Mar 29, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Insights  Source

[ AnalyticsWeek BYTES]

>> October 2, 2017 Health and Biotech analytics news roundup by pstein

>> Andrea Gallego / @BCG on Managing Analytics Practice by v1shal

>> Tackling 4th Industrial Revolution with HR4.0 – Playcast – Data Analytics Leadership Playbook Podcast by v1shal

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[ NEWS BYTES]

>>
 ‘Small Wins’ Called Key to Success in Harnessing Big Data in Healthcare – GenomeWeb Under  Big Data

>>
 Facebook wants to rid of engagement bait with machine learning – ZDNet Under  Machine Learning

>>
 Counter intelligence: Japan brings out the bazooka in AI war with China – South China Morning Post Under  Artificial Intelligence

More NEWS ? Click Here

[ FEATURED COURSE]

Learning from data: Machine learning course

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This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applicati… more

[ FEATURED READ]

The Misbehavior of Markets: A Fractal View of Financial Turbulence

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Mathematical superstar and inventor of fractal geometry, Benoit Mandelbrot, has spent the past forty years studying the underlying mathematics of space and natural patterns. What many of his followers don’t realize is th… more

[ TIPS & TRICKS OF THE WEEK]

Strong business case could save your project
Like anything in corporate culture, the project is oftentimes about the business, not the technology. With data analysis, the same type of thinking goes. It’s not always about the technicality but about the business implications. Data science project success criteria should include project management success criteria as well. This will ensure smooth adoption, easy buy-ins, room for wins and co-operating stakeholders. So, a good data scientist should also possess some qualities of a good project manager.

[ DATA SCIENCE Q&A]

Q:Do you know / used data reduction techniques other than PCA? What do you think of step-wise regression? What kind of step-wise techniques are you familiar with?
A: data reduction techniques other than PCA?:
Partial least squares: like PCR (principal component regression) but chooses the principal components in a supervised way. Gives higher weights to variables that are most strongly related to the response

step-wise regression?
– the choice of predictive variables are carried out using a systematic procedure
– Usually, it takes the form of a sequence of F-tests, t-tests, adjusted R-squared, AIC, BIC
– at any given step, the model is fit using unconstrained least squares
– can get stuck in local optima
– Better: Lasso

step-wise techniques:
– Forward-selection: begin with no variables, adding them when they improve a chosen model comparison criterion
– Backward-selection: begin with all the variables, removing them when it improves a chosen model comparison criterion

Better than reduced data:
Example 1: If all the components have a high variance: which components to discard with a guarantee that there will be no significant loss of the information?
Example 2 (classification):
– One has 2 classes; the within class variance is very high as compared to between class variance
– PCA might discard the very information that separates the two classes

Better than a sample:
– When number of variables is high relative to the number of observations

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Dr. Nipa Basu, @DnBUS

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Dr. Nipa Basu, @DnBUS

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[ QUOTE OF THE WEEK]

Torture the data, and it will confess to anything. – Ronald Coase

[ PODCAST OF THE WEEK]

@JohnTLangton from @Wolters_Kluwer discussed his #AI Lead Startup Journey #FutureOfData #Podcast

 @JohnTLangton from @Wolters_Kluwer discussed his #AI Lead Startup Journey #FutureOfData #Podcast

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[ FACT OF THE WEEK]

According to Twitter’s own research in early 2012, it sees roughly 175 million tweets every day, and has more than 465 million accounts.

Sourced from: Analytics.CLUB #WEB Newsletter

Slow progress forces Navy to change strategies for cloud, data centers

computer-data-center
The Department of the Navy isn’t making as much progress on data center consolidation and moving to the cloud as it wants to. So the Navy is moving the initiatives under a new owner and coming down hard on those who are standing in the way.

“Later this year, we will make an organizational change to our approach to data center consolidation. The Data Center and Application Optimization (DCAO) program office will move from under Space and Naval Warfare Systems Command (SPAWAR) headquarters to under Program Executive Office-Enterprise Information Systems (PEO-EIS) as a separate entity or program office,” said John Zangardi, the Navy’s deputy assistant secretary for command, control, computers, intelligence, information operations and space and acting chief information officer. “This will better align consolidation efforts with network efforts and more fully leverage the Next Generation Enterprise Network (NGEN) contract.

So we will build on their application experience. The DCAO will be responsible for establishing a working model for Navy cloud hosting service brokerage. This will be for the delivery of application hosting via commercial and federal agencies. Culturally, we have to make this shift from a mistaken belief that all our data has to be near us and somewhere where I can do and hug the server, instead of someplace where I don’t know in the cloud. This is a big shift for many within the department. It’s not going to be an easy transition.”

Since 2012, the Navy has made some progress. Zangardi, who spoke at the 14th annual Naval IT Day sponsored by AFCEA’s Northern Virginia chapter, said over the last three years, the Navy has consolidated 290 systems and apps across 45 sites. But overall, he said getting bases and commands to move faster just isn’t happening.

The Navy plans to officially move the data center consolidation office into the PEO-EIS office in July.

Testing the cloud access point

Knowing the difficulties and challenges over the past few years, Zangardi said he’s taking several steps to help ease the pain.

First, he said his office picked three data centers that are lagging behind and required them to develop a plan to consolidate and move their data to a centralized data center.

Second, the Navy is rationalizing large scale apps. Zangardi said too often people hold their applications and servers close.

“I spend a lot of time thinking about the cloud access point (CAP) and our data centers. My objective is to move stuff as quickly as possible. The applications we are looking at right now to move to our cloud access point, the ones I’m most interested in moving right now, would come out of the N4 world, so we are talking about things like maintenance or aviation type of stuff so think logistics,” he said. “We’re also looking at enterprise resource planning (ERP). Can we move our ERP to a cloud type of solution to drive in more efficiencies? I think most of the things we are looking at, at least upfront, would be business sort of applications.”

The third way to ease the pain is by using pilot programs to get commands and bases comfortable with the idea of letting go of their servers and data.

