Feb 27, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Conditional Risk  Source

[ AnalyticsWeek BYTES]

>> 4 Considerations for Bringing Predictive Capabilities to Market by analyticsweek

>> Aug 15, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> How to Choose a Feature Selection Method For Machine Learning by administrator

Wanna write? Click Here

[ FEATURED COURSE]

Introduction to Apache Spark

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Learn the fundamentals and architecture of Apache Spark, the leading cluster-computing framework among professionals…. more

[ FEATURED READ]

The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t

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People love statistics. Statistics, however, do not always love them back. The Signal and the Noise, Nate Silver’s brilliant and elegant tour of the modern science-slash-art of forecasting, shows what happens when Big Da… more

[ TIPS & TRICKS OF THE WEEK]

Fix the Culture, spread awareness to get awareness
Adoption of analytics tools and capabilities has not yet caught up to industry standards. Talent has always been the bottleneck towards achieving the comparative enterprise adoption. One of the primal reason is lack of understanding and knowledge within the stakeholders. To facilitate wider adoption, data analytics leaders, users, and community members needs to step up to create awareness within the organization. An aware organization goes a long way in helping get quick buy-ins and better funding which ultimately leads to faster adoption. So be the voice that you want to hear from leadership.

[ DATA SCIENCE Q&A]

Q:Give examples of data that does not have a Gaussian distribution, nor log-normal?
A: * Allocation of wealth among individuals
* Values of oil reserves among oil fields (many small ones, a small number of large ones)

Source

[ VIDEO 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

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

With data collection, ‘the sooner the better’ is always the best answer. – Marissa Mayer

[ PODCAST OF THE WEEK]

Discussing Forecasting with Brett McLaughlin (@akabret), @Akamai

 Discussing Forecasting with Brett McLaughlin (@akabret), @Akamai

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

More than 5 billion people are calling, texting, tweeting and browsing on mobile phones worldwide.

Sourced from: Analytics.CLUB #WEB Newsletter

Three Upcoming Talks on Big Data and Customer Experience Management

I have recently written on Big Data’s role in Customer Experience Management (CEM) and how companies can extract great insight from their business data when different types of business data are integrated with customer feedback data. I have been invited to share my thoughts on Big Data and Customer Experience Management at three upcoming conferences in May and June (see conference details below).

Big Data and CEM

Big Data refers to the idea that companies can extract value from collecting, processing and analyzing vast quantities of data. Businesses who can get a better handle on these data will be more likely to outperform their competitors who do not. In my talks, I will explore how businesses can apply Big Data principles to their existing business data, including customer feedback, to gain deeper customer insight. Incorporating customer feedback metrics into a Big Data strategy will help companies:

  1. answer bigger questions about their customers
  2. spread a customer-centric culture in the company through increased collaboration among different departments
  3. use objective measures of customer loyalty to build better predictive models

Upcoming Big Data Talks

These talks will be my first public appearances as the Chief Customer Officer of TCELab. Here are the presentation titles and corresponding conference information.

  • Using your Big Data to Improve Customer Loyalty (May 15): At VOCFusion - May 14-17, Las Vegas, NV.
  • Big Data has Big Implications for Customer Experience Management (May 18): At Score Conference - May 16-18, Boston, MA.
  • Integrating Customer Experience Metrics Into Your Big Data Plan (June 25): At Redes Sociales y sCRM – June 25-26, Bogota, Colombia.

I am looking forward to participating in each of these conferences. If you are attending any of these events, I hope to see you there!

Source: Three Upcoming Talks on Big Data and Customer Experience Management by bobehayes

How is AI Going to affect the Financial Industry in 2018?

Reading Time: 4 minutesGive this a thought: Could the 2008-10 recession in the US been prevented if they could forecast the stock market,  predict risks or detect frauds using machine learning and artificial intelligence? The answer lies in the ability of machines to perform diverse, intelligent tasks for us in the form of machine learning and artificial intelligence.

