Apr 27, 17: #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

[ AnalyticsWeek BYTES]

>> Intel bets on real-time analytics with high-end processor refresh by anum

>> In a Word: The Customer Sentiment Index by bobehayes

>> Why Your Company Should Use Data Science to Make Better Decisions by anum

Wanna write? Click Here

[ NEWS BYTES]

>>
 Court grants stay in FTC data security complaint against LabMD – Health Data Management Under  Data Security

>>
 WWE Royal Rumble 1990 Match Time and Statistics – Cageside Seats (blog) Under  Statistics

>>
 Social Listening in 2017: The Next Frontier in Social Media – MarketingProfs.com (subscription) Under  Sentiment Analysis

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[ FEATURED COURSE]

Probability & Statistics

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This course introduces students to the basic concepts and logic of statistical reasoning and gives the students introductory-level practical ability to choose, generate, and properly interpret appropriate descriptive and… more

[ FEATURED READ]

The Industries of the Future

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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]

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 JOB Q&A]

Q:Explain the difference between “long” and “wide” format data. Why would you use one or the other?
A: * Long: one column containing the values and another column listing the context of the value Fam_id year fam_inc

* Wide: each different variable in a separate column
Fam_id fam_inc96 fam_inc97 fam_inc98

Long Vs Wide:
– Data manipulations are much easier when data is in the wide format: summarize, filter
– Program requirements

Source

[ VIDEO OF THE WEEK]

Data-As-A-Service (#DAAS) to enable compliance reporting

 Data-As-A-Service (#DAAS) to enable compliance reporting

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[ QUOTE OF THE WEEK]

I keep saying that the sexy job in the next 10 years will be statisticians. And I’m not kidding. – Hal Varian

[ PODCAST OF THE WEEK]

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

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

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[ FACT OF THE WEEK]

Every person in the US tweeting three tweets per minute for 26,976 years.

Sourced from: Analytics.CLUB #WEB Newsletter

Customer Loyalty and Goal Setting

All companies who use customer loyalty surveys strive to see increases in their customer loyalty scores. Improving customer loyalty has been shown to have a positive impact on business results and long-term business success. Toward that end, executives implement various company-wide improvements in hopes that improvements in customer loyalty scores will follow.

One common method for improving performance is goal setting. There is a plethora of research on the effectiveness of goal setting in improving performance. In the area of customer satisfaction, what typically occurs is that management sees that their customer loyalty score is 7.0 (on a 0-10 scale) at the start of the year. They then set a customer loyalty goal of 8.0 for the end of the fiscal year. What happens at the end of the year? The score remains about 7.0. While their intentions are good, management does not see the increases in loyalty scores that they set out to attain. What went wrong? How can this company effectively use goal setting to improve their customer loyalty scores?

Here are a few characteristics of goals that improve the probability that goals will improve performance:

Specific. Goals need to be specific and clearly define what behaviors/actions are going to be taken to achieve the goal and in what time-frame or frequency these behaviors/actions should take place. For example, a goal stating, “Decrease the number of contacts with the company a customer needs to resolve an issue” does little to help employees focus their efforts because there is no mention of a rate/frequency associated with the decrease. A better goal would be, “Resolve customer issues in three or fewer contacts.”

Measurable. A measurement system needs to be in place to track/monitor progress toward the goal. The measurement system is used to determine whether the goal has been achieved and provides a feedback loop to the employees who are achieving the goal.

A common problem with using customer loyalty scores as the metric to track or monitor improvements is that satisfaction goals are still vague with respect to what the employees can actually do to impact satisfaction/loyalty scores. Telling the technical support department that the company’s customer loyalty goal is 8.0 provides no input on how that employee can affect that score. A better measure for the technical support department would be “satisfaction with technical support” or other technical support questions on the survey (e.g., “technical support responsiveness,” technical support availability”). We know that satisfaction with technical support is positively related to customer loyalty. Using these survey questions for goal setting has a greater impact on changing their behaviors compared to using vague loyalty questions. Because satisfaction with technical support is related to customer loyalty, improvements in technical support satisfaction should lead to improvements in loyalty scores.

An even better measure would be to use operational metrics for goal setting. The company must first identify the key operational metrics that are statistically related to customer satisfaction/loyalty. This process involves in-depth research via linkage analysis (e.g., linking satisfaction scores with operational measures such as hold time, turnaround time, and number of transfers) but the payoffs are great; once identified, the customer-centric operational metrics can be used for purposes of goal setting.

Difficult but attainable. Research has shown that difficult goals lead to better performance compared to goals that are easy. Difficult goals focus attention to the problem at hand. Avoid setting goals, however, that are too difficult and, consequently, not achievable. One way to set difficult and attainable goals is to use historical performance data to determine the likelihood of achieving different performance levels.

Relevant. Goals for the employees should be appropriate for the employees’ role; can the employee impact the goal? Additionally, the goal should be relevant to both the employee and the organization. Holding employees to be responsible for goals that are outside of their control (e.g., technical support representatives being responsible for product quality) is unfair and can lead to low morale.

