Jun 21, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data Storage  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Two More Customer Experience Facts and Suggestions You Can’t Ignore [INFOGRAPHIC] by bobehayes

>> What Is a Residential IP, Data Center Proxy and what are the Differences? by thomassujain

>> Ashok Srivastava(@aerotrekker) @Intuit on Winning the Art of #DataScience #FutureOfData #Podcast by admin

Wanna write? Click Here

[ NEWS BYTES]

>>
 Global BPO Business Analytics Market Share 2018 WNS Global … – The Mobile Herald Under  Business Analytics

>>
 Fund Me, KC: WISE IoT tech aims to lower energy bills, decrease carbon footprint – Startland News Under  IOT

>>
 Can State-of-the-Art Machine Learning Tools Give New Life to Household Survey Data? – Modern Diplomacy Under  Machine Learning

More NEWS ? Click Here

[ FEATURED COURSE]

Deep Learning Prerequisites: The Numpy Stack in Python

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The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence… more

[ FEATURED READ]

On Intelligence

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Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one strok… more

[ TIPS & TRICKS OF THE WEEK]

Winter is coming, warm your Analytics Club
Yes and yes! As we are heading into winter what better way but to talk about our increasing dependence on data analytics to help with our decision making. Data and analytics driven decision making is rapidly sneaking its way into our core corporate DNA and we are not churning practice ground to test those models fast enough. Such snugly looking models have hidden nails which could induce unchartered pain if go unchecked. This is the right time to start thinking about putting Analytics Club[Data Analytics CoE] in your work place to help Lab out the best practices and provide test environment for those models.

[ DATA SCIENCE Q&A]

Q:What is the life cycle of a data science project ?
A: 1. Data acquisition
Acquiring data from both internal and external sources, including social media or web scraping. In a steady state, data extraction and routines should be in place, and new sources, once identified would be acquired following the established processes

2. Data preparation
Also called data wrangling: cleaning the data and shaping it into a suitable form for later analyses. Involves exploratory data analysis and feature extraction.

3. Hypothesis & modelling
Like in data mining but not with samples, with all the data instead. Applying machine learning techniques to all the data. A key sub-step: model selection. This involves preparing a training set for model candidates, and validation and test sets for comparing model performances, selecting the best performing model, gauging model accuracy and preventing overfitting

4. Evaluation & interpretation

Steps 2 to 4 are repeated a number of times as needed; as the understanding of data and business becomes clearer and results from initial models and hypotheses are evaluated, further tweaks are performed. These may sometimes include step5 and be performed in a pre-production.

5. Deployment

6. Operations
Regular maintenance and operations. Includes performance tests to measure model performance, and can alert when performance goes beyond a certain acceptable threshold

7. Optimization
Can be triggered by failing performance, or due to the need to add new data sources and retraining the model or even to deploy new versions of an improved model

Note: with increasing maturity and well-defined project goals, pre-defined performance can help evaluate feasibility of the data science project early enough in the data-science life cycle. This early comparison helps the team refine hypothesis, discard the project if non-viable, change approaches.

Steps 2 to 4 are repeated a number of times as needed; as the understanding of data and business becomes clearer and results from initial models and hypotheses are evaluated, further tweaks are performed. These may sometimes include step5 and be performed in a pre-production.

Deployment

Operations
Regular maintenance and operations. Includes performance tests to measure model performance, and can alert when performance goes beyond a certain acceptable threshold

Optimization
Can be triggered by failing performance, or due to the need to add new data sources and retraining the model or even to deploy new versions of an improved model

Note: with increasing maturity and well-defined project goals, pre-defined performance can help evaluate feasibility of the data science project early enough in the data-science life cycle. This early comparison helps the team refine hypothesis, discard the project if non-viable, change approaches.

Steps 2 to 4 are repeated a number of times as needed; as the understanding of data and business becomes clearer and results from initial models and hypotheses are evaluated, further tweaks are performed. These may sometimes include step5 and be performed in a pre-production.

Deployment

Operations
Regular maintenance and operations. Includes performance tests to measure model performance, and can alert when performance goes beyond a certain acceptable threshold

Optimization
Can be triggered by failing performance, or due to the need to add new data sources and retraining the model or even to deploy new versions of an improved model

Note: with increasing maturity and well-defined project goals, pre-defined performance can help evaluate feasibility of the data science project early enough in the data-science life cycle. This early comparison helps the team refine hypothesis, discard the project if non-viable, change approaches.

