Big Data: The 5 Vs Every person Should Know

Big Data: The 5 Vs Every person Should Know that all are essential

 

Big Data is a huge point. It will transform our globe entirely and is not a passing craze that will certainly disappear. To know the sensation that allows data, it is usually explained utilizing 5 Vs: Volume, Velocity, Range, Veracity and Value

I assumed it may be worth simply restating what these 5 Vs are, in simple and easy language:.

Quantity refers to the vast amounts of data created every secondly. Just think about all the emails, twitter messages, photos, video clips, sensing unit data etc. we create and discuss every second. We are not speaking Terabytes yet Zettabytes or Brontobytes. On Facebook alone we send out 10 billion messages daily, click the “like’ button 4.5 billion times and upload 350 million new images every day. If we take all the information generated in the world between the start of time and 2008, the exact same amount of data will certainly soon be generated every min! This increasingly makes data sets too big to shop and assess utilizing standard database technology. With large information technology we could now store and use these data sets with the aid of distributed systems, where parts of the data is saved in various locations and combined by software program.

Speed describes the speed at which brand-new information is generated and the speed at which data moves around. Merely think of social networks messages going viral in seconds, the rate at which bank card transactions are looked for fraudulent activities, or the nanoseconds it takes trading systems to analyze social networks networks to pick up signals that set off choices to acquire or sell shares. Large data modern technology enables us now to evaluate the data while it is being produced, without ever putting it into data sources.

B2B

Selection refers to the various kinds of information we could now utilize. In the past we concentrated on structured information that neatly matches tables or relational databases, such as financial information (e.g. sales by item or area). Actually, 80 % of the world’s data is now disorganized, and therefore can’t easily be put into tables (consider pictures, video sequences or social media sites updates). With huge information technology we could now utilize differed sorts of information (structured and unstructured) including messages, social networks talks, photos, sensing unit data, video clip or voice recordings and bring them along with even more standard, organized information.

Accuracy describes the messiness or trustworthiness of the information. With several kinds of big data, top quality and precision are less manageable (merely think about Twitter posts with hash tags, abbreviations, typos and colloquial speech and also the reliability and precision of content) however large data and analytics innovation now enables us to work with these type of information. The quantities usually offset the absence of high quality or accuracy.

Worth: Then there is another V to think about when checking out Big Data: Worth! It is all well and great having accessibility to huge data however unless we could turn it into value it is pointless. So you could securely say that ‘worth’ is one of the most vital V of Big Data. It is very important that businesses make a business situation for any sort of try to collect and leverage large information. It is so simple to come under the talk catch and plunge into large data initiatives without a clear understanding of costs and benefits.

I have assembled this little presentation for you to make use of when talking about or discussing the 5 Vs of big data:.

 

Big Data: The 5 Vs Every person Should Know that all are essential



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Big Data: The 5 Vs Everyone Needs to Know

Big Data: The 5 Vs Everyone Need to Know that are essential

 

Big Data is a large point. It will certainly alter our globe totally and is not a passing fad that will vanish. To recognize the sensation that allows information, it is typically described utilizing 5 Vs: Quantity, Velocity, Assortment, Honesty and Worth

I assumed it may be worth simply restating just what these 5 Vs are, in plain and simple language:.

Volume describes the substantial amounts of data created every secondly. Simply think of all the e-mails, twitter messages, photos, video clips, sensor information and so on we create and share every second. We are not chatting Terabytes however Zettabytes or Brontobytes. On Facebook alone we send 10 billion messages every day, click the “like’ button 4.5 billion times and upload 350 million brand-new pictures every single day. If we take all the information produced on the planet in between the start of time and 2008, the exact same amount of data will soon be created every min! This increasingly makes data sets as well large to store and examine using typical database innovation. With big information modern technology we can now hold and utilize these data sets with the help of dispersed devices, where parts of the data is held in various areas and brought together by software program.

Big Data: The 5 Vs Everyone Needs to Know

Speed refers to the speed at which brand-new data is produced and the rate at which data moves around. Merely think of social media messages going viral in seconds, the speed at which credit card transactions are looked for deceitful tasks, or the milliseconds it takes trading systems to assess social networking sites networks to get signals that trigger decisions to get or market shares. Big information modern technology enables us now to assess the information while it is being generated, without ever putting it into databases.

Range refers to the different sorts of information we could now use. In the past we concentrated on structured information that properly matches tables or relational databases, such as economic data (e.g. sales by product or area). Actually, 80 % of the globe’s data is now disorganized, and for that reason can’t quickly be embeded tables (consider photos, video clip sequences or social networks updates). With large data modern technology we can now take advantage of differed kinds of information (structured and disorganized) consisting of messages, social networking sites chats, pictures, sensor data, video or voice recordings and bring them in addition to more conventional, structured data.

Honesty refers to the messiness or credibility of the data. With numerous forms of huge data, quality and reliability are much less controlled (merely think of Twitter posts with hash tags, abbreviations, typos and colloquial speech in addition to the reliability and accuracy of content) yet big information and analytics technology now enables us to collaborate with these sort of data. The volumes often make up for the absence of top quality or reliability.

Value: Then there is another V to take into account when taking a look at Big Information: Value! It is all well and great having accessibility to big data however unless we can turn it into worth it is ineffective. So you can securely say that ‘value’ is the most crucial V of Big Information. It is essential that businesses make a company situation for any type of attempt to collect and leverage large data. It is so easy to fall into the talk catch and start large information campaigns without a clear understanding of prices and perks.

I have put together this little discussion for you to make use of when talking about or talking about the 5 Vs of big information:

Big Data: The 5 Vs Everyone Needs to Know 

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Big Data Evolutionary Forecasting

Jonathan Losos

After comparing the DNA from different anole lizard species in the Caribbean, scientists found predictable patterns in their evolution.