But how do machine learning and artificial intelligence impact the financial industry? The International Data Corporation (IDC) has predicted AI revenues to surge past $47 billion in 2020 and is poised to become the most important technology in the financial sector in India. Interestingly, Prime Minister Narendra Modi on February 18, stated that with AI, bots, and robots, productivity will increase. He emphasised that AI should be “Made in India” and “Made to Work for India,” thus making a strong case for professionals who want to upskill via machine learning certification.

This is set to give a massive boost to careers in artificial intelligence and machine learning. People trained in different branches of science,  mathematics or just a degree in technical engineering, can take up a machine learning course to gain a valuable machine learning certification.

The financial industry in India or the Banking, Financial Services and Insurance (BFSI) sector in India is a fast-evolving one. How then, do banks and associated organisations save time, costs and yet add value to their operations for smooth functioning? In India, Artificial Intelligence (AI) has begun to play a major role in solving some of the most vital problems faced by both companies as well as customers.  Not just banks, but nearly every company whether public or private in BFSI has started using AI.

Let’s explore how machine learning and artificial intelligence will impact the financial industry in 2018.

Advisory: Robo-advisory at fingertips

Source

Feb 20, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data security  Source

[ AnalyticsWeek BYTES]

>> 2017 Trends in Data Modeling by jelaniharper

>> Best Practices for Using Context Variables with Talend – Part 3 by analyticsweekpick

>> Does the Future Lie with Embedded BI? by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Data Mining

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Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations… more

[ FEATURED READ]

Antifragile: Things That Gain from Disorder

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Antifragile is a standalone book in Nassim Nicholas Taleb’s landmark Incerto series, an investigation of opacity, luck, uncertainty, probability, human error, risk, and decision-making in a world we don’t understand. The… 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 random forest? Why is it good?
A: Random forest? (Intuition):
– Underlying principle: several weak learners combined provide a strong learner
– Builds several decision trees on bootstrapped training samples of data
– On each tree, each time a split is considered, a random sample of m predictors is chosen as split candidates, out of all p predictors
– Rule of thumb: at each split m=?p
– Predictions: at the majority rule

Why is it good?
– Very good performance (decorrelates the features)
– Can model non-linear class boundaries
– Generalization error for free: no cross-validation needed, gives an unbiased estimate of the generalization error as the trees is built
– Generates variable importance

Source

[ VIDEO OF THE WEEK]

Discussing #InfoSec with @travturn, @hrbrmstr(@rapid7) @thebearconomist(@boozallen) @yaxa_io

 Discussing #InfoSec with @travturn, @hrbrmstr(@rapid7) @thebearconomist(@boozallen) @yaxa_io

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Numbers have an important story to tell. They rely on you to give them a voice. – Stephen Few

[ PODCAST OF THE WEEK]

@DrewConway on fabric of an IOT Startup #FutureOfData #Podcast

 @DrewConway on fabric of an IOT Startup #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Walmart handles more than 1 million customer transactions every hour, which is imported into databases estimated to contain more than 2.5 petabytes of data.

Sourced from: Analytics.CLUB #WEB Newsletter

Five Scales to Measure Customer Satisfaction

While customer satisfaction may be thought of as one concept, there’s isn’t a single “official” way to measure it.

By one estimate there are more than 40 instances of different customer satisfaction scales described in the published literature.

That, in part, is a consequence of how common satisfaction is as a measure. Satisfaction is measured on more than just brands, products, and features. It’s used to gauge one’s attitude toward one’s job, life, public service, and the quality of a relationship to name a few.

Satisfaction can be measured using either multiple items in a questionnaire or only a single item scale. And it’s the format of the scales themselves that is the subject of this article.

We’ve written about 15 different types of rating scales as well as about different ways to assess people’s satisfaction with a brand or a specific “attribute” satisfaction with products or features.