Steps 2 to 4 are repeated a number of times as needed; as the understanding of data and business becomes clearer and results from initial models and hypotheses are evaluated, further tweaks are performed. These may sometimes include step5 and be performed in a pre-production.

Deployment

Operations
Regular maintenance and operations. Includes performance tests to measure model performance, and can alert when performance goes beyond a certain acceptable threshold

Optimization
Can be triggered by failing performance, or due to the need to add new data sources and retraining the model or even to deploy new versions of an improved model

Note: with increasing maturity and well-defined project goals, pre-defined performance can help evaluate feasibility of the data science project early enough in the data-science life cycle. This early comparison helps the team refine hypothesis, discard the project if non-viable, change approaches.

Source

[ VIDEO OF THE WEEK]

Andrea Gallego(@risenthink) / @BCG on Managing Analytics Practice #FutureOfData #Podcast

 Andrea Gallego(@risenthink) / @BCG on Managing Analytics Practice #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The goal is to turn data into information, and information into insight. – Carly Fiorina

[ PODCAST OF THE WEEK]

Dave Ulrich (@dave_ulrich) talks about role / responsibility of HR in #FutureOfWork #JobsOfFuture #Podcast

 Dave Ulrich (@dave_ulrich) talks about role / responsibility of HR in #FutureOfWork #JobsOfFuture #Podcast

Subscribe 

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[ FACT OF THE WEEK]

73% of organizations have already invested or plan to invest in big data by 2016

Sourced from: Analytics.CLUB #WEB Newsletter

25 quotes for anyone seeking customer service excellence

25 quotes for anyone seeking customer service excellence
25 quotes for anyone seeking customer service excellence

Increasingly, the CEO’s of all the progressive organizations are focusing on customer experience as a competitive business differentiator. If not already, it should be high on the list of every company aspiring to be successful. Need some convincing? Take a look at the following 25 inspiring and useful customer service quotes as said by CEO’s of company we all admire:

