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The Latest News, Events and Developments

The Modern John Henry: Why Executives Are Wrong to Favor Intuition over Analytics – by Loraine Lawson in IT Business Edge

Ironically, one of the most common barriers to companies adopting analytics isn’t the business case, but executives’ faith in their own memory.

Roei Ganzarski, CEO of the predictive analytics firm BoldIQ, told Information Week that he daily encounters leaders who reject the idea of using analytics over their own business intuition.

Ah, man versus the machine — it’s an old, old debate for literature, philosophy, and more recently, brain researchers. To be honest, the more I read, the more I think the gig is up for business intuition.

As least, that’s what the data says, according to Andrew McFee, co-director of the Initiative on the Digital Economy in the MIT Sloan School of Management. According to McFee, “…there have been a raftload of studies comparing the predictions of human experts vs. those of algorithms, and …in the great majority of them the algorithms have been at least as good as or significantly better than the humans,” McFee wrote in a Harvard Business Review blog post. “In a meta-analysis conducted by William Grove and colleagues of 136 research studies, for example, expert judgments were clearly better than their purely data-driven equivalents in only eight cases.”

In fact, with what we now know about the brain, the harder it gets to justify intuition over algorithms. From memory formation to mental fatigue brought on by too many choices, research tends to favor the idea that we are much more fallible than we would ever want to believe.

Marcelo Gleiser, a theoretical physicist, natural philosopher and professor at Dartmouth College, expressed this problem eloquently in a recent NPR post.

“We can thank the brain for tricking us into building a sense of the ‘real,’” Gleiser writes. “What we call reality is the result of our brain’s very complex integration of external stimuli: sights, sounds, tastes, touch and smells. We perceive nothing in the actual present.”

To be fair, the same is somewhat true about analytics, despite all the hype about real-time analytics. The difference is that analytics is based on a collective sense of reality — not one person’s reality. Or as Barrett Thompson, GM of pricing excellence solutions for Zilliant, told Information Week, predictive analytics is “…the distilled wisdom and experience of five hundred salespeople who encountered tens of thousands, or hundreds of thousands, of unique selling circumstances.”

The pro-intuition crowd isn’t without a champion, however. Analytics expert Tom Davenport has written about the important role of intuition in Big Data projects.

I checked out Davenport’s full HBR post. It’s worth noting that his examples aren’t so much about decisions business leaders made, but rather about new ideas or theories they had that were confirmed or, in many cases, actually made possible by Big Data.

To me, Davenport’s examples are creative acts — which actually fall outside the domain of many executive decisions, if you think about it — even the strategic one.

As Information Week notes, what we’re talking about is significant, but daily, decisions that are largely based on guessing—not facts—even though you have access to the data.

For example: Which route is the most cost-effective for our delivery drivers? Where are we making overpayments or fraudulent charges?

Businesses often have this data, but struggle to analyze it. This is where executives play John Henry, stubbornly fighting the machine despite what’s best for business. But in such cases, Big Data analytics outplays intuition.

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Big Data Debate: Do Analytics Trump Intuition? – by Jeff Bertolucci in Information Week

C-suite types often scoff at the notion that software can outperform insights acquired from years in the trenches. Fear not: Analytics will complement, not replace us.

Will the next evolution in big data remove human intuition from key business decisions and rely exclusively on data-driven analytics?

Probably not, but organizations will increasingly rely on analytics to make real-time decisions based on a rising tide of big data, predicts Roei Ganzarski, CEO of BoldIQ, a Seattle-based optimization company. This doesn’t mean, however, that managers warmly embrace analytics over intuition. In fact, the opposite is often true. C-suite types often scoff at the notion that software can outperform insights acquired from years in the trenches.

“We run into that every day,” says Ganzarski in a phone interview with InformationWeek. “Our response is, ‘It’s not that we think you’re doing something wrong.'” Rather, he says, analytics provide “an additional tool that enables you to do things the human mind simply can’t do. We’re not here to replace you. We’re here to enhance your ability to make decisions.”