Across much of the published literature, satisfaction scales fall into three broad categories and subcategories:

  1. Satisfaction scales
    • Unipolar rating
    • Unipolar thermometer
    • Bipolar rating
  2. Performance scales
  3. Disconfirmation scales

Somewhat confusingly, satisfaction scales also have a subgroup called satisfaction scales. I’ve broken this group into three further subgroups (unipolar rating scales, unipolar thermometers, and bipolar rating scales) as some research suggests that they have different reliability and discriminating characteristics. That gives us five common ways you’ll see satisfaction measured, with some notes about how particular scales have performed in published research. All variations of these scales can be used in our MUIQ research platform.

  1. Unipolar Scales

When you think of satisfaction scales, unipolar scales are probably what you have in mind. In the unipolar scale, only satisfaction is considered (not dissatisfaction), and it’s typically presented on a continuum (sometimes fully labeled) from completely satisfied to not at all satisfied. These unipolar satisfaction scales typically have between 2 and 101 points or use a graphical scale with faces or a line or slider for participants to mark. Below is an example of a five-point fully labeled (no-numbers) version from Aiello and Czepiel (1979).

Unipolar scales can also be labeled on just the endpoints (a topic for a future article), such as the one below. With unipolar satisfaction scales you’re using only one concept — satisfaction — and aren’t including an opposite (usually dissatisfaction).

One possible advantage to using only satisfaction (and not dissatisfaction) is you avoid having a clear opposite item. As simple as it may sound, dissatisfaction may not be interpreted as the opposite of satisfaction by your respondents (depending on the context). If it’s not the opposite it may add more error (less reliability) in responses as people may interpret midpoints differently.

  1. Unipolar Feeling Thermometer

A special case of the unipolar rating scale is something that sort of acts like a satisfaction thermometer. Respondents are asked to select the percent satisfied they are, from not at all satisfied (0% satisfied) to completely satisfied (100% satisfied), which takes up 11 points on the scale.

For example, a study by Westbrook (1980) used an 11-point feeling thermometer with completely satisfied on the left. A more recent study by Maritz (2011) [pdf] used a web-based version and found that their unipolar feeling thermometer performed best relative to other satisfaction scales. Feeling thermometers can use fewer points (e.g., six points with 20% point increments), but with fewer points of discrimination they would likely be slightly less reliable and offer less discriminating capability.

  1. Bipolar Satisfaction

While looking quite similar, the bipolar satisfaction scale uses the putative opposites on the ends (poles) of the rating scales. This is usually an extreme version of satisfaction at one end and an extreme version of dissatisfaction at the other. While it seems that dissatisfaction is the natural opposite of satisfaction, there’s some “>evidence [pdf] that people treat dissatisfying events differently and that such events play an equally important (if not more important) role in loyalty. If dissatisfaction isn’t the opposite, then it also makes the neutral point more problematic.

Bipolar satisfaction scales don’t need to be anchored by only satisfaction and dissatisfaction. In a study by Westbrook in 1980, he used a Delighted to Terrible scale (shown below) and found that it had nominally higher reliability relative to other satisfaction scales he tested. Those results are interesting considering the study’s quite varied use of root words (satisfied, delighted, unhappy, terrible, and pleased).

There is some evidence [pdf] satisfaction includes both hedonic and utilitarian qualities. The Delighted to Terrible scale anchors may tap into the more hedonic qualities of satisfaction whereas the unipolar thermometers may be more utilitarian.

  1. Performance

A satisfaction performance scale asks participants to consider the adequacy/performance of a product or experience. It can be presented as an overall judgment or more specifically about attributes such as the following four-point scale adapted from a self-reported health rating scale.

Performance scales can also be used to gauge the quality of an experience. For example, they can be used to rate the overall quality of a hotel and more specifically the room, check-in experience, and hotel restaurant. For rating product performance this could be about a mobile phone overall and specifically about the battery life, camera quality, and user interface. Here are examples of two bipolar performance scales adapted from Churchill and Surprenant (1982).

  1. Disconfirmation

A likely less familiar but still important way to measure satisfaction is to understand how well expectations were met (confirmed) or not met (disconfirmed). Measures of disconfirmation tap into a simple idea of assessing how well a product or experience was better or worse than expected, such as the example below.