  1. “Your most unhappy customers are your greatest source of learning.”- Bill Gates
    This quote resonates well for a growing organization as customers should be the source of all learning and innovation in an enterprise.
  2. “It is not the strongest of the species that survives, nor the most intelligent, but the one most responsive to change.” –Charles Darwin
    I love the way how Charles Darwin has simply explained one of the most important concepts in the history of human evolution. This applies very well to business evolution.
  3. “Well done is better than well said.” –Benjamin Franklin
    As described in this quote, the importance of successful execution cannot be undermined and understated.
  4. “Spend a lot of time talking to customers face to face. You’d be amazed how many companies don’t listen to their customers.” –Ross Perot
    It’s a great quote that addresses a big concern that needs to be fixed by large organizations, if they want to maintain their edge in the future.
  5. “If you do build a great experience, customers tell each other about that. Word of mouth is very powerful.”
    -Jeff Bezos, CEO Amazon.com
    Amazon is a pioneer in customer experience and clearly outlines its marketing edge – word of mouth driven by happy customers
  6. “The customer experience is the next competitive battleground.” –Jerry Gregoire, CIO, Dell Computers
    Today customer experience is a key differentiating factor for a large number of commodity products.
  7. “Quality in a service or product is not what you put into it. It is what the client or customer gets out of it.”
    -Peter Drucker
    Quality of a product needs to be seen and defined from the eyes of the customer. Customer should be able to experience the value and the quality and decide its worthiness.
  8. “Customers don’t expect you to be perfect. They do expect you to fix things when they go wrong.” –Donald Porter, V.P. British Airways
    This clearly defined the new customers of the current era. Customers understand that companies are made of people/ humans and want to connect with them and get help when they need it the most.
  9. “The more you engage with customers the clearer things become and the easier it is to determine what you should be doing.” –John Russell, President, Harley Davidson
    It is absolutely true that staying close to the customers would always help an organization learn and adapt to compete effectively.
  10. “You’ll never have a product or price advantage again. They can be easily duplicated, but a strong customer service culture can’t be copied.” –Jerry Fritz
    We all understand that true strategic advantage is difficult to imitate. Customer service is one of those things that can deliver strong competitive advantage. We all know the examples of Apple, Southwest airlines etc.
  11. “In the world of Internet Customer Service, it’s important to remember your competitor is only one mouse click away.” –Doug Warner
    We should remember that we live in a digital era and customers have a lot of choice when it comes to making speedy online decisions. It is inevitable to have a strong online presence for any successful business.
  12. “Know what your customers want most and what your company does best. Focus on where those two meet.” –Kevin Stirtz
    This is where the rubber meets the road. It is important for a company to know its strengths and understand its customers to deliver a successful product.
  13. “Loyal customers, they don’t just come back, they don’t simply recommend you, they insist that their friends do business with you.” –Chip Bell, Founder Chip Bell Group
    This is the reason why businesses want loyal customers. Apple has done a great job at this.
  14. “Customer service is not a department, it’s everyone’s job.” –Anonymous
    Today, customer service is a differentiator for a business and it cannot be left only in the hands of the customer representatives. Everyone in the company should have an ear and an eye for improving customer experience.
  15. “When people talk about successful retailers and those that are not so successful, the customer determines at the end of the day who is successful and for what reason.” –Jerry Harvey
    The ultimate power lies in the hands of the customers and they are responsible for making a product/ service successful.
  16. “A lot of companies have chosen to downsize, and maybe that was the right thing for them. We chose a different path. Our belief was that if we kept putting great products in front of customers, they would continue to open their wallets.” –Steve Jobs
    Apple is a symbol of customer focused innovation and great customer experience. This is something that competition and businesses should understand and learn from.
  17. “You’ve got to look for a gap, where competitors in a market have grown lazy and lost contact with the readers or the viewers.” –Rupert Murdoch
    Competitive research should focus on the customer experience gaps to innovate and differentiate on new and existing products.
  18. “Excellent firms don’t believe in excellence – only in constant improvement and constant change.” –Tom Peters
    Change is the true underdog that if done correctly can lead to success and profit.
  19. “Every contact we have with a customer influences whether or not they’ll come back. We have to be great every time or we’ll lose them.” –Kevin Stirtz
    This is the new normal for today’s customer. And, this needs to be followed to sustain the customer base.
  20. “Here is a simple but powerful rule: always give people more than what they expect to get.” –Nelson Boswell
    This has been widely tested and understood, but difficult to follow and execute. But, if done right, it always pays off.
  21. “Statistics suggest that when customers complain, business owners and managers ought to get excited about it. The complaining customer represents a huge opportunity for more business.” –Zig Ziglar
    Data is true and statisticians are helpful. A complaining customer is giving you one more chance to improve before he leaves you and goes to the competitor. So, do listen to him and help him.
  22. “Every client you keep, is one less that you need to find.” –Nigel Sanders
    This is the mantra to maintaining your growth and profitability.
  23. “If you make customers unhappy in the physical world, they might each tell 6 friends. If you make customers unhappy on the Internet, they can each tell 6,000 friends” –Jeff Bezos
    The new digital world comes with its own benefits and disadvantages and an unhappy digitally savvy customer is the last thing a business wants.
  24. “If we keep doing what we’re doing, we’re going to keep getting what we’re getting.” –Stephen Covey
    Great quote to describe the needs for change in this changing world.
  25. “We don’t want to push our ideas on to customers, we simply want to make what they want.” –Laura Ashley
    This is another quote that tells the power of the customers and the need to stay close to them to learn, innovate and provide them what they want. This is a win win for everyone.

I loved writing this blog and hope that you enjoy reading it.

Source

The First and Only – Big Data Search Engine Powered by Apache® Spark™

PALO ALTO, California — June 18, 2015 — Maana, a pioneer in search engine technology for big-data-fueled solutions, today announced it has successfully built its industry agnostic, end-to-end search and discovery platform on Apache® Spark™. Spark enables Maana to perform massive-scale processing for machine learning and data mining. With Spark, Maana successfully overcomes the limitations of Hadoop’s MapReduce framework, especially with regard to performance and integration.

“Maana is the first and only big data search engine powered by Spark. Switching to Spark was a business decision we didn’t take lightly, and afterwards we never looked back,” said Babur Ozden, founder and CEO of Maana.

Maana runs natively on Spark and uses its in-memory caching, which enables Maana to re-use parts of the computation. The in-memory caching is designed to increase application performance by holding frequently requested data in memory, reducing the need for database queries to get that data.

“Maana has been running on Spark for over a year and a half. For Maana, working with extremely large technical datasets from numerous different sources requires both speed and sophisticated analysis. We weren’t getting the results with Hadoop MapReduce v1. Spark, the industry standard, is easier to use and the best alternative to the more exotic solutions available,” said Donald Thompson, founder and CTO of Maana. “The growing ecosystem around Spark allows our data scientists and our customers to use the languages and tools they are already familiar with.”

