Jul 18, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data shortage  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

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

>> Are APIs becoming the keys to customer experience? by analyticsweekpick

>> Could these 5 big data projects stop climate change? by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

Process Mining: Data science in Action

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Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be ap… more

[ FEATURED READ]

The Misbehavior of Markets: A Fractal View of Financial Turbulence

image

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]

Keeping Biases Checked during the last mile of decision making
Today a data driven leader, a data scientist or a data driven expert is always put to test by helping his team solve a problem using his skills and expertise. Believe it or not but a part of that decision tree is derived from the intuition that adds a bias in our judgement that makes the suggestions tainted. Most skilled professionals do understand and handle the biases well, but in few cases, we give into tiny traps and could find ourselves trapped in those biases which impairs the judgement. So, it is important that we keep the intuition bias in check when working on a data problem.

[ DATA SCIENCE Q&A]

Q:Explain selection bias (with regard to a dataset, not variable selection). Why is it important? How can data management procedures such as missing data handling make it worse?
A: * Selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved
Types:
– Sampling bias: systematic error due to a non-random sample of a population causing some members to be less likely to be included than others
– Time interval: a trial may terminated early at an extreme value (ethical reasons), but the extreme value is likely to be reached by the variable with the largest variance, even if all the variables have similar means
– Data: “cherry picking”, when specific subsets of the data are chosen to support a conclusion (citing examples of plane crashes as evidence of airline flight being unsafe, while the far more common example of flights that complete safely)
– Studies: performing experiments and reporting only the most favorable results
– Can lead to unaccurate or even erroneous conclusions
– Statistical methods can generally not overcome it

Why data handling make it worse?
– Example: individuals who know or suspect that they are HIV positive are less likely to participate in HIV surveys
– Missing data handling will increase this effect as it’s based on most HIV negative
-Prevalence estimates will be unaccurate

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]

You can have data without information, but you cannot have information without data. – Daniel Keys Moran

[ PODCAST OF THE WEEK]

@AlexWG on Unwrapping Intelligence in #ArtificialIntelligence #FutureOfData #Podcast

 @AlexWG on Unwrapping Intelligence in #ArtificialIntelligence #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

In the developed economies of Europe, government administrators could save more than €100 billion ($149 billion) in operational efficiency improvements alone by using big data, not including using big data to reduce fraud and errors and boost the collection of tax revenues.

Sourced from: Analytics.CLUB #WEB Newsletter

Jul 11, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Statistics  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Aureus at InsureTech Connect 2017, Las Vegas by analyticsweek

>> Are You Headed for the Analytics Cliff? by analyticsweek

>> Big data: The critical ingredient by analyticsweekpick

Wanna write? 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]

The Future of the Professions: How Technology Will Transform the Work of Human Experts

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This book predicts the decline of today’s professions and describes the people and systems that will replace them. In an Internet society, according to Richard Susskind and Daniel Susskind, we will neither need nor want … 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:What is the Law of Large Numbers?
A: * A theorem that describes the result of performing the same experiment a large number of times
* Forms the basis of frequency-style thinking
* It says that the sample mean, the sample variance and the sample standard deviation converge to what they are trying to estimate
* Example: roll a dice, expected value is 3.5. For a large number of experiments, the average converges to 3.5

Source

[ VIDEO OF THE WEEK]

@Schmarzo @DellEMC on Ingredients of healthy #DataScience practice #FutureOfData #Podcast

 @Schmarzo @DellEMC on Ingredients of healthy #DataScience practice #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data are becoming the new raw material of business. – Craig Mundie

[ PODCAST OF THE WEEK]

#FutureOfData with @CharlieDataMine, @Oracle discussing running analytics in an enterprise

 #FutureOfData with @CharlieDataMine, @Oracle discussing running analytics in an enterprise

Subscribe 

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[ FACT OF THE WEEK]

571 new websites are created every minute of the day.

Sourced from: Analytics.CLUB #WEB Newsletter

BARC Survey Shows New Benefits from Embedded Analytics

Application teams are embedding analytics in their products at an increasingly rapid pace. More than 85 percent of application teams have embedded dashboards, reports, and analytics in their software products, according to Logi’s 2018 State of Embedded Analytics Report. And they’re seeing value from their efforts: 92 percent of respondents say enhancing their products with analytics has increased competitive differentiation, with over 90 percent crediting it with improving win rates, increasing adoption, and reducing customer churn.