As with the other satisfaction scales, disconfirmation scales can be asked using different labels and scale points such as the one shown below, adapted from Churchill and Surprenant (1982).

There is some evidence that these disconfirmation scales may be a better measure of failing to meet expectations than comparing pre- and post-expectation measures.

And other research has also found that these disconfirmation scales may better correlate with customer retention than satisfaction or performance scales. (See Rust et al. 1994.)

Discussion and Takeaway

A review of the literature on measuring customer satisfaction revealed:

There are three common satisfaction scale types. It may be helpful to think of satisfaction scales as falling into three broad types: satisfaction scales, performance scales, and disconfirmation scales. Satisfaction scales are likely the most familiar to researchers and can be subdivided into unipolar rating scales, unipolar thermometers, and bipolar rating scales (providing five satisfaction scale types). Other factors will affect the reliability, validity, and sensitivity of satisfaction scales, including orientation (positive vs. negative options first), labeling (full vs. partial) and the number of scale points. This is the subject of many past and future articles.

Satisfaction may be multi-dimensional. While satisfaction may be thought of as a single construct, and in some contexts may be unidimensional, there is also evidence it has multiple dimensions in other contexts. Possible additional dimensions of satisfaction include dissatisfaction, hedonic vs. utilitarian satisfaction, performance, and disconfirmation. One emerging model is that performance drives disconfirmation, which in turn drives satisfaction. (See Oliver 1993 and Danaher and Haddrell 1996.)

Multiple measures may be better but longer. So which scale(s) do you use? The literature provides mixed results on which type of satisfaction scale is “best” and some studies — e.g., Churchill and Surprenant (1982) — suggest a combination of all three can provide a better picture of satisfaction. From a practical standpoint, however, researchers are often limited in the number of questions they can ask (especially about the same construct). So, while using more items will likely increase the reliability and validity of your scale (especially if satisfaction is multidimensional), it may be modest and not enough to offset the added load of more questions. If you are able, there’s little harm in asking all three types of satisfaction questions and then comparing results (and look for the connection between these complementary types of satisfaction).

There is no universal “best scale.” The published research provides mixed results on the quality of each of these satisfaction scales and which one is “best.” The unclear winner is most likely due to two main reasons. First, the scales are evaluated in often very different contexts, with different types of participants gauging things from consumer electronics, flu vaccines, restaurants, and hotels to name a few. That context suggests some scales (e.g., performance scales) may be more appropriate in some situations but not in others. The second reason is that the actual scales vary in their presentation style and confound the effects. The number of points, the labels used, whether they’re fully or partially labeled, the number of items, and the orientation (positive vs. negative first) make disentangling the effects more challenging.

Research into satisfaction is broad and ongoing. Some customer satisfaction articles I reviewed for this article date back fifty years, with many of the more influential papers appearing in the 1980s. My hope is that by delineating the distinction between the most common types of scales and knowing the relevant studies, other researchers can better select the right scale and address potential shortcomings or see how these different aspects complement each other to provide a better picture of satisfaction, loyalty, and future consumer behavior.

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Why Using the ‘Cloud’ Can Undermine Data Protections

By Jack Nicas

While the increasing use of encryption helps smartphone users protect their data, another sometime related technology, cloud computing, can undermine those protections.

The reason: encryption can keep certain smartphone data outside the reach of law enforcement. But once the data is uploaded to companies’ computers connected to the Internet–referred to as “the cloud”–it may be available to authorities with court orders.
“The safest place to keep your data is on a device that you have next to you,” said Marc Rotenberg, head of the Electronic Privacy Information Center. “You take a bit of a risk when you back up your device. Once you do that it’s on another server.”

Encryption and cloud computing “are two competing trends,” Mr. Rotenberg said. “The movement to the cloud has created new privacy risks for users and businesses. Encryption does offer the possibility of restoring those safeguards, but it has to be very strong and it has to be under the control of the user.”