By: Carl Zimmer

July 17, 2014

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Michael Lässig can be certain that if he steps out of his home in Cologne, Germany, on the night of Jan. 19, 2030 — assuming he’s still alive and the sky is clear — he will see a full moon.

Lässig’s confidence doesn’t come from psychic messages he’s receiving from the future. He knows the moon will be full because physics tells him so. “The whole of physics is about prediction, and we’ve gotten quite good at it,” said Lässig, a physicist at the University of Cologne. “When we know where the moon is today, we can tell where the moon is tomorrow. We can even tell where it will be in a thousand years.”

Early in his career, Lässig made predictions about quantum particles, but in the 1990s, he turned to biology, exploring how genes evolved. In his research, Lässig was looking back in time, reconstructing evolutionary history. Looking ahead to evolution’s future was not something that biologists bothered doing. It might be possible to predict the motion of the moon, but biology was so complex that trying to predict its evolution seemed a fool’s errand.

But lately, evolution is starting to look surprisingly predictable. Lässig believes that soon it may even be possible to make evolutionary forecasts. Scientists may not be able to predict what life will be like 100 million years from now, but they may be able to make short-term forecasts for the next few months or years. And if they’re making predictions about viruses or other health threats, they might be able to save some lives in the process.

“As we collect a few examples of predictability, it changes the whole goal of evolutionary biology,” Lässig said.

Replaying the Tape of Life

If you want to understand why evolutionary biologist have been so loathe to make predictions, read “Wonderful Life,” a 1989 book by the late paleontologist Stephen Jay Gould.

Michael Lässig

The book is ostensibly about the Cambrian explosion, a flurry of evolutionary innovation that took place more than 500 million years ago. The oldest known fossils of many of today’s major animal groups date to that time. Our own lineage, the vertebrates, first made an appearance in the Cambrian explosion, for example.

But Gould had a deeper question in mind as he wrote his book. If you knew everything about life on Earth half a billion years ago, could you predict that humans would eventually evolve?

Gould thought not. He even doubted that scientists could safely predict that any vertebrates would still be on the planet today. How could they, he argued, when life is constantly buffeted by random evolutionary gusts? Natural selection depends on unpredictable mutations, and once a species emerges, its fate can be influenced by all sorts of forces, from viral outbreaks to continental drift, volcanic eruptions and asteroid impacts. Our continued existence, Gould wrote, is the result of a thousand happy accidents.

To illustrate his argument, Gould had his readers imagine an experiment he called “replaying life’s tape.” “You press the rewind button and, making sure you thoroughly erase everything that actually happened, go back to any time and place in the past,” he wrote. “Then let the tape run again and see if the repetition looks at all like the original.” Gould wagered that it wouldn’t.

Although Gould only offered it as a thought experiment, the notion of replaying the tape of life has endured. That’s because nature sometimes runs experiments that capture the spirit of his proposal.

Predictable Lizards

For an experiment to be predictable, it has to be repeatable. If the initial conditions are the same, the final conditions should also be the same. For example, a marble placed at the edge of a bowl and released will end up at the bottom of the bowl no matter how many times the action is repeated.

Biologists have found cases in which evolution has, in effect, run the same experiment several times over. And in some cases the results of those natural experiments have turned out very similar each time. In other words, evolution has been predictable.

One of the most striking cases of repeated evolution has occurred in the Caribbean. The islands there are home to a vast number of native species of anole lizards, which come in a staggering variety. The lizards live in the treetops, on forest floors and in open grassland. They come in a riot of colors and shapes. Some are blue, some are green and some are gray. Some are huge and bold while others are small and shy.

To understand how this diversity evolved, Jonathan Losos of Harvard University and his students gathered DNA from the animals. After they compared the genetic material from different species, the scientists drew an evolutionary tree, with a branch for every lizard species.

Jonathan Losos measuring a lizard in the field.

When immigrant lizards arrived on a new island, Losos found, their descendants could evolve into new species. It was as if the lizard tape of life was rewound to the same moment and then played again.

If Gould were right, the pattern of evolution on each island would look nothing like the pattern on the other islands. If evolution were more predictable, however, the lizards would tend to repeat the same patterns.

Losos and his students have found that evolution did sometimes veer off in odd directions. On Cuba, for example, a species of lizard adapted to spending a lot of time in the water. It dives for fish and can even sprint across the surface of a stream. You won’t find a fishing lizard on any other Caribbean island.

For the most part, though, lizard evolution followed predictable patterns. Each time lizards colonized an island, they evolved into many of the same forms. On each island, some lizards adapted to living high in trees, evolving pads on their feet for gripping surfaces, along with long legs and a stocky body. Other lizards adapted to life among the thin branches lower down on the trees, evolving short legs that help them hug their narrow perches. Still other lizards adapted to living in grass and shrubs, evolving long tails and slender trunks. On island after island, the same kinds of lizards have evolved.

“I think the tide is running against Gould,” Losos said. Other researchers are also finding cases in which evolution is repeating itself. When cichlid fish colonize lakes in Africa, for example, they diversify into the same range of forms again and again.

“But the question is: What’s the overall picture?” Losos asked. “Are we cherry-picking the examples that work against him, or are we going to find that most of life is deterministic? No one is going to say Gould is completely wrong. But they’re not going to say he’s completely right either.”

Evolution in a Test Tube

Natural experiments can be revealing, but artificial experiments can be precise. Scientists can put organisms in exactly the same conditions and then watch evolution unfold. Microbes work best for this kind of research because scientists can rear billions of them in a single flask and the microbes can go through several generations in a single day. The most spectacular of these experiments has been going on for 26 years — and more than 60,000 generations — in the lab of Richard Lenski at Michigan State University.