“PEO-EIS and SPAWAR Systems Center Atlantic are piloting a cloud access point in conjunction with the commercial cloud service provider. It’s currently operating under an interim authority to test,” Zangardi said. “These organizations have the right expertise to develop the approach for the department to leverage the cloud. However, the CAP pilot is in its early stages. Essentially right now we are doing table top testing.

Our objective over the next year is to move from a pilot effort to what I would term a productionized commercial cloud. What do I mean by productionized? Simply to me it means an industry leveraged approach that can scale to demand from users. This capability should be secure, provide lower costs for storage and data and facilitate mobility.”

One big question about this consolidation effort is how to break out the 17 or 19 data centers that fall under the NGEN Intranet, and put them under the PEO- EIS team with the other data centers.

Zangardi said the Navy is considering an approach to this, but it’s still in the early stages.

Private cloud works just fine

While the Navy is open to using commercial or public clouds, the Marine Corps is going its own way.

Several Marine Corps IT executives seemed signal that the organization will follow closely to what the Navy is doing, but put their own twist on the initiative.

One often talked about example of this is the Marines decision to not move to the Joint Regional Security Stacks (JRSS) that is part of the Joint Information Environment (JIE) until at least version 2 comes online in 2017. Marine Corps CIO Gen. Kevin Nally said the decision not use the initial versions of JRSS is because Marine Corps’ current security set up is better and cheaper than version 1 or 1.5.

More see – http://www.federalnewsradio.com/412/3857833/Slow-progress-forces-Navy-to-change-strategies-for-cloud-data-centers

Source: Slow progress forces Navy to change strategies for cloud, data centers by analyticsweekpick

Mar 22, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Statistically Significant  Source

[ AnalyticsWeek BYTES]

>> Uber: When Big Data Threatens Local Democracy by analyticsweekpick

>> Share a Joke for a Chance to Win the New Book, Total Customer Experience by bobehayes

>> Measuring Customer Satisfaction and Loyalty (3rd Edition)! by bobehayes

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[ NEWS BYTES]

>>
 Big Data Security Market 17.1% CAGR Led by Encryption Software Solutions to 2022 – Broadway World Under  Big Data Security

>>
 Exclusive: IS Risk Analytics – a Year in Review – Finance Magnates Under  Risk Analytics

>>
 Adobe Campaign Releases Connector for Hadoop – KMWorld Magazine Under  Hadoop

More NEWS ? Click Here

[ FEATURED COURSE]

The Analytics Edge

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This is an Archived Course
EdX keeps courses open for enrollment after they end to allow learners to explore content and continue learning. All features and materials may not be available, and course content will not be… more

[ FEATURED READ]

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

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Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for e… more

[ TIPS & TRICKS OF THE WEEK]

Data aids, not replace judgement
Data is a tool and means to help build a consensus to facilitate human decision-making but not replace it. Analysis converts data into information, information via context leads to insight. Insights lead to decision making which ultimately leads to outcomes that brings value. So, data is just the start, context and intuition plays a role.

[ DATA SCIENCE Q&A]

Q:What is: collaborative filtering, n-grams, cosine distance?
A: Collaborative filtering:
– Technique used by some recommender systems
– Filtering for information or patterns using techniques involving collaboration of multiple agents: viewpoints, data sources.
1. A user expresses his/her preferences by rating items (movies, CDs.)
2. The system matches this user’s ratings against other users’ and finds people with most similar tastes
3. With similar users, the system recommends items that the similar users have rated highly but not yet being rated by this user

n-grams:
– Contiguous sequence of n items from a given sequence of text or speech
– ‘Andrew is a talented data scientist”
– Bi-gram: ‘Andrew is”, ‘is a”, ‘a talented”.
– Tri-grams: ‘Andrew is a”, ‘is a talented”, ‘a talented data”.
– An n-gram model models sequences using statistical properties of n-grams; see: Shannon Game
– More concisely, n-gram model: P(Xi|Xi?(n?1)…Xi?1): Markov model
– N-gram model: each word depends only on the n?1 last words

Issues:
– when facing infrequent n-grams
– solution: smooth the probability distributions by assigning non-zero probabilities to unseen words or n-grams
– Methods: Good-Turing, Backoff, Kneser-Kney smoothing

Cosine distance:
– How similar are two documents?
– Perfect similarity/agreement: 1
– No agreement : 0 (orthogonality)
– Measures the orientation, not magnitude

Given two vectors A and B representing word frequencies:
cosine-similarity(A,B)=?A,B?/||A||?||B||

Source

[ VIDEO OF THE WEEK]

Pascal Marmier (@pmarmier) @SwissRe discusses running data driven innovation catalyst

 Pascal Marmier (@pmarmier) @SwissRe discusses running data driven innovation catalyst

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data that is loved tends to survive. – Kurt Bollacker, Data Scientist, Freebase/Infochimps

[ PODCAST OF THE WEEK]

@TimothyChou on World of #IOT & Its #Future Part 1 #FutureOfData #Podcast

 @TimothyChou on World of #IOT & Its #Future Part 1 #FutureOfData #Podcast

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[ FACT OF THE WEEK]

Retailers who leverage the full power of big data could increase their operating margins by as much as 60%.

Sourced from: Analytics.CLUB #WEB Newsletter

Gaming Your Brain

GEORGE YAO WALKED into the bathroom of his unfeasibly small $1,450-a-month San Francisco apartment and slid each of his five iPads into a zip-lock freezer bag. He was preparing, with some ceremony, to defend his world title. Three months earlier, in January 2013, Yao had reached the top of the global leaderboards for Clash of Clans, a medieval warfare-themed strategy game in which a player builds defenses, trains troops and attacks other players’ fortifications. The achievement had cost him dearly. At the peak of his obsession, Yao would easily spend $400 a week in the game to help him climb the leaderboards, an unbudgeted outlay that prevented him from going out with his friends on the weekend (or renting an apartment in which he could fit much more than a couch). To maintain his position, which had made his online handle, Jorge Yao, familiar to millions of Clash players around the world, Yao was running five parallel game accounts, playing them off against each other simultaneously. His focus was so single-minded that he even took his iPads into the shower so he could monitor his games through the plastic bags.