Apple is fighting a government request that it help the Federal Bureau of Investigation unlock the iPhone of Syed Rizwan Farook, the shooter in the December terrorist attack in San Bernardino, Calif.

The FBI believes the phone could contain photos, videos and records of text messages that Mr. Farook generated in the final weeks of his life.

The data produced before then? Apple already provided it to investigators, under a court search warrant. Mr. Farook last backed up his phone to Apple’s cloud service, iCloud, on Oct. 19.

Encryption scrambles data to make it unreadable until accessed with the help of a unique key. The most recent iPhones and Android phones come encrypted by default, with a user’s passcode activating the unique encryption key stored on the device itself. That means a user’s contacts, photos, videos, calendars, notes and, in some cases, text messages are protected from anyone who doesn’t have the phone’s passcode. The list includes hackers, law enforcement and even the companies that make the phones’ software: Apple and Google.

However, Apple and Google software prompt users to back up their devices on the cloud. Doing so puts that data on the companies’ servers, where it is more accessible to law enforcement with court orders.

Apple says it encrypts data stored on its servers, though it holds the encryption key. The exception is so-called iCloud Keychain data that stores users’ passwords and credit-card information; Apple says it can’t access or read that data.

Officials appear to be asking for user data more often. Google said that it received nearly 35,000 government requests for data in 2014 and that it complies with the requests in about 65% of cases. Apple’s data doesn’t allow for a similar comparison since the company reported the number of requests from U.S. authorities in ranges in 2013.

Whether they back up their smartphones to the cloud, most users generate an enormous amount of data that is stored outside their devices, and thus more accessible to law enforcement.

“Your phone is an incredibly intricate surveillance device. It knows everyone you talk to, where you are, where you live and where you work,” said Bruce Schneier, chief technology officer at cybersecurity firm Resilient Systems Inc. “If you were required to carry one by law, you would rebel.”

Google, Yahoo Inc. and others store users’ emails on their servers. Telecom companies keep records of calls and some standard text messages.
Facebook
Inc. and Twitter Inc. store users’ posts, tweets and connections.

Even Snapchat Inc., the messaging service known for photo and video messages that quickly disappear, stores some messages. The company says in its privacy policy that “in many cases” it automatically deletes messages after they are viewed or expire. But it also says that “we may also retain certain information in backup for a limited period or as required by law” and that law enforcement sometimes requires it “to suspend our ordinary server-deletion practices for specific information.”

Snapchat didn’t respond to a request for comment.

Write to Jack Nicas at jack.nicas@wsj.com
(END) Dow Jones Newswires
02-18-161938ET
Copyright (c) 2016 Dow Jones & Company, Inc.

Originally Posted at: Why Using the ‘Cloud’ Can Undermine Data Protections by analyticsweekpick

Feb 13, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Human resource  Source

[ FEATURED COURSE]

Data Mining

image

Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations… more

[ FEATURED READ]

Thinking, Fast and Slow

image

Drawing on decades of research in psychology that resulted in a Nobel Prize in Economic Sciences, Daniel Kahneman takes readers on an exploration of what influences thought example by example, sometimes with unlikely wor… more

[ TIPS & TRICKS OF THE WEEK]

Data Analytics Success Starts with Empowerment
Being Data Driven is not as much of a tech challenge as it is an adoption challenge. Adoption has it’s root in cultural DNA of any organization. Great data driven organizations rungs the data driven culture into the corporate DNA. A culture of connection, interactions, sharing and collaboration is what it takes to be data driven. Its about being empowered more than its about being educated.

