The World Is Complex; Advanced Analytics Can Simplify
Remember high school or college research projects?  Remember trying to find, and then pouring over, thick tomes that might or might not have the specific information you were looking for?  Remember the frustration of realizing that you probably didn’t have all the necessary information when you sat down to write your paper?


Those frustrations haven’t gone away, but they have been eased a bit with the convenience of the Internet, and advanced analytics hold even more promise.  Combine the tools and you have a powerful method of discerning truth in what seems an extremely complex world.  Currently, advanced analytics are used primarily by large private companies and governmental organizations to increase profitability and transparency; however, they also have possibilities for individuals needing to research or track virtually anything.  And as prices for business intelligence dashboards come down, it isn’t hard to imagine advanced analytics becoming more mainstream among the general public.
This is not to say that you’ll be able to use advanced analytics to research virtually anything from home or your dorm room in the near future.  However, it isn’t hard to imagine such a scenario over the next 20 years or so.  Of course then, with “one version of the truth” all those trying to convince you of “their way” may have more difficulties, which is not necessarily a bad thing.  Manufacturers, retail companies and high-tech firms and of course government and quasi-government agencies will be first, but they will be blazing trails more of us can follow.
–Warren B. Causey 

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Spring Is The Time For Home Improvement—Analyze It

Executives at home improvement companies such as Lowes, Home Depot, and other chain hardware & garden centers, are well aware that a major portion of their business occurs in the springtime.  Most people wait until the weather turns warm to begin home improvement projects; fences, new rooms, landscaping, swimming pools, and other endeavors.  However, there are a lot of other events that cause home improvement bugs to bite.  These include moving to a new or different home, damage from a storm, home aging, or just your general unexpected thing that needs fixing.
Tracking all these variables in order to have the right materials and products on-hand at the right time is a function of advanced analytics.  Some companies utilize these tools well, others can’t keep up with today’s changing business environment.  Usually large chains have sophisticated computer analytics systems that help them prepare for the busy seasons by ramping up production or inventory and adding the necessary staff.  With the increasing penetration of advanced analytics at lower costs, smaller firms also are beginning to learn the benefits and catch up with the bigger sharks. 
As spring is upon us, look for more and more firms to be target-marketing to the season as a result of advanced analytics.
–Warren B. Causey

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Analytics For The Entrepreneur
There are eternal optimists who start small businesses all the time—even in a generally down economy.  In many cases, preliminary research includes, “Well, that looks like a good place for a store,” or, “I know how to do a specific skill, why not start my own business”.
There was a day when that type of research was all you needed, or at least was what sufficed for many successes and even more failures.  Today, however, between the Internet, myriads of data sources and advanced analytics/business intelligence, there are many more ways to research and help promote success, even in small businesses.
Organizations such as the Chamber of Commerce, and a myriad of other federal and state agencies and organizations, track business development and results and monitor America’s progress.  If you know a lot about certain things or have specific skills, you still can learn more by digging into the data now generally available.  Advanced analytics and business intelligence doesn’t have to be just the purview of large organizations.  If you want to go into business—which basically is what America is about, or has been in the past—do your research up front.  Experience is a very hard teacher, it’s better to have a good look at what you’re getting into.
(Warren B. Causey)

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Cisco Presents Our Internet of Everything Future
We have a new flavor of Internet of Things (IoT). Cisco just endorsed its $14 trillion vision for flowerpot sensors connected over a global network.
But unlike other IoT evangelists GE and Intel, Cisco wants to connect more than just things. The reigning champ of networking gear calls their recipe the “Internet of Everything,” which integrates people, process, data and, of course, things into a web of connectivity. These networked connections, according to Cisco, turn “information into actions that create new capabilities, richer experiences, and unprecedented economic opportunity for businesses, individuals, and countries.”

