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

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[  COVER OF THE WEEK ]

image
Conditional Risk  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Assess Your Data Science Expertise by bobehayes

>> Six Practices Critical to Creating Value from Data and Analytics [INFOGRAPHIC] by bobehayes

>> How to Choose a Database for Your Predictive Project by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Deep Learning Prerequisites: The Numpy Stack in Python

image

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

[ FEATURED READ]

Storytelling with Data: A Data Visualization Guide for Business Professionals

image

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]

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: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]

Understanding #Customer Buying Journey with #BigData

 Understanding #Customer Buying Journey with #BigData

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

I’m sure, the highest capacity of storage device, will not enough to record all our stories; because, everytime with you is very valuable da

[ 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]

In 2015, a staggering 1 trillion photos will be taken and billions of them will be shared online. By 2017, nearly 80% of photos will be taken on smart phones.

Sourced from: Analytics.CLUB #WEB Newsletter

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

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[  COVER OF THE WEEK ]

image
Accuracy  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Removing Silos & Operationalizing Your Data: The Key to Analytics – Part 7, Business Intelligence on Big Data/Data Lakes by analyticsweekpick

>> Self-Reported Intentions vs Actual Behaviors: Comparing Two Employee Turnover Metrics by bobehayes

>> Dec 06, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

Wanna write? Click Here

[ FEATURED COURSE]

CPSC 540 Machine Learning

image

Machine learning (ML) is one of the fastest growing areas of science. It is largely responsible for the rise of giant data companies such as Google, and it has been central to the development of lucrative products, such … more

[ FEATURED READ]

Data Science from Scratch: First Principles with Python

image

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn … 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:What is: lift, KPI, robustness, model fitting, design of experiments, 80/20 rule?
A: Lift:
It’s measure of performance of a targeting model (or a rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model. Lift is simply: target response/average response.

Suppose a population has an average response rate of 5% (mailing for instance). A certain model (or rule) has identified a segment with a response rate of 20%, then lift=20/5=4

Typically, the modeler seeks to divide the population into quantiles, and rank the quantiles by lift. He can then consider each quantile, and by weighing the predicted response rate against the cost, he can decide to market that quantile or not.
“if we use the probability scores on customers, we can get 60% of the total responders we’d get mailing randomly by only mailing the top 30% of the scored customers”.

KPI:
– Key performance indicator
– A type of performance measurement
– Examples: 0 defects, 10/10 customer satisfaction
– Relies upon a good understanding of what is important to the organization

More examples:

Marketing & Sales:
– New customers acquisition
– Customer attrition
– Revenue (turnover) generated by segments of the customer population
– Often done with a data management platform

IT operations:
– Mean time between failure
– Mean time to repair

Robustness:
– Statistics with good performance even if the underlying distribution is not normal
– Statistics that are not affected by outliers
– A learning algorithm that can reduce the chance of fitting noise is called robust
– Median is a robust measure of central tendency, while mean is not
– Median absolute deviation is also more robust than the standard deviation

Model fitting:
– How well a statistical model fits a set of observations
– Examples: AIC, R2, Kolmogorov-Smirnov test, Chi 2, deviance (glm)

Design of experiments:
The design of any task that aims to describe or explain the variation of information under conditions that are hypothesized to reflect the variation.
In its simplest form, an experiment aims at predicting the outcome by changing the preconditions, the predictors.
– Selection of the suitable predictors and outcomes
– Delivery of the experiment under statistically optimal conditions
– Randomization
– Blocking: an experiment may be conducted with the same equipment to avoid any unwanted variations in the input
– Replication: performing the same combination run more than once, in order to get an estimate for the amount of random error that could be part of the process
– Interaction: when an experiment has 3 or more variables, the situation in which the interaction of two variables on a third is not additive

80/20 rule:
– Pareto principle
– 80% of the effects come from 20% of the causes
– 80% of your sales come from 20% of your clients
– 80% of a company complaints come from 20% of its customers

Source

[ VIDEO OF THE WEEK]

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

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

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data really powers everything that we do. – Jeff Weiner

[ PODCAST OF THE WEEK]

@AnalyticsWeek #FutureOfData with Robin Thottungal(@rathottungal), Chief Data Scientist at @EPA

 @AnalyticsWeek #FutureOfData with Robin Thottungal(@rathottungal), Chief Data Scientist at @EPA

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

235 Terabytes of data has been collected by the U.S. Library of Congress in April 2011.

Sourced from: Analytics.CLUB #WEB Newsletter

Big data and digital skills essential for future retail success

retail-park-540x334

The Sector insights: skills and performance challenges in the retail sector (PDF) report by the UK Commission for Employment and Skills warned that retailers must embrace modern technology in the supply chain through to the shop floor.