Obviously, the human brain still trumps computers at myriad tasks. But software is significantly better at split-second analysis, he says, providing a transportation industry example. “Let’s talk about next-day and same-day deliveries. People talk about that as the Holy Grail of distribution and supply chain — where you make an order and it shows up on your door that very day.” This presents the sort of big-data challenge suited for analytics “Once the order has been put in place, how does [the delivery company] make sure its vehicle-and-driver network is set up in such a way that, within milliseconds, it can tell you which vehicle and driver should do the pickup and delivery so that everything is done on time and in a profitable manner?”

The Human Algorithm: Barrett Thompson is general manager of pricing excellence solutions for Zilliant, an optimization company that helps businesses use in-house data to make better sales and pricing decisions. In a January interview with InformationWeek on the algorithm-versus-intuition topic, Thompson pointed out that the algorithmic approach to decision-making is based on the collective experiences of multiple individuals within a business.

“Let’s say I’m looking at set of sales transactions completed over the past year, and I have five million of them sitting in the data warehouse,” says Thompson. “What I have captured in that data, what I have to find a way to unlock, is the distilled wisdom and experience of five hundred salespeople who encountered tens of thousands, or hundreds of thousands, of unique selling circumstances.”

A predictive algorithm, he says, doesn’t create knowledge out of thin air. Rather it’s an “accelerator” of what an organization already knows. It’s driven by human experience, by “data points you’ve lived through.”

“I can’t see what the other 499 salespeople did, and I can’t even remember what I did in March of last year. I make so many decisions that they become lost in memory,” Thompson said. “If I had a software tool, an algorithm that could remind me of what I’ve learned, and reveal to me what everyone else has learned… I could look at the guidance coming out of the algorithm as a distilled and refined experience from myself and people just like me.”

Of course, the big caveat here is that Thompson and Ganzarski are heavily vested in the analytics camp. Neither is an unbiased observer of the intuition-versus-algorithm debate.

In a December 2013 Harvard Business Review blog on intuition’s role in a data-driven organization, analytics expert and author Tom Davenport argued that major big-data projects are often driven by intuition:

Major big data projects to create new products and services are often driven by intuition as well. Google’s self-driving car, for example, is described by its leaders as a big data project. Sebastian Thrun, a Google Fellow and Stanford professor, leads the project. He had an intuition that self-driving cars were possible well before all the necessary data, maps, and infrastructure were available. Motivated in part by the death of a friend in a traffic accident, he said in an interview that he formed a team to address the problem at Stanford without knowing what he was doing.

Fair enough, chalk one up for intuition. But in the enterprise, analytics will play an increasingly influential role in the intuitive process.

Jeff Bertolucci is a technology journalist in Los Angeles who writes mostly for Kiplinger’s Personal Finance, The Saturday Evening Post, and InformationWeek

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BoldIQ Team Driverless cars are cool, but the network layer that will run them is even cooler – a BoldIQ byline in Venture Beat

Google has shown us that autonomous, driverless cars are now a reality, at least from a technological perspective. These vehicles are capable of reacting to unforeseen disruptions such as jaywalkers, roving bicyclists, or slowing traffic.

It is exciting and scary to think of the world ten years from now replete with these technological advancements. Our world will need to adjust.

Consider the air, where unmanned aerial vehicles or drones are already being used – and where a fierce battle is being waged between the FAA and prospective and current drone users. Concerns around safety, security, and more are all part of the huge debate in one of the more controlled environments we have: our airspace.

If this is what is happening in aviation, can you imagine what will happen when “autobots” hit our streets?

The environment of ground transportation is much larger and more dynamic than aviation, yet it has significantly fewer (if any) regulations in place. Drivers can go anywhere using any route they chose. Travel plans are not known in advance; and speed, while supposedly controlled by rules of the road, is often unpredictable and inconsistent.

All this causes near-collisions every day with licensed and trained humans behind the wheel. Imagine what will happen when we throw driverless vehicles into the mix. This is a highly complex dynamic network that requires real-time tools to manage it in order to be safe and efficient.

To do this, there are two layers of the network that should be addressed, combining both safety and efficiency. The first layer is the individual vehicles themselves. Google is doing a phenomenal job addressing this layer. The second layer is what I will call the network layer — the fleets of unmanned vehicles in aggregate. If we plan and execute the network layer correctly and in an adaptive fashion, we will actually need fewer vehicles (human-driven or driverless) to meet the demands of our day-to-day lives, whether that entails deliveries or public transportation.