About Maana

Maana is pioneering new search technology for big data. It helps corporations drive significant improvements in productivity, efficiency, safety, and security in the operations of their core assets. Investors include: Chevron Technology Ventures, ConocoPhillips Technology Ventures, Frost Data Capital, GE Ventures, and Intel Capital. Maana is privately held with offices in Palo Alto, California and Bellevue, Washington. Visit us at www.maana.io.

To read the original press release on Maana, click here.

Originally Posted at: The First and Only – Big Data Search Engine Powered by Apache® Spark™

Jun 14, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Complex data  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ NEWS BYTES]

>>
 I want to be a data scientist … what will my salary be? – The Globe and Mail Under  Data Scientist

>>
 The Impact of Empathy On Data Security And Productivity – Forbes Under  Data Security

>>
 Social Media Content Producer – Pedestrian TV Under  Social Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Hadoop Starter Kit

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Hadoop learning made easy and fun. Learn HDFS, MapReduce and introduction to Pig and Hive with FREE cluster access…. 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]

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:Do you think 50 small decision trees are better than a large one? Why?
A: * Yes!
* More robust model (ensemble of weak learners that come and make a strong learner)
* Better to improve a model by taking many small steps than fewer large steps
* If one tree is erroneous, it can be auto-corrected by the following
* Less prone to overfitting

Source

[ VIDEO OF THE WEEK]

Andrea Gallego(@risenthink) / @BCG on Managing Analytics Practice #FutureOfData #Podcast

 Andrea Gallego(@risenthink) / @BCG on Managing Analytics Practice #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

If you can’t explain it simply, you don’t understand it well enough. – Albert Einstein

[ PODCAST OF THE WEEK]

Nick Howe (@Area9Nick @Area9Learning) talks about fabric of learning organization to bring #JobsOfFuture #Podcast

 Nick Howe (@Area9Nick @Area9Learning) talks about fabric of learning organization to bring #JobsOfFuture #Podcast

Subscribe 

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[ FACT OF THE WEEK]

According to execs, the influx of data is putting a strain on IT infrastructure. 55 percent of respondents reporting a slowdown of IT systems and 47 percent citing data security problems, according to a global survey from Avanade.

Sourced from: Analytics.CLUB #WEB Newsletter

Tips To Hunt For That Great Travel Deal [video]

Tips To Hunt For That Great Travel Deal Have you ever found yourself in a flux chasing after websites, agents, travel blogs, coupons to find your great travel deal? I am no different and spend good chunk of hours on travel deal hunting. I came across this amazing video by Jason Cochran on WalletPop.com, Jason walks us through easy to follow steps, helping us get to that great travel deal faster. Hope these tips will help you get to your travel-deal faster as well. These are great suggestions, I have been using few of them myself, and they work great.

Let me know if there are any other tips/tricks that you use and are not covered in the video below.

Source: Tips To Hunt For That Great Travel Deal

Jun 07, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data Accuracy  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ NEWS BYTES]

>>
 Understanding The Role Of Artificial Intelligence In Payments – Forbes Under  Artificial Intelligence

>>
 Streaming Analytics Market Overview and Revenue Till 2024 – Business Services Under  Streaming Analytics

>>
 Big-data stewardship takes the main stage at DataWorks Summit in Berlin – SiliconANGLE (blog) Under  Big Data Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Pattern Discovery in Data Mining

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Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern disc… more

[ FEATURED READ]

On Intelligence

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Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one strok… more

[ TIPS & TRICKS OF THE WEEK]

Grow at the speed of collaboration
A research by Cornerstone On Demand pointed out the need for better collaboration within workforce, and data analytics domain is no different. A rapidly changing and growing industry like data analytics is very difficult to catchup by isolated workforce. A good collaborative work-environment facilitate better flow of ideas, improved team dynamics, rapid learning, and increasing ability to cut through the noise. So, embrace collaborative team dynamics.