Clash of Clans is made by the Finnish game studio Supercell. It launched in August 2012 and rapidly became one of the top five highest-grossing titles in Apple’s App Store. In 2013, when Yao and his invitation-only clan, North44, were at their peak, Clash of Clans helped create $555 million of revenue for the company. The next year, Supercell’s revenue tripled to $1.7 billion — a seemingly inexplicable sum produced by a roster of games that, like Clash, are free to download and can be played without spending a dime. So how is Supercell generating all that money? By relying on players who don’t simply want to enjoy the game but who want to win. Players who, like Yao, are willing to spend a great deal of cash.

“The game became my life,” Yao tells me from his new home in London. “I played every single day in order to maintain my ranking. It became a daily routine, even on the weekends. I was exhausted.” During a three-month period at the top of the game’s leaderboard, when he came home from his job as a compliance analyst on IT projects, Yao estimates that he spent close to $3,000 buying “gems,” units of virtual currency that can be used to speed up every aspect of the game, from the time it takes to build fortifications to the time it takes to train virtual troops. The habit left him close to broke. After Yao joined North44, one of the hundreds of thousands of groups of players who support one another and wage war on rival clans, a sympathetic team member became Yao’s patron, funding his gem habit. Later, another wealthy clan mate who lived in the United Arab Emirates bought Yao three iPads to allow him to further maximize his time in the game. What had started as a diversion had mushroomed into an obsession. Yao’s immersion was total. He’d twitch his way through the long nights on cans of Red Bull, his dedication on the virtual battlefield filling Supercell’s coffers.

 

 

Yao’s devotion to Clash was freely given but also, from Supercell’s point of view, carefully planned, developed and nurtured. Clash is the most successful so-called “freemium” game, a relatively new breed of app that aims to lock players into a spending habit. The game’s developer earns its money not at the point of sale, but at the point of absorption — and during the past decade, the techniques for captivating players have vastly improved. Every time you play a game, its maker is, in all probability, recording everything you do and using that information to keep you playing longer and spending more.

These techniques are related to those seen in the gambling industry, where slot machines are designed to create a sense of total immersion in the game, so players continue to hit the button for as long as possible. That’s particularly true for more repetitive freemium games, like Candy Crush, by the design studio King. “These games allow you to zone out,” says Natasha Dow Schull, an MIT professor who has been researching the links between freemium video games and slots. “They all follow this high-hit-frequency/low-volatility model that puts you into a trance where your worries drop away. It’s a mechanism for escape even more than it is a mechanism for excitement.”

Schull, the author of “Addiction by Design,” a study of digital slot machines in Las Vegas, argues that many slots players don’t necessarily know that escape is what they’re seeking until they get hooked by the flashing rhythms of the machines and find the allure of the trance irresistible. More than jackpots, they crave the feeling that the real world and all its problems are melting away. Schull believes that many play the repetitive freemium games to achieve a similar type of relief. The pull of games that involve strategy and world building, like Clash of Clans, she says, is less analogous to that of slot machines — but their developers are no less devoted to leveraging every bit of data their players produce into profit. Whether King or Supercell, video game companies have built their entire business on one premise: lengthening our stay within their virtual worlds.

IN THE PAST FEW YEARS, video game studios have made both an art and a science of optimizing their designs. Employees with job titles like “data scientist” study whether a player is more likely to click on a button if it’s square or round, or whether a particular advertisement is likely to break that trancelike state and cause someone to close the game. The tiniest improvement can have fortune-changing effects for a game studio. “If you can make a change to, say, a menu color that results in your 10 million players spending an average of just a penny more every month, it adds up fast,” one analyst tells me.

These techniques are familiar in the gambling industry. For some time now, Schull says, slot machine manufacturers have hired mathematical engineers to make “personalized volatility algorithms.” Drawing on data from the gambler’s playing history (as tracked by ubiquitous casino loyalty cards), these algorithms tailor the rhythms of a machine’s payout to suit the current player’s mentality, whether it’s a person who prefers to take high-risk gambles or someone who makes only safe bets for modest gains.

In the video game industry, many larger developers make extensive use of psychologists and professional economists to improve the game — and its bottom line — in subtle ways. Riot Games, creator of League of Legends, arguably the most popular freemium eSport in the world, employs a team of data scientists to study the 5 terabytes of data generated by the 2 million players who log in each day. They use the information to ensure the game is fairly balanced and that one particular character isn’t too powerful.

“It’s been a fundamental shift in our understanding,” says Seth Killian, a designer who worked on Street Fighter IV, the competitive fighting game that has sold more than 8 million copies since its original launch in 2008. “A game’s menus, virtual stores and so on used to be viewed as an afterthought, but the scope of free-to-play games has shown that what goes on around the game can be just as important to your financial success as what’s happening during a match.”

The formulas are heavily guarded. In early 2009, an employee of Zynga, creator of FarmVille, downloaded 70 files from his computer to a USB storage device two weeks before leaving to join rival social game publisher Playdom (now owned by ESPN’s parent company, Disney). The files included a document known as The Playbook, which Zynga claimed is a “recipe book” that contains its “secret sauce” for designing profitable Facebook games. Unbeknownst to the employee and three of his colleagues, who had also downloaded the file before they left to work for Playdom, Zynga hired a forensics team to go through their computers after they left. “Most of us had emailed or copied the files, which is technically illegal, but which is pretty normal for the game industry,” says one of the employees, who asked to not be named.

Zynga sued Playdom and the four employees for theft of this document, which, Zynga claimed in its lawsuit, was “the result of years of testing, development, trial and error, analyzing customer behavior, … optimization … and collective know-how.” In the right hands the information could be used, the company argued, to make hugely profitable games. The suit was settled for undisclosed terms one year later, but not before one of the defendants was held in contempt of court for deleting the file from his computer in a cover-up attempt, receiving a $4,000 fine and a 10-day prison sentence (the sentence was suspended).