And with fewer vehicles on the road, there is less of a safety risk at the individual vehicle level and the added benefit of less traffic, congestion, and waste. The ripple effects of this can, of course, be extended to a price reduction for deliveries and public transportation, etc.

This network needs to be structured yet rapidly adoptable and adaptable to meet the needs of the public, from both transportation and safety aspects. It is one thing for a single vehicle to sense a jaywalker and apply the brakes. It is another for the network to adapt itself in real-time to the butterfly effect of that car braking: the other cars braking; the human-driven car tailgating and not braking in time and colliding into the autonomous vehicle in front of it; the traffic jam created due to this incident; the ensuing delays; the delivery company needing to adjust its plans to meet its same-day delivery commitments; and the public transport authority needing to get the commuters to work on time.

As we move steadily and surely into an on-demand world, one which blends human and artifcially intelligent resources, we must think beyond a single resource, a single delivery or bus route. Using already available big data to streamline operations in real time is a highly effective way to manage a constantly changing environment, all while using the vehicles to the best of their capabilities. Optimizing real-time data streamed in from the vehicles can make responding and adjusting their usage in the real world possible, and it can give the public and regulators some of the comfort needed as we explore the possibilities.

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‘Send in the Drones’ featuring BoldIQ – by Zach Noble in FCW

Drones, UAVs, UAS — call them what you will, remotely piloted aircraft are poised to make huge inroads in the national airspace. And although publ0ic perception might link unmanned aircraft systems with intelligence agencies and the military, the federal government’s UAS user base extends well beyond spies and soldiers.

Agencies as diverse as NASA, Customs and Border Protection, and the Forest Service are all experimenting with UAS and deploying the systems in novel ways. Drones “can reach hard-to-fl y areas and maneuver well at low altitudes,”said Jeff Sloan, a UAS operator at the U.S. Geological Survey. “They give us data there’s no way you could get with a manned aircraft.” NASA is sending drones through hurricanes and volcanic plumes to collect data, while USGS is using the technology to map changing landscapes. The Border Patrol is scanning for lawbreakers from above, and the Forest Service hopes to better monitor the spread of wildfi res. Drones might soon be able to effectively deliver critical supplies in disaster and searchand- rescue situations. In short, the technology can save money, provide superior data and keep people out of harm’s way. Nevertheless, civilian agencies’ adoption of UAS is not a straight path forward, and regulatory hurdles and institutional caution are slowing the technology’s adoption.

Regulatory restrictions: One major impediment to faster drone adoption is the Federal Aviation Administration. Charged with regulating the nation’s airspace, the agency is naturally reticent to open the drone fl oodgates. And the FAA’s reach extends further than many think. In a recent myth-busting release, the FAA reaffi rmed that its jurisdiction starts at ground level, not at 400 feet as commonly stated, and NASA scientists confi rmed that FAA rules follow the space agency to parts of the Atlantic Ocean and the Arctic. In 2012, Congress tasked the FAA with developing a plan for safely integrating UAS into the national airspace by Sept. 30, 2015, and until that plan is completed, civilian agencies must obtain special FAA clearances to use drones. Public operators, including civilian agencies and numerous universities, held 613 active clearances, called certifi cates of authorization, as of April 8. Besides the trouble of obtaining authorization from the FAA, agencies must also follow rules that, although well-intentioned, can neutralize the benefi ts of UAS. For many smaller drone models, like those deployed by USGS, FAA rules require operators to maintain line-of-sight contact with the vehicles, which Sloan said limits the drones’ utility. The requirement also keeps the Forest Service from sending drones into the smoke of wildfi res, thereby undermining one of the biggest potential benefi ts of UAS: gaining a vantage point unattainable by human pilots.