Accepted (or mutually set). For goal setting to increase performance, employees should be allowed to participate in setting their goals. Goals that are not accepted by the recipient are not likely to be internalized and motivating. A good approach would be to get employees involved early in the process of goal setting. Let them help in identifying the problem, selecting (or understanding) the key measures to track, and setting the goal.

Summary

The following are key characteristics of effective goals:

  • Specific
  • Measurable
  • Difficult but attainable
  • Relevant
  • Accepted (or mutually set)

Goal setting can be an effective management tool. Incorporating this methodology can build a customer-centric culture by ensuring employees’s behaviors are guided by measures that matter to the customer.

Source: Customer Loyalty and Goal Setting

Two Underutilized Heroes of Data & Innovation: Correlation & Covariance

Two Underutilized Heroes of Data & Innovation: Correlation & Covariance
Two Underutilized Heroes of Data & Innovation: Correlation & Covariance

Yes, Data driven innovation is fun and it gets most done in less. But let’s talk about a math that is not as much known as it should be in the enterprise world. Correlation & Covariance are two such values that are most underutilized and have the tendency to cause maximum impact and disruption to any complicated business model.

First, a quick high level math primer (picked from Wiki): In probability theory and statistics, the mathematical descriptions of covariance and correlation are very similar.[1][2] Both describe the degree of similarity between two random variables or sets of random variables.
Correlation refers to any of a broad class of statistical relationships involving dependence.
Whereas, Covariance is a measure of how much two random variables change together. If the greater values of one variable mainly correspond with the greater values of the other variable, and the same holds for the smaller values, i.e., the variables tend to show similar behavior, the covariance is positive.[1] In the opposite case, when the greater values of one variable mainly correspond to the smaller values of the other, i.e., the variables tend to show opposite behavior, the covariance is negative. Anyways, over with the math talk, you could find more information by searching for covariance & correlations and if you are not blown away by it’s capabilities, do take out some extra time for reading about cross-correlation & cross covariance. You will get into the world of predictive modeling and so much more savvy stuff that you could do with these two interesting and powerful concepts.

On a traditional note, a company is analytically as smart as the analytics team it entails. But, on an interesting note, it does not have to be like this. A smarter business model like utilizing correlation & covariance on your captured data could do the heavy lifting for you and help you focus on the areas that are really causing some significant impact to your business. As you must have already read, by definition covariance and correlation can help you understand relationship between 2 random sets of data.

What is happening in most of the companies that I spoke with is that most of us have utilized this math while handling known sets of data within the periphery of a project. For an example, a project data and its variables can be correlated together for finding any hidden relations. If these relationships are not determined, it could cost your businesses a significant impact. If you are not at this yet, stop reading now and get your correlation & covariance mojo active at the least within the projects.

If your organization is already doing it within projects, you are part of that savvy organization which takes success and failures of their projects too seriously for them to be left with professionals. Now, you might need to ask, what next. Where is the next big wave? Innovation is the next big thing that is riding on the data that correlation/covariance could provide your organization. How about doing it within different projects, departments, silos etc. Consider for a case where one project is impacting the other. So, one tiny dependency on a remote department could cause a significant impact to totally unrelated department in the business.

Yes, you guessed right, we are talking about a big-data problem, or may be one of the biggest big-data problems for your organization.

Correlation and covariance have the power to identify those hidden relationships that you would have never guessed existed and then helps you find the extent of their dependency. How much one variable varies with the other. Once you have a model in place to comb your organization’s data for any correlations and thereby finding their covariance, you would understand how much one event is linked to other and by what degree. This would help your business identify high impact areas that you could then map to high performance. All you need to do is understand if the identified relationship is known or unknown. If it’s known, yes, you have validated that sometimes world is as sane as you expect it to be, and If not, wallah, you just identified a potential area to investigate and worry about, to make sure all relationships in your business are accounted for.

If data combing is done properly for any possible correlations and covariance, you could assure nothing will ever fall through the crack again. Your radar will always pick potential areas as soon as their relationship is established. And yes, that will save your business some cash and help it run optimally.

So, to do a quick recap:
1. Make sure you understand what correlation/covariance is, and for added bonus, read about cross correlation & cross covariance.
2. Make sure your project or projects in your company are leveraging correlation/covariance in finding hidden dependencies that could jeopardize the success of your project.
3. Make sure, you have big-data setup that could help connect data across various projects, departments & business units for finding possible correlations and their covariance.
4. Make sure you have right triggers, alarms and action plan setup for investigating any identified relationships further.
5. Make sure you have an automated system that combs the business data and help identifies possible cracks in real time.

If you are done with those 5 steps, your business is destined for consistent improvements and sustained data driven innovations.

And yes, as I always rant, you don’t have to do it in-house. Probably, for better business sense, get it made outside and then once it is validated, bring it in-house. All you need is a good data analytics/visualization platform that could take any number of structured and un-structured data and find correlations between them.

