To enable DOD for a particular class (like the particle we used in the previous entry), i.e., to distribute its different data members in separate memory locations, we change the class source code to turn it into a class template particle<Access> where Access is a framework-provided entity in charge of granting access to the external data members with a similar syntax as if they were an integral part of the class itself. Now,particle<Access> is no longer a regular class with value semantics, but a mere proxy to the external data without ownership to it. Importantly, it is the members and not theparticle objects that are stored: particles are constructed on the fly when needed to use its interface in order to process the data.
"Hard." "Huge." "Dramatic." These are the words used to describe the kind of change required to truly become data-driven. Most companies simply aren’t up to the task. Have no fear, though: It’s the boss’s fault. If you ask business leaders to name their strategic challenges, “making fact-based business decisions based on data” tops the list (48 percent of respondents to the Forbes survey). But when you ask them how willing they are to trust that data, a less rational picture emerges. A Fortune survey of 720 senior business leaders that revealed that 62 percent tend to trust their gut rather than data, and 61 percent indicated real-world insight tops hard analytics when making decisions. In other words, the problem with truly embracing big data starts at the top.
Finally, since the “Thing” is a thing, it must meet a specific need or bring a value to be useful. As such, we must also account for its ability to meet functional expectations as a core existence requirement. Once the core physical needs of “Things” are met, and before external connectivity is possible, security is needed. To be quite clear: Security is key for IoT adoption, and thus needs to be addressed for individual “Things” that can be externally accessible. Accessibility does not mean just connectivity. It also applies to things that can be physically “cracked” open, where lack of security could put stored data at risk. ... This truth really has to be faced early on in the creation of each IoT device. Every “Thing” in IoT requires a means to encode, encrypt and authenticate its data.
So the good news is you have taken the plunge, recognised that you and your organisation need to embrace the digital present, hired an appropriate partner to help with your transformation and are ready to get started. The bad news is that your chances of success aren't great, but there are plenty of things to do to help move the process along. Unless you are incredibly naive and believe that your transformation partner is just going to 'do' everything for you--like an agency--then you will appreciate that this is about changing from within with a little help from the outside. As such it's down to you to do everything you can to make it work, so here are some things to watch out for.
One common feedback we've heard consistently is about the need for a prescriptive guidance on how to design the network infrastructure for disaster recovery. This helps to guarantee the best possible RTO by bringing their replicated virtual machines located in either the secondary data center or Microsoft Azure. This whitepaper is directed to IT professionals who are responsible for architecting, implementing, and supporting business continuity and disaster recovery (BCDR) infrastructure, and who want to leverage Microsoft Azure Site Recovery (ASR) to support and enhance their BCDR services. This paper discusses practical considerations for System Center Virtual Machine Manager server deployment, the pros and cons of stretched subnets vs. subnet failover, and how to structure disaster recovery to virtual sites in Microsoft Azure.
Anita Wang, TrendForce's notebook analyst, noted that "Tablets have yet to evolve beyond their main role as entertainment devices". However, Microsoft's Surface range "and Apple's upcoming 12.9-inch iPad change their functions depending on situations. They therefore can assist in the expansion of tablet applications by capturing a share of the business application market." Shipments of Microsoft Surfaces will grow from 1.5 million in the first half of 2015 to 2.6 million in the second half, according to TrendForce. It says: "The success of Surface 3 also proves that 2-in-1 PCs with better specs have the potential to expand into the business application market. Based on TrendForce's analysis, Microsoft's tablet shipments this year will soar 52 percent year on year and hit the four-million-unit mark."
A Forbes review of numbers from Wanted Analytics showed manufacturers listed 15% of Big Data related job openings at mid-year, compared with "professional, scientific and technical services" (25%), Information Technologies (17%), finance and insurance (9%) and retail trade (8%). Put differently: outside of IT itself, manufacturing is hiring more data gurus than any other single industry. Big Data positions include data analysts and scientists, solution architects, data platform engineers and Linux/Java/Hadoop/SQL engineers. While pure size plays a role – one in six private-sector US employees works in manufacturing – there is no question that factories are punching above their historical weight when it comes to data.
The Merriam-Webster dictionary defines artificial intelligence (AI) as "An area of computer science that deals with giving machines the ability to seem like they have human intelligence," and offers an alternate definition of "the power of a machine to copy intelligent human behavior." The first AI programs were developed in the 1950s and weren't commonly used in business. The big data analytics revolution has finally taken AI out of the halls of academia and research institutions, and has plugged it into commercial applications for business. Commercialized AI is still fundamentally new for many companies, though. The keys to business success with AI are to know how to use it and know what results to expect.
For many businesses, big data emerged in recent years with great expectations—that it could answer all questions about customer desires and behaviors. Today, however, many people believe big data alone can’t deliver what they want: actionable information with which they can make effective decisions that serve their customers and their bottom line. Many companies are trying to figure out what value big data can give them, and how to gather, mine and make sense of it. Unfortunately, big data can present several challenges within a company, which is why most big data projects fail. More importantly, however, smart companies are starting to figure out that big data alone isn’t a sufficient source of customer intelligence.
You can think of an autonomous system in the computer world as a city with many streets. A network prefix is similar to one street with many houses. An IP address is like an address for a particular house in the real world, while a packet is the equivalent of a car travelling from one house to another using the best possible route. Taking this comparison to its logical conclusion, the BGP routing protocol is analogous to your trusty GPS navigator. Like Google's Waze application, the best route is determined by different factors, such as traffic congestion, roads temporarily closed for maintenance, etc. The path is calculated dynamically dependiing on the situation of the network nodes, which are like roads and junctions on a GPS map.
Quote for the day: "The best minute you spend is the one you invest in people." -- Ken Blanchard