Zynga isn’t the only company intent on protecting its design secrets. Twice a month, Riot Games, the League of Legends maker, hires an actor to visit its studio in southern California and roam its halls, chatting with staff while pretending to be a new employee. The company, founded in 2006 by two USC graduates, is expanding so quickly that there are often fresh faces among its more than 1,000 employees. But new hires, like every other staff member, must wear an identification badge at all times. The actor, meanwhile, does not wear one. Any Riot employee who finds and reports the imposter wins a prize. Beneath the playfulness is a serious aim: to train employees to be observant about who should and shouldn’t be walking the developer’s corridors. When you’re custodian of the most widely played video game on the planet, you must protect your design secrets with Sauron-like vigilance.

AS GEORGE YAO’S experience with Clash of Clans lengthened, he began to perceive the tricks the game employed to draw him in and lure him to spend. He noticed how, when he reached the top of the leaderboards, he’d spend less on the game (“I dropped from $400 a week to around $250”) because there were fewer players at his level for the game to match him against in battles. He also observed that he spent less money in those weeks when Supercell did not update the game with new features.

In the final months of his obsession, Clash of Clans became what Yao described as almost “a second job.” He estimates that he would play 70 to 80 hours a week and says that he lost 20 pounds during his reign because he often simply forgot to eat. For Yao, who still holds the record for longest run atop the Clash leaderboard, it was a lifestyle he could not sustain. “I played every single day in order to maintain my ranking, and after six months I felt there was not much left I could accomplish in the game itself,” he says. “I wanted to quit on top rather than fizzle out slowly.” On June 17, 2013, Yao took to Facebook to announce to his 20,000 followers that he was quitting the game. He’d escaped. “It was such a relief,” he says quietly.

No sooner than Yao posted his message on Facebook, he heard from a surge of game studios wanting to know about what he’d learned in his year on the freemium front line. First, rival game publishers made consultancy offers, hoping to learn Clash of Clans’ secrets. Then came a formal job offer. Yao was bored with his life in the finance industry, so at the same time he quit the game, he also quit his profession. He moved to London to join Space Ape Games, where he now works as manager of live operations across a number of the studio’s freemium titles. Samurai Siege, the first game Yao worked on, is a “carbon copy” of Clash of Clans, he says.

Yao’s work mainly involves tweaking in-game tournaments to increase participation: for example, ensuring that the prizes being offered are worthwhile enough for the top-tier players to compete for but “not too hardcore that lower-tier players don’t have a shot at winning them.” He is, however, coy about how the company uses data to make the game more profitable.

Freemium game companies in Asia appear more willing to talk. Barcode Footballer was the top-grossing freemium game in Japan last summer. “There are many times that we have changed the game based on player data,” says Keisuke Osone, who heads the team of data analysts at the game’s developer, CYBIRD, in the Shibuya area of Tokyo. This can involve changing the location of menu options on the screen, or even adapting the game to when players have the most money jangling around their pockets. “People tend to time their spending in the game around the day their salary is paid, so we tend to run special offers and promote the most desirable in-game items around that time,” Osone says. “In Japan payday is usually on the 25th of each month, so we generally run campaigns on that date.” The team also tweaks its digital storefront to make the most of the fact that “people like to buy things on the first day of every month” because, Osone says, they have just been billed for the previous month’s purchases by the mobile phone operator.

“Every decision my team makes is catalyzed by data,” says Eric Seufert, vice president of user acquisition and engagement at Rovio, the Swedish developer of the mobile game phenomenon Angry Birds. Often, the game will run two similar versions of the same ad to see which garners more clicks. This, Seufert explains, does not necessarily lead the company to run the more popular ad. It might garner more clicks from certain players, but at the same time, it might drive away others who are bigger spenders. Another ad may attract fewer hits but also prove less bothersome to the more desirable players. Seufert, who previously worked for the freemium communication service Skype, says that getting those decisions right is a matter of life or death for a game world. If too many players decide to quit or move to a rival game, the virtual world dies, potentially taking its developer down with it.

Survival is often based on finding the highest spending players, known as whales (a term that originated in the gambling industry), and keeping them invested. Will Luton, author of “Free-to-Play: Making Money From Games You Give Away” — and a player who spends about $2,000 a year in Magic, The Gathering Online — estimates that only 2 to 5 percent of a freemium game’s audience will spend any money in the game. Of these paying players, he says, a tiny percentage of big spenders typically provide up to 50 percent of the revenue. Confoundingly, other reports, like a study from the market research company NPD in 2012, claim that 40 percent of those who have played a freemium game have made in-app payments. Since only a handful of companies release their figures, definitive data on this issue are hard to come by. It’s safe to say, though, that the makers of freemium games want as large a user base as possible, filled with plenty of whales and small fish alike.

Sometimes, the whales are smaller (physically, at least) than you might expect. Last October the Belgian news site Nieuwsblad reported that a teenage boy had spent no less than $46,000 on in-game purchases in Game of War: Fire Age, using his grandfather’s credit card. He later claimed that he did not realize he was spending real money. Stories of children ringing up preposterous bills are so widespread that in 2014 the Federal Trade Commission issued a successful complaint against Apple, arguing that the company did not adequately inform account holders that entering their password would open a 15-minute window during which children could incur unlimited charges in a freemium game. Apple agreed to provide full refunds to consumers, paying a minimum of $32.5 million, to settle the complaint. Google also made a $19 million settlement with the FTC, while Amazon has vowed to fight similar charges. What will remain even after all the FTC’s business is settled: the uneasy fact that psychologists and economists are working to draw players, including children, into that slot-machine-like spending trance. (In 2012, Zynga formalized the relationship between the two industries when it announced a partnership with bwin.party, an online poker and casino games provider.)

Of course, there is plenty that’s positive about a game like Clash of Clans: it teaches planning and strategy, encourages care and dedication and, as seen in Yao’s case, can foster new friendships. But for the player who succumbs to the algorithm, it can become not only a financial burden but an emotional and psychological drain. In 2013, a few months after he quit Clash, Yao told The New York Times that he couldn’t stand the sight of the game.

Yet today, just as he once labored to upgrade his troops in Clash, he works to turn casual players into legions of George Yaos — players who give their all to the game, perhaps even beyond the limits of what they can afford, as they seek to find the flow state in which, as MIT’s Schull puts it, the world’s problems disappear. It can become a dispiriting cycle: Players pick up the game to escape the pressures of the world but, in time, the game can mimic those same pressures and become its own kind of stress and labor — one that can, in some cases, even invade the shower. Does Yao ever struggle with the idea that he is working to draw others into a pattern of play from which he was relieved to escape?