Now a new survey from the Business Application Research Center (BARC) indicates even more value may come from embedded analytics. According to The BI Survey 2018, the world’s largest annual survey of business intelligence (BI) software users, companies that encourage more users to adopt BI also see additional business benefits from their BI projects.

Related: New Study: Top 3 Trends in Embedded Analytics

 “Companies claiming to have achieved the most benefit from their BI tools (‘Best-in-Class’) have on average nine percent more BI users than those achieving the least benefit (‘Laggards’), suggesting that there is a relationship between the number of BI users and the degree of benefits an organization gains.” writes BARC in the report. “This should provide an incentive for businesses to maximize BI tool penetration and train as many employees as possible to use their BI tool.”

If more BI users means more business benefits, the natural question becomes, how do you get more BI users? As Logi’s own data from the 2018 State of Embedded Analytics Report shows, the best way to increase adoption of BI tools is to embed analytics in the applications people already use.Adoption of standalone vs embedded

In fact, embedded analytics sees twice the adoption rates of standalone BI solutions. Why? Because business users want to stay in one place, not jump from application to application to get what they need. In the 2017 State of Analytics Adoption Report, over 83 percent of business professionals expressed a strong desire to stay in one application, when and where a decision is needed, instead of wasting precious time switching applications. People clearly want their information in context of where they work, and embedded analytics delivers on this need.

According to our survey, 67 percent of application teams say time spent in their applications increased after they embedded analytics. On top of that, they cite the substantial business benefits of embedding analytics:

  • 96 percent of companies said embedded analytics contributes to overall revenue growth
  • 94 percent said it boosts user satisfaction
  • 93 percent said they’ve improved user experiences

 

Ready to embed analytics in your application? Gartner outlines best practices on evaluating solutions in its analyst paper, “5 Best Practices for Choosing an Embedded Analytics Platform Provider.”

 

Source by analyticsweek

Guide to business intelligence and health IT analytics

large_article_im1353_Data_Analytics

Introduction
Technology is frequently used as a tool through which healthcare providers and their IT departments can monitor and improve the business and personal performance of every aspect of their organization. For example, an analytics program that is deployed to examine a patient population’s medical data can then become the starting point for a provider’s business intelligence program. The results found by mining patient data can inform future care decisions and help the IT team discover any technology-related operational malfunctions.

There’s no doubt technology can be a valuable asset to healthcare practitioners when used properly, but convincing them to use new technology hasn’t been a cinch. Some physicians neglect clinical decision support tools in favor of consulting a colleague. A downside of healthcare organizations installing new technology containing patient data is that it creates additional security concerns. The ability for new technology to analyze data without improperly exposing protected health information will be key to determining how much it can improve the delivery of healthcare.

1Business intelligence
Applications of healthcare business intelligence

There is more data than ever for healthcare providers to use to maximize their operational efficiency. Information derived from social media and captured on patients’ mobile health devices are two examples. This section covers how providers are using business intelligence tools to analyze data and improve the experience of their patients. Business intelligence through cloud computing is an option for providers, but it comes with its own set of security issues.

Tip
Discover how providers apply business intelligence to big data

Social media is yet another source of data through which providers can monitor patients and health trends. Learn how they can apply this data to their business goals. Continue Reading

Tip
Business advantages of cloud have the attention of healthcare organizations

Security is a particularly strong concern for healthcare organizations that deploy cloud services. Continue Reading

Tip
Five keys to mastering healthcare business intelligence

A successful business intelligence program starts with good data. What’s required to turn that data into meaningful analysis may be a surprise. Continue Reading

Tip
Tips for patching analytics, business intelligence errors

Find out why healthcare analytics and business intelligence technology can fail, even after those systems are up and running. Continue Reading

Tip
Boost in computing power multiples power of health IT

Cloud computing and artificial intelligence are only two of the business intelligence tools that are molding the future of healthcare. Continue Reading
2Analytics at the point of care
Clinical decision support and health IT analytics

How can providers mine health data for information without exposing patients’ private information? That important question is examined in this section of the guide. Also, learn why some physicians have accepted the analysis provided to them via clinical decision support tools and why others still refuse to consult this form of technology for a second opinion when making a decision about a patient’s care. Like every other form of technology, healthcare analytics resources are only as good as their security and backup measures allow them to be. A cybersecurity expert explains how to approach protecting your health IT department from today’s threats.