Originally Posted at: Two Underutilized Heroes of Data & Innovation: Correlation & Covariance

Apr 20, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

 

Issue #15    Web Version
Contact Us: info@analyticsweek.com

[  ANNOUNCEMENT ]

I hope this note finds you well. Please excuse the brief interruption in our newsletter. Over past few weeks, we have been doing some A/B testing and mounting our Newsletter on our AI led coach TAO.ai. This newsletter and future versions would be using capability of TAO. As with any AI, it needs some training, so kindly excuse/report the rough edges.

– Team TAO/AnalyticsCLUB

[  COVER OF THE WEEK ]

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Weak data  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Collaborative Analytics: Analytics for your BigData by v1shal

>> Colleges are using big data to identify when students are likely to flame out by analyticsweekpick

>> Rise of Data Capital by Paul Sonderegger by thebiganalytics

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[ NEWS BYTES]

>>
 Strategy Analytics: Android accounts for 88% of smartphones shipped in Q3 2016 – GSMArena.com Under  Analytics

>>
 Did you know we’re sedentary but less obese than average? So says Miami statistics website – Miami Herald Under  Statistics

>>
 MHS grad sinks Steel Roots in cyber security – News – North of … – Wicked Local North of Boston Under  cyber security

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Statistical Thinking and Data Analysis

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This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and n… more

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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]

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 JOB Q&A]

Q:What is cross-validation? How to do it right?
A: It’s a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. Mainly used in settings where the goal is prediction and one wants to estimate how accurately a model will perform in practice. The goal of cross-validation is to define a data set to test the model in the training phase (i.e. validation data set) in order to limit problems like overfitting, and get an insight on how the model will generalize to an independent data set.

Examples: leave-one-out cross validation, K-fold cross validation

How to do it right?

the training and validation data sets have to be drawn from the same population
predicting stock prices: trained for a certain 5-year period, it’s unrealistic to treat the subsequent 5-year a draw from the same population
common mistake: for instance the step of choosing the kernel parameters of a SVM should be cross-validated as well
Bias-variance trade-off for k-fold cross validation:

Leave-one-out cross-validation: gives approximately unbiased estimates of the test error since each training set contains almost the entire data set (n?1n?1 observations).

But: we average the outputs of n fitted models, each of which is trained on an almost identical set of observations hence the outputs are highly correlated. Since the variance of a mean of quantities increases when correlation of these quantities increase, the test error estimate from a LOOCV has higher variance than the one obtained with k-fold cross validation

Typically, we choose k=5 or k=10, as these values have been shown empirically to yield test error estimates that suffer neither from excessively high bias nor high variance.
Source

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[ VIDEO OF THE WEEK]

Data-As-A-Service (#DAAS) to enable compliance reporting

 Data-As-A-Service (#DAAS) to enable compliance reporting

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[ 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 David Rose, @DittoLabs

 #BigData @AnalyticsWeek #FutureOfData #Podcast with David Rose, @DittoLabs

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[ FACT OF THE WEEK]

By then, our accumulated digital universe of data will grow from 4.4 zettabyets today to around 44 zettabytes, or 44 trillion gigabytes.

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*This Newsletter is hand-curated and autogenerated using #TEAMTAO & TAO, excuse some initial blemishes. As with any AI, it may get worse before it will get relevant, excuse us with your patience & feedback.
Let us know how we could improve the experience using: feedbackform

Copyright © 2016 AnalyticsWeek LLC.

Bob Hayes to Address Vovici Vision 2010 Users Conference, May 10-12, 2010

Dulles, VA – November 2, 2009 – Vovici, the leading provider of survey software and enterprise feedback management (EFM) solutions, will hold its user conference, Vision 2010, May 10-12, 2010 in Reston, Virginia.

Vision 2010 will bring together feedback management leaders and experts across multiple industries to participate in compelling educational sessions, training, and peer networking opportunities. Among the confirmed keynote presenters will be three customer loyalty luminaries:

  • Jeb Dasteel, Chief Customer Officer of Oracle (NASDAQ: ORCL)
  • Jeanne Bliss, author of I Love You More Than My Dog and Chief Customer Officer
  • Bob Hayes, Ph. D., author of Beyond the Ultimate Question and recognized loyalty expert

“At Oracle, executive leadership is relentlessly focused on listening to customers and prioritizing feedback to drive customer strategy at all levels,” said Dasteel. Dasteel has been with Oracle for 11 years, five of which have been spent running Oracle’s Global Customer Programs and as CCO for the last year. Dasteel was named the 2009 Chief Customer Officer of the Year at the first Chief Customer Officer Summit.

Jeanne Bliss spent 25 years in the role of Chief Customer Officer at Lands’ End, Allstate, Microsoft, Mazda and Coldwell Banker. Today her firm, CustomerBLISS consults around the world, teaching and guiding companies and leaders how to wrap their business around customer relationships and business prosperity. “Leading companies understand the importance of listening to customers, using feedback to deliver an experience with impact, and creating a lasting bond,” noted Bliss. Her first book, Chief Customer Officer (Jossey-Bass, 2006), was based on 25 years of reporting to the CEOs of five major corporations.