“I do consider that,” he says. “Some players don’t know any better. They can be competing for something and they don’t like what they’re doing, but they do it anyway.”

To read the original article on ESPN, click here.

Originally Posted at: Gaming Your Brain by analyticsweekpick

Talent Analytics: Old Wine In New Bottles?

talent
A day does not pass without my receiving multiple emails announcing webinars, publications, and workshops focused on talent analytics. Talent analytics has become an important area in both consulting firms and corporations.

Most of the discussion of analytics emphasizes how much can be gained by better talent utilization and the ability of analytics and big data to improve overall organizational performance. It is hard to disagree with this, but it is also important to point out that talent analytics is not a new area by any means. There is a bit of an “old wine in new bottles” about it.

At least since the 1940s, some companies and governments have used talent analytics to improve their selection and talent management activities. But, and it’s a very big “but,” very few have. The capability has been there, as has evidence to show that organizational performance can be improved by using data and evidence based talent management. Despite this, the use of talent analytics to guide practice has been rare.

There is extensive literature in academia about the failure of companies to use best practices when it comes to talent management, and a strong argument is made in this literature for evidence-based management. The simple fact is, when it comes to dealing with people, many managers trust their “intuition” and “common sense” more than they trust data. This seems to be particularly true when it comes to data gathered by academics and researchers who are not part of their company.

So what is new? Why the flurry of interest in talent analytics? There are a number of reasons. One is that with the growth of information technology, it is much easier to gather data and analyze it. In the past, much of the work on talent analytics was done by a few large corporations (e.g. IBM, AT&T, 3M) who had the staff and resources to do what often were time consuming analytics that required staff expertise or “worse yet”, it was done by academics.

Today, the expertise to implement the results of analytics may not be present in most organizations but the data and the ability to analyze it is readily available even in small organizations. Thus, in more and more organizations, it is not possible to justify not acting on data because it was “not invented (or collected) here.”

Perhaps the major reason for the greater attention being paid to talent analytics is the growing importance of talent. More and more organizations are dependent on the performance of their talent for their source of competitive advantage. Thus, any improvement in talent performance pays off on the bottom line. This obviously produces a greater incentive for companies to pay attention to talent and to use analytics to help make talent management decisions.

Finally, new kinds of data can be collected that may prove useful in increasing the power of talent analytics. For example, a recent study found that the number of internet messages sent and received by individuals is a good predictor of their turnover. This measure did not exist a decade ago, and until recently could not be easily captured and analyzed.

Overall, at this point in time, there are clearly a number of reasons to believe that talent analytics will continue to grow and be more powerful and that more organizations will use them. The data and computational power are available and, to some degree, the expertise is available to do the analytics and facilitate the implementation of the findings. But, and it is an important “but”, it may not be enough to result in very many organizations practicing data-based talent management. In order for this to happen, organizations need the “right culture” and leadership approaches–ones that value evidence-based decision making.

In the absence of a culture, which focuses and values data and practices evidence-based decisions, the availability of data is unlikely to have a great impact. Those who collect and analyze the data need to not just “report” it, they need to be able to tell a story that makes it clear how it relates to the organization’s effectiveness. Decision makers need to make it clear that data wins out over opinion!

Without the right kind of culture, leaders who demand data-based talent decisions, and good storytelling, the availability of data is not likely to make a great difference in how organizations manage their talent. For example, they will still make most hiring decisions based on interviews, which have little to no predictive validity! It is not enough to create new wine that is superior to what we have had for decades. It must be put in new bottles that highlight its strategic importance and makes it impossible to ignore!

Article originally published HERE.

Originally Posted at: Talent Analytics: Old Wine In New Bottles? by analyticsweekpick

Mar 15, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Accuracy  Source

[ AnalyticsWeek BYTES]

>> @ChuckRehberg / @TrigentSoftware on Translating Technology to Solve Business Problems #FutureOfData by v1shal

>> Surviving the Internet of Things by v1shal

>> Achieving tribal leadership in 5 easy steps by v1shal

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>>
 Cloud vulnerabilities are being ignored by the enterprise | ZDNet – ZDNet Under  Cloud Security

>>
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Pattern Discovery in Data Mining

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Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

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Analytics Strategy that is Startup Compliant
With right tools, capturing data is easy but not being able to handle data could lead to chaos. One of the most reliable startup strategy for adopting data analytics is TUM or The Ultimate Metric. This is the metric that matters the most to your startup. Some advantages of TUM: It answers the most important business question, it cleans up your goals, it inspires innovation and helps you understand the entire quantified business.

[ DATA SCIENCE Q&A]

Q:What is principal component analysis? Explain the sort of problems you would use PCA for. Also explain its limitations as a method?

A: Statistical method that uses an orthogonal transformation to convert a set of observations of correlated variables into a set of values of linearly uncorrelated variables called principal components.

Reduce the data from n to k dimensions: find the k vectors onto which to project the data so as to minimize the projection error.
Algorithm:
1) Preprocessing (standardization): PCA is sensitive to the relative scaling of the original variable
2) Compute covariance matrix ?
3) Compute eigenvectors of ?
4) Choose kk principal components so as to retain xx% of the variance (typically x=99)

Applications:
1) Compression
– Reduce disk/memory needed to store data
– Speed up learning algorithm. Warning: mapping should be defined only on training set and then applied to test set

2. Visualization: 2 or 3 principal components, so as to summarize data

Limitations:
– PCA is not scale invariant
– The directions with largest variance are assumed to be of most interest
– Only considers orthogonal transformations (rotations) of the original variables
– PCA is only based on the mean vector and covariance matrix. Some distributions (multivariate normal) are characterized by this but some are not
– If the variables are correlated, PCA can achieve dimension reduction. If not, PCA just orders them according to their variances

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Reimagining the role of data in government

 Reimagining the role of data in government

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We chose it because we deal with huge amounts of data. Besides, it sounds really cool. – Larry Page

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@RCKashyap @Cylance on State of Security & Technologist Mindset #FutureOfData #Podcast

 @RCKashyap @Cylance on State of Security & Technologist Mindset #FutureOfData #Podcast

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This year, over 1.4 billion smart phones will be shipped – all packed with sensors capable of collecting all kinds of data, not to mention the data the users create themselves.