Lenski launched the experiment with a single E. coli microbe. He let it divide into a dozen genetically identical clones that he then placed in a dozen separate flasks. Each flask contained a medium — a cocktail of chemicals mixed into water — that Lenski created especially for the experiment. Among other ingredients, it contained glucose for the bacteria to feed on. But it was a meager supply, which ran out after just a few hours. The bacteria then had to eke out their existence until the next morning, when Lenski or his students transferred a little of the microbe-laced fluid into a fresh flask. With a new supply of glucose, they could grow for a few more hours. Lenski and his students at Michigan State have been repeating this chore every day since.

Lenski thought the tape of life would replay differently with each rewind. But that’s not what happened.

At the outset, Lenski wasn’t sure what would happen, but he had his suspicions. He expected mutations to arise randomly in each line of bacteria. Some would help the microbes reproduce faster while others would be neutral or even harmful. “I imagined they’d be running off in one direction or another,” Lenski said.

In other words, Lenski thought the tape of life would replay differently with each rewind. But that’s not what happened. What Lenski witnessed was strikingly similar to the evolution that Jonathan Losos has documented in the Caribbean.

Lenski and his students have witnessed evolutionary oddities arise in their experiment — microbial versions of the Cuban fishing lizards, if you will. In 2003, Lenski’s team noticed that one line of bacteria had abruptly switched from feeding on glucose to feeding on a compound called citrate. The medium contains citrate to keep iron in a form that the bacteria can absorb. Normally, however, the bacteria don’t feed on the citrate itself. In fact, the inability to feed on citrate in the presence of oxygen is one of the defining features of E. coli as a species.

But Lenski has also observed evolution repeat itself many times over in his experiment. All 12 lines have evolved to grow faster on their meager diet of glucose. That improvement has continued to this day in the 11 lines that didn’t shift to citrate. Their doubling time — the time it takes for them to double their population — has sped up 70 percent. And when Lenski and his students have pinpointed the genes that have mutated to produce this improvement, they are often the same from one line to the next.

“That’s not at all what I expected when I started the experiment,” Lenski said. “I evidently was wrong-headed.”

Getting Complex Without Getting Random

Lenski’s results have inspired other scientists to set up more complex experiments.Michael Doebeli, a mathematical biologist at the University of British Columbia, wondered how E. coli would evolve if it had two kinds of food instead of just one. In the mid-2000s, he ran an experiment in which he provided glucose — the sole staple of Lenski’s experiment — and another compound E. coli can grow on, known as acetate.

Doebeli chose the two compounds because he knew that E. coli treats them very differently. When given a choice between the two, it will devour all the glucose before switching on the molecular machinery for feeding on acetate. That’s because glucose is a better source of energy. Feeding on acetate, by contrast, E. coli can only grow slowly.

Something remarkable happened in Doebeli’s experiment — and it happened over and over again. The bacteria split into two kinds, each specialized for a different way of feeding. One population became better adapted to growing on glucose. These glucose-specialists fed on the sugar until it ran out and then slowly switched over to feeding on acetate. The other population became acetate-specialists; they evolved to switch over to feeding on acetate even before the glucose supply ran out and could grow fairly quickly on acetate.

When two different kinds of organisms are competing for the same food, it’s common for one to outcompete the other. But in Doebeli’s experiment, the two kinds of bacteria developed a stable coexistence. That’s because both strategies, while good, are not perfect. The glucose-specialists start out growing quickly, but once the glucose runs out, they slow down drastically. The acetate-specialists, on the other hand, don’t get as much benefit from the glucose. But they’re able to grow faster than their rivals once the glucose runs out.

Doebeli’s bacteria echoed the evolution of lizards in the Caribbean. Each time the lizards arrived on an island, they diversified into many of the same forms, each with its own set of adaptations. Doebeli’s bacteria diversified as well — and did so in flask after flask.

To get a deeper understanding of this predictable evolution, Doebeli and his postdoctoral researcher, Matthew Herron, sequenced the genomes of some of the bacteria from these experiments. In three separate populations they discovered that the bacteria had evolved in remarkable parallel. In every case, many of the same genes had mutated.

Although Doebeli’s experiments are more complex than Lenski’s, they’re still simple compared with what E. coli encounters in real life. E. coli is a resident of the gut, where it feeds on dozens of compounds, where it coexists with hundreds of other species, where it must survive changing levels of oxygen and pH, and where it must negotiate an uneasy truce with our immune system. Even if E. coli’s evolution might be predictable in a flask of glucose and acetate, it would be difficult to predict how the bacteria would evolve in the jungle of our digestive system.

E. Coli

And yet scientists have been surprised to find that bacteria evolve predictably inside a host.Isabel Gordo, a microbiologist at the Gulbenkian Institute of Science in Portugal, and her colleagues designed a clever experiment that enabled them to track bacteria inside a mouse. Mice were inoculated with a genetically identical population of E. coliclones. Once the bacteria arrived in the mice’s guts, they started to grow, reproduce and evolve. And some of the bacteria were carried out of the mouse’s body with its droppings. The scientists isolated the experimental E. coli from the droppings. By examining the bacteria’s DNA, the scientists could track their evolution from one day to the next.

The scientists found that it took only days for the bacteria to start evolving. Different lineages of E. coli picked up new mutations that made them reproduce faster than their ancestors. And again and again, they evolved many of the same traits. For example, the original E. coli couldn’t grow if it was exposed to a molecule called galactitol, which mammals make as they break down sugar. However, Gordo’s team found that as E. coli adapted to life inside a mouse, it always evolved the ability to withstand galactitol. The bacteria treated a living host like one of Lenski’s flasks — or an island in the Caribbean.

Evolution’s Butterfly Effect

Each new example of predictable evolution is striking. But, as Losos warned, we can’t be sure whether scientists have stumbled across a widespread pattern in nature. Certainly, testing more species will help. But Doebeli has taken a very different approach to the question: He’s using math to understand how predictable evolution is overall.