Bob Hayes, Ph.D., is the president and founder of Business Over Broadway. He is a recognized expert in customer satisfaction and loyalty measurement, and has conducted survey research for enterprise companies, including Siebel Systems, Oracle, Agilent Technologies, and Cisco Systems. “There are key ingredients to a successful customer feedback program. Adoption of these elements is critical to improving both customer relationship management and customer loyalty, and Vision 2010 will offer a great opportunity to learn how to accomplish these,” said Hayes.

To register for Vision 2010, please visit: https://www.vovici.com/about/events.aspx

“Vovici is the Voice of the Customer platform that is helping Fortune 500 companies to emotionally connect to customers,” said Greg Stock, chairman and CEO of Vovici. “We are very excited to bring this amazing group together to share insights and proven methodologies that actually achieve higher level business objectives and make the customer’s vision a reality.”

Source: Bob Hayes to Address Vovici Vision 2010 Users Conference, May 10-12, 2010 by bobehayes

Every step you take: Who owns our mobile health data?

Gadgets that track your steps, sleeping and heart rate could help us live longer and cut national healthcare costs by billions – or so we are told.

Microsoft has just launched its first wearable health gadget, the Band, in the US ahead of its global launch.

Similar products from Samsung and Google are already on the market and early next year the much-hyped Watch from Apple will go on sale.

Millions of us are going to be having our most intimate bodily functions monitored by these gadgets, creating more health data than has ever existed before.

Why do these machines help us stay fit and more importantly what happens to all that information we are generating and sharing?

Tim Cook introducing the Apple Watch
Apple will soon follow Microsoft and Google into the mobile health device market

Massive market

Before the giants of the tech world realised that wearable, health-focused gadgets were the new big thing the market was already thriving.

In March the European Commission published its green paper on mobile health, which contained some mind-boggling statistics.

It suggests that 97,000 apps are on sale in the mobile health sector, which includes tracking apps but also apps that help patients make appointments and keep track of medication.

It predicts that by 2017 more than 1.5 billion people around the world will be using these apps, generating total revenues of £14.5bn ($23bn).

In the EU alone it is estimated that these apps and gadgets could reduce health costs by £77.5bn (99bn euros).

Sector pioneers

Most of the growth has come from start-ups that saw the potential early and now face a competitive onslaught from the big technology companies.

Five years ago French firm Withings launched its wireless scales – the device feeds data back to you, by plotting a graph of your weight over time.

“It started with the scales because we thought that was the one dimension that would make sense for people to track,” Julien De Preaumont, chief marketing officer at Withings, says.

“The first rule of data is to make people aware of their health to make them realise how their weight is evolving.

black wireless scales by Withtings
The wireless scales by Withings uses data visualisation to help dieters lose weight

“The curve reveals the impact of life changes, it will show how a divorce, a diet or a new job will affect your weight.”

After the scales took off, Withings launched wearable gadgets that track your movement, heart rate, blood pressure and sleep.

The company maintains that the data it collects belongs to the user only.

But it has published reports revealing the most obese cities in France and the US, as well as another study showing sleep patterns across Europe.

Withings says this does not compromise the privacy of the individual user’s data because it is aggregated and anonymised.

Business games

While Withings has grown to be a global business, US firm Fitbit has also seen its business thrive beyond its borders.

Founded in 2007 Fitbit offers wireless scales, wearable devices that monitor movement, heart rate, sleep and blood pressure, and is evangelical about the motivating power of targets and data on our health.

Fitbit also offers companies its gadgets and software for corporate use.

Its “corporate wellness” scheme started in the US and companies can use the scheme to get a rebate on their taxes.

A screengrab from a Fitbit challenge
Games and challenges can be used to motivate people to compete against each other

Clients so far include blue-chip multinationals such as BP and Time Warner.

Employees can sign up and different divisions can compete against each other over the number of steps taken or stairs climbed.

“The key is to make the product sticky,” says Gareth Jones from Fitbit, and the key to that is gamification.

“Our software incorporates challenges like daily showdowns and weekend warriors which motivate people and keep them coming back.”

But should employees be worried about sharing their every movement, 24 hours a day with a corporate scheme?

“We don’t have data about this, it’s very much a choice of the individual as to whether they sign in for the programme. We see the result of that as purely the people who agree to participate and the people who don’t,” says Mr Jones.

“We might share with the corporate administrator information that 50 people have been invited and 45 have said yes. How the company uses that information is up to the company.”

‘In the hands of the people’

The potential of all the data that is now being collected is huge, both for business and for public health bodies.

Imagine going to the doctor and being able to show them how much exercise you do, how much sleep you get and your blood pressure for the last year.

While the insurance industry is using mobile applications for arranging appointments and giving health information, they are yet to fully embrace the use of wearable devices and the data they collect, though it is a development that could completely change their business as many research papers suggest.

Meanwhile the use of the data for medical research is also a long way off.

Professor John Newton from Public Health England would like to see a more joined-up approach.

“We’ve got the world of apps, a huge investment from the technology companies, but the healthcare sector hasn’t made the link,” he says.