[ DATA SCIENCE Q&A]

Q:Give examples of bad and good visualizations?
A: Bad visualization:
– Pie charts: difficult to make comparisons between items when area is used, especially when there are lots of items
– Color choice for classes: abundant use of red, orange and blue. Readers can think that the colors could mean good (blue) versus bad (orange and red) whereas these are just associated with a specific segment
– 3D charts: can distort perception and therefore skew data
– Using a solid line in a line chart: dashed and dotted lines can be distracting

Good visualization:
– Heat map with a single color: some colors stand out more than others, giving more weight to that data. A single color with varying shades show the intensity better
– Adding a trend line (regression line) to a scatter plot help the reader highlighting trends

Source

[ VIDEO OF THE WEEK]

Big Data Introduction to D3

 Big Data Introduction to D3

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Everybody gets so much information all day long that they lose their common sense. – Gertrude Stein

[ PODCAST OF THE WEEK]

Solving #FutureOfOrgs with #Detonate mindset (by @steven_goldbach & @geofftuff) #FutureOfData #Podcast

 Solving #FutureOfOrgs with #Detonate mindset (by @steven_goldbach & @geofftuff) #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

And one of my favourite facts: At the moment less than 0.5% of all data is ever analysed and used, just imagine the potential here.

Sourced from: Analytics.CLUB #WEB Newsletter

Why Cloud and streaming will save the music business

Analysis: Despite rejection by acts including Taylor Swift the streaming model could be set to take off.

You’ve heard the story before: a music industry that couldn’t adapt quickly enough to digital and is paying the price.

Record labels report sluggish, declining revenues as artists struggle to make a decent buck from their hard work and become disillusioned with the process.

Nobody’s quite sure where to cast the blame. Lars Ulrich of thrash metal band Metallica notably attracted criticism for blaming the downloaders, while Thom Yorke of Radiohead blames the industry itself. Perhaps it’s time to end the blame game and start looking at where the solution is going to come from.

Since its peak in the late 1990s when the industry rode high on a cresting wave of CD sales, the industry has taken its time to realise more significant revenue streams from digital channels. According to the International Federation for the Phonographic Industry (IFPI), 2014 was the first year to see the industry deriving the same proportion of revenues from digital channels as physical format sales, at 46 percent.

Breaking down this digital chunk further reveals that revenues from downloads globally fell by 8 percent in 2014. It is streaming services where the music industry is seeing its biggest and most sustained growth. The IFPI estimates 41 million people paid for music subscription services in 2014, a fivefold increase since 2010. In addition, revenues had grown by 39 percent in 2014 and grew consistently across major markets.

Artists are divided on streaming.

Certainly Spotify in particular has attracted criticism for not paying artists enough. The business-minded Taylor Swift removed her music from Spotify, anticipating (correctly) that she would be able to sell huge numbers of her most recent album 1989. On the other hand, English singer-songwriter Ed Sheeran credited the streaming service with allowing him to sell out Wembley Stadium.

However the artists feel, the market is growing. Working behind the scenes, but well placed to benefit from the growing streaming market as much as any stakeholder, are B2B cloud-based music providers such as Omnifone. According to founder and Chief Engineer Phil Sant, streaming will see the industry getting back to health within a few years.

“The music industry sort of stumbled into digital and added digital on the side. It’s taken quite a while for it to recognise that it’s a digital business. We recognised that the music industry was going to turn digital eventually, and it’s actually only really pivoting now…it will end up many times bigger than it was what the peak of CDs.”

In fact, Sant argues that it is the immaturity of the streaming market that has held back revenues and hence royalties, which has been a primary bugbear of critics such as Beck.

“There are seven billion people on the earth. There are currently 30 million, growing quickly to 40 million, music subscribers. 7 billion minus 35 million is still 7 billion. It’s really in its infancy still.

“Imagine what’s going to happen when there are 1 billion subscribers, which is where it will get. There will be more money available to everybody – all the rights holders, all the authors and all the musicians.”

Omnifone works by collecting content from record labels, including recordings and accompanying materials. They then host this on Amazon Web Services.

“What we ingest from labels are their high-resolution assets – the highest possible quality they’ve got,” says Sant. “A studio quality master is about 300 MB, whereas if we delivered that to a tiny mobile phone in India it would be about 600k. We ingest that, the associated artwork, the meta-data and the usage data.

