Being good at data analytics starts with the mastery of fundamental tools and techniques in statistics. However, the real challenge in practice is asking the right questions, selecting the most appropriate tools, and communicating the data in a way that is meaningful to the audience. Along the way, the true expert navigates between the sand traps of human biases and statistical confusions.
"One bit of advice: it is important to view knowledge as sort of a semantic tree — make sure you understand the fundamental principles, i.e., the trunk and big branches, before you get into the leaves/details or there is nothing for them to hang on to." - Elon Musk
CIOs, saddled with legacy apps and development projects, rarely have the time to become chiefs of information. Larger companies need focus to the various roles in IT bleeding into business: CIO, CTO, CDO, CDS.
[First appeared with a different title in LinkedIn on October 18, 2015]
If you haven't been following the upsurge of interest in the physics and astronomy communities about dark matter (and if you haven't, that's nothing to be ashamed of), realize that there is near conclusive evidence that our universe [Ed. Is there any other?] consists of baryonic and non-baryonic matter.
IoT is making huge waves and has tremendous promise. Can we borrow the whole idea of IoT and apply it to non-physical "things" inside a company? With IoT, we can instill intelligence into our universe. With IoT-C, we can make this happen specifically for the non-physical entities in our corporate world.
The Utility of an Integrated Knowledge Management Approach for Analytics and Decision-Making in Healthcare
In an increasingly connected world, lack of explicit consideration of the entities and their interrelationships in the decision-making process results in solutions that are sub-optimal, inefficient, expensive, or unsustainable. This paper examines two decision-making models: Military Decision-Making Process (MMDP) and DECIDE.
Data science has many sides to it. It isn't just for data scientists with advanced degrees. Data science is not just a cool way to run exotic algorithms. It is also about collecting it, consolidating, and presenting it in a timely way. For this to be effective and efficient, everyone needs to participate in different ways.
Thinking, in the context of important topics, is a complex and sophisticated skill that is underrated. There are many useful frameworks to assist in the process. We also have to overcome our biases and learn to think from first principles.
The process versus outcome rubric helps you focus on process, since a good outcome is not always a result of good process or a bad outcome is not always a result of bad process. Good processes create belief in the efficacy of culture in a company.
[First appeared in LinkedIn on August 12, 2014]
Many, many years ago, programmers sat down and wrote code without any thought of deployment sullying their minds. Life was simpler back then.
They wrote code for MSDOS x86 machines (640K RAM, anyone?), or Unix. Linux, of course, was the result of Linus Torvald's stirring call to "the times when men were men and wrote their own device drivers."