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Virtualization – A Look to the Future

Cloud-stock-getty

 

Several years ago, we began using virtualization technologies as means to test servers and use resources more effectively. When VMware became a hypervisor, very few vendors actually supported a virtual infrastructure. So, virtualization was left behind in the classroom, and the development environment within numerous organizations.

With the awareness quickly rising, administrators saw that server resources were being wasted dramatically and that virtualization was a way to curtail that. And with that, the pressure rose on vendors to support a virtual state. From there, server virtualization made its way into almost all data center environments as more organizations adopted the technology to help align their business needs.

Now – we’ve entered the next frontier…. We’re way beyond simple server virtualization and are exploring new avenues to make virtualization an even more powerful platform. Let’s take a look at some of these technologies.

  • Application Delivery. If we can virtualize a server, why not apps? Popularity with products like XenApp, ThinApp, and now Cloud Volumes continues to increase. Administrators are able to stream or delivery applications to the end-user without actually deploying them at the end-point. This sort of control and manageability makes app virtualization very plausible. In fact, many of the big Fortune 500 organizations have some type of application virtualization deployed already. The next iteration of application and virtualization will absolutely revolve around secure clientless delivery. HTML5 allows you to stream entire applications directly to a web browser. This can helped revolutionize how end-points are being deployed and how organizations control resources.
  • Hosted/Virtual/Cloud Desktops. People very realized that VDI isn’t as easy as it may seem. Numerous underlying components can make this technology a bit cumbersome. Today, there has been a resurgence behind VDI and the delivery of complete virtual desktops. Similar to applications, HTML5 can also steam entire desktops directly to a browser. The other big aspect is how far the data center has come as well. Converged infrastructure, better resource controls and more use-cases are actually resulting in more VDI deployments today. The future, however, might be a bit different. The concept of a “desktop” as we know it might be going away as the focus shifts even more towards the delivery of applications and data.
  • Network Virtualization (SDN and NFV). Also known as software defined networks (SDN), network virtualization has allowed the administrator much greater control over a network infrastructure. Where one physical NIC had its limitations, new technologies allow for numerous virtual networking designations on a corporate network. Another big network virtualization push revolves around network functions virtualization (NFV). You can now virtualize specific network functions and allow them to run as individual nodes connecting with other communication and networking services. For example, you can have virtual machines or appliances running as virtual load balancers, firewalls, and even WAN optimizers.
  • Security Abstraction. There will always be room in the IT world for more traditional unified threat management devices. However, hardened physical appliances aside, more organizations have deployed security platforms on top of a VM. The flexibility to clone security appliances, place them at various points within the organization and assign specific functions to them makes security virtualization very appealing. Imagine having a security appliance VM only doing DLP, IPS/IDS. This type of deployment can be very strategic and beneficial. Furthermore, you’re going to see a lot more virtual services specifically designed to protect your cloud. Inter-cloud connectivity needs a good security practice. This is where more virtual appliances helping bind security services spanning multiple cloud services are really going to help.
  • User Virtualization. With IT consumerization and mobility making a presence, more organizations have been looking for ways to abstract the user layer from devices, applications and end-points. And so, user virtualization was born. Solutions from technologies like AppSense provide a way for a user to transfer their personalized settings from application to application and from platform to platform. Basically, users are able to carry their settings with them as they migrate from various systems and applications. Furthermore, you can tie the user’s compute profile between various end-points and even cloud resources.
  • Storage Virtualization. A single storage controller can be logically carved up so well, that they appear to be their own standalone units to the administrator. Using storage more efficiently is on the front page of many project lists. Controller multi-tenancy is just one example of how storage virtualization plays a role in today’s IT world. Another big example is what’s happening around software-defined storage. An organization’s ability to completely abstract every storage resource and point it to a virtual layer for management is absolutely a reality. Today’s heterogeneous storage architecture is asking for a better way to manage silo’d disks, storage arrays, and cloud resources.
  • Server Virtualization. This stays on the list only because server virtualization continues to evolve and expand. With entire platforms being designed for server virtualization, more emphasis is being placed on how to better use a virtual environment. There continues to be a need for virtualizing the server and to better incorporate virtualization efficiencies into the modern data center. However, a lot of future conversation around server virtualization revolves around commodity server systems. Remember, your hypervisor is a lot more power than ever before. Future capabilities will allow you to create even better underlying server resource management solutions to help keep your underlying data center very agile.

The list will most likely grow as more environments seek ways to be even more efficient. Already, virtualization technologies are helping many businesses cut costs, regain control, and allow for greater growth with their infrastructure. The most important point to remember here is that the logical (virtual) layer will be critical to help connect your data center to your users – and to the cloud.

 

Originally posted at: http://www.datacenterknowledge.com/archives/2015/05/15/virtualization-look-future/

 

Source: Virtualization – A Look to the Future by analyticsweekpick

Is big data dating the key to long-lasting romance?

If you want to know if a prospective date is relationship material, just ask them three questions, says Christian Rudder, one of the founders of US internet dating site OKCupid.

  • “Do you like horror movies?”
  • “Have you ever travelled around another country alone?”
  • “Wouldn’t it be fun to chuck it all and go live on a sailboat?”

Why? Because these are the questions first date couples agree on most often, he says.

Mr Rudder discovered this by analysing large amounts of data on OKCupid members who ended up in relationships.

Dating agencies like OKCupid, Match.com – which acquired OKCupid in 2011 for $50m (£30m) – eHarmony and many others, amass this data by making users answer questions about themselves when they sign up.

Some agencies ask as many as 400 questions, and the answers are fed in to large data repositories. Match.com estimates that it has more than 70 terabytes (70,000 gigabytes) of data about its customers.

Applying big data analytics to these treasure troves of information is helping the agencies provide better matches for their customers. And more satisfied customers mean bigger profits.