News
The ups and downs of clinical decision support

A years-long government-sponsored study turned up some surprising results about the efficacy of analytics. Continue Reading

Podcast
Cybersecurity pro examines threats in healthcare

Analytics are no use unless healthcare organizations protect their data. Mac McMillan dishes out advice on what security precautions to take. Continue Reading

Tip
Privacy a top concern during clinical analysis

An analytics expert explains under which circumstances a patient’s identifying information should be available. Continue Reading

Tip
Analytics becoming a way of life for providers

Discover why more healthcare organizations are using analytics tools to keep up with regulatory changes. Continue Reading

Tip
Real-time analytics slowly working its way into patient care

Find out why physicians are wary of becoming too reliant on clinical decision support tools. Continue Reading

Tip
Quantity of data challenges healthcare analysts

There are a few simple steps health IT analysts should follow when examining data they are unfamiliar with. Continue Reading

Tip
Analytics backups preserve clinical decision support

Too many healthcare organizations take analytics for granted and don’t realize what would happen to their workflows if their backups failed. Continue Reading
3Population health management
How technology controls population health

Population health management, or the collective treatment of a group of patients, is an area that has matured along with the use of technology in healthcare. Though technology has come a long way, there are still hurdles, including those involving the exchange of health information among care facilities, that are causing hospitals to achieve treatment advances at different rates. This section contains information on why participating in an accountable care organization is one way for healthcare providers to commit to improving their population’s health and why that commitment has proven elusive for some.

Feature
Population health management in the home

Find out when healthcare services could become part of your cable bill. Continue Reading

Tip
Accountable care progress held up by technology

Technology that supports health information exchange is being adopted at a plodding rate. Learn why this is affecting accountable care organizations. Continue Reading

Feature
Karen DeSalvo, M.D. explains her public health mission

Karen DeSalvo goes into why public health goals shouldn’t be brushed aside. Continue Reading

Feature
Clinical decision support education a must

Too many physicians still don’t know how to use clinical decision support technology to their advantage. Continue Reading

Podcast
Chief information officer walks through population health process

A CIO of a New Jersey hospital system shares his organizations’ technology-based population health plan and how it will lead them to accountable care. Continue Reading

Feature
National health IT coordinator talks population health

The head of the Office of the National Coordinator for Health IT explains her career background and the early days of her government tenure. Continue Reading

Note: This article originally appeared in TechTarget. Click for link here.

Originally Posted at: Guide to business intelligence and health IT analytics by analyticsweekpick

Jul 04, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Fake data  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? 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]

The Misbehavior of Markets: A Fractal View of Financial Turbulence

image

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]

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:Explain what a long-tailed distribution is and provide three examples of relevant phenomena that have long tails. Why are they important in classification and regression problems?
A: * In long tailed distributions, a high frequency population is followed by a low frequency population, which gradually tails off asymptotically
* Rule of thumb: majority of occurrences (more than half, and when Pareto principles applies, 80%) are accounted for by the first 20% items in the distribution
* The least frequently occurring 80% of items are more important as a proportion of the total population
* Zipf’s law, Pareto distribution, power laws

Examples:
1) Natural language
– Given some corpus of natural language – The frequency of any word is inversely proportional to its rank in the frequency table
– The most frequent word will occur twice as often as the second most frequent, three times as often as the third most frequent…
– The” accounts for 7% of all word occurrences (70000 over 1 million)
– ‘of” accounts for 3.5%, followed by ‘and”…
– Only 135 vocabulary items are needed to account for half the English corpus!

2. Allocation of wealth among individuals: the larger portion of the wealth of any society is controlled by a smaller percentage of the people

3. File size distribution of Internet Traffic

Additional: Hard disk error rates, values of oil reserves in a field (a few large fields, many small ones), sizes of sand particles, sizes of meteorites

Importance in classification and regression problems:
– Skewed distribution
– Which metrics to use? Accuracy paradox (classification), F-score, AUC
– Issue when using models that make assumptions on the linearity (linear regression): need to apply a monotone transformation on the data (logarithm, square root, sigmoid function…)
– Issue when sampling: your data becomes even more unbalanced! Using of stratified sampling of random sampling, SMOTE (‘Synthetic Minority Over-sampling Technique”, NV Chawla) or anomaly detection approach

Source

[ VIDEO OF THE WEEK]

@JohnNives on ways to demystify AI for enterprise #FutureOfData #Podcast

 @JohnNives on ways to demystify AI for enterprise #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

You can have data without information, but you cannot have information without data. – Daniel Keys Moran

[ 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

Subscribe 

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[ FACT OF THE WEEK]

As recently as 2009 there were only a handful of big data projects and total industry revenues were under $100 million. By the end of 2012 more than 90 percent of the Fortune 500 will likely have at least some big data initiatives under way.

