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.
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 outperformÂ their competitors who do not.
I recently wrote about theÂ implications of Big Data on the practice of CEMÂ 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.Â 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. Â I offer up one example below.Â The example was first presented in my book on CEM best practices,Â Beyond the Ultimate Question, and highlights Oracle’s use of Big Data principles to improve their service request (SR) process.
Jeb Dasteel, Oracle’s senior vice president and chief customer officer, understands the value of integrating different data sources with their customer metrics:
âIt 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.â
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 customers can request help in the form of service requests (SRs). Â 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:
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.
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.
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.).
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 customersâ satisfaction with the SR, this study showed that the initial response time had no impact on the satisfaction of customers.
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.Â The analyses showed that not all operational metrics are predictive of customer satisfaction; Â 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.
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:
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.
Predictive analytics in healthcare has long been the wave of the future: an ultimate goal to which everyone aspires but few can claim success.Â 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.Â 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.Â 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 itâs too late. âFinding a precise and quick way to determine which patients are at high risk of developing the disease is critically important,â said study co-author Hien Nguyen, Associate Professor of Internal Medicine and Medical Director of EHRs at UC Davis. âWe wanted to see if EHRs could provide the foundation for knowing when aggressive diagnosis and treatment are needed and when they can be avoided.â
At Massachusetts General Hospital, an analytics system called QPID is helping providers ensurethat they donât miss critical patient data during admission and treatment.Â 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. âSurgeons, even the world-renown surgeons, do not want to operate on a patient whoâs going to die on the table,â explained Dr. David Ting, Associate Medical Director for Information Systems at the Massachusetts General Physicians Organization.Â âThe last thing they want to do is do harm to a patient or do something inappropriately.Â Â 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.â
Precision medicine, personalized care, and genomics
âPrecision medicineâ entered the healthcare industryâs lexicon in a big way earlier this year during President Obamaâs State of the Union address.Â The Presidentâs vision for a nationwide patient databank sparked hopes of a renewed commitment to genomic research and the development of personalized treatments, but the NIH isnât the only one who has been using big data to predict the course of diseases related to a patientâs 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 patientsâ needs.Â 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.Â 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.
âWe could save four lives for every hundred people we treatedâ 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. âThis would be unparalleled, compared to almost any other intervention we could make in medicine.Â This study begins to show the positive effects big data can have, when combined with administrative health records.â
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.
âEmergency department crowding is a complex problem affecting more than 130 million patient visits per year in the US,â writes Joshua E. Hurwitz, lead author of a study detailing the effects of an online patient flow simulator. âIn the current world of scarce resources and little margin for error, it is essential to rigorously identify the speciï¬c causes of crowding, so that targeted management interventions can have maximal effect.â
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.Â 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 patientâs chart. âThis 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,âÂ saidÂ 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.
âClassifying readmissions as potentially preventable or not preventable can be used to improve hospital performance,â wrote the authors of the study comparing an algorithm to human review of readmissions cases. âAdministrators 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.â
Originally posted via “Four Use Cases for Healthcare Predictive Analytics, Big Data”
A swell of consumer data â from sales numbers to social media feeds â has bumped up demand for workers who can help businesses turn that information into profit, and Iowaâs universities are jumping in to help.
âYou canât pick up a newspaper or turn on the TV without somebody yelling âbig dataâ at you. Itâs a reality,â 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.
âI think culturally weâve become a data-driven world â¦ we just need to have as much information as we can and figure out whatâs important in that information,â said Daniel Alexander, the co-director of Drake Universityâs data analytics program.
âWhere data science comes in is taking these vast sorts of unreadable databases and (distilling) them into something people can use.â
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 masterâs program in Cedar Rapids.
It also plans to start offering a certificate program in Des Moines.
âEveryone is collecting tons and tons of data. They donât know what to do with it,â Street said. âThey need to know how to turn it into money.â
In February, Iowa State University announced its own master of business analytics program.
âTease out their secretsâ
Although the traditional view of âbig dataâ involves countless numbers and rows in an Excel spreadsheet, professors at each university say theyâre 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.
âBig datasets donât like to give up their secrets really easily, so weâre 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,â 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.
âWeâre looking for people that have the skills to take that data, turn it into information and then use it to make business decisions,â said Terry Lillis, chief financial officer for Principal Financial Group.
âCrank this upâ
There is already high demand for these jobs, Street and others said. Itâs only slated to increase.
âOur 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,â Street said.
At Iowa State, Sree Nilakanta said that although the university already had classes teaching analytics, increased demand prompted a specific program.