US internet dating revenues top $2bn (£1.2bn) annually, according to research company IBISWorld. Just under one in 10 of all American adults have tried it.

Morecambe & Wise with Glenda Jackson as Cleopatra
If Cleopatra had used big data analytics perhaps she wouldn’t have made the ultimately fatal decision to hook up with Mark Anthony

The market for dating using mobile apps is particularly strong and is predicted to grow from about $1bn in 2011 to $2.3bn by 2016, according to Juniper Research.

Porky pies

There is, however, a problem: people lie.

To present themselves in what they believe to be a better light, the information customers provide about themselves is not always completely accurate: men are most commonly economical with the truth about age, height and income, while with women it’s age, weight and build.

Mr Rudder adds that many users also supply other inaccurate information about themselves unintentionally.

“My intuition is that most of what users enter is true, but people do misunderstand themselves,” he says.

For example, a user may honestly believe that they listen mostly to classical music, but analysis of their iTunes listening history or their Spotify playlists might provide a far more accurate picture of their listening habits.

Lovers on a picnic
Can big data analytics really engineer the perfect match?

Inaccurate data is a problem because it can lead to unsuitable matches, so some dating agencies are exploring ways to supplement user-provided data with that gathered from other sources.

With users’ permission, dating services could access vast amounts of data from sources including their browser and search histories, film-viewing habits from services such as Netflix and Lovefilm, and purchase histories from online shops like Amazon.

But the problem with this approach is that there is a limit to how much data is really useful, Mr Rudder believes.

“We’ve found that the answers to some questions provide useful information, but if you just collect more data you don’t get high returns on it,” he says.

Social engineering

This hasn’t stopped Hinge, a Washington DC-based dating company, gathering information about its customers from their Facebook pages.

The data is likely to be accurate because other Facebook users police it, Justin McLeod, the company’s founder, believes.

Man pressing "Like" button
Dating site Hinge uses Facebook data to supplement members’ online dating profiles

“You can’t lie about where you were educated because one of your friends is likely to say, ‘You never went to that school’,” he points out.

It also infers information about people by looking at their friends, Mr McLeod says.

“There is definitely useful information contained in the fact that you are a friend of someone.”

Hinge suggests matches with people known to their Facebook friends.

“If you show a preference for people who work in finance, or you tend to like Bob’s friends but not Ann’s, we use that when we curate possible matches,” he explains.

The pool of potential matches can be considerable, because Hinge users have an average of 700 Facebook friends, Mr McLeod adds.

‘Collaborative filtering’

But it turns out that algorithms can produce good matches without asking users for any data about themselves at all.

For example, Dr Kang Zhao, an assistant professor at the University of Iowa and an expert in business analytics and social network analysis, has created a match-making system based on a technique known as collaborative filtering.

Dr Zhao’s system looks at users’ behaviour as they browse a dating site for prospective partners, and at the responses they receive from people they contact.

“If you are a boy we identify people who like the same girls as you – which indicates similar taste – and people who get the same response from these girls as you do – which indicates similar attractiveness,” he explains.

Model of the word love on a laptop
Do opposites attract or does it come down to whether you share friends and musical taste?

Dr Zhao’s algorithm can then suggest potential partners in the same way websites like Amazon or Netflix recommend products or movies, based on the behaviour of other customers who have bought the same products, or enjoyed the same films.

Internet dating may be big business, but no-one has yet devised the perfect matching system. It may well be that the secret of true love is simply not susceptible to big data or any other type of analysis.

“Two people may have exactly the same iTunes history,” OKCupid’s Christian Rudder concludes, “but if one doesn’t like the other’s clothes or the way they look then there simply won’t be any future in that relationship.”

Originally posted via “Is big data dating the key to long-lasting romance?”

Originally Posted at: Is big data dating the key to long-lasting romance? by analyticsweekpick

Mar 08, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data Storage  Source

[ AnalyticsWeek BYTES]

>> Turning Business Users into Citizen Data Scientists by jelaniharper

>> The real-time machine for every business: Big data-driven market analytics by thomassujain

>> The Case For Pure Play Virtualization by analyticsweekpick

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[ NEWS BYTES]

>>
 The road to better health outcomes – The Daily Star Under  Big Data Analytics

>>
 Heico (HEI) Receiving Somewhat Positive Media Coverage, Analysis Shows – StockNewsTimes Under  Sentiment Analysis

>>
 Post Graduate Certificate Program in Business Analytics – Mathrubhumi English Under  Business Analytics

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Pattern Discovery in Data Mining

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Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners

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If you are looking for a book to help you understand how the machine learning algorithms “Random Forest” and “Decision Trees” work behind the scenes, then this is a good book for you. Those two algorithms are commonly u… more

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Strong business case could save your project
Like anything in corporate culture, the project is oftentimes about the business, not the technology. With data analysis, the same type of thinking goes. It’s not always about the technicality but about the business implications. Data science project success criteria should include project management success criteria as well. This will ensure smooth adoption, easy buy-ins, room for wins and co-operating stakeholders. So, a good data scientist should also possess some qualities of a good project manager.

[ DATA SCIENCE Q&A]

Q:What are feature vectors?
A: * n-dimensional vector of numerical features that represent some object
* term occurrences frequencies, pixels of an image etc.
* Feature space: vector space associated with these vectors

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@AnalyticsWeek Panel Discussion: Marketing Analytics

 @AnalyticsWeek Panel Discussion: Marketing Analytics

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In God we trust. All others must bring data. – W. Edwards Deming

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@BrianHaugli @The_Hanover ?on Building a #Leadership #Security #Mindset #FutureOfData #Podcast

 @BrianHaugli @The_Hanover ?on Building a #Leadership #Security #Mindset #FutureOfData #Podcast

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Facebook stores, accesses, and analyzes 30+ Petabytes of user generated data.

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2017 Trends in Data Modeling

The projected expansion of the data ecosystem in 2017 is causing extremely deliberate, systematic challenges for organizations attempting to exploit the most effective techniques available for maximizing data utility.

The plenitude of cognitive computing options, cloud paradigms, data science, and mobile technologies for big data has demonstrated its business value in a multitude of use cases. Pragmatically, however, its inclusion alongside conventional data management processes poses substantial questions on the back end pertaining to data governance and, more fundamentally, to data modeling.