Doebeli’s work draws on pioneering ideas that geneticists like Sewall Wrightdeveloped in the early 1900s. Wright pictured evolution like a hilly landscape. Each point on the landscape represents a different combination of traits — the length of a lizard’s legs versus the width of its trunk, for example. A population of lizards might be located on a spot on the landscape that represents long legs and a narrow trunk. Another spot on the landscape would represent short legs and a narrow trunk. And in another direction, there’s a spot representing long legs and a thick trunk.

The precise combinations of traits in an organism will influence its success at reproducing. Wright used the elevation of a spot on the evolutionary landscape to record that success. An evolutionary landscape might have several peaks, each representing one of the best possible combinations. On such a landscape, natural selection always pushes populations up hills. Eventually, a population may reach the top of a hill; at that point, any change will lead to fewer offspring. In theory, the population should stay put.

The future of evolution might seem easy to predict on such a landscape. Scientists could simply look at the slope of the evolutionary landscape and draw a line up the nearest hill.

“This view is just simply wrong,” said Doebeli.

That’s because the population’s evolution changes the landscape. If a population of bacteria evolves to feed on a new kind of food, for example, then the competition for that food becomes fierce. The benefit of specializing on that food goes down, and the peak collapses. “It’s actually the worst place to be,” Doebeli said.

“Over short periods of time, it is predictable, if you have enough information. But you can’t predict it over long periods of time.”

To keep climbing uphill, the population has to veer onto a new course, toward a different peak. But as it travels in a new direction, it alters the landscape yet again.

Recently, Doebeli and Iaroslav Ispolatov, a mathematician at the University of Santiago in Chile, developed a model to understand how evolution works under these more complicated conditions. Their analysis suggests that evolution is a lot like the weather — in other words, it’s difficult to predict.

In the early 1960s, a scientist at the Massachusetts Institute of Technology namedEdward Lorenz developed one of the first mathematical models of weather. He hoped that they would reveal repeatable patterns that would help meteorologists predict the weather more accurately.

But Lorenz discovered just the opposite. Even a tiny change to the initial conditions of the model led, in time, to drastically different kinds of weather. In other words, Lorenz had to understand the model’s initial conditions with perfect accuracy to make long-term predictions about how it would change. Even a slight error would ruin the forecast.

Mathematicians later dubbed this sensitivity chaos. They would find that many systems — even surprisingly simple ones — behave chaotically. One essential ingredient for chaos is feedback — the ability for one part of the system to influence another, and vice versa.  Feedback amplifies even tiny differences into big ones. When Lorenz presented his results, he joked that the flap of a butterfly’s wings in Brazil could set off a tornado in Texas.

Evolution has feedbacks, too. A population evolves to climb the evolutionary landscape, but its changes alter the landscape itself. To see how these feedbacks affected evolution, Doebeli and Ispolatov created their own mathematical models.  They would drop populations onto the evolutionary landscape at almost precisely the same spot. And then they followed the populations as they evolved.

In some trials, the scientists only tracked the evolution of a few traits, while in others, they tracked many. They found that in the simple models, the populations tended to follow the same path, even though they started out in slightly different places. In other words, their evolution was fairly easy to predict.

But when the scientists tracked the evolution of many traits at once, that predictability disappeared. Despite starting out under almost identical conditions, the populations veered off on different evolutionary paths. In other words, evolution turned to chaos.

Doebeli and Isplolatov’s research suggests that for the most part, evolution is too chaotic to be predicted with any great accuracy. If they are right, then the successes that scientists like Losos and Lenski have had in finding predictable evolution are the exceptions that prove the rule. The future of evolution, for the most part, is as fundamentally unknowable as the future of the weather.

This conclusion may seem strange coming from Doebeli. After all, he has conducted experiments on E. coli that have shown just how predictable evolution can be. But he sees no contradiction. “It’s just a matter of time scales,” he said. “Over short periods of time, it is predictable, if you have enough information. But you can’t predict it over long periods of time.”

Darwin’s Prophets

Even over short periods of time, accurate forecasts can save lives. Meteorologists can make fairly reliable predictions about treacherous weather a few days in advance. That can be enough time to evacuate a town ahead of a hurricane or lay in supplies for a blizzard.

Richard Lenski thinks that recent studies raise the question of whether evolutionary forecasting could also provide practical benefits. “I think the answer is definitely yes,” he said.

To predict which strains would dominate the 2002-2003 flu season (right), Michael Lässig and Marta Łuksza counted the number of beneficial mutations in the strains from the previous season (left).

One of the most compelling examples comes from Lässig. Using his physics background, he is working on a way to forecast the flu.

Worldwide, the flu kills as many as 500,000 people a year. Outside of the tropics, infections cycle annually from a high in winter to a low in summer. Flu vaccines can offer some protection, but the rapid evolution of the influenza virus makes it a moving target for vaccination efforts.

The influenza virus reproduces by invading the cells in our airway and using their molecular machinery to make new viruses. It’s a sloppy process, which produces many new mutants. Some of their mutations are harmful, crippling the viruses so that they can’t reproduce. But other mutations are harmless. And still others will make new flu viruses even better at making copies of themselves.

As the flu virus evolves, it diverges into many different strains. A vaccine that is effective against one strain will offer less protection against others. So vaccine manufacturers try to provide the best defense each flu season by combining the three or four most common strains of the flu.

There’s a problem with this practice, however. Manufacturing a new season’s flu vaccines takes several months. In the United States and other countries in the Northern Hemisphere, vaccine manufacturers must decide in February which strains to use for the flu season that starts in October. They often make the right prediction. But sometimes a strain that’s not covered by the vaccine unexpectedly comes to dominate a flu season. “If something goes wrong, it can cost thousands of lives,” Lässig said.