Quality, more than in many other industries, is key to music. Omnifone employs an expert audio engineer and ‘golden ears’, trained by James Guthrie, the man responsible for mastering and producing Pink Floyd’s ‘Dark Side of the Moon’. Alongside Spotify, Omnifone hosts Neil Young’s music service Pono. Carried on an idiosyncratic pyramid-shaped device, the service uses only high quality recordings. The ‘Heart of Gold’ hitmaker created Pono to tackle what he sees as the poor quality of MP3s and iTunes files.

“Although we’ve got the highest resolution available here, in the early days we were squeezing tracks down to the smallest format possible for tiny little feature phones,” Sant continues. “We found when dealing with thousands of labels that they all had different approaches to compression.

“We couldn’t give the users that. [The audio engineer] convinced me to go to the labels and convince them of the security, and we took lossless studio quality from them from day one. We have the biggest collection of 41 million lossless tracks in the world.”

The availability of such a large collection means that it’s not difficult for new players to launch into the market if they have a unique proposition. Omnifone removes the need for artists to collect their own music database, meaning that competition in this burgeoning market will remain healthy.

As adoption of subscription services increases, we should expect musicians and the industry to start taking it more seriously as a channel. This will mean better service, better revenues and ultimately, perhaps, better music.

 

Source: Why Cloud and streaming will save the music business

Big Data: Career Opportunities Abound in Tech’s Hottest Field

As big data continues to grow, companies around the world are on the hunt for technical recruits — a shift experts predict will continue through 2014 and beyond. WinterWyman’s NY Tech Search division, for example, has seen a 300% increase in demand for data scientists and engineers since 2013. The hottest sectors for big data growth are ad tech, financial services, ecommerce and social media

The hottest sectors for big data growth are ad tech, financial services, ecommerce and social media — those with the highest opportunity for revenue.

According to Dice, a few of the U.S. cities making the most big data hires include New York, Washington D.C./Baltimore area, Boston and Seattle.

Below is a quick guide for both companies and job seekers seeking big data opportunities.

Q&A

What is big data?
Big data is the advance in data management technology, which allows for an increase in the scale and manipulation of a company’s data. It allows companies to know more about their customers, products and their own infrastructure. More recently, people have become increasingly focused on the monetization of that data.

How can companies benefit by using big data; and more importantly, which industries use it?
Big data is everywhere; the volume of data produced, saved and mined is mind-boggling. Today, companies use data collection and analysis to formulate more cogent business strategies. This will continue to be an emerging area for all industries.

What is the current hiring landscape for big data? What are the salary ranges?
Currently, tech positions in big data are hard to fill because the demand is overwhelming and the talent pool is so small. It is difficult to find job candidates with the specific skill sets needed while balancing the cost of that talent. Companies need to ensure they can make money off of the “data” to justify offering candidates a large salary.

The highest demand is for data engineers who can code, utilize data analytics and manipulate for marketing purposes. The newest — and most sought-after role — is for data scientists who can integrate big data into both the company’s IT department and business functions.

These positions are all within a salary range of $90-$180,000

These positions are all within a salary range of $90-$180,000, depending on the individual role and experience. The typical time to hire is less than three weeks.

What is a data scientist?
Data scientists integrate big data technology into both IT departments and business functions. Many have a formal education in computer science and math, focusing on architecture/code, modeling, statistics and analytics. There is also a trend toward data-oriented master’s degree programs being offered at many colleges and universities. A data scientist must also understand the business applications of big data and how it will affect the business organization, and be able to communicate with IT and business management.

What can job seekers do to get the skills they need for a job in big data?
You need to be a marketable programmer already, or enroll in a program/school like General Assembly. To help make you more marketable to transition to a big data job, aim to work on projects using platforms like Hadoop or Mongo.