[ DATA SCIENCE Q&A]

Q:Given two fair dices, what is the probability of getting scores that sum to 4? to 8?
A: * Total: 36 combinations
* Of these, 3 involve a score of 4: (1,3), (3,1), (2,2)
* So: 3/36=1/12
* Considering a score of 8: (2,6), (3,5), (4,4), (6,2), (5,3)
* So: 5/36

Source

[ VIDEO OF THE WEEK]

Jeff Palmucci @TripAdvisor discusses managing a #MachineLearning #AI Team

 Jeff Palmucci @TripAdvisor discusses managing a #MachineLearning #AI Team

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

@CRGutowski from @GE_Digital on Using #Analytics to #Transform Sales #FutureOfData #Podcast

 @CRGutowski from @GE_Digital on Using #Analytics to #Transform Sales #FutureOfData #Podcast

Subscribe 

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[ 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

Twitter Cuts Off DataSift To Step Up Its Own Big Data Business

In the push for more revenue growth, Twitter has been building up its business in areas like advertising and commerce, but a move made late Friday night points to another area where the company is setting its sights: big data analytics.

Twitter announced that it will be terminating agreements with third parties for reselling firehose data — the unfiltered, full stream of Tweets and all related metadata that goes along with them.

Instead, it will use its own in-house big data analytics team, which it developed around its acquisition of Gnip in 2014, to seek to build direct relationships with the data companies, brands and others that use Twitter data to measure consumer sentiment, market trends and other moving targets that can be better understood by tracking online conversations — a transition it says it hopes to have completed by mid-August.

DataSift, the biggest company to be affected by Twitter’s move, services thousands of businesses who in turn serve thousands more. Unsurprisingly it moved quickly to post its own reaction to the termination and its own determination to push ahead in its own business.

NTT Data, which deals only in Japanese Tweets, is still listed as a Twitter firehose partner at the time of writing, but Twitter has confirmed to me that NTT is also affected by Friday’s announcement.

This is both a very unsurprising and sudden move, from the looks of it.

Talking to Nick Halstead, the CEO and founder of DataSift, he said that his company was “blindsided” by Twitter’s announcement, which it made without any warning to DataSift. He said that before this, the pair had been discussing a renewal of the deal. And while DataSift recently added Facebook — Twitter’s big social advertising competitor — as a firehose partner, it didn’t appear that this would impact those discussions.

“We were in the middle of negotiations with everything pointing to Twitter wanting to still continue to be a part of an open ecosystem,” he said, “but this is clearly now not true.”

On the other hand, for those who have been following how Twitter has grown as a business, the company’s move to cut off third-party firehose relationships should come as no surprise.

The company has made no secret of its bigger philosophy about how it interfaces with third parties in general. In its (in)famous ‘quadrant’ diagram, the company outlined its position towards third parties that added value to what Twitter was doing versus those that effectively overlapped with Twitter’s own efforts: those who were building Twitter clients that “mimic” Twitter’s own experience in reproducing the Twitter stream were getting cut off.

You can think of last year’s move by Twitter to acquire Gnip — another firehose reseller who competed with DataSift — as a step Twitter was taking to move its interests into one more area of that quadrant.

At the time the acquisition was seen mainly as a response to Apple’s acquisition of Topsy, who had been another firehose partner. And DataSift went so far as to reassure people that its status with Twitter would not be affected. But now it’s clear that Twitter had other things in mind, too.

Zach Hofter-Shall, head of Twitter ecosystem, said as much in his blog post late Friday:

 “One of the reasons Twitter acquired Gnip was because Twitter believes the best way to support the distribution of Twitter data is to have direct data relationships with its data customers – the companies building analytic solutions using Twitter’s data and platform,” he wrote. “Direct relationships help Twitter develop a deeper understanding of customer needs, get direct feedback for the product roadmap, and work more closely with data customers to enable the best possible solutions for the brands that rely on Twitter data to make better decisions…The acquisition of Gnip was the first step toward developing more direct relationships with data customers.”

In fact, whether they wanted to believe it or not, these companies were told by Twitter that they would be getting cut off nearly a year ago, we understand.

The direct relationships Twitter has with data customers, meanwhile, are also starting to take a new kind of form. Just last month, Twitter made its first investment in a startup through its new investment vehicle. The recipient? Dataminr, one of the companies that analyses Twitter firehose data, in its case to track news and financial data.

The reason why Twitter wants to tap into more big data business, of course, comes down to one big reason: money.

Since going public, Twitter has regularly faced questions about user growth. On one hand, that has led it to many iterations as it tries to snag more consumers who are not already regular Twitter users. On the other, it has increasingly focused on ways that it can better monetise what it already has.