Plunging in unprepared: The FAA is not the only source of UAS hiccups; other agencies have made some mistakes along the way. In 2007, for example, the Forest Service spent $100,000 on a pair of SkySeer drones that it planned to use to spot illegal marijuana-growing operations on federal land. Unfortunately, the agency lacked trained operators and FAA approval. Jeff Ruch, executive director of Public Employees for Environmental Responsibility, said the Forest Service’s poorly planned purchase is evidence of a “boys with toys” attitude toward emerging technology. “There was no planning,” said Ruch, whose organization publicized the issue. “They saw the Border Patrol’s use of drones and said, ‘Oh, that’s neat.’” The Forest Service’s drones have now been slated for wildfi re tracking, he added, but “it’s not clear if that transition will take.”

Drones on a budget: Regulatory restrictions have all but forced civilian agencies to be followers in the realm of UAS development, but there’s a substantial benefi t to letting the military and private industry take the lead: Interested agencies can pick up drones for free. The Interior Department’s USGS owns a fl eet, valued at $15 million, of 20 T-Hawks (20-pound drones made by Honeywell) and 15 tiny hand-launched, remote-control Ravens made by AeroVironment. Although USGS has spent around $1 million on UAS operator training and sensor systems, it paid nothing for the drones themselves. “Our Ravens are from 2005,” said Mike Hutt, UAS project manager at USGS. “The military has moved three generations past those initial Raven models, so they’re surplussing the old ones to us.” That military/civilian cooperation has been a boon to USGS. The free Ravens “really helped us cut our teeth on what we can and can’t do with drones,” he added. NASA is another agency that is beating swords into plowshares. The agency’s Airborne Science Program has been dabbling in UAS since the early 1990s and currently uses such varied drones as the 25,000-pound Global Hawk, the customized- for-science Predator variant Ikhana and the small, maneuverable Dragon Eye. NASA’s fleet of Dragon Eyes was acquired for free from the Marine Corps. “We take whatever we can get,” said Bruce Tagg, manager of the Airborne Science Program. “Our scientists are very entrepreneurial; they have their eyes on just about everything.” NASA obtained the Dragon Eyes through the Rube Goldberg process that is interagency procurement: A NASA scientist heard the Marine Corps was getting rid of the drones and asked about having them sent to NASA. The drones went fi rst to the Interior Department, then to the General Services Administration and, fi nally, as a result of the scientist’s persistence, to NASA.

Weighing costs and benefits: Although UAS can bring many benefi ts, saving money is not always one of them. “There’s a misconception that these UAVs are so much cheaper than manned aircraft,” said Matt Fladeland, NASA’s UAS manager. “For [small drones such as] Dragon Eyes, that might be true, but for bigger systems like the Global Hawks, there’s not much difference [in cost] between running them and running a twin-engine B200.” Between the costs of fuel, trained operators and support systems, fl ying a large UAS can be just as expensive as a manned fl ight. Tagg said the real benefi t of large drones is not that they save money but that the unmanned craft can stay aloft for 24 hours in situations where a manned aircraft would last half as long. When monitoring a developing hurricane, for example, the extra airtime can be hugely benefi cial, he added. Small drones bring more direct savings. “In smaller areas — 10 kilometers by 10 kilometers — UAS are very good for surveying and bring us a substantial cost savings,” said Hutt, who estimated a 10-to-1 savings over traditional manned fl ights. Drones also enable agencies to save in other ways. For instance, instead of relying on satellite imagery, USGS can get better photos for less money by strapping a GoPro camera to a low-fl ying drone. USGS uses data-processing software to make sense of the images collected by drones and gain a sophisticated sense of topography, vegetation cover and more. “We’re fi nding that $1,000 cameras are giving us data that we used to rely on $400,000 mapping tools to get,” Hutt said.

The path forward: The peaceful potential of UAS seems indisputable. “UAS will assist public safety agencies in responding to natural disasters, locating missing persons or helping to fi ght wildfi res,” said Melanie Hinton, senior communications manager at the Association for Unmanned Vehicle Systems International. “In addition, UAS will help farmers care for their crops, [help] to identify diseases, and more precisely and safely spray pesticides.” The organization estimates that the fi rst decade of widespread UAS adoption could produce an $82 billion economic boost in the United States. The military likes to say drones are used for dull, dangerous and dirty missions, Hutt said, “but we’re focused on better Earth science applications, greater safety and savings.” He said he expects FAA regulations, especially the requirement that operators maintain line-of-sight contact with drones, will ease as better radar systems and transponders are developed to keep drones out of the way of other aircraft and one another. Industry insiders are developing new tools, but they say the existing technology is exciting in its own right. “Lost-link procedures are pretty standard now, and fl ight planning is getting better,” said Hutt, adding that although they are not fully autonomous, many drones have sophisticated programming to handle emergency landings and extended fl ights on their own.

