What could we do to create a breakthrough? Could the DIY movement be brought to the Big Data & Analytics area? It has certainly worked in some other areas. For instance, the 3D printing business. Of course, 3D printing had already been invented and was in existence, but the start of the DIY 3D printing race really made it possible for this business to flourish. It’s funny to notice that it was spurred by the possibilities from crowd-funding, making it possible for the first DIY 3D print companies to start building their business. A great documentary call ‘Print the Legend’ is available on Netflix, in case you are interested to know more. The same goes for Internet of Things (IoT). One could argue that IoT is no longer a new thing, but to many it is still a starting business.
Morin first took a few moments to speak about V-Locity, what the company describes as "I/O Reduction Software." The product appears to aggregate I/O from Windows and virtual machine (VM) software from VMware and Microsoft and then optimize storage reads and writes. The product does this by gathering up small I/O requests and making the system read and write larger amounts of data in a single I/O. It also more intelligently places data, rather than using the first available space. ... By optimizing writes to be written in a more contiguous fashion, the size of an I/O consistently increases. In other words, instead of writing four 4Kb blocks of a 16Kb file, V-locity enables the system to write a single 16Kb write, requiring a single I/O operation.
The DevOps movement essentially stems from the agile development movement. "Agile is working," Randell said. "Three quarters of [development] teams are doing some kind of agile. It comes down to the focus on delivering to the users. It's not a one size fits all situation." Development teams are now the first line of defense for effective and smooth-running apps, Randell added. The move to DevOps has changed the app testing process. Testing now becomes embedded into the development teams as part of the process. By iterating frequently and delivering rapidly, apps are tested and debugged on a more continual basis. "It really does turn DevOps into a machine," Jones said, noting even Microsoft no longer has a QA department which has led to the delivery of new code to fix bugs faster.
Putting the ultimate user of the product front and centre results in a focus on making products people want to use, and High Tech Anthropology® (HTA) is considered to be the key to how they achieve this. HTA is an approach to user needs identification, requirements elicitation and product design that draws elements from a variety of existing disciplines and adds some Menlo-specific tools and approaches. As with the other Menlonian practices, it sits atop the solid foundation of the Menlo culture – collaboration, trust, respect and teamwork are baked in from the beginning. Without this solid foundation the practices would not work. The underlying culture is the secret sauce of Menlo Innovation’s success.
The recommended way of dealing with the leap second is to stop the clock for a second, but on a computer, that's not practical: The computer and its clock will keep running and have to be jumped back, with the same second seemingly occurring twice -- a chance for high-frequency stock traders in Asia and California to make -- or lose -- a fortune. Software, then, must deal with the consequences of that repeated second, or find a way to fake it, perhaps by gradually adjusting the clock over the last few minutes of the day. But the sheer variety of clever ways to handle the extra second is part of the problem, as systems using different methods drift slowly out of sync with one another before slowly realigning once again.
Monsanto's enterprise IoT strategy started as a way to reduce inefficiencies in its supply chain, such as preventing seed loss. Seeds that experience heat stress, for example, are unlikely to germinate. By outfitting the semi-trucks that transport seed from fields to processing facilities with sensors that measure temperature and geolocation, Monsanto's IT department was able to build a virtual window into the transportation environment. Doing so gave the business an advantage: "Now, with IoT, if our grain gets heat stressed, we can dynamically route it to cooling centers or route it to the front of a receiving line to get the grain processed," said Fred Hillebrandt, infrastructure architect at the agro-chemical and technology company in St. Louis.
What many companies are experiencing is a gap between their investments in the underlying technologies of Big Data, and the anticipated benefits. The macro trends across many industries around challenged sales growth, margins, and consumer loyalty reflect this reality. The gap looks a bit differently between companies at the bleeding edge of adoption and those just beginning their journeys. Yet there is one thing in common between the two: wide-ranging views on what Big Data represents in terms of value creation and how it fits within the organization. CEOs and their leadership teams have enough to worry about and focus on, without getting into the weeds of Big Data technologies.
Blau cites BlackBerry as an example. "Even though [BlackBerry] still seemingly has fairly decent enterprise support today, it's not enough," says Blau. "You have to have that whole ecosystem." Angela Yochem, CIO of logistics and transportation company BPD International, says third-party partnerships are almost always a good thing for enterprise vendors. "These partnerships allow enterprise customers to benefit from innovations and support structures well outside of a single vendor capability." Apple is similar to Google in this regard. Both companies focus on what they do best, instead of "customizing elaborate support models and relationship management teams for major customers," Yochem says. "Consumers and enterprise customers alike are becoming more comfortable with this sort of constrained model."
The lazy man figured out ways to eliminate wasteful movements, conserve energy and still get the job done. "If necessity is the mother of invention, then maybe laziness is the mother of innovation," he said. One of the ways CIOs can encourage laziness? Brian encouraged CIOs to embrace the DRY principle -- Don't Repeat Yourself. Automate what you can so that employees can devote time to the most important problems. ... "Impatience leads to better tools," Brian said. "We get impatient with the tools we have, so we build a better one, and it solves the problem in a better way." That's also why impatience benefits from laziness and hubris, characteristics that can ensure the business doesn't take on too much technical debt.
Understanding data is about extracting insights from the data to answer questions that will help executives drive their business forward. Do we invest in products or services to improve customer loyalty? Would we get greater ROI by hiring more staff or invest in new equipment? Getting insights from data is no simple task, often requiring data science experts with a variety of different skills. Many pundits have offered their take on what it takes to be a successful data scientist. Required skills include expertise in business, technology and statistics. In an interesting study published by O'Reilly, researchers (Harlan D. Harris, Sean Patrick Murphy and Marck Vaisman) surveyed several hundred practitioners, asking them about their proficiency in 22 different data skills.
Quote for the day:
"Opportunity always involves some risk. You can?t steal second base & keep your foot on first!" ~ Joseph Helle