That’s where the big-data services come in. Twitter’s data firehose, from what we understand, makes up a relatively small portion of DataSift’s revenues. The company makes 20% of its revenues from licensing data, with that data including Twitter but also more than 20 other networks. The remaining 80% comes from data processing.* Cutting off the firehose to DataSift, Twitter hopes, will potentially give it access to (and better returns on) the customer deals that DataSift held before.

(The big question now will be whether Twitter manages to convince enough of the people who used to buy data through DataSift to turn directly to Twitter for those needs instead.)

“Twitter believes that creating a closed market for their data allows them to generate more revenue,” Halstead told TechCrunch. “We believe and others believe that an open ecosystem is important for a brand to understand what is going on in the market.”

As for where DataSift is turning next, the company says it is signing on more social networks to provide its own firehose data feeds. No comment from DataSift on which feed will be next, but it’s notable LinkedIn is not yet a partner. The social network for the working world is clearly looking for more ways of using its data for analytics, and this seems an obvious route to do that.

DataSift is also still able to work with Twitter data: if a third party buys data from Twitter, it can supply it to DataSift by way of a “connector” so that it can still be parsed by DataSift’s algorithms. However, this will mean significantly lower revenues for DataSift in the process from that feed. And  armed with its Facebook deal and other developments in the pipeline, DataSift is pressing ahead with business. The company is currently in the process of raising a new round of funding — a Series D round. To date, DataSift has raised nearly $78 million.

Update: Mark Suster writes that DataSift returns 95% of data revenue back to Twitter. The 20/80 ratio we were describing referred to revenues from data firehose licensing versus data processing revenues for DataSift. We’ve updated the passage above to clarify this.

Originally posted via “Twitter Cuts Off DataSift To Step Up Its Own Big Data Business”

Originally Posted at: Twitter Cuts Off DataSift To Step Up Its Own Big Data Business by analyticsweekpick

December 12, 2016 Health and Biotech analytics news roundup

Here’s the latest in health and biotech analytics:

Potential for wide-scale whole-genome sequencing in humans using nanopore approaches: Researchers have recently sequenced an entire human genome using Oxford Nanopore’s hand-held MinION devices. These instruments cost less than $1000 plus consumables, and have the potential to change the economics of sequencing.

Advanced Plan for Health Upgrades Next-Generation Predictive Analytics On Poindexter Population Health Management Platform: The platform currently assigns broad risk scores to patients based on demographic data. Now, the company claims to be able to predict the likelihood of specific catastrophic illnesses.

Can Blockchain Give Healthcare Payers Better Analytical Insight?: Jennifer Resnick summarizes a report from Deloitte on the technology. Potential uses include reducing overhead, detecting fraud, and managing provider directories.

Health Tech 2016: A Year To Recalibrate: David Shaywitz writes about the apparent gap between the promise of personalized medicine and actual progress. He understands that new techniques need to actually show real-world value, yet also sees the need for optimism as an impetus for advancement

Originally Posted at: December 12, 2016 Health and Biotech analytics news roundup by pstein

May 31, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Correlation-Causation  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Jul 27, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> Creating “Data Culture” with Self-Service Analytics by jelaniharper

>> Digital currencies – The risk of relying on human perfection by checcaaird

Wanna write? Click Here

[ NEWS BYTES]

>>
 38% Of Gen Z And Millennials Trust Digital Influencers, Says … – Tubefilter Under  Social Analytics

>>
 The Courier » Blanchard Valley receives top 100 hospital award – The Courier Under  Health Analytics

>>
 Improving the Customer Experience Takes Behind-The-Scenes Work – Customer Think Under  Customer Experience

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]

On Intelligence

image

Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one strok… 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:Given two fair dices, what is the probability of getting scores that sum to 4? to 8?
A: * Total: 36 combinations
* Of these, 3 involve a score of 4: (1,3), (3,1), (2,2)
* So: 3/36=1/12
* Considering a score of 8: (2,6), (3,5), (4,4), (6,2), (5,3)
* So: 5/36

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A Good Patient Experience Does not Start with Medical Spending

Patient experience (PX) has become an important topic for US hospitals. The Centers for Medicare & Medicaid Services (CMS) will be using patient feedback about their care as part of their reimbursement plan for acute care hospitals (see Hospital Value-Based Purchasing (VBP) program). According to QualityNet, the purpoase of the VBP program is to promote better clinical outcomes for patients and improve their experience of care during hospital stays. Not surprisingly, hospitals are focusing on improving the patient experience to ensure they receive the maximum of their incentive payments. But what is the cost of a good patient experience? Does increased hospital spending on medical services translate into a better patient experience?