Roei Ganzarski, CEO of software developer BoldIQ, is particularly bullish on drones. “The civilian market, once it’s opened up, will be a lot bigger than the military market,” he said. Real-time optimization of data is BoldIQ’s stock-in-trade, and Ganzarski said software can make sense of drone data nearly instantaneously. Noting that concerns about the prevalence of drones are similar to public fears surrounding the advent of commercial aviation, he added, “There’s a view that [drones] will be flying around like mosquitoes, en masse, crashing into each other.” But with modern programming, drone fleets can be integrated with one another and the surrounding environment, and dynamic optimization will allow drones to react quickly and competently to changes in the environment, Ganzarski said. “The tech barriers [to UAS integration] don’t exist,” he said. “The barrier is the fear of the unknown.”

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BoldIQ and Drones In Action: 5 Non-Military Uses – by Elena Malykhina in Information Week

The Federal Aviation Administration (FAA) estimates that as many as 7,500 commercial drones — ranging in size from the large wingspan of a Boeing 737 to a small radio-controlled model airplane — will be hovering in the US airspace by 2018. Beyond the military, there are numerous potential uses for drones, or unmanned aircraft systems (UAS), such as law enforcement, storm tracking, search and rescue, and aerial surveying. But managing drones domestically comes with its own challenges, which still need to be addressed by the US government and the private companies involved.

The FAA in December set up six sites to test drone operations around the country. The congressionally mandated sites are tasked with conducting research into the certification and operational requirements for safely integrating commercial drones into the national airspace. The six sites include the University of Alaska, the state of Nevada, the North Dakota Department of Commerce, Texas A&M University/Corpus Christi, Virginia Tech, and Griffiss International Airport in Rome, New York.

The FAA’s move to set up drone test locations follows the release of a roadmap in November, addressing current and future policies, regulations, and procedures that will be required as drones continue to become more mainstream. “We have made great progress in accommodating public UAS operations, but challenges remain for the safe long-term integration of both public and civil UAS in the national airspace system,” FAA administrator Michael Huerta said in the document’s introductory letter.

Safety tops the list, especially when it comes to the logistical challenges of managing drones. “Buildings, antennas, manned airplanes, and other drones can make it a chaotic place, and safety needs to be the number-one focus of those managing drone implementation,” said Roei Ganzarski, CEO at BoldIQ, in an interview with InformationWeek Government. BoldIQ, a provider of optimization software, recently completed analysis of Silent Guardian, a solar-electric drone to highlight the benefits of using hybrid technology.

Companies managing drones need to consider logistical planning involving individual drone operations, coordinated drone fleet management, and incorporating drones into a “manned airspace,” all while processing enormous amounts of real-time data, according to Ganzarski. “When assessing a fleet of drones operating autonomously or even semi-autonomously, it becomes impossible for the human brain to process and manage the data to keep the entire system operating smoothly. It requires sophisticated real-time dynamic optimization software,” he said.

Beyond logistics, another issue is the security of the drones themselves, and the cargo they may be carrying. It’s vital that systems are in place to protect these expensive technologies while in flight and on the ground. Privacy is also a major concern for the public. Organizations need to make sure that UAS equipped with cameras do not violate privacy laws, said Ganzarski.

At the moment, almost all commercial drones are banned by the FAA. But that should change in 2015, when the agency expects to release its guidelines for safely operating drones. In the meantime, government agencies, a number of universities, and a handful of private companies are putting robotic aircraft to good use — and in some cases challenging the FAA’s authority.

A judge agreed March 6 the FAA had overreached fining businessman Raphael Pirker, who used a model aircraft to take aerial videos for an advertisement. The judge said the FAA lacked authority to apply regulations for aircraft to model aircraft. That may open the skies to a lot more privately controlled drones.