Sourced from: Analytics.CLUB #WEB Newsletter

Enhancing CRM with Real-Time, Distributed Data Integrations

The world of CRM is relatively slow to change. These repositories still excel at making available numerous types of largely historical data based on user accounts, frequently asked questions, manual notes, and databases containing these and other forms of customer information.

According to UJET CEO Anand Janefalkar, however, CRM is much less effective for real-time data, particularly those spawned from heterogeneous settings involving contemporary applications of the Internet of Things and mobile technologies: “It just takes a different level of focus to not only reduce the latency, not only shift it’s intent, but also have a specific focus on real-time interactions and user experience.”

Nonetheless, there are a number of contemporary developments taking place within the CRM space (and that for customer service in general) that are designed to enrich the customer experience and the service organizations provide their endpoint customers in velocities equitable to that of modern mobile and big data technologies.

Prudent usage of these mechanisms produces “bi-directional, smart, high-bandwidth communication so that way, there are no artificial limits, and there’s all of the options available for someone to really curate and configure their vision of the user journey,” Janefalkar mentioned.

Embedded, Cloud-Based Data Integrations
Embedding contemporary contact center options, typically in the form of widgets or adaptors, inside of CRM makes them suddenly viable for a host of real-time data sources. Many contact center solutions are facilitated through the cloud, so that they offer omni-channel experiences in which users can communicate with the enterprise via text, chats, mobile apps, web sites, phone calls, and just about any other form of electronic communication. Highly competitive platforms “design an extremely meticulous user experience to enable agents and customers to communicate visually and contextually,” Janefalkar said.

By embedding the adaptors for these solutions into CRM, organizations can now make available an assortment of low latent data which otherwise would have proved too arduous to assemble quickly enough—and which can drastically improve customer service. Examples of these data sources include “photos, videos, screenshots, sensor data, [which] is either requested by an agent or sent from the web site or the smart phone app to the agent,” Janefalkar revealed. “All of that gets stored into the CRM in real time.” With this approach, CRM is suddenly equipped with a multitude of largely unstructured data to associate with specific customers.

Decentralized Use Cases
The practical business value of enhancing CRM with low latent data integrations from distributed sources varies according to verticals, yet is always almost demonstrable. Perhaps the most significant factor about this methodology is it enables for low latent integrations of data from distributed sources outside of enterprise firewalls. In insurance, for example, if a customer gets into a fender bender, he or she can use a mobile application to present digital identification to the law enforcement officer summoned, then inform the process with contextual and visual information regarding the encounter. This information might include photographs or even videos of the scene, all of which is transmitted alongside any other digital information attained at the time (such as the other party’s contact and insurance information), and embedded into “the case or the ticket,” Janefalkar said.

The resulting workflow efficiency contributes to faster resolutions and better performance because “when the first agent is contacted, they take this information and it gets logged into the CRM,” Janefalkar explained. “And then, that gets passed over to a claims assessor. All that information’s already there. The claims assessor doesn’t have to call you back and ask the same questions, ask you to send an email with the photos that you have. Obviously, since it’s after the fact you wouldn’t have access to a video of the site, because you may not have taken it.”

Visual and Contextual Data Integrations
The rapid integration of visual and contextual decentralized data inside CRM to expedite and improve customer service is also an integral approach to handling claims of damaged or incorrect items from e-commerce sites. There’s also a wide range of applicability in other verticals, as well.

The true power of these celeritous integrations of data within CRM is they expand the utility of these platforms, effectively modernize them at the pace of contemporary business, and “make them even better by providing a deep integration into the CRMs so that all of the data and business rules are fetched in real time, so that the agent doesn’t have to go back and forth between different tabs or windows,” Janefalkar said. “But also, when the conversation is done and then the photos and the secure information, they’re not going through any different source. It gets completely archived from us and put back into the source of truth, which usually is the CRM.”