Medicare Spending Per Beneficiary (MSPB)

Medicare tracks how much they spend on each patient with Medicare who is admitted to a hospital compared to the amount Medicare spends per hospital patient nationally. Also known as “Medicare Spending per Beneficiary (MSPB)”, this measure assesses the cost of care. By measuring cost of care with this measure, CMS hopes to increase the transparency of care for consumers and recognize hospitals that are involved in the provision of high-quality care at lower cost to Medicare.

The MSPB measure for each hospital is calculated as the ratio of the MSPB Amount for the hospital divided by the median MSPB Amount across all hospitals. A hospital with an MSPB value of 1.o indicates that the hospital’s spending per patient is average. A hospital with an MSPB value less than 1.0 indicates that the hospital’s spending per patient is less than the average hospital. A hospital with an MSPB value greater than 1.0 indicates that the hospital’s spending per patient is greater than the average hospital.

Figure 1. Patient Loyalty by Medicare Spending per Beneficiary.

Patient Experience

Patient experience (PX) reflects the patients’ perceptions about their recent inpatient experience. PX is collected by a survey known as HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems). HCAHPS (pronounced “H-caps“) is a national, standardized survey of hospital patients and was developed by a partnership of public and private organizations and was created to publicly report the patient’s perspective of hospital care.

The survey asks a random sample of recently discharged patients about important aspects of their hospital experience. The data set includes patient survey results for over 3800 US hospitals on ten measures of patients’ perspectives of care (e.g., nurse communication, pain well controlled). I combined two general questions (Overall hospital rating and recommend) to create a patient advocacy metric. Thus, a total of 9 PX metrics were used. Across all 9 metrics, hospital scores can range from 0 (bad) to 100 (good). You can see the PX measures for different US hospital here.

Figure 2. Patient Experience by Medicare Spending per Beneficiary

The Relationship Between Medicare Spend and Patient Loyalty/Experience

Hospitals were divided into 10 groups based on their MSPB score. Figure 1 contains the plot of patient advocacy for each of the 10 MSPB levels. Figure 2 contains the plot of patient experience ratings for each of the 10 MSPB levels.

There were statistically significant differences across the 10 segments.  Although these differences were statistically significant, the differences were not substantial. Specifically, the MSPB segments accounted for about 4.5% of the variance in patient metrics.

We might expect hospitals that spend more on medical services per patient would receive higher patient experience ratings. The patients are, after all, receiving more resources directed toward them compared to hospitals who spend less on medical services. If anything, we see that as the cost of care goes up, patient experience actually decreases (however slightly).

Improving Patient Loyalty and the Patient Experience

Improving patient loyalty and the patient experience clearly does not start with medical spend. The results show that hospitals who spend less on medical services compared to other hospitals who spend more on medical services receive comparable marks on their patient experience and patient loyalty scores.
Table 1. Adoption Rates of Customer Feedback Program Practices of Loyalty Leaders and Loyalty Laggards
Figure 3. Adoption Rates of Customer Feedback Program Practices of Loyalty Leaders and Loyalty Laggards

One possible approach to understand patient experience/loyalty differences across hospitals is to understand how hospitals build their patient experience (PX) programs. How mature is their PX program? Do they even have a PX program? In other industries, we know that loyalty leading companies structure their customer experience programs differently than loyalty lagging companies (see Figure 3). Specifically, loyalty leaders: 1) have top executive support of the customer program, 2) communicate all aspects of the program throughout the company and 3) integrate their customer feedback with other business data for deep dive customer research. I suspect these same processes (or something similar) are key to a successful PX program (e.g., high patient loyalty and patient experience) in the hospital setting. But that is an empirical question.

Research on the role of PX programs in hospitals would help hospitals better understand the necessary ingredients they need to improve the patient experience. Many hospitals receive very low marks on their HCAHPS ratings, suggesting they will be penalized on their Medicare payments. This PX program research needs to identify best practices. Findings could help individual hospitals improve their HCAHPS score by adopting/incorporating best practices into their PX programs. Additionally, findings could help the healthcare industry overall by sharing best practices across all hospitals that would remove inefficiencies in healthcare delivery while improving patient satisfaction with their care.

Source: A Good Patient Experience Does not Start with Medical Spending