“If you were able to make the link between a hospital service like a diabetic clinic with a patient’s mobile phone data, they could tell immediately whether that person’s diabetes was going out of control.”

His message is clear: “Put the data into the hands of the people who can use it to make a difference.”

Like all the new data that is being recorded and analysed the possibilities are massive but the ethical and privacy issues surrounding our personal information will not go away quickly.

Originally posted via “Every step you take: Who owns our mobile health data?”

Originally Posted at: Every step you take: Who owns our mobile health data?

Apr 13, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

 

Issue #15    Web Version
Contact Us: info@analyticsweek.com

[  ANNOUNCEMENT ]

I hope this note finds you well. Please excuse the brief interruption in our newsletter. Over past few weeks, we have been doing some A/B testing and mounting our Newsletter on our AI led coach TAO.ai. This newsletter and future versions would be using capability of TAO. As with any AI, it needs some training, so kindly excuse/report the rough edges.

– Team TAO/AnalyticsCLUB

[  COVER OF THE WEEK ]

image
Data security  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> The What and Where of Big Data: A Data Definition Framework by bobehayes

>> The Cost Of Too Much Data by v1shal

>> Unraveling the Mystery of Big Data by v1shal

Wanna write? Click Here

[ NEWS BYTES]

>>
 How a Data Scientist’s Job ‘Play in Front’ than other BI and Analytic Roles – CIOReview Under  Data Scientist

>>
 AI, Machine Learning to Reach $47 Billion by 2020 – Infosecurity Magazine Under  Machine Learning

>>
 Software to “Encode the Mindset” of Lawyers – Lawfuel (blog) Under  Prescriptive Analytics

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[ FEATURED COURSE]

Lean Analytics Workshop – Alistair Croll and Ben Yoskovitz

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Use data to build a better startup faster in partnership with Geckoboard… more

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Storytelling with Data: A Data Visualization Guide for Business Professionals

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

[ TIPS & TRICKS OF THE WEEK]

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 JOB Q&A]

Q:What is cross-validation? How to do it right?
A: It’s a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. Mainly used in settings where the goal is prediction and one wants to estimate how accurately a model will perform in practice. The goal of cross-validation is to define a data set to test the model in the training phase (i.e. validation data set) in order to limit problems like overfitting, and get an insight on how the model will generalize to an independent data set.

Examples: leave-one-out cross validation, K-fold cross validation

How to do it right?

the training and validation data sets have to be drawn from the same population
predicting stock prices: trained for a certain 5-year period, it’s unrealistic to treat the subsequent 5-year a draw from the same population
common mistake: for instance the step of choosing the kernel parameters of a SVM should be cross-validated as well
Bias-variance trade-off for k-fold cross validation:

Leave-one-out cross-validation: gives approximately unbiased estimates of the test error since each training set contains almost the entire data set (n?1n?1 observations).

But: we average the outputs of n fitted models, each of which is trained on an almost identical set of observations hence the outputs are highly correlated. Since the variance of a mean of quantities increases when correlation of these quantities increase, the test error estimate from a LOOCV has higher variance than the one obtained with k-fold cross validation

Typically, we choose k=5 or k=10, as these values have been shown empirically to yield test error estimates that suffer neither from excessively high bias nor high variance.
Source

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

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[ QUOTE OF THE WEEK]

Processed data is information. Processed information is knowledge Processed knowledge is Wisdom. – Ankala V. Subbarao

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

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[ FACT OF THE WEEK]

140,000 to 190,000. Too few people with deep analytical skills to fill the demand of Big Data jobs in the U.S. by 2018.

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Invite top local professionals to your office

 

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*This Newsletter is hand-curated and autogenerated using #TEAMTAO & TAO, excuse some initial blemishes. As with any AI, it may get worse before it will get relevant, excuse us with your patience & feedback.
Let us know how we could improve the experience using: feedbackform

Copyright © 2016 AnalyticsWeek LLC.

How Oracle Uses Big Data to Improve the Customer Experience

Data Silo for each Business Data Source

Customer experience management (CEM) programs are no stranger to the use of data. CEM professionals use data to gain insight about their customers to help improve the customer experience and optimize customer loyalty. Not surprisingly, CEM programs typically rely on customer feedback as their main data source (e.g., social media, customer emails, tech support notes, formal customer surveys). Customer feedback data, however, are only one type of business data that are used to improve business decisions.

Big Data

The concept of Big Data is broad one and I consider it an amalgamation of different areas that help us try to get a handle on, insight from and use out of data. Big Data, including the tools, processes and solutions to wrangle the ever-increasing size, complexity and velocity of business data, can help companies extract value from collecting, processing and analyzing vast quantities of data. Businesses who can get a better handle on these data will be more likely to outperform their competitors who do not.

I recently wrote about the implications of Big Data on the practice of CEM and how Big Data providers can help companies integrate all their different business data (e.g., operational, financial, constituency, customer) to understand how different data sources impact customer satisfaction and loyalty. With the ever-increasing hype around the promise of Big Data, there has been a call for practitioners to provide real world examples of Big Data solutions in use.  I offer up one example below. The example was first presented in my book on CEM best practices, Beyond the Ultimate Question, and highlights Oracle’s use of Big Data principles to improve their service request (SR) process.