If it’s terrestrial, it should compute. If it computes, it should be connected to the Internet.
Per Cisco’s thinking, none of this will happen without cloud computing and big data. Or Cisco’s knack for building IP-based platforms with jumbo scale.
So we’re going to connect physical objects together whether they like it or not, or need to be connected or not. Even Cisco recognizes new privacy models and security capabilities must be created to make an Internet of Everything (IoE) economy go. But where do we get $14 trillion connecting people with their pets and plants, or industrial robots to their plant workers?
Primary drivers of IoE:
  •       Asset productivity and cost reductions
  •       Employee productivity
  •       Supply-chain and logistics efficiency
  •       Innovation
  •       Customer Experience

Cisco based its calculations and analysis on 21 use cases, including one for smart factories, or factory automation. And Cisco says the manufacturing industry has the most money at stake (27 percent). Overall, the United States will see the biggest share of the IoE economy (32 percent).
IoE looks good for manufacturers. Cisco pegged smart factories as a favored use case.
The IoE assumes machines will be both easier to program and more adaptable. In addition, connections to the cloud for analytics will better integrate capital, labor and technology.
Other smart factory IoE superlatives:
  •       Allow greater customization and smaller runs
  •       Improve product quality through better sensors
  •       Reduce waste from materials and energy

But do we buy it? Can we really get rich and happy by connecting the unconnected?
Let’s start with today’s $70 trillion world GDP. Then accept Cisco’s claims we have exactly $14.4 trillion of IoE at stake over the next 10 years. And, Cisco claims 99.4 percent of physical objects destined for the IoE are now unconnected.
The numbers are intoxicating.
In fact, these are the kind of numbers that cause an entrepreneur’s heart to flutter and an electrical engineer’s head to shake. And the kind of blue-sky research that generates more questions than answers.
A dose of hyperbole tailored for futurists and shareholders, maybe. But we measure and hack, hack and measure. That’s what we do. Cisco is, at least, painting the broad brushstrokes of the next decade. And the real winners won’t be the ones making the 50-cent flowerpot sensors.
(Rory Crump)

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How Big Data Saved Intel $100 Million
It should be no surprise that Intel is profiting from big data; heavy in R & D through good times and bad, the Santa Clara chip giant has discovered three ways to reduce manufacturing costs by leveraging unused data. Over the past two years Intel has parlayed over a dozen data-driven projects into improvements in speed, quality and security.
In fact, Intel is weaving analytics into its many layers. Beyond manufacturing, the company is using predictive analytics to boost its reseller network with faster, tighter market data. No doubt Intel is a big data believer, maybe even downright giddy over future plans for measuring and analyzing more areas of their business.

Intel’s data ecosystem
Analytics are contagious at Intel. With designers and manufacturing sold on the benefits, projects are popping up throughout the company. It’s worth checking out Intel’s video series on big data to get a lesson on building an analytics culture.
As more manufacturers begin to experiment with big data, Intel makes a great test lab for great minds and fat wallets. But think about Intel’s matrix of a supply chain, the sheer number of end users they touch, and, of course, the urgency tied to a chipmaker’s roadmap. Also consider the opportunities hidden in all that data which Intel produces and how they could analyze it.
So the resources and test dummies are in great supply at Intel, but so are the numbers. Meaning the big numbers Intel has saved using predictive analytics to carve better silicon – $100 million in costs savings across the company. Ron Kasabian, Intel’s general manager of big data solutions, shared his company’s manufacturing successes in this article with InformationWeek.
At Intel, speed and quality have become intertwined. Intel does a quality check on every chip it produces. “We run a huge number of complicated tests on every single chip that comes through the manufacturing process,” Kasabian told InformationWeek.
Intel wants to find bugs, fix them, and squeeze time in the process. Intel squeezes time by analyzing historical data logged during manufacturing and cutting down on the thousands of tests required to produce a perfect chip. These tests drill down to the wafer level and focus on specific chips that need more, or less, testing.
According to Intel, predictive analytics saved $3 million in manufacturing costs in 2012. Small change to them, but the reported savings covered just one line of Intel Core processors.
“We’re talking about five terabytes an hour.” Did that get your attention?
Actually, per Kasabian, we’re talking five terabytes of data generated by a highly automated manufacturing line at Intel. So Intel’s goal is to detect failures within true big data volumes. In practice, information is pulled from log files and test machines, and then analyzed to reveal any hiccups. Specifically, Intel looks for deviations from normal tolerances along a manufacturing process. Analytics deliver data stealth enough to identify specific steps needing a fix.
To build a security platform, Intel took its own distribution of Hadoop software last year for a test drive. The result processes 200 billion server events and can flag security threats inside of 30 minutes.
Kasabian talks of network intrusion devices (NIDs) that check packets across Intel’s network. Thousands of NIDs feed data into Hadoop. Hadoop captures and classifies the data in preparation for massive parallel processing (MPP). In the end, Intel is hunting anomalies which they are capable of spotting throughout its server network. The entire security system was built and deployed last summer.
Kasabian said 2012 included about 14 big data projects for Intel worth over $100 million in cost savings or avoidance. Even better, Kasabian claims Intel has just scratched the surface of what predictive analytics can accomplish in such a capital-intensive business. 
(Rory Crump)