The report said that big data harvested from customer loyalty schemes and online activity can play a significant role in marketing products to customers and boosting sales.

The retail sector has been at the forefront of big data use, which helped it to weather the recent recession by using information gleaned from data analysis to boost supply chain efficiency and revenues.

However, the report said that properly embracing such technology requires retailers to have employees with the right digital skills.

“This requires new ICT-related skills to take advantage of new business opportunities facilitated by social media for advertising and marketing,” the report said.

“Larger retailers are leading the uptake of new, innovative technologies which are creating shifts in the way that customer service is delivered and managed, changing the profile of the marketing function to incorporate an increased focus on data.

“This leads to a pressing need to attract and retain appropriately skilled workers in order to respond to these changes.

“Smaller retailers are at risk of being left behind unless they recognise the impact of these changes and respond by investing appropriately in their own skills and knowledge, to think more strategically about their business, and embrace appropriate new technologies.”

The report explained that the pace of technology is putting the retail sector at risk of misaligning the skills it has with the needs of businesses looking to use IT to drive performance.

Handling hardware
o2o-feeling-and-looking

The report also found that the integration of hardware such as beacon technology, self-service tills and virtual ‘browse and order’ hubs is forcing the need for shop workers and managers to develop skills that make use of new technologies that help the business and improve customer service.

“Retailers will need to continue to upskill existing staff to respond to the growing use and sophistication of technology,” the report said.

“Findings from the primary research confirm that in-store technologies are also requiring diversification and a higher-level skills base on the shop-floor.

“For example, staff are increasingly required to not only use more advanced technologies, but to interact with customers face to face and to guide them through the retailer’s online presence using mobiles and tablets.”

This is being driven in part by the need to have staff that can deal with customers who are better informed about a company’s products and prices because they have access to multiple shopping options provided through online retail and mobile shopping apps and services.

But the report warned that improving the digital skills of shop floor workers may infringe on their selling ability.

“There is a risk that the focus on IT skills and product knowledge can overshadow the importance of sales skills,” it said.

Furthermore, the report suggested that the skills gap is more pertinent with the older generation of retail workers who will need to be taught new skills, while at the same time businesses will need to keep attracting younger, digital-savvy workers.

“New technology requires workers to have up-to-date IT skills, which can be a challenge for older workers who are less likely to have good IT skills than younger workers,” it said.

“To continue to attract younger workers, the opportunity to use and develop technology-based skills and knowledge within a retail career should be promoted.”

The opportunities in the retail world to tap into big data and other technologies are well known, but finding the right skills amid the UK’s digital skills gap will not be easy for some retailers, despite Tesco’s dismissal of such challenges.

Furthermore, the recent launch of Apple Pay in the UK has brought more contactless payment options into the retail world, and the sector is likely to see increasing use of technology in physical outlets.

Alternatively, technology could replace staff completely, as seen with IBM Watson Analytics used to generate big data in London’s unmanned Honest Café coffee shops.

Note: This article originally appeared in V3. Click for link here.

Source

Making Sense of the 2018 Gartner Magic Quadrant for Data Integration Tools

It’s an exciting time to be part of the data market.  Never before have we seen so much innovation and change in a market, especially in the areas of cloud, big data, machine learning and real-time data streaming.  With all of this market innovation, we are especially proud that Talend was recognized by Gartner as a leader for the third time in a row in their 2018 Gartner Magic Quadrant for Data Integration Tools and remains the only open source vendor in the leaders quadrant.

According to Gartner’s updated forecast for the Enterprise Infrastructure Software market, data integration and data quality tools are the fastest growing sub-segment, growing at 8.6%. Talend is rapidly taking market share in the space with a 2017 growth rate of 40%, more than 4.5 times faster than the overall market.

The Data Integration Market: 2015 vs. 2018

Making the move from challengers to market leaders from 2015 to today was no easy feat for an emerging leader in cloud and big data integration. It takes a long time to build a sizeable base of trained and skilled users while maturing product stability, support and upgrade experiences. 

While Talend still has room to improve, it’s exciting recognition of all the investments Talend has made to see our score improve like that.

Today’s Outlook in the Gartner Magic Quadrant

Mark Byer, Eric Thoo, and Etisham Zaidi are not afraid to change things up in the Gartner Magic Quadrant as the market changes, and their 2018 report is proof of that.  Overall, Gartner continued to raise their expectations for the cloud, big data, machine learning, IoT and more.  If you read each vendor’s write up carefully and take close notes, as I did, you start to see some patterns. 

