Peter Levine, a venture capital investor with Andreesen Horowitz, claims: “I’m going to take you out to the edge to show you what the future looks like.” He takes us on a “crazy” tour of the history and future of cloud computing — from the constant turns between centralized to distributed computing, and even to his “Forrest Gump rule” of investing in these shifts.
But… how can we say cloud computing is coming to an “end” when it hasn’t even really started yet?? Because the edge — where self-driving cars and drones are really data centers with wheels or wings — is where it’s at. So where does machine learning in the enterprise come in? How does this change IT? Read more
Ken Molay posted an article “Thinking Differently About Thinking Different” that starts as follows: “People love pointing to non-conformist geniuses to show the power unleashed when you break away from conventional norms and boldly follow your own vision: Picasso and his cubist painting. Miles Davis and his cool jazz movement. Mahatma Mohandas Gandhi and his nonviolent non-cooperation independence movement. What many people miss in these stories is that to break away from tradition effectively, each of these people studied, practiced, and understood the traditional approaches first. Picasso didn’t start out painting crazy flattened faces with both eyes on one side of the nose… He spent many years learning and practicing realistic painting techniques. Miles Davis studied music theory and trumpet technique at Juilliard. Gandhi studied law and jurisprudence in London.” Read more
On Feb. 20 Mike Gualtieri from Forrester wrote: “Prescriptive analytics is about using data and analytics to improve decisions and therefore the effectiveness of actions. Isn’t that what all analytics should be about? A hearty “yes” to that because, if analytics does not lead to more informed decisions and more effective actions, then why do it at all? Many wrongly and incompletely define prescriptive analytics as the what comes after predictive analytics. Our research indicates that prescriptive analytics is not a specific type of analytics, but rather an umbrella term for many types of analytics that can improve decisions. Think of the term “prescriptive” as the goal of all these analytics — to make more effective decisions — rather than a specific analytical technique.” You may want to get the full report “Prescriptive Analytics: The Black Belt Of Digital Decisions“
“To solve any business problem, you must first start with a business question. Some vendors speak too much about “magic.” In their eyes, you combine data, machine learning and cognitive artificial intelligence, and you get magic. That may be true someday in the future, but today, projects should start with a well-defined question, such as:
- Can I recommend products to my visitors?
- Can I predict who is going to leave me for competition among my current customer base?
- Can I predict next month?
When developers are brought into the fold to deploy models and improve applications that answer real business problems, enterprises see results from machine learning” Read more
The spring is around the corner and it’s time for fun. Play with this Magic Square and try to figure out the “business logic” behind its decisions.
Ron Ross just published a provocative post “IT Departments Should be Evacuated – Agree/Disagree?” In particular, he writes: “The days of traditional departmental IT staff having unfettered access to the financial assets of the corporate budget will end sooner than you might think. The current way of building business systems is unsustainable. If you think the cloud was something, just wait! I look at agile software development as the death throes of traditional IT. Beyond it there’s nowhere left to go to accelerate except to elevate the level of human interfaces with machines. Economics will demand it.” Agree/Disagree?
Sapiens gathered five of its leading subject matter experts for a quick Q&A to get real about artificial intelligence (AI) in the insurance industry. A quote: “Question. How insurers can prepare for a more “intelligent” future? Answer: The best way for insurers to prepare is to organize their data and make it accessible. Today’s insurers are coping with mountains of data. They will need to quickly transform this data into business decisions by using advanced analytics tools, and this organized data will be the foundation that machine learning will build upon.” Read more