3 tips for hiring companies

  • 1. Don’t delay time to hire: These candidates have a lot of career options. They often have multiple job offers and are not on the market long. Wait too long, and they won’t be available.
  • 2. Promote your company: It’s good to have a distinct company culture, but candidates are more concerned with how their job will evolve, who they will work with, what technology they will use, etc. Be sure that you’re hitting all of these key points throughout the interview process and on your company website’s “careers” section.
  • 3. Practice flexibility when considering candidates’ qualifications: Because of the limited number of qualified candidates, companies must be open to considering candidates from different industries who have transferable job skills. Focus on a candidate’s potential to learn and grow with the company rather than strict prerequisites for hard skills.

3 tips for job seekers

  • 1. You have options: The market is strong, and this is a great time to be looking for employment. You have negotiating power when it comes to salary, benefits, etc.
  • 2. Contract or permanent jobs abound: Most job candidates can convert a contract/temporary position to permanent employment. There are more opportunities today than in the past few years to transition from a contract position to a full-time one. That being said, developing a strong portfolio of contract/freelance work can prove lucrative — you’ll need to decide what option works best for your needs, goals and schedule.
  • 3. Your technical skills are hot: People with strong tech backgrounds on their resumes are being bombarded with offers. You can afford to be selective about the company you decide to ultimately join.

Predictions for the future of big data

In my expert opinion, there will be a continued hiring demand for big data-related positions in industries such as mobile, healthcare and financial services — but industries that have the ability to monetize big data, such as ad tech, will likely have a longer, deeper and steeper hiring demand for big data-related positions.

What is so exciting is that big data applies to almost all industries. As a data scientist, you can work for any number of companies or industries. As an employer, it’s all about finding the right talent to fit your big data needs.

Originally posted via “Big Data: Career Opportunities Abound in Tech’s Hottest Field”

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Feb 06, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Human resource  Source

[ AnalyticsWeek BYTES]

>> Big Data Analytics Goes Hollywood by analyticsweekpick

>> Your essential weekly guide to Artificial Intelligence – July 17 by administrator

>> 2017 Trends in Big Data by jelaniharper

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[ FEATURED COURSE]

Applied Data Science: An Introduction

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As the world’s data grow exponentially, organizations across all sectors, including government and not-for-profit, need to understand, manage and use big, complex data sets—known as big data…. more

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Storytelling with Data: A Data Visualization Guide for Business Professionals

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Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You’ll discover the power of storytelling and the way to make data a pivotal point in your story. Th… more

[ TIPS & TRICKS OF THE WEEK]

Save yourself from zombie apocalypse from unscalable models
One living and breathing zombie in today’s analytical models is the pulsating absence of error bars. Not every model is scalable or holds ground with increasing data. Error bars that is tagged to almost every models should be duly calibrated. As business models rake in more data the error bars keep it sensible and in check. If error bars are not accounted for, we will make our models susceptible to failure leading us to halloween that we never wants to see.

[ DATA SCIENCE Q&A]

Q:Provide a simple example of how an experimental design can help answer a question about behavior. How does experimental data contrast with observational data?
A: * You are researching the effect of music-listening on studying efficiency
* You might divide your subjects into two groups: one would listen to music and the other (control group) wouldn’t listen anything!
* You give them a test
* Then, you compare grades between the two groups

Differences between observational and experimental data:
– Observational data: measures the characteristics of a population by studying individuals in a sample, but doesn’t attempt to manipulate or influence the variables of interest
– Experimental data: applies a treatment to individuals and attempts to isolate the effects of the treatment on a response variable

Observational data: find 100 women age 30 of which 50 have been smoking a pack a day for 10 years while the other have been smoke free for 10 years. Measure lung capacity for each of the 100 women. Analyze, interpret and draw conclusions from data.

Experimental data: find 100 women age 20 who don’t currently smoke. Randomly assign 50 of the 100 women to the smoking treatment and the other 50 to the no smoking treatment. Those in the smoking group smoke a pack a day for 10 years while those in the control group remain smoke free for 10 years. Measure lung capacity for each of the 100 women.
Analyze, interpret and draw conclusions from data.

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#BigData @AnalyticsWeek #FutureOfData #Podcast with Joe DeCosmo, @Enova

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Joe DeCosmo, @Enova

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[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

#FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

 #FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

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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%.

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