Oracle Understands Value of Integrating Data Silos

Jeb Dasteel, Oracle’s senior vice president and chief customer officer, understands the value of integrating different data sources with their customer metrics:

“It is important to understand how the operational measures that we use to drive our business correlate to the satisfaction of our customers. Our studies have helped determine the areas of operational performance that are the key drivers of our customer’s satisfaction. This has provided an opportunity to focus our improvement initiatives specifically on those areas that are of greatest importance to our customers.”

Jeb Dasteel, SVP, Chief Customer Officer, Oracle
from, Beyond the Ultimate Question

By integrating different types of metrics (from disparate data silos), Oracle is able to expand how they think about their customer experience improvement initiatives. Rather than focusing solely on their customer metrics to gain customer insights, Oracle links different data sources to get a holistic understanding of all the business areas that impact customer loyalty. Here is how they accomplished this Big Data project.

Oracle’s Service Request Process

Oracle customers can request help in the form of service requests (SRs).  The quality of these SRs are typically measured using objective operational metrics that are automatically generated in their CRM system. Oracle’s system tracks many operational metrics. For this illustration, we will look at three:

  • Total Time to Resolve (Close Date – Open Date)
  • Initial Response Time
  • Number of SR Ownership Changes

In addition to the operational metrics that are captured as part of their SR process, Oracle solicits feedback from their customers about the quality of their specific SR experience (via transaction-based survey). These customer feedback data are housed in a separate system apart from the operational metrics.

Oracle wanted to understand how their operational metrics were related to satisfaction with the service request.

Data Federation of Operational Metrics and Customer Metrics

Figure 1. Data Model for Linking Operational Metrics and Customer Metrics (result of data federation)

Oracle used data federation to pull together metrics from the two disparate data sources (operational metrics and one for customer satisfaction metrics). The data were linked together at the transaction level. The data model for this Big Data project appears in Figure 1.

After the data were linked together, segments for each operational variable were created (from low to high values) to understand how customer satisfaction varied over different levels of the operational metric.

Results of Analyses

Analyses revealed some interesting insights about how the three operational metrics impact customer satisfaction with the transaction. The relationship of each operational metric with overall satisfaction with the SR is presented in Figures 2, 3 and 4.

Using Total Time to Resolve the SR, Oracle found that customers were more satisfied with their SRs that were resolved more quickly compared to customers whose SRs took longer to resolve (See Figure 2.).

Figure 2. Relationship between time to resolve SR and customer satisfaction with SR

Using Initial Response Time to the SR, Oracle found that customers were no more satisfied or dissatisfied with their SRs whether the initial response time was fast or slow (See Figure 3.). Despite the expectations that the Initial Response Time to the SR would greatly impact the customers’ satisfaction with the SR, this study showed that the initial response time had no impact on the satisfaction of customers.

Figure 3. Relationship between initial response time and customer satisfaction with SR

Using Number of Ownership Changes, Oracle found that customers were more satisfied with their SRs that had fewer ownership changes compared to customers whose SRs had more ownership changes (See Figure 4.).

The application of Big Data solutions at Oracle has provided much insight regarding how the management of customers through the Service Request process can be facilitated with the use of operational metrics. The analyses showed that not all operational metrics are predictive of customer satisfaction;  initial response time was unrelated to customer satisfaction, suggesting that monitoring metrics associated with that aspect of the SR process is unnecessary in improving customer satisfaction. To improve the customer experience with the SR process (e.g., improve customer satisfaction), changes to the SR process are best directed at elements of the SR process that will impact the resolution time and the number of ownership changes.

Figure 4. Relationship between number of SR ownership changes and customer satisfaction with SR

Benefits of Big Data

Linking disparate data silos proved useful for Oracle. They were able to identify the operational metrics that were important to customers. More importantly, they were able to identify operational metrics that were not important to driving customer satisfaction. Demonstrating the statistical relationship between operational metrics and customer satisfaction and operational metrics can help you in three ways:

  1. Build/Identify customer-centric business metrics: You can identify/create key operational metrics that are statistically linked to customer satisfaction and focus only those that are important to your customers.
  2. Manage customer relationships using objective operational metrics: Driving business growth now becomes a process of using the operational metrics to manage customer relationships. Big Data studies can help you identify appropriate operational performance goals (using operational metrics) that ensure customers will be satisfied.
  3. Reward employee behavior that will drive customer satisfaction: Because of their reliability and specificity, operational metrics are good candidates for use in goal setting and employee incentive programs.  Rewarding employee performance based on customer-centric operational metrics ensures employees are aligned with the needs of the customers.

Summary

Proper application of Big Data principles helps expand the types of metrics you can use as part of your customer experience strategy. By taking a customer-centric approach in their analyses of their Big Data, Oracle was able to link operational metrics to customer feedback metrics to identify how the operational metrics are related to customer satisfaction. This type of approach to understanding all your business data will help you build customer-centric operational metrics, manage customer relationships using operational metrics and reward employees based on operational metrics that matter to the customer.