A few years ago, Lässig started to study the vexing evolution of the flu. He focused his attention on the rapidly evolving proteins that stud the shell of the flu virus, called hemagglutinin. Hemagglutinin latches on to receptors on our cells and opens up a passageway for the virus to invade.

When we get sick with the flu, our immune system responds by building antibodies that grab onto the tip of the hemagglutinin protein. The antibodies prevent the viruses from invading our cells and also make it easier for immune cells to detect the viruses and kill them. When we get flu vaccines, they spur our immune system to make those antibodies even before we get sick so that we’re ready to wipe out an infection as soon as it starts.

Scientists have been sequencing hemagglutinin genes from flu seasons for more than 40 years. Poring over this trove of information, Lässig was able to track the evolution of the viruses. He found that most mutations that altered the tip of the hemagglutinin protein helped the viruses reproduce more, probably because they made it difficult for antibodies to grab onto them. Escaping the immune system, they can make more copies of themselves.

Michael Lässig lecturing.

Each strain of the flu has its own collection of beneficial mutations. But Lässig noticed that the viruses also carry harmful mutations in their hemagglutinin gene. Those harmful mutations make hemagglutinin less stable and thus less able to open up cells for invasion.

It occurred to Lässig that these mutations might determine which strains would thrive in the near future. Perhaps a virus with more beneficial mutations would be more likely to escape people’s immune systems. And if they escaped destruction, they would make more copies of themselves. Likewise, Lässig theorized, the more harmful mutations a virus had, the more it would struggle to invade cells.

If that were true, then it might be possible to predict which strains would become more or less common based on how many beneficial and harmful mutations they carried. Working with Columbia University biologist Marta Łuksza, he came up with a way to score the evolutionary potential of each strain of the flu. For each beneficial mutation, a strain earned a point. For each harmful one, Lässig and Łuksza took a point away.

The scientists examined thousands of strains of the flu that have been sampled since 1993. They would calculate the score for every strain in a given year and then use that score to predict how it would fare the following year. They correctly forecast whether a strain would grow or decline about 90 percent of the time. “It’s a simple procedure,” Lässig said. “But it works reasonably well.”

Lässig and his colleagues are now exploring ways to improve their forecast. Lässig hopes to be able to make predictions about future flu seasons that the World Health Organization could consult as they decide which strains should be included in flu vaccines. “It’s just a question of a few years,” he said.

The flu isn’t the only disease that evolutionary forecasting could help combat. Bacteria are rapidly evolving resistance to antibiotics. If scientists can predict the path that the microbes will take, they may be able to come up with strategies for putting up roadblocks.

Forecasting could also be useful in fighting cancer. When cells turn cancerous, theyundergo an evolution of their own. As cancer cells divide, they sometimes gain mutations that let them grow faster or escape the immune system’s notice. It may be possible to forecast how tumors will evolve and then plan treatments accordingly.

Beyond its practical value, Lässig sees a profound importance to being able to predict evolution. It will bring the science of evolutionary biology closer to other fields like physics and chemistry. Lässig doesn’t think that he’ll be able to forecast evolution as easily as he can the motion of the moon, but he hopes that there’s much about evolution that will prove to be predictable. “There’s going to be a boundary, but we don’t know where the boundary is,” he said.

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Big Data companies to watch

These big data companies are ones to watch

Daly and Newton/Getty Images—OJO Images RFWhich companies are breaking new ground with big data technology? We ask 10 industry experts.

It’s hard enough staying on top of the latest developments in the technology industry. That’s doubly true in the fast-growing area known as big data, with new companies, products and services popping up practically every day.

There are scores of promising big data companies, butFortune sought to cut through the noise and reached out to a number of luminaries in the field to ask which big data companies they believe have the biggest potential. Which players are really the ones to watch?

That question, we learned, is rather difficult to answer.

“A list of ‘big data companies’ is interesting because of the definition,” said Dean Abbott, co-founder and chief data scientist of Smarter Remarketer. “Is a ‘big data’ company one that is pure-play analytics with big data? A company that does predictive analytics often with big data but not always—like Beyond the Arc or Elder Research? A large company that has a group that usually does ‘big data analytics’ but it’s only a small part of what they do as a company—like HP  HP 0.19% , SAP, Dell, even GE  GE -0.02% ?”

‘One of the most interesting ones I’ve seen’

There was certainly consensus on some of the big data companies that industry experts said were notable. At least two of 10 experts we polled named MapRMemSQLDatabricks,PlatforaSplunkTeradataPalantirPremiseDatameer,ClouderaHortonworksMongoDB, and Trifacta as shining examples in the space.

MemSQL, for example, is “an in-memory relational database that would be effective for mixed workloads and for analytics,” said Svetlana Sicular, an analyst at Gartner. “While SAP draws so much attention to in-memory databases by marketing their Hana database, MemSQL seems to be a less expensive and more agile solution in this space.”

Splunk, meanwhile, “has an excellent technology, and it was among the first big data companies to go public,” Sicular said. “Now, Splunk also has a strong product called Hunk—Splunk on Hadoop—directly delivering big data solutions that are more mature than most products in the market. Hunk is easy to use compared to many big data products, and generally, most customers I spoke with expressed their love to Splunk without any soliciting on my side.”

Palantir Technologies, which focuses on data analysis for public sector clients, also received high marks. “I’d have to put Palantir at the top of the list” of the startups in the big data space, said Tom Davenport, an IT management professor at Babson College and author of the book, Big Data @ Work.

DJ Patil, vice president of product at RelateIQ, said San Francisco-based Trifacta—which makes a “data transformation platform” promising increase productivity for data analysts—was a company to watch. “One of the most interesting ones I’ve seen,” said Patil, who serves as a technical advisor to the company.

Two miles away, cross-town peer Datameer is also remarkable, said Carla Gentry, founder of Analytical-Solution. “There are lots more companies out there, but most of the them just end up being a BI platform that anyone with an engineer could have started.” Datameer is different, she said.

