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

 

Issue #15    Web Version
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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

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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.
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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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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

B9318261199Z.1_20150802191505_000_GTDBGKV3J.1-0
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

Study claims 1 in 4 cancer research papers contains faked data

You could be forgiven for thinking there’s a bit of a crisis going on in biomedical science these days. Tenured academic positions are few and far between—and are often dependent upon the researcher’s success in obtaining scarce funding. The pressure to succeed, measured by publications, is sometimes blamed for leading less-scrupulous scientists to break the rules. A new paper by Morton Oskvold, a Norwegian scientist, will fan those flames, as it makes the bold claim that 25 percent of cancer biology papers contain duplicated data. Is something rotten in our research labs?

There has been a real uptick in scientific misconduct in recent years, but it’s not going unchallenged. Post-publication peer review, where papers are critiqued publicly on the Internet by other scientists, is putting the literature to the test. And journals are taking a tougher line with authors to ensure that they include all the relevant details, not just the ones that make them look good.

Some of this comes in response to high-profile publications like one from researchers at the biotech company Amgen, who tried to reproduce the findings of 53 “landmark” preclinical cancer research papers but were only able to do so for six of them.

Oskvold’s paper, published in Science and Engineering Ethics, looked at cancer biology papers published in three journals (International Journal of Oncology, Oncogene, and Cancer Cell) during 2013. He selected 40 papers from each journal at random and then systematically examined the data in each, looking for images (or elements in images) that appeared more than once. In papers where these elements were found, Oskvold then dug deeper, also looking at other publications from the same authors to see if there was evidence of reused data.

The images Oskvold focused on are photographs of Western blots (where proteins are separated by weight and labeled with antibodies) and microscope images (again, often labeled with fluorescent antibodies).

The results are rather startling—a quarter of the papers showed identical images in two or more figures, a finding that was consistent across all three journals. However, once one digs a little deeper into the results, some of the findings that Oskvold calls problematic turn out to be a bit less clear-cut. That’s because the data duplications fall into one of two categories. Just over half of the papers with duplications pass off the same image as two completely different experiments. That is clearly outside the bounds of acceptable behavior for scientists, and bravo to Oskvold for calling them to account.

This kind of thing is not OK. The same data is presented as different experiments in two different papers.
This kind of thing is not OK. The same data is presented as different experiments in two different papers.
This is a much less clear case. Many researchers would tell you there was no problem splitting up a gel into sub-figures like this.
This is a much less clear case. Many researchers would tell you there was no problem splitting up a gel into sub-figures like this.

But in the other cases, the duplications are data from the same experimental conditions. For example, using a subset of a Western blot in one figure, then another subset (including the same control) in a second figure. Oskvold calls the publication into doubt because it raises uncertainty about whether or not sufficient experiments were actually performed—it’s not enough to do it once, shout “eureka!” and send off the manuscript. But many other scientists take issue with this hardline view, something evident from a lengthy discussion of Oskvold’s findings at PubPeer (Oskvold is Peer 1).

There are legitimate reasons for reusing the same data in more than one figure. As mentioned, budgets are tight, reagents aren’t cheap, and it’s often prudent to run a Western blot with eight or ten (or more) samples at once. However, dumping all this data out at once might not be the most effective way of communicating a researcher’s results; using subsets of an experiment to communicate specific points may be more effective. In fact, there’s evidence of exactly this kind of duplication in one of Oskvold’s own publications.

Oskvold contacted each of the journals about his findings, as well as the authors for the 29 papers where he found duplication (he also started PubPeer threads for each one). Only one of the authors responded (accepting responsibility for mixing up the images), along with a second unverified author (who claimed the journal made the error during page layout). He didn’t hear back from any of the three journal editorial boards.

While we don’t think that the initial claim—a quarter of cancer research is fake—is accurate, the fact that it’s closer to one in eight should still be troubling. A lot of responsibility rests with the authors who write these papers, as well as the reviewers and journal editors who accept them for publication. With bandwidth and storage as cheap as they are now, there’s no good reason why one shouldn’t be asked to submit the raw data for each experiment when submitting a paper.

Sadly, the pressure to puff up one’s findings probably isn’t going away any time soon. So, unless there’s an organized strengthening of standards, problems like these probably won’t go away either.

To read the original article on ars technica, click here.

Source

Big data analytics startup Sqrrl raises $7M

Sqrrl, a Cambridge-based big data analytics startup, has raised $7 million in Series B funding and also unveiled a new software aimed at detecting and responding to cybersecurity threats.

This brings total funding to date for the company to $14.2 million, with investors including Rally Ventures, Atlas Venture and Matrix Partners.

The company says it makes software to uncover hidden patterns, trends and links in data. On Wednesday, Sqrrl also announced the launch of its new software, Sqrrl Enterprise 2.0, which focuses “on the challenges posed by cybersecurity threats and vulnerabilities that nearly every organization faces today.”

“Sqrrl is at the intersection of two of the most important trends facing the enterprise: cybersecurity and Big Data,” said Zenas Hutcheson, partner at Rally Ventures. “Sqrrl’s technology can help both Fortune 1000 companies and government agencies prevent themselves from becoming the next cyber incident headline story.”

The company’s customers include several undisclosed Fortune 500 companies and large government agencies.

Thirty-five employees work at the company’s headquarters in Cambridge and Sqrrl plans to hire 25 more this year, according to Ely Kahn, co-founder and Director of Business Development at Sqrrl.

Originally posted via “Big data analytics startup Sqrrl raises $7M”

Source: Big data analytics startup Sqrrl raises $7M by anum