Originally Posted at: How Oracle Uses Big Data to Improve the Customer Experience

Four Use Cases for Healthcare Predictive Analytics, Big Data

Predictive analytics in healthcare has long been the wave of the future: an ultimate goal to which everyone aspires but few can claim success.  While the landscape is changing for healthcare predictive analytics as more organizations figure out how to harness big data and implement the right infrastructure for generating actionable insights from a slew of new sources, some providers may still be wondering how the pie-in-the-sky world of big data can actually work for them.

Luckily, a number of pioneering organizations have taken it upon themselves to test the waters of healthcare predictive analytics, generating use cases that spur interest and help carve a path through the wilderness.

In this article, HealthITAnalytics.com explores some of the ways healthcare organizations have already found success by turning big data into a strategic asset that can help providers react quickly and effectively to the ongoing challenges of quality care delivery.

Hospital quality and patient safety in the ICU

The ICU is another area where predictive analytics is becoming crucial for patient safety and quality care.  The most vulnerable patients are prone to sudden downturns due to infection, sepsis, and other crisis events which are often difficult for busy staff to predict.  However, a number of organizations have been working on integrating bedside medical device data into sensitive algorithms that detect plummeting vitalsigns hours before humans have a clue.

At the University of California Davis, researchers are using routinely collected EHR data as the fodder for an algorithm that gives clinicians an early warning about sepsis, which has a 40 percent mortality rate and is difficult to detect until it’s too late. “Finding a precise and quick way to determine which patients are at high risk of developing the disease is critically important,” said study co-author Hien Nguyen, Associate Professor of Internal Medicine and Medical Director of EHRs at UC Davis. “We wanted to see if EHRs could provide the foundation for knowing when aggressive diagnosis and treatment are needed and when they can be avoided.”

At Massachusetts General Hospital, an analytics system called QPID is helping providers ensurethat they don’t miss critical patient data during admission and treatment.  The system is also used to predict surgical risk, helping match patients with the right course of action that will keep them safest during their care. “Surgeons, even the world-renown surgeons, do not want to operate on a patient who’s going to die on the table,” explained Dr. David Ting, Associate Medical Director for Information Systems at the Massachusetts General Physicians Organization.  “The last thing they want to do is do harm to a patient or do something inappropriately.  The system automates searches using national guidelines, and then it essentially shows the results in a dashboard with a red, yellow, or green risk indicator for the surgeon to see.”

Precision medicine, personalized care, and genomics

“Precision medicine” entered the healthcare industry’s lexicon in a big way earlier this year during President Obama’s State of the Union address.  The President’s vision for a nationwide patient databank sparked hopes of a renewed commitment to genomic research and the development of personalized treatments, but the NIH isn’t the only one who has been using big data to predict the course of diseases related to a patient’s genetic makeup.

Healthcare predictive analytics has been particularly instrumental in the fight against cancer, and has also helped to target the development of preventative measures related to heart disease, diabetes, and even food poisoning based on genetic research.

Population health management, risk stratification, and prevention

Population health management is as much about prevention as it is about treatment, and healthcare predictive analytics equip providers with the tools they need to be proactive about their patients’ needs.  Targeting patients based on their past behaviors can help to predict future events, such as a diabetic ending up in the emergency room because he did not refill his medication or a child with asthma requiring a hospital admission due to environmental triggers of her disease.

By harnessing EHR data, providers can even identify links between previously disparate diseases.  A risk score developed by Kaiser Permanente researchers in 2013 allows clinicians to predict diabetic patients who are likely to develop dementia in the future, while the Army is attempting to curb the rampant rate of veteran suicides by leveraging a predictive risk model to identify patients who may be likely to harm themselves.

“We could save four lives for every hundred people we treated” with better data-driven care coordination and follow-up after a hospital stay for a psychiatric episode, said Lt. Gen. Eric B. Schoomaker, a former surgeon general of the Army and a professor of military and emergency medicine at the Uniformed Services University of the Health Sciences. “This would be unparalleled, compared to almost any other intervention we could make in medicine.  This study begins to show the positive effects big data can have, when combined with administrative health records.”

Healthcare predictive analytics can even prevent bottlenecks in the urgent care department or emergency room by analyzing patient flow during peak times to give providers the chance to schedule extra staff or make other arrangements for access to care.

“Emergency department crowding is a complex problem affecting more than 130 million patient visits per year in the US,” writes Joshua E. Hurwitz, lead author of a study detailing the effects of an online patient flow simulator. “In the current world of scarce resources and little margin for error, it is essential to rigorously identify the specific causes of crowding, so that targeted management interventions can have maximal effect.”

Reducing preventable hospital readmissions

As hospitals begin to feel the financial pinch of high 30-day readmission rates, they are turning to predictive analytics to keep patients at home.  At the University of Pennsylvania, informaticists can look at prior hospitalization histories to flag patients who may be returning to the inpatient setting within 30 days.