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Utilities: Time to get on the optimization bandwagon – a BoldIQ byline in SmartGridNews

Here’s an article penned by the CEO of a company that has built an optimization engine that works for many different industries, including electric power. I wanted to run it not to spotlight any company in particular, but to underline the growing sophistication of grid optimization options. Sophisticated solutions are emerging from national laboratories, universities, non-profit research companies, established grid vendors and innovative startups like the one highlighted here. – Jesse Berst

The US Energy Information Administration expects that the global energy market will see demand grow by more than half its present level by 2035 because of a growing world population. This increased demand and consumption will exceed the renewable rate at which many resources can be produced and the overall sustainability of today’s culture could be in danger. More demand and a dwindling supply of nonrenewable resources is cause for concern and cities need to start taking action now to minimize the risk.

Typically, utilities match demand by increasing supply and the infrastructure to support it, but there are other options that can save valuable time, money and resources. Instead of spending capital to build additional generators and power plants to continue producing enough energy to meet rising demands, power companies should learn to optimize their resources to maximize their availability.

New optimization technologies smart grid, modern grid, smart grid technology, grid optimization, electric utilities New dynamic real-time optimization technologies are available to disrupt how energy firms think about their grid and how they handle unforeseen interruptions.

Dynamic real-time resource optimization software was leveraged in a test of data from a large energy-focused organization in the Pacific Northwest. The test sought to learn how to meet network level loads while reducing overall operating costs and use of network assets. In a testing environment, the optimization software delivered in a matter of seconds and in some cases milliseconds, a nine-day hour by hour resource use plan for its 128 power sources.

Compared to the current optimization software in use, the solutions met all of the required load while matching and in some cases reducing the overall network cost. In addition to the planning, the optimization engine enables real-time disruption recovery. For example when shutting down a power source unexpectedly, as could happen in the real-world, the engine provided a new actionable recovery plan in 0.03 seconds that allowed the operation to maintain the same level of power output with fewer resources.

The same power from fewer resources What this means is that power companies could distribute the same amount of power (or perhaps even more), using fewer power sources. In a world where demand is increasing and resources are decreasing, this has tremendous implications. Retiring old inefficient power plants without having to necessarily replace them may now in fact be a realistic option for consideration. As predictions continue to foreshadow increased strain on the energy market, utility companies should take a modern sophisticated view of the action they should take now to be better equipped to handle the future.

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How the FAA can test real-time disruptions at drone test sites – a BoldIQ byline in Government Product

The FAA will have its hands full as it begins rigorous testing of unmanned drones at six recently announced testing sites. These sites are faced with managing the dense system of drones among themselves as well as other aircraft in the flying environment. This setup will require extensive logistical planning and coordination between agencies.

The billion-dollar future of the drone industry relies on flying expensive “test subjects” around the sky to best prepare for safe integration into U.S. airspace. This large task needs to have a structured, and yet rapidly adoptable and modifiable plan, to best utilize the time and resources involved in testing. When managed testing is over, the need for a plan grows exponentially. An “air space grid” is one way to manage this mandated drone testing. For safety, control and efficiency in the flight zone, researchers could create a grid in the sky — an intricate four-dimensional mesh of available and optional flight paths — for drones to fly through and test real-world scenarios. Researchers can then apply real-time dynamic optimization for drones to utilize the grid in a safe and efficient manner. Additionally, test sites will need to survey the capabilities of drones in the real-world environment. Unpredictable disruptions like extreme weather impacts, unexpected aircraft in the grid and malfunctions must be addressed as these changes to the operating environment can create chaos in the sky. When disruptions arise, real-time adjustments will need to be made by unmanned aircraft, just like manned aircraft.

Leveraging Big Data that is already available to optimize operations in real time during testing and beyond is an effective way to avoid collisions and manage a constantly changing environment. This arrangement can also ensure that the drones are utilized to the best of their capabilities. Optimizing data can make responding to and adjusting drone usage in the real world possible. In addition, it gives the FAA a jump-start on a complex regulatory and operating program.