‘Graphs have a great future’

Some of the less well-known companies received the highest praise.

Tamr, for instance, is “an exciting startup in data curation, so that would be my nomination,” said Gregory Piatetsky-Shapiro, president and editor of KDnuggets.com.

Neo Technology, the company behind open source graph database Neo4j, is another that Gartner’s Sicular pointed out. “I think graphs have a great future since they show data in its connections rather than a traditional atomic view,” she said. “Graph technologies are mostly unexplored by the enterprises but they are the solution that can deliver truly new insights from data.” (She also named PivotalThe Hive andConcurrent.)

DataStaxWibiDataAerospikeAyasdi and ClearStory were all part of analyst Curt Monash‘s “obvious inclusion” list, he said, while AutomaticPlanet LabsSight MachineDataPad,InteranaWise.ioLendUpDeclaraSentinel LabsFliptopSift ScienceImport.io and Segment.io were among those named by data scientist Pete Skomoroch.

Paxata and Informatica were both cited by Ovum analyst Tony Baer; IBM  IBM 0.18% SyntasaActian and Tableau were four named by George Mason University professor and data scientist Kirk Borne.

“There are a number of startups in security and machine learning that are emerging,” Baer said. “What’s missing right now are startups that look at data governance, stewardship, lifecycle management for big data. Right now IBM is largely alone, but I’m expecting there will be more startup action to come.”

‘Most of these companies will go away’

If you’ve reached this point in the article, you will have read 42 recommendations by our panel of experts. All of them are foremost technology companies; most exist specifically to perpetuate big data technology.

But some experts said that the most interesting big data companies aren’t big data companies at all. Established companies with traditional products and services are starting to develop offerings based on big data, Davenport said. Those include agriculture giant Monsanto  MOO , back-office operations stalwart Intuit  INTU -0.16% , and the trucking company Schneider.

“To me, it’s much easier to create a startup than it is to change your entire way of thinking about information and how it relates to your business operation,” Davenport said. “One of the really exciting things about big data is when you use it to create new products and services.”

He added with hesitation: “It’s early days still, and we don’t know how easy it will be for companies to make money off these things.”

It is inevitable that there will eventually be a thinning of the big data herd, experts said.

“Most of these companies will go away because the most important part of the big data movement will be how to use data operationally—to make decisions for the business,” Smarter Remarketer’s Abbott said, “rather than who can merely crunch more data faster.”



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Accenture Revenue Analytics and Business Intelligence

Revenue Analytics and Business Intelligence

Overview

Why Accenture

Specific Services

Revenue agencies face an environment of electronic filing, reduced resources, new taxpayer expectations and complex fraud schemes.

  • How can revenue agencies use data to improve risk management practices?
  • How can revenue agencies use data to better understand taxpayer behaviors?
  • How can revenue agencies improve debt collection, returns processing and audit activities?

More and more, leading revenue agencies are capturing significant amounts of data from taxpayers and third party providers. Many are asking questions like these and want to better analyze this data to understand taxpayer behaviors, improve fraud detection and risk management and enhance compliance enforcement.

Some are looking to business intelligence and predictive analytics to create new value from data. They are using quantitative methods to derive actionable insights and outcomes from data to meet these objectives and improve agency functions. Standard practice in the private sector, analytics can help revenue agencies achieve high performance by:

  • Generating more revenue.
  • Enhancing compliance via targeted compliance enforcement activities.
  • Improving audit, collections and returns processing procedures.
  • Optimizing use of personnel and resources.
  • Improving and streamlining taxpayer service.
  • Customizing service delivery channels based on taxpayer preference.
  • Improving operational visibility.
  • Understanding voluntary compliance.
  • Enabling faster and better decision making.
  • Optimizing the return on existing business and technology investments.
  • Reducing tax fraud.


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Big Data Today

Incredible Ways Big Data Is Made use today’s technology

 

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The term ‘Big Information’ is as massive in management and technology as Justin Bieber and Miley Cyrus are in music. Like with various other mega buzzwords, lots of claim huge information is all talk and no activity. This couldn’t be further from the fact. With this post, I would like to demonstrate how huge data is made use of today to include actual worth.

Eventually, every aspect of our lives will be affected by large information. Nonetheless, there are some locations where big data is currently making a real difference today. I have classified the application of big data into 10 areas where I view one of the most extensive use along with the highest perks [For those of you which wish to take a go back below and comprehend, in basic terms, just what huge information is, check out the posts in my Big Information Guru column]

1. Understanding and Targeting Consumers
This is just one of the most significant and most advertised locations of huge information use today. Right here, huge data is utilized to far better know consumers and their habits and preferences. Firms are keen to broaden their standard data collections with social media data, web browser logs as well as text analytics and sensing unit information to get a much more total image of their clients. The big objective, in many cases, is to create anticipating versions. You could remember the instance of UNITED STATE retailer Target, who is now able to really properly forecast when one of their clients will certainly expect a child. Making use of huge information, Telecommunications companies can now better anticipate client spin; Wal-Mart can forecast just what items will certainly offer, and car insurance coverage firms know how well their consumers in fact drive. Also federal government election campaigns could be optimized making use of large information analytics. Some think, Obama’s succeed after the 2012 governmental election campaign was because of his group’s remarkable capability to utilize huge information analytics.