Real-time EHR data analytics helped a Texas hospital cut readmissions by five percent by drawing on nearly 30 data elements included in the patient’s chart. “This is one of the first prospective studies to demonstrate how detailed data in EMRs can be used in real-time to automatically identify and target patients at the highest risk of readmission early in their initial hospitalization when there is a lot that can be done to improve and coordinate their care, so they will do well when they leave the hospital,” said Ethan Halm, MD, MPH, Professor of Internal Medicine and Clinical Sciences and Chief of the Division of General Internal Medicine at UT Southwestern.

Meanwhile, the Kaiser Permanente system has been working to refine its readmissions algorithms in order to better understand which returns to the hospital are preventable and which are not, a crucial distinction for value-based reimbursements.

“Classifying readmissions as potentially preventable or not preventable can be used to improve hospital performance,” wrote the authors of the study comparing an algorithm to human review of readmissions cases. “Administrators can sort potentially preventable readmissions into categories that are actionable for improvement. They can identify trends over time or across reporting units. Classifying readmissions as potentially preventable or not preventable can also be used to establish accountability across reporting units and reward top performers.”

Originally posted via “Four Use Cases for Healthcare Predictive Analytics, Big Data”

Originally Posted at: Four Use Cases for Healthcare Predictive Analytics, Big Data

Looking for a career? ‘Big data’ analysts in high demand

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A swell of consumer data — from sales numbers to social media feeds — has bumped up demand for workers who can help businesses turn that information into profit, and Iowa’s universities are jumping in to help.

“You can’t pick up a newspaper or turn on the TV without somebody yelling ‘big data’ at you. It’s a reality,” said Nick Street, a professor of management sciences at the University of Iowa.

Within the last year, several Iowa universities have announced plans to start or expand academic programs to address the growth of data and the demand for workers who can tell businesses what it all means.

Drake University will roll out a new data analytics program this school year. The program comes along with plans for a $65 million, six-building complex revolving around science, technology, engineering and math.

“I think culturally we’ve become a data-driven world … we just need to have as much information as we can and figure out what’s important in that information,” said Daniel Alexander, the co-director of Drake University’s data analytics program.

“Where data science comes in is taking these vast sorts of unreadable databases and (distilling) them into something people can use.”

The University of Iowa has had a business analytics undergraduate program for a few years. Earlier this year, however, the university said it will start offering a master’s program in Cedar Rapids.

It also plans to start offering a certificate program in Des Moines.

“Everyone is collecting tons and tons of data. They don’t know what to do with it,” Street said. “They need to know how to turn it into money.”

In February, Iowa State University announced its own master of business analytics program.

‘Tease out their secrets’

Although the traditional view of “big data” involves countless numbers and rows in an Excel spreadsheet, professors at each university say they’re taking a different path.

Instead of just needing someone who can compile a bunch of figures, they said companies need analysts who can both understand the data and meaningfully interpret it to others.

“Big datasets don’t like to give up their secrets really easily, so we’re trying to train students who can collect data, who can develop these datasets, but more importantly can mine them, can understand them, can tease out their secrets,” Alexander said.

Getting at those secrets is important for all companies, especially if it leads to more sales, happier customers and a better bottom line.

“We’re looking for people that have the skills to take that data, turn it into information and then use it to make business decisions,” said Terry Lillis, chief financial officer for Principal Financial Group.

‘Crank this up’

There is already high demand for these jobs, Street and others said. It’s only slated to increase.

“Our corporate partners here are wanting more. They want us to crank this up so they can get those skills in their workplace at all levels,” Street said.

At Iowa State, Sree Nilakanta said that although the university already had classes teaching analytics, increased demand prompted a specific program.

“There is now a specific demand from companies saying, ‘We want analytics professionals,’ ” said Nilakanta, who chairs ISU’s information systems department. “It’s easier now to put a label on it.”

While some technology companies have used data analytics for years, other industries are realizing the larger implications.

“Google started hiring, Facebook started hiring and then everybody figured out that we need to get into this game,” Nilakanta said.

Fast-growing profession

The U.S. Bureau of Labor Statistics expects the employment of statisticians to grow 27 percent between 2012 and 2022, faster than the 11 percent average. Computer programmer employment is expected to grow by 8 percent.

“Everybody is looking for these types of individuals,” Lillis said.

The bureau doesn’t track specific “big data” jobs, instead splitting job projections among other fields, such as statisticians and computer programmers.

In a 2011 report, consulting firm McKinsey & Co. projected the United States would have a shortage of 140,000 to 190,000 people with “deep analytical skills” who would know how to analyze big data.

Job search site Glassdoor.com puts the national average salary for business analysts at about $65,000 a year.

Part of that increased demand, Street said, comes from the need to have people familiar with data in all parts of a company.

“The tradition is, you hire one or two Ph.Ds and you expect all kinds of brilliance to come out. Well, that’s not sustainable,” he said. “You need people to know how to think with data at every level of the organization, and that’s what they’re looking for.”

Read more @ http://www.desmoinesregister.com/story/tech/2015/08/02/iowa-universities-data-business-analytics-programs/31034415/

Source: Looking for a career? ‘Big data’ analysts in high demand