[unable to retrieve full-text content]Why is this distinction important? Because it’s critical to understanding how leading-organizations are investing in new data engineering skills that exploit advanced analytics to create new sources of business and operational value.
Original Post: What’s the Difference Between Data Integration and Data Engineering?
[unable to retrieve full-text content]Democratization is defined as the action/development of making something accessible to everyone, to the “common masses.” AI | ML | DL technology stacks are complicated systems to tune and maintain, expertise is limited, and one minimal change of the stack can lead to failure.
Original Post: Democratizing Artificial Intelligence, Deep Learning, Machine Learning with Dell EMC Ready Solutions
[unable to retrieve full-text content]It’s really hard to find predictions about the future made in the 1950’s. I decided to review the most popular sci-fi movies from 1950’s, and provide my perspective as to what these movies might tell us about 2018.
Original Post: Back to the Future: 2018 Big Data and Data Science Prognostications
[unable to retrieve full-text content]As I watched the impending battle between the White Walkers and humanity, I couldn’t help but identify a number of lessons that we can learn from Jon Snow’s battle with the leader of the White Walkers… and the power of Valyrian steel!
Original Post: Lessons from Game of Thrones: Stopping the White Walkers of Data Monetization
[unable to retrieve full-text content]The best way to reduce operating and business costs and risks is to prevent them!
Original Post: Why Use Data Analytics to Prevent, Not Just Report
[unable to retrieve full-text content]It seems Isaac Asimov didn’t envision needing a law to govern robots in these sorts of life-and-death situations where it isn’t the life of the robot versus the life of a human in debate, but it’s a choice between the lives of multiple humans!
Original Post: Asimov’s 4th Law of Robotics
[unable to retrieve full-text content]This blog introduces the basics of reinforcement learning. We are going to see how reinforcement learning might help us to address these challenges; to work smarter at the edge when brute force technology advances will not suffice.
Original Post: Transforming from Autonomous to Smart: Reinforcement Learning Basics
[unable to retrieve full-text content]While I have talked frequently about the concept of Analytic Profiles, I’ve never written a blog that details how Analytic Profiles work. So let’s create a “Day in the Life” of an Analytic Profile to explain how an Analytic Profile works to capture and “monetize” your analytic assets.
Original Post: The Key to Data Monetization
[unable to retrieve full-text content]This post outlines a data analysis exercise undertaken by students in a recent University of San Francisco MBA class, in which they were forced to make difficult data science trade-offs between gathering data, preparing the data and performing the actual analysis.
Original Post: Golden State Warriors Analytics Exercise
Okay, let me get this out there: I find the term “Citizen Data Scientist” confusing. Gartner defines a “citizen data scientist as “a person who creates or generates models that leverage predictive or prescriptive analytics but whose primary job function is outside of the field of statistics and analytics.” While we teach business users to “think like a data scientist” in their ability to identify those variables and metrics that might be better predictors of performance, I do not expect that the business stakeholders are going to be able to create and generate analytic models. I do not believe, nor do I expect, that the business stakeholders are going to be proficient enough with tools like SAS or R or Python or Mahout or MADlib to 1) create or generate the models, and then 2) be proficient enough to be able to…
Original Post: Citizen Data Scientist, Jumbo Shrimp, and Other Descriptions That Make No Sense