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Manufacturing needs to keep track of energy issues

Manufacturing in the U.S. has always had a pretty good deal when it comes to energy, particularly electricity.  Because manufacturing can help build businesses and jobs, utility regulators have looked with upon it with favor on such enterprises, making electricity prices for manufacturers are considerably lower than those for residential customers—in most cases by less than half.  Manufacturing and commercial enterprises use about 60% of the electricity generated in the U.S., the rest is for residential use.  Unfortunately, however, those lower prices for manufacturing as well as residential customers may be going up due to new regulations; but a good way to keep track of all these changes is to use analytics to better adjust when changes happen.
The U.S. is in the midst of a great political debate regarding energy.  Those on the left are pushing hard for renewable energy, which is more expensive than energy generated by traditional fossil fuels, primarily coal.  The current federal administration, through a flood of regulations issued primarily by the Environmental Protection Agency but also the Department of Energy, has placed coal virtually out of reach for generating electricity. 
The only saving grace in all this is that new recovery methods—particularly fracking—for natural gas have made it possible for utilities to switch generation to natural gas and continue to hold electricity prices down more than would have been possible without fracking.  Coal fired plants across the U.S. either are being converted to natural gas or shut down.  The Southern Cos., which serve a large swath of the Southern U.S., for example, have reduced coal generation from more than 80% three or four years ago to about 40% today.
Fracking, however also is under attack by environmentalists who heavily dominate the political left in the U.S.  Should they succeed in their efforts to slow or stop fracking, again there would be tremendous upward price pressure on electricity.  Manufacturers need to keep track of these debates and these regulations because if electricity prices go up dramatically—as many on the political left seek—they, like their residential customers, could be hit hard.  One of the best ways to do that necessary tracking is to have advanced analytics and business intelligence to track everything involved with energy, including the regulations and political debates.
(Warren B. Causey)

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Railroads Another Heavy Industry Using Analytics Well
One industry that has used analytics well to move into a modern area is railroads.  As most are aware, passenger trains are pretty much a thing of the past, except for the government-run and subsidized ones; the automobile pretty much replaced them for long-distance travel.  Then the Interstate system and large trucks, including double-bottom ones took a lot of business from freight trains.  However, the latter have not gone away, in fact, in many respects, they are thriving.
One of the reason you still see a lot of long freight trains these days is that virtually everything to do with them has been computerized and analyzed—with advanced analytics.  Freight trains no longer need cabooses because of on-board communications and video systems; they are carrying long trains of freight because analytical systems help railroads optimize their routes, and their loading a host of other variables.  Analytical computer systems quantify loads, distances to travel, costs and profits per mile and do it all in a fraction of the time it took 30 years ago.
We tend to think of advanced analytics and business intelligence as recently developed phenomenon.  They aren’t, they have been evolving over time as computing power became more available and more effective in helping run businesses.  If you want to look at advanced analytics at work, gaze at the next freight train you see when you walk by a railroad.
–Warren B. Causey

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