In my opinion, the latest report from Gartner indicates that in general, you have to pick your poison, you can have a point solution with less mature products and support and a very limited base of trained users in the market, or go with a vendor that has product breadth, maturity and a large base of trained users, but with expensive, complex and hard to deploy solutions.

Talend’s Take on the 2018 Gartner Magic Quadrant for Data Integration Tools

In our minds, this has left a really compelling spot in the market for Talend as the leader in the new cloud and big data use cases that are increasingly becoming the mainstream market needs. For the last 10+ years, we’ve been on a mission to help our customers liberate their data. As data volumes continue to grow exponentially along with growth in business users needing access to that data, this mission has never been more important. This means continuing to invest in our native architecture to enable customers to be the first to adopt new cutting-edge technologies like serverless, containers which significantly reduce total cost of ownership and can run on any cloud.

Talend also strongly believes that data must become a team sport for businesses to win, which is why governed self-service data access tools like Talend Data Preparation and Talend Data Streams are such important investments for Talend.  It’s because of investments like these that we believe Talend will quickly become the overall market leader in data integration and data quality. As I said at the beginning of the blog, our evolution has been a journey and we invite you to come along with us. I encourage you to download a copy of the report,  try Talend for yourself and become part of the community.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. 
GARTNER is a federally registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and is used herein with permission. All rights reserved.

 

The post Making Sense of the 2018 Gartner Magic Quadrant for Data Integration Tools appeared first on Talend Real-Time Open Source Data Integration Software.

Source: Making Sense of the 2018 Gartner Magic Quadrant for Data Integration Tools by analyticsweekpick

Jun 13, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[  COVER OF THE WEEK ]

image
Fake data  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ FEATURED COURSE]

CS109 Data Science

image

Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data managem… more

[ FEATURED READ]

The Industries of the Future

image

The New York Times bestseller, from leading innovation expert Alec Ross, a “fascinating vision” (Forbes) of what’s next for the world and how to navigate the changes the future will bring…. more

[ TIPS & TRICKS OF THE WEEK]

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

[ DATA SCIENCE Q&A]

Q:Explain likely differences between administrative datasets and datasets gathered from experimental studies. What are likely problems encountered with administrative data? How do experimental methods help alleviate these problems? What problem do they bring?
A: Advantages:
– Cost
– Large coverage of population
– Captures individuals who may not respond to surveys
– Regularly updated, allow consistent time-series to be built-up

Disadvantages:
– Restricted to data collected for administrative purposes (limited to administrative definitions. For instance: incomes of a married couple, not individuals, which can be more useful)
– Lack of researcher control over content
– Missing or erroneous entries
– Quality issues (addresses may not be updated or a postal code is provided only)
– Data privacy issues
– Underdeveloped theories and methods (sampling methods…)

Source

[ VIDEO OF THE WEEK]

#FutureOfData with Rob(@telerob) / @ConnellyAgency on running innovation in agency

 #FutureOfData with Rob(@telerob) / @ConnellyAgency on running innovation in agency

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

It is a capital mistake to theorize before one has data. Insensibly, one begins to twist the facts to suit theories, instead of theories to

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @MPFlowersNYC, @enigma_data

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @MPFlowersNYC, @enigma_data

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

YouTube users upload 48 hours of new video every minute of the day.

Sourced from: Analytics.CLUB #WEB Newsletter

Helping Humans with Healthcare Analytics

[soundcloud url=”https://api.soundcloud.com/tracks/616679784″ params=”color=#ff5500&auto_play=false&hide_related=false&show_comments=true&show_user=true&show_reposts=false&show_teaser=true” width=”100%” height=”166″ iframe=”true” /]

Healthcare: everyone needs it, it’s a rapidly technologizing industry, and it produces immense amounts of data every day. To get a sense of where analytics fit into this vital market, I got on the phone with Hamza Jap-Tjong, CEO and Co-Founder of GeriMedica Inzicht, a GeriMedica subsidiary. GeriMedica is a multi-disciplinary electronic medical record (EMR) company servicing the elderly care market and as such, their SaaS platform is filled with data of all kinds. Recently, they rolled out analytics that practitioners could use to improve the quality of care (vs the prior main use case in healthcare analytics, which was done by the billing and finance departments). This helps keep practitioners focused on helping patients vs spending (wasting) hours in a software product. Hamza and I spoke about the state of healthcare analytics, how it can improve care for patients, and where the industry is going.