2. Understanding and Optimizing Business Processes
Huge data is also increasingly utilized to maximize company processes. Stores manage to optimize their stock based on forecasts produced from social networks information, web search trends and weather report. One certain company process that is seeing a great deal of huge data analytics is supply chain or delivery route optimization. Right here, geographical positioning and radio frequency recognition sensors are utilized to track goods or shipping cars and maximize routes by integrating real-time web traffic information, and so on. Human Resources company procedures are also being improved utilizing large data analytics. This consists of the optimization of talent purchase Moneyball design, in addition to the measurement of business society and team involvement making use of big data devices.
3. Individual Metrology and Efficiency Optimization

Large information is not merely for companies and governments however additionally for all of us independently. We could now take advantage of the data created from wearable tools such as smart watches or smart bracelets. Take the Up band from Jawbone as an instance: the armband accumulates data on our calorie usage, activity levels, and our sleep patterns. While it offers people rich ideas, the genuine value is in evaluating the collective information. In Jawbone’s case, the company now collects 60 years worth of rest data every night. Evaluating such quantities of information will certainly bring totally new insights that it could feed back to specific users. The many others location where we take advantage of big information analytics is finding love – online this is. A lot of on the internet dating sites apply huge information tools and algorithms to locate us the most proper matches.
4. Improving Health care and Hygienics

The computer power of huge information analytics enables us to decode entire DNA strands in minutes and will allow us to find brand-new cures and much better know and predict condition designs. Simply consider just what happens when all the individual information from wise watches and wearable devices can be used to apply it to countless people and their various diseases. The professional tests of the future will not be restricted by small example sizes however can potentially consist of everybody! Big information techniques are currently being utilized to monitor infants in an expert early and sick baby system. By taping and evaluating every heart beat and breathing design of every infant, the system managed to create algorithms that can now forecast infections 24 hours prior to any sort of bodily signs show up. By doing this, the group can step in early and save delicate babies in an atmosphere where every hour counts. Exactly what’s more, large information analytics enable us to keep an eye on and forecast the advancements of upsurges and disease break outs. Integrating information from clinical documents with social networks analytics enables us to keep an eye on flu episodes in real-time, just by listening to what folks are saying, i.e. “Feeling rubbish today – in bed with a cold”.

5. Improving Sports Efficiency
Most exclusive sports have actually now embraced big information analytics. We have the IBM SlamTracker tool for tennis competitions; we use video analytics that track the performance of every player in a soccer or baseball video game, and sensor innovation in sporting activities devices such as container balls or golf clubs permits us to obtain comments (using cell phones and cloud servers) on our game and the best ways to enhance it. Lots of exclusive sports groups likewise track sportsmens away from the sporting environment using wise innovation to track nutrition and rest, and also social media discussions to monitor psychological health and wellbeing.

6. Improving Science and Studio
Science and studio is currently being transformed by the new opportunities huge data brings. Take, for example, CERN, the Swiss nuclear physics lab with its Huge Hadron Collider, the globe’s largest and most highly effective particle accelerator. Experiments to open the tricks of our cosmos just how it began and functions – generate huge amounts of information. The CERN data center has 65,000 processors to analyze its 30 petabytes of data. However, it makes use of the computer powers of hundreds of computer systems distributed across 150 data centers worldwide to evaluate the information. Such computer powers can be leveraged to transform so many various other areas of science and medical.

7. Optimizing Machine and Gadget Efficiency
Large information analytics help equipments and gadgets become smarter and more independent. For example, huge data devices are utilized to operate Google’s self-driving auto. The Toyota Prius is matched with electronic cameras, GPS in addition to powerful computers and sensors to safely drive when driving without the intervention of human beings. Big information tools are also made use of to maximize energy grids making use of information from smart meters. We could even use large information tools to enhance the performance of computers and information storage facilities.

8. Improving Security and Police.
Big information is used greatly in boosting protection and enabling police. I make certain you recognize the revelations that the National Security Firm (NSA) in the UNITED STATE uses huge data analytics to foil terrorist plots (and perhaps spy on us). Others make use of big data techniques to find and prevent online assaults. Polices make use of large information tools to capture bad guys or even forecast criminal activity and bank card companies use large data use it to identify deceptive purchases.

9. Improving and Optimizing Cities and Countries
Big information is used to boost lots of aspects of our cities and nations. As an example, it permits cities to maximize visitor traffic flows based on real time web traffic information along with social networking sites and weather data. A number of cities are presently piloting huge data analytics with the purpose of turning themselves into Smart Cities, where the transportation infrastructure and utility processes are all joined up. Where a bus would await a delayed train and where traffic indicates forecast traffic quantities and operate to decrease jams.

10. Financial Trading
My final classification of huge data application originates from monetary trading. High-Frequency Trading (HFT) is a location where large information discovers a bunch of use today. Below, big data algorithms are made use of to make trading choices. Today, the majority of equity trading now happens by means of information formulas that significantly think about signals from social networks networks and news web sites to make, buy and sell decisions in split secs.
For me, the 10 categories I have outlined right here stand for the locations where huge data is used one of the most. Certainly there are many various other applications of big information and there will certainly be lots of new classifications as the tools come to be more widespread.

Big Data

 



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Big Push into Big Data

Midsize Companies Plan Big Push into Big Data

Ann All
Ann All |   ARTICLES   |   POSTED 28 APR, 2014

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Big Data is not just within the purview of big companies. That’s the takeaway of new research from Competitive Edge Research Reports. The firm’s study, which was commissioned by Dell, found that 41 percent of midsize companies are already involved in one or more Big Data projects while 55 percent are planning initiatives.

Midsize companies that have moved from planning into implementing Big Data projects are reporting successes, the research found.

About half of respondents with existing initiatives gave themselves high marks in six strategic tasks: improving speed and quality of decision-making, improving product quality, identifying and taking advantage of business opportunities, understanding constituent sentiment, understanding customer needs and predicting future trends that may impair achievement of business goals. In contrast, fewer than 30 percent of respondents that were still just planning Big Data projects scored themselves well in most of these categories.

The gap was greatest in the improved decision-making category, in which 50 percent of companies with Big Data in production said they performed “very well” in this area, vs. 23 percent of companies that are still in the planning stage.

Big Data Spending

Buoyed by these early wins, respondents expect to boost their Big Data budgets by at least $2 million over the next two years. The research firm predicts the average Big Data budget will reach $6 million during this time frame.