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UAV Comparative Analysis Completed Projects Significant Capital and Operating Cost Reductions, when Selecting the Silent Guardian

Bye Aerospace, Inc. and BoldIQ, Inc. concluded an extensive analysis that projects the long endurance Silent Guardian, a solar-electric hybrid unmanned aerial vehicle (UAV), deployed with BoldIQ’s dynamic operations optimization software, offers twice the mission productivity (effective time on station versus total sortie flight hours) at less than half the operating costs.

The Silent Guardian UAV, in development by Bye Aerospace, is designed as a self-deployable high altitude long endurance (HALE) hybrid aircraft able to provide global range for persistent Intelligence, Surveillance and Reconnaissance (ISR) to support commercial, defense and security requirements. With a broad range of military and civil applications, the solar-electric Silent Guardian answers the market need for enhanced mission capability at a significantly reduced fleet size, reduced operating budget, and reduced environmental impact.

BoldIQ’s dynamic operations optimization software is a proven solution that enables significant capital and operating cost reductions for customers. More missions using less assets and lower operating costs. BoldIQ’s software was selected by Bye Aerospace to complement the Silent Guardian, together providing customers with an unparalleled UAV solution.

The detailed analysis verifies the cost and mission efficiencies of Silent Guardian and three other comparable UAVs currently in service. More than 180 different hypothetical mission scenarios varying in range, length, and parallel operations, were evaluated across an area of seven million square miles. In order to neutralize the potential differences created by operations scheduling among the various UAVs, BoldIQ applied its operations optimization software to all four UAVs equally. With a highly efficient operating schedule for all UAVs, the results demonstrated that the Silent Guardian will perform the same number and mix of missions with 16% to 64% less aircraft, and significantly reduced operating costs, when compared to the other three leading UAVs.

“The results clearly highlight the significant cost and performance benefits made possible by the very long endurance Silent Guardian,” said Kerry Beresford, Bye Aerospace’s Senior Vice President – Government Programs. “It also draws attention to the enormous environmental benefits of applying hybrid technology in aerospace by significantly reducing fuel burn and harmful emissions.”

“The UAV operating environment is ever changing and is constantly being impacted by numerous factors both in planning and in real-time. Compounding this complexity is the harsh environment of shrinking budgets,” said Roei Ganzarski, President and CEO of BoldIQ. “Real-time dynamic optimization provides operators with the edge they need – the ability to increase efficiency and productivity, while reducing waste, and lowering capital costs. Combine that with the momentous operating improvements of the Silent Guardian and the solution is unmatched.”

George E. Bye, President and CEO of Bye Aerospace, said the projections confirm what Silent Guardian UAV will achieve. “I would like to give a special thanks to the team at BoldIQ,” he said. “Their report quantifies the Silent Guardian’s endurance and cost advantages. As we continue with the detailed design phase of Silent Guardian, we are verifying the substantial cost savings and significantly increased efficiencies to ISR missions.”

BoldIQ Promotes Roei Ganzarski to CEO

BoldIQ, Inc., a real-time dynamic operations optimization software company, today announced the appointment of Roei Ganzarski as Chief Executive Officer. The board of directors promoted Roei from the role of president and chief operating officer because of his leadership expertise. Roei’s predecessor, Eyal Levy who served as chairman and interim CEO, will continue his role as executive chairman of the company, and will continue to focus on strategic development and customer advocacy.

Roei has been with BoldIQ since 2012 serving as the president and chief operating officer responsible for the company’s growth and overall business.
“Roei is an experienced leader with a keen business sense,” said Eyal Levy, executive chairman, BoldIQ. “We are now well positioned to take BoldIQ into the next phase of evolution and growth under his leadership.”

Prior to joining BoldIQ, Roei spent 13 years with the Boeing family of companies. Most recently, he served as chief customer officer for Boeing’s Flight Services division where he was responsible for leading all customer and market facing activity worldwide. Roei is a graduate of Wharton’s Advanced Management Program. Along with a BA in Economics from the University of Haifa he earned an MBA from the University of Washington. Roei currently services as a member of the advisory board at Zealyst, the Washington Technology Industry Association board, and in 2013 was named chairman of the University of Washington Foster School of Business Global Business Advisory board.