The State of Healthcare Analytics

As previously mentioned, the healthcare industry creates tons of data every day from a wide array of sources.

“I think tons of data might be an understatement,” says Hamza, citing a Stamford study. “They were talking about data on the scale of exabytes (where each exabyte is 1,000 gigabytes). Where doesn’t that data come from? Fitbits, iPhones, fitness devices on your person… healthcare data is scattered everywhere: not only treatment plans and records created by practitioners, but also stored in machines (X-rays, photographs, etc.).”

Data is the new oil, but without the right tools, the insights locked in that data can’t help anyone. At present, few healthcare organizations (let alone frontline practitioners) are taking advantage of the data at their disposal to improve patient care. Moreover, these teams are dealing with amounts of information so vast that they are impossible to make sense of without help (like from a BI or analytics platform). They also can’t combine these datasets to gain a complete picture without help, either. Current software offerings, even if they have some analytical capabilities for the data that they capture, often can’t mash it up with other datasets.

“In my opinion, we could really improve the data gathering,” Hamza says. “As well as the way we use that data to improve patient care. What we know is that when you look at doctors, nurses, physical therapists, everybody close to the care process, close to the patient, is hankering for data and insights and analytics and we see that there isn’t at the moment a tool that is good enough or easy enough for them to use to gain the insights that they are looking for.”

Additionally, the current generation of medical software has a high barrier to entry/learning curve when it comes to getting useful insights out. All these obstacles prevent caregivers from helping clients as much as they might with easier-to-use analytics.

Improving Patient Care (and Improving Analytics for Practitioners)

Analytics and insight-mining systems have huge potential to improve patient care. Again, healthcare data is too massive for humans to handle unaided. However, there’s hope: Hamza mentioned that AI systems were already being used in medical settings to aggregate research and present an array of options to practitioners without them having to dig through numerous sources themselves.

“A doctor or a nurse does not work nine-to-five. They work long shifts and their whole mindset is focused on solving the mystery and helping the patient. They do not have time to scour through all kinds of tables and numbers. They want an easy-to-understand dashboard that tells a story from A-to-Z in one glance and answers their question.”

This is a huge opportunity for software and analytics companies to help improve patient care and user experience. Integrating easy-to-understand dashboards and analytics tools within medical software lowers the barrier to entry and serves up insights that practitioners can use to make better decisions. The next step is also giving clinicians tools to build their own dashboards to answer their own questions.

The Future of Healthcare Analytics

Many healthcare providers might not know how much analytics could be improving their lives and the care they give their patients. But they certainly know that they’re spending a lot of time gathering information and putting it into systems (and, again, that they have a ton of data). This is slowly changing today and will only accelerate as time goes on. The realization of how much a powerful analytics and BI system could help them with data gathering, insight harvesting, and providing better care will drive more organizations to start using a software’s analytics capabilities as a factor in their future buying decisions.

Additionally, just serving up insights won’t be enough. As analytics become more mainstreamed, users will want the power to dig into data themselves, perform ad hoc analyses, and design their own dashboards. With the right tools and training, even frontline users like doctors and nurses can be empowered to become builders, creating their own dashboards to answer the questions that matter most to them.

“We have doctors who are designers,” Hamza says. “They are designing their own dashboards using our entire dataset, combining millions of rows and records to get the answers that they are looking for.”

Builders are everywhere. Just as the healthcare space is shifting away from only using analytics in financial departments and putting insights into the hands of frontline practitioners, the right tools democratize the ability to create new dashboards and even interactive analytics widgets and empower anyone within an organization to get the answers and build the tools they need.

Creating Better Experiences

When it comes to the true purpose of healthcare analytics, Hamza summed it up perfectly:

“In the end, it’s all about helping end users create a better experience.”

The staggering volume of data that the healthcare industry creates presents a huge opportunity for analytics to find patterns and insights and improve the lives of patients. As datasets become more massive and the analytical questions become more challenging, healthcare teams will rely more and more on the analytics embedded within their EMR systems and other software. This will lead them to start using the presence (or lack thereof) and quality of those analytics when making buying decisions. Software companies that understand this will build solutions that answer questions and save lives; the ones that don’t might end up flatlining.

Source

What’s the True Cost of a Data Breach?

The direct hard costs of a data breach are typically easy to calculate. An organization can assign a value to the human-hours and equipment costs it takes to recover a breached system. Those costs, however, are only a small part of the big picture.