Left unchecked, these concerns could potentially compromise any front-end merit while cluttering data-driven methods with unnecessary silos and neglected data sets. The key to addressing them lies in the implementation of swiftly adjustable data models which can broaden to include the attributes of the constantly changing business environments in which organizations compete.

According to TopQuadrant Executive VP and Director of TopBraid Technologies Ralph Hodgson, the consistency and adaptability of data modeling may play an even more dire role for the enterprise today:

“You have physical models and logical models, and they make their way into different databases from development to user acceptance into production. On that journey, things change. People might change the names of some of the columns of some of those data bases. The huge need is to be able to trace that through that whole assembly line of data.”

Enterprise Data Models
One of the surest ways to create a flexible enterprise model for a top down approach to the multiple levels of modeling Hodgson denoted is to use the linked data approach reliant upon semantic standards. Although there are other means of implementing enterprise data models, this approach has the advantages of being based on uniform standards applicable to all data which quickly adjust to include new requirements and use cases. Moreover, it has the added benefit of linking all data on an enterprise knowledge graph which, according to Franz CEO Jans Aasman, is one of the dominant trends to impact the coming year. “We don’t have to even talk about it anymore,” Aasman stated. “Everyone is trying to produce a knowledge graph of their data assets.”

The merit of a uniform data model for multiple domains throughout the enterprise is evinced in Master Data Management platforms as well; one can argue the linked data approach of ontological models merely extends that concept throughout the enterprise. In both cases, organizations are able to avoid situations in which “they spend so much time trying to figure out what the data model looks like and how do we integrate these different systems together so they can talk.” Stibo Systems Director of Vertical Solutions Shahrukh Arif claimed. “If you have it all in one platform, now you can actually realize that full value because you don’t have so spend so much time and money on the integrations and data models.”

Data Utility Models
The consistency of comprehensive approaches to data modeling are particularly crucial for cloud-based architecture or for incorporating data external to the enterprise. Frequently, organizations may encounter situations in which they must reconcile differences in modeling and metadata when attaining data from third-party sources. They can address these issues upfront by creating what DISCERN Chairman and CEO Harry Blount termed a “data utility model”, in which “all of the relevant data was available and mapped to all of the relevant macro-metadata, a metamodel I should say, and you could choose which data you want” from the third party in accordance with the utility model. Actually erecting such a model requires going through the conventional modeling process of determining business requirements and facilitating them through IT—which organizations can actually have done for them by competitive service providers. “Step one is asking all the right questions, step two is you need to have a federated, real-time data integration platform so you can take in any data in any format at any time in any place and always keep it up to date,” Blount acknowledged. “The third requirement is you need to have a scalable semantic graph structure.”

Relational Data Modeling (On-Demand Schema)
Data modeling in the relational world is increasingly impacted by the modeling techniques associated with contemporary big data initiatives. Redressing the inherent modeling disparities between the two is largely a means of accounting for semi-structured and unstructured data in relational environments primarily designed for structured data. Organizations are able to hurdle this modeling issue through the means of file formats which derive schema on demand. Options such as JSON and Avro are ideal for those who “want what is modeled in the big data world to align with what they have in their relational databases so they can do analytics held in their main databases,” Hodgson remarked.

One of the boons of utilizing Avro is the complete traceability it provides for data in relational settings—although such data may have originated from more contemporary unstructured sources associated with big data. The Avro format, and other files in this vein, allow modelers to traverse both relational schema requirements with what may be a lack of such schema intrinsic to most big data. According to Hodgson, Avro “still has the ontological connection, but it still talks in terms of property values and columns. It’s basically a table in the same sense you find in a spreadsheet. It’s that kind of table but the columns all align with the columns in a relational database, and those columns can be associated with a logical model which need not be an entity-relationship model. It can be an ontology.”

Predictive Models
Predictive models have been widely impacted by cognitive computing methods and other aspects of data science–although these two realms of data management are not necessarily synonymous with classic statistically-trained predictive models. Still, the influx of algorithms associated with various means of cognitive computing are paramount to the creation of predictive models which illustrate their full utility on unstructured big data sets at high velocities. Organizations can access entire libraries of machine learning and deep learning models from third-party vendors through the cloud, and either readily deploy them with their own data or “As a platform, we allow customers to build their own models or extend our models in service of their own specific needs” indico Chief Customer Officer Vishal Daga said.

The result is not only a dramatic reduction in the overall cost, labor, and salaries of hard to find data scientists to leverage cognitive computing techniques for predictive models, but also a degree of personalization—facilitated by the intelligent algorithms involved—enabling organizations to tailor those models to their own particular use cases. Thus, AI-centered SaaS opportunities actually reflect a predictive models on-demand service based on some of the most relevant data-centric processes to date.

Enterprise Representation
The nucleus of the enduring appositeness of data modeling is the increasingly complicated data landscape—including cognitive computing, a bevy of external data sources heralded by the cloud and mobile technologies in big data quantities—and the need to effectually structure data in a meaningful way. Modeling data is the initial step to gleaning its meaning and provides the basis for all of the different incarnations of data modeling, regardless of the particular technologies involved. However, there appears to be a burgeoning sense of credence associated with doing so on an enterprise-wide scale as “Knowing how data’s flowing and who it’s supporting, and what kind of new sources might make a difference to those usages, it’s all going to be possible when you have a representation of the enterprise,” Hodgson commented.

Adding further conviction to the value of enterprise data modeling is the analytic output facilitated by it. All-inclusive modeling techniques at the core of enterprise-spanning knowledge graphs appear well-suited for the restructuring of the data sphere caused by the big data disruption—particularly when paired with in-memory, parallel processing graph-aware analytics engines. “As modern data diversity and volumes grow, relational database management systems (RDBMS) are proving too inflexible, expensive and time-consuming for enterprises,” Cambridge Semantics VP of Engineering Barry Zane said. “Graph-based online analytical processing (GOLAP) will find a central place in everyday business by taking on data analytics challenges of all shapes and sizes, rapidly accelerating time-to-value in data discovery and analytics.”

 

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