Source: Enhancing CRM with Real-Time, Distributed Data Integrations by jelaniharper

Jun 27, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Accuracy  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Tackling 4th Industrial Revolution with HR4.0 by v1shal

>> Three Types Of Context To Make Your Audience Care About Your Data by analyticsweek

>> Remote DBA Experts- Improve Business Intelligence with The Perfect Analytical Experts by thomassujain

Wanna write? 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]

Introduction to Graph Theory (Dover Books on Mathematics)

image

A stimulating excursion into pure mathematics aimed at “the mathematically traumatized,” but great fun for mathematical hobbyists and serious mathematicians as well. Requiring only high school algebra as mathematical bac… more

[ TIPS & TRICKS OF THE WEEK]

Keeping Biases Checked during the last mile of decision making
Today a data driven leader, a data scientist or a data driven expert is always put to test by helping his team solve a problem using his skills and expertise. Believe it or not but a part of that decision tree is derived from the intuition that adds a bias in our judgement that makes the suggestions tainted. Most skilled professionals do understand and handle the biases well, but in few cases, we give into tiny traps and could find ourselves trapped in those biases which impairs the judgement. So, it is important that we keep the intuition bias in check when working on a data problem.

[ DATA SCIENCE Q&A]

Q:What are confounding variables?
A: * Extraneous variable in a statistical model that correlates directly or inversely with both the dependent and the independent variable
* A spurious relationship is a perceived relationship between an independent variable and a dependent variable that has been estimated incorrectly
* The estimate fails to account for the confounding factor

Source

[ VIDEO OF THE WEEK]

@DrewConway on creating socially responsible data science practice #FutureOfData #Podcast

 @DrewConway on creating socially responsible data science practice #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

What we have is a data glut. – Vernon Vinge

[ PODCAST OF THE WEEK]

@JohnNives on ways to demystify AI for enterprise #FutureOfData #Podcast

 @JohnNives on ways to demystify AI for enterprise #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

By 2020, we will have over 6.1 billion smartphone users globally (overtaking basic fixed phone subscriptions).

Sourced from: Analytics.CLUB #WEB Newsletter

New Mob4Hire Report “The Impact of Mobile User Experience on Network Operator Customer Loyalty” Ranks Performance Of Global Wireless Industry

Mob4Hire, in collaboration with leading customer loyalty scientist Business Over Broadway, today announced its Summer Report 2010 of its “Impact of Mobile User Experience on Network Operator Customer Loyalty” international research, conducted during the Spring. The 111-country survey analyzes the impact of mobile apps across many dimensions of the app ecosystem as it relates to customer loyalty of network operators.

Read the full press release here: http://www.prweb.com/releases/2010/08/prweb4334684.htm; The report is available at http://www.mob4hire.com/services/global-mobile-research for only $495 Individual License (1-3 people), $995 Corporate License (3+ people).

Source by bobehayes

Jun 20, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Insights  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> How Airbnb Uses Big Data And Machine Learning To Guide Hosts To The Perfect Price by analyticsweekpick

>> My Conversation with Oracle on Customer Experience Management by bobehayes

>> Accelerating Discovery with a Unified Analytics Platform for Genomics by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

R Basics – R Programming Language Introduction

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Learn the essentials of R Programming – R Beginner Level!… 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 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:Is mean imputation of missing data acceptable practice? Why or why not?
A: * Bad practice in general
* If just estimating means: mean imputation preserves the mean of the observed data
* Leads to an underestimate of the standard deviation
* Distorts relationships between variables by “pulling” estimates of the correlation toward zero

Source

[ VIDEO OF THE WEEK]

Understanding Data Analytics in Information Security with @JayJarome, @BitSight

 Understanding Data Analytics in Information Security with @JayJarome, @BitSight

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[ QUOTE OF THE WEEK]

For every two degrees the temperature goes up, check-ins at ice cream shops go up by 2%. – Andrew Hogue, Foursquare

[ 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

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[ FACT OF THE WEEK]

39 percent of marketers say that their data is collected ‘too infrequently or not real-time enough.’

Sourced from: Analytics.CLUB #WEB Newsletter

Life Might Be Like a Box of Chocolates, But Your Data Strategy Shouldn’t Be

“My momma always said, “Life was like a box of chocolates. You never know what you’re gonna get.” Even if everyone’s life remains full of surprises, the truth is that what applied to Forrest Gump in the 1994 movie by Robert Zemeckis, shouldn’t apply to your data strategy. As you’re making the very first steps into your data strategy, you need to first know what’s inside your data. And this part is critical.  To do so, you need the tools and methodology to step up your data-driven strategy.

<<ebook: Download our full Definitive Guide to Data Governance>>

Why Data Discovery?