Every organization that has experienced a significant data breach knows this firsthand. Besides direct financial costs, there are actually lost business, third-party liabilities, legal expenses, regulatory fines, and damaged goodwill. The true cost of a data breach encompasses much more than just direct losses.

Forensic Analysis. Hackers have learned to disguise their activity in ways that make it difficult to determine the extent of a breach. An organization will often need forensic specialists to determine how deeply hackers have infiltrated a network. Those specialists charge between $200 and $2,000 per hour.

Customer Notifications. A company that has suffered a data breach has a legal and ethical obligation to send written notices to affected parties. Those notices can cost between $5 and $50 apiece.

Credit Monitoring. Many companies will offer credit monitoring and identity theft protection services to affected customers after a data breach. Those services cost between $10 and $30 per customer.

Legal Defense Costs. Customers will not hesitate to sue a company if they perceive that the company failed to protect their data. Legal costs between $500,000 and $1 million are typical for significant data breaches affecting large companies. Companies often mitigate these high costs with data breach insurance because it covers liability and notification costs, among others.

Regulatory Fines and Legal Judgments. Target paid $18.5 million after a 2013 data breach that exposed the personal information of more than 41 million customers. Advocate Health Care paid a record $5.5 million fine after thieves stole an unsecured hard drive containing patient records. Fines and judgments of this magnitude can be ruinous for a small or medium-sized business.

Reputational Losses. Quantifying the value of lost goodwill and standing within an industry after a data breach is impossible. That lost goodwill can translate into losing more than 20 percent of regular customers, plus revenue depletions exceeding 30 percent. There’s also the cost of missing new business opportunities.

The total losses that a company experiences following a data breach depend on the number of records lost. The average per-record loss in 2017 was $225. Thus, a small or medium-sized business that loses as few as 1,000 customer records can expect to realize a loss of $225,000. This explains why more than 60 percent of SMBs close their doors permanently within six months of experiencing a data breach.

Knowing the risks, companies can focus on devoting their cyber security budget to prevention and response. The first line of defense is technological, including network firewalls and regular employee training. However, hackers can still slip through the cracks, as they’re always devising new strategies for stealing data. A smart backup plan includes a savvy response and insurance to cover the steep costs if a breach occurs. After all, the total costs are far greater than just business interruption and fines; your reputation is at stake, too.

Originally Posted at: What’s the True Cost of a Data Breach? by thomassujain

Jun 06, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[  COVER OF THE WEEK ]

image
Data Storage  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Building Data Science AI Teams by @MikeTamir / @UberATG #FutureOfData #Podcast by v1shal

>> Achieving tribal leadership in 5 easy steps by v1shal

>> Data Science and Big Data: Two very Different Beasts by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

CS109 Data Science

image

Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data managem… more

[ FEATURED READ]

Hypothesis Testing: A Visual Introduction To Statistical Significance

image

Statistical significance is a way of determining if an outcome occurred by random chance, or did something cause that outcome to be different than the expected baseline. Statistical significance calculations find their … 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:How do you know if one algorithm is better than other?
A: * In terms of performance on a given data set?
* In terms of performance on several data sets?
* In terms of efficiency?
In terms of performance on several data sets:

– ‘Does learning algorithm A have a higher chance of producing a better predictor than learning algorithm B in the given context?”
– ‘Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets”, A. Lacoste and F. Laviolette
– ‘Statistical Comparisons of Classifiers over Multiple Data Sets”, Janez Demsar

In terms of performance on a given data set:
– One wants to choose between two learning algorithms
– Need to compare their performances and assess the statistical significance

One approach (Not preferred in the literature):
– Multiple k-fold cross validation: run CV multiple times and take the mean and sd
– You have: algorithm A (mean and sd) and algorithm B (mean and sd)
– Is the difference meaningful? (Paired t-test)

Sign-test (classification context):
Simply counts the number of times A has a better metrics than B and assumes this comes from a binomial distribution. Then we can obtain a p-value of the HoHo test: A and B are equal in terms of performance.

Wilcoxon signed rank test (classification context):
Like the sign-test, but the wins (A is better than B) are weighted and assumed coming from a symmetric distribution around a common median. Then, we obtain a p-value of the HoHo test.

Other (without hypothesis testing):
– AUC
– F-Score

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

What we have is a data glut. – Vernon Vinge

[ 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 

iTunes  GooglePlay

[ FACT OF THE WEEK]

40% projected growth in global data generated per year vs. 5% growth in global IT spending.

Sourced from: Analytics.CLUB #WEB Newsletter