Curious about OpenStack? Don’t miss this episode of Engineers Unplugged, where Kenneth Hui (@hui_kenneth) and Gabriel Chapman (@bacon_is_king) explain the difference between OpenStack the project, product, and service using bacon as an analogy. Don’t watch hungry.
Cue the unicorns.
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This is Engineers Unplugged, where technologists talk to each other the way they know best, with a whiteboard. The rules are simple:
Episodes will publish weekly (or as close to it as we can manage)
You know the Internet of Everything (IoE) is gaining traction when you hear about it from the guy changing your oil. Earlier this month I was dropping off my car for its regular service when the technician began asking me how the Internet of Everything will change automobile maintenance and repair. Twenty minutes later – after we had discussed how quickly cars are becoming smarter and connected – I was on my way home with yet another example of just how fast the Internet of Everything is coming our way.
IoE — the networked connection of people, process, data, and things — is spawning business opportunities in just about every walk of life. However, the proliferation of traditional and new data sources and the movement of data to the cloud are making it harder for businesses to access all their data assets. Research shows that through 2017, a whopping 90 percent of the information assets from big data analytic efforts will be limited to specific project siloes and — more importantly — unleverageable across multiple business processes. [Source: Gartner “Predicts 2014: Big Data”] Read More »
The theme of this year’s Cyber Security Awareness Month is “Our Shared Responsibility.” At Cisco, security is everyone’s responsibility – from our trustworthy development processes, to innovation enabling our customers and partners to address threats on end points, networks, and in the cloud. That is why Cisco is setting the industry standard for meeting the security needs demanded by the Internet of Everything (IoE).
Over the next six years, the number of devices connected to the Internet is going to reach 50 billion, creating some pretty unique opportunities and dilemmas as companies and industries are connecting people and devices to one another in ways we’ve never seen before, changing the way we work and live.
As the number of connected devices in the “Internet of Things” increases exponentially, organizations must keep security top of mind as the number and type of attack vectors increases alongside the quantity of data IoE creates. This shift is creating a daunting challenge for companies and those responsible to defend the infrastructure.
I recently did a video blog on the IoE from the security perspective. Take a look and let me know what you think in the comments.
The recently concluded Predictive Analytics World 2014 in Boston, Massachusetts, was chock full of insights from organizations who have successfully implemented analytics in a variety of settings. A few points stood out and I will attempt to capture them here:
1. Focus on ROI measures: This is spoken of very often, but frequently in an attempt to develop the “right” or “perfect” model, the focus on ROI sometimes begins to waver. Being driven by ROI implies understanding which variables are controllable by the business, which data observations are of real interest and sometimes making adjustments to accomplish that. This may mean considering variables that are otherwise not significant, or oversampling certain data observations and so forth. But a relentless focus on ROI will yield the desired results.
2. Eschew Complexity: Seek simpler models, fewer variables, and explanations that make sense. Given results, the human mind will find ways to explain it – so do not rely on interpretability as a defense of your models. But let the sheer simplicity of models tell their own story.
3. Ensure algorithmic Data Preparation: As all practitioners know, Data Cleansing and Preparation is 80% of the effort – but what is sometimes forgotten is that Data Preparation is not a one time effort, but is subject to the algorithms being considered. Understand not only the assumptions, but the limitations of the algorithms being considered – and do for the algorithms, what the algorithm cannot do for itself.
4. Consider Ensemble Techniques: Ensemble methods such as Random Forest, Gradient Boosting and others have proven repeatedly to provide stable and usable results. Master these techniques and more.
5. Simulations are often a good Communication technique – All practitioners understand that the ultimate success or failure of their efforts depends upon successful communication of their findings. Leverage simulation of your results, and the likelihood of success or failure of your model in real situations to help further communicate and define your findings.
And incidentally, if you wish to understand the analytics maturity of your organization, visit this link at the INFORMS website!