“I am honored and excited about this new opportunity to help propel BoldIQ forward,” said Roei Ganzarski, CEO, BoldIQ. “In today’s dynamic and complex business environment, companies must be able to make intelligent operational decisions in real time to succeed and stay competitive. BoldIQ provides customers with that competitive edge in real-time and I am thrilled to be part of this team.”

Amazon Prime Air Will Fly on Big Data’s Wings – by Beth Schultz, Editor in Chief, AllAnalytics.com

Amazon, one day in the not-so-distant future, wants to set the air abuzz with package-delivery drones. Today, it will have to be satisfied with buzz of a more personal sort, as industry watchers analyze the feasibility of the proposed Jetson-like Amazon Prime Air service.

Should anyone have missed this news, let me recap. In a 60 Minutes interview that aired Dec. 1, Amazon CEO Jeff Bezos described his vision for a world in which you can order that to-die-for sweater you’ve just seen on Amazon and be wearing it 30 minutes later. Amazon would use mini-drones, called octocopters for the eight blades they sport, to deliver the package to your doorstep — optimistically, as early as 2017.

The plan comes with caveats, to be sure. For one, you’d have to live in a metropolitan area near an Amazon fulfillment center — but the idea is fascinating nonetheless. As 60 Minutes reporter Charlie Rose said during the program, Amazon wants “to sell everything to everybody around the world, as fast as possible.”

Bezos readily admits that much work remains to make mini-drone deliveries on a large, commercial scale possible, and skepticism is certainly rampant. But we already know the power of prescriptive modeling and optimization in fine-tuning a global package delivery network, from our conversations with Jack Levis, director of process management at UPS. (See Inside Analytics: UPS Delivers the Goods.) So maybe the idea isn’t quite so impossible after all — algorithmically speaking and from a data perspective, at least.

For insight on that, we turned to Roei Ganzarski, COO and president of BoldIQ, which had powered the now-defunct (but not for lack of optimization) DayJet on-demand air taxi service. BoldIQ is in use within a number of other industries that have real-time needs and lots of disruptions with which to contend. Ganzarski brings his experience as a former Boeing executive to the discussion, as well.

He’s among those who say we can count on Amazon to make this happen, “as funky an idea as it is.” And in so doing, it will conquer four distinct worlds: same-day logistics; drone operations; aviation, with the introduction of unmanned aerial vehicles into airspace; and big data, which is a new world in its own right but also encapsulates the three others, he told us.

So let’s zero in on the big data nature of the Prime Air concept, as did Ganzarski: All this is going to be happening in real-time, disrupting all those worlds, each of which produces huge amounts of data and requires huge amounts of compute power. It’s not doable, of course, without advanced optimization software.

Amazon has to find a way to know in real-time as every package request comes in whether it has a drone to take it on, Ganzarkski says. If no, it’ll put it on a truck and push out delivery time to a day or more. If yes, it needs to know all the ripple effects of that decision.

He continued: If Drone No. 1 is taking this package, once it arrives at its destination, what is its next path going to be, and its next one after that? And as things change, like a new demand comes in, or a drone breaks down, or a wind burst comes in and changes the weather pattern in that urban area, what’s the next move? Amazon will have to be able to immediately — and I’m talking milliseconds — re-network and re-schedule what its drones are doing with the packages… and at the same time, let the customers know what to expect.

There is a precedent, of a sort, in on-demand private aviation, he notes. In this market, an aircraft could be at any one of the 5,000 or so airports that accept private aviation. The service also requires a pilot and copilot who are not only certified to fly that aircraft, but who also have enough rest time as specified by the FAA. Demand, generally from wealthy individuals and corporate executives, can come at any time and require a flight to and from any of those airports.

Which aircraft is selected depends on the number of passengers and their luggage: There’s a lot that goes into the matching between the right — not just the one that can — aircraft to fly them with the right pilot and copilot so the entire network meshes in a seamless fashion, so the customer gets what he wants, and the operator is able to serve that demand while being profitable and safe.

So the beginning stages of the full-on capability, whether via a software package from a vendor such as BoldIQ or developed internally at a company like UPS or, undoubtedly, Amazon, are out there and operational already. The big data piece won’t be the problem, it seems. What will be, as you see it?

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