon Valley
by Melia Russell, Business Reporter for The San Francisco Chronicle
Dubbed the “sexiest job of the 21st century” by Harvard Business Review. Data science uses modeling and analysis skills of statistics combined with the programming and machine learning tools of computer science to find patterns in data and extract insights. But sexiness hasn’t lured enough people into the field, especially around Silicon Valley.
Does Third-Party Data Help You Understand Your Customer and Improve Targeting?
by Simon Poole, VP of Analytics Services at Lityx.com featured on Martech Advisors
Marketers generally accept that third-party data provides foundational knowledge for any marketer to use to gather general components that make up their customer base. But, it can also be effectively used to enable more precise target marketing that can dramatically improve marketing KPIs.
Did you see an interesting article in the last week? Share it with us! Send it to astuttle [at] lityx.com.
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