“The early success midmarket companies are seeing with their Big Data initiatives will encourage more growth and investment, and additional returns on that investment will be achieved as they dive further into different datasets and embrace ever-improving analytic capabilities,” said Darin Bartik, executive director, product management, information management, Dell Software, in a statement.

The study offers insight into the tools that will likely win be included in Big Data budgets. The three tools ranked as most valuable were: real-time processing of data and analytics, predictive analytics and data visualization, to convert processed data into actionable insights.

Big Data Challenges, Best Practices

While the study found many positive trends, some daunting Big Data challenges still remain. Named most frequently as challenges: wide variety of new data types and structures, cited by 40 percent of respondents; sheer volume of data slows processing, mentioned by 34 percent; and budget limitations to improve data analysis capabilities, 32 percent.

Perhaps the most valuable insights in the study are the factors respondents named as key to their Big Data success. Forty-one percent of them tapped strong collaboration between business units and IT organizations as a success factor. Similarly, IDC Retail Insights recently noted that companies with more advanced Big Data capabilities were far more likely than their less mature peers to involve both business and IT in their Big Data projects.

4 Tips on Choosing a Supply Chain Planning and Optimization (SCP&O) Solution

IDC Retail Insights recommends that companies establish a collaborative governance structure involving lines of business, IT and a separate analytics group. With Big Data initiatives, high achievers tend to put IT in a leadership role, a finding that runs counter to the common practice of having lines of business lead technology-driven business initiatives, IDC Retail Insights found. Under such models, IT is responsible for overall strategy, planning and application development, with business responsible for evaluating the capabilities created by IT and the ultimate business outcomes. The analytics group handles management of data, content and analytics.

The Dell-commissioned research recognizes “encouraging signs of shared responsibilities taking shape among the management ranks.” For example, while 76 percent of respondents said IT was the most responsible for implementing Big Data projects, 56 percent mentioned sales management in a leadership role. This emphasis on customer-facing issues reinforces a need for close collaboration, the report notes.

Ann All is the editor of Enterprise Apps Today and eSecurity Planet. She has covered business and technology for more than a decade, writing about everything from business intelligence to virtualization.

 



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IBM’s Multi-Billion-Dollar Cloud/Big Data

Looking Far Ahead: IBM’s Multi-Billion-Dollar Cloud/Big Data Analytics Strategic Initiative

Rob Enderle
Rob Enderle |   UNFILTERED OPINION   |   POSTED 11 JUL, 2014

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Slide ShowThe Differences Between Hardware Design and Software Development

Yesterday, IBM announced its massive $3 billion investment effort to create a processor uniquely suited to the demands of the cloud and Big Data systems. This has both strategic and timing implications to the market. Efforts like this speak to the level of maturity in the market as well as IBM’s commitment to leading it. The announcement anticipates a massive improvement in processor scaling down to 7 nanometers and begins to flesh out what the Big Data world will look like in 2020.

 

Why Do You Care About 2020?

Often the mistake that both technology companies and IT managers make is focusing too much on the tactical and working to solve the problems of today. This tactical focus generally puts everyone in firefighting or whack-a-mole mode, constantly on the verge, and sometimes over it, of being overwhelmed by the changes they can barely keep up with.

The reason to maintain at least a five-year view is so that strategic efforts like Big Data analytics, which consume massive resources, aren’t prematurely obsolete and you can anticipate the world that will exist once they have matured. This eye on the future is often what distinguishes firms that survive decades from those that don’t make it to their first 10-year anniversary. The surviving firms have anticipated and prepared for changes.

What the IBM Announcement Means

This announcement means the market has reached the point where large solutions providers are beginning to build solutions from the ground up, not cobble together technologies that were designed for other things into a kludge that sort of works. The data analytics and cloud solutions increasingly demand a level of performance and granularity that wasn’t imagined when current chip technologies were incubated, and big vendors like IBM now understand what current technologies don’t do. Thus, they have a roadmap to create something that works better.

 

5 Tips for Better S&OP Scenario Planning

They are now looking beyond 7nanometer technology into quantum computingcarbon nanotubes and neurosynaptic computing. The first two are being worked on by a number of vendors now and promise, but have yet to deliver, massive improvements in processing speed and levels of security well beyond what we have today. Neurosynaptic computing promises a near-human-like capability to anticipate and proactively respond to future events by systems, taking products like IBM’s Watson to a future where they largely train themselves and can actually figure out more quickly the answers to questions that users haven’t even thought to ask. Carbon nanotubes (CNTs), in particular, could help define the smartphones and personal devices of the future. 

For those next-generation devices, which will be accessing these increasingly intelligent Watson-like back-end systems (think of Siri or more likely Microsoft’s Cortana on steroids), you’ll need advanced, low-powered transistors that are far more efficient. Many of those devices will be wearable, and to keep heat and power costs down to manageable levels, the data centers of tomorrow will find CNTs and Tunnel Field Effect Transistors (TFETs) critical. Finally, graphene will be critical to the future, as silicon runs out of performance headroom and a more efficient and powerful alternative is increasingly required.

They are layering on advancements in silicon photonics, which massively improves the speed of data transport because Big Data cloud-hosted jobs tend to be very fluid with regard to where they are located and have to be moved depending on their relative importance to the company and the proximity of the user.

Wrapping Up: How IBM Lasts

IBM’s announcement both showcases that the cloud and Big Data analytics requirements of today can now be projected into the future and that it is turning its massive R&D engine into creating the technologies that future will require. This is a massive effort by IBM, showcasing the kind of commitment that made it the longest-lasting technology vendor in the market. The firm is a survivor largely because it is able to step outside of the day-to-day tactical concerns and invest in the world of tomorrow so it has a place in it. That’s a decent example that more firms in every segment should follow.

 



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