With increased affordability and accessibility of data storage over recent years, data lakes have increased in popularity. This left IT teams with a growing number of diverse known and unknown datasets polluting the data lake in volume and variety every day. As a consequence, everyone is facing a data backlog.  It can take weeks for IT teams to publish new data sources in a data warehouse or data lakes. At the same time, it takes hours for line-of-business workers or data scientists to find, understand and put all that data into context. IDC found that only 19 percent of the time spent by data professionals and business users can really be dedicated to analyzing information and delivering valuable business outcomes

Given this new reality, the challenge is now to overcome these obstacles by bringing clarity, transparency and accessibility to your data as well as to extract value from legacy systems and new applications alike. Wherever the data resides (in a traditional data warehouse or hosted in a cloud data lake), you need to establish proper data screening, so you can get the full picture and make sure you have the entire view of the data flow coming in and out your organization.

Know Your Data

When it’s time to get started working on your data, it’s critical to start exploring the different data sources you wish to manage. The good news is that the newly released Talend Data Catalog coupled with the Talend Data Fabric is here to help.

As mentioned in this post, Talend Data Catalog will intelligently discover all the data coming into your data lake so you get an instant picture of what’s going on in any of your datasets.

One of the many interesting use cases of Talend Data Catalog is to identify and screen any datasets that contain sensitive data so that you can further reconcile them and apply data masking, for example, to enable relevant people to use them within the entire organization. This will help reduce the burden of any data team wishing to operationalize regulations compliance across all data pipelines. To discover more about how Talend Data Catalog will help to be compliant with GDPR, take a look at this Talend webcast.

Auto Profiling for All with Data Catalog

Auto-profiling capabilities of Talend Data Catalog facilitate data screening for non-technical people within your organization. Simply put, the data catalog will provide you with automated discovery and intelligent documentation of the datasets in your data lake. It comes with easy to use profiling capabilities that will help you to quickly assess data at a glance. With trusted and auto profiled datasets, you will have powerful and visual profiling indicators, so users can easily find and the right data in a few clicks. 

Not only can Talend Data Catalog bring all of your metadata together in a single place, but it can also automatically draw the links between datasets and connect them to a business glossary. In a nutshell, this allows organizations to:

  • Automate the data inventory
  • Leverage smart semantics for auto-profiling, relationships discovery and classification
  • Document and drive usage now that the data has been enriched and becomes more meaningful

Go further with Data Profiling

Data profiling is a technology that will enable you to discover your datasets in-depth and accurately assess multiple data sources based on the six dimensions of data quality. It will help you to identify if and how your data is inaccurate, inconsistent, incomplete.

Let’s put this in context. Think about a doctor’s exam to assess a patient’s health. Nobody wants to be in the process of having surgery without a precise and close examination. The same applies to data profiling. You need to understand your data before fixing it. As data will often come into the organization as either inoperable, in hidden formats, or unstructured an accurate diagnosis will help you to have a detailed overview of the problem before fixing it. This will save your time for you, your team and your entire organization because you will have primarily mapped this potential minefield.

Easy profiling for power users with Talend Data Preparation: Data profiling shouldn’t be complicated. Rather, it should be simple, fast and visual. For use cases such as Salesforce data cleansing, you may wish to gauge your data quality by delegating some of the basic data profiling activities to business users. They will then be able to do quick profiling on their favorite datasets. With tools like Talend Data Preparation, you will have powerful yet simple built-in profiling capabilities to explore datasets and assess their quality with the help of indicators, trends and patterns.

Advanced profiling for data engineers: Using Talend Data Quality in the Talend Studio, data engineers can start connecting to data sources to analyze their structure (catalogs, schemas, and tables), and stores the description of their metadata in its metadata repository. Then, they can define available data quality analysis including database, content analysis, column analysis, table analysis, redundancy analysis, correlation analysis, and more. These analyses will carry out the data profiling processes that will define the content, structure, and quality of highly complex data structures. The analysis results will be then displayed visually as well.

To go further into data profiling take a look at this webcast: An Introduction to Talend Open Studio for Data Quality.

Keep in mind that not your data strategy should first and foremost start with data discovery. Failure to profile your data would obviously put your entire data strategy at risk. It’s really about analyzing the ground to make sure your data house could be built on solid foundations.

The post Life Might Be Like a Box of Chocolates, But Your Data Strategy Shouldn’t Be appeared first on Talend Real-Time Open Source Data Integration Software.

Originally Posted at: Life Might Be Like a Box of Chocolates, But Your Data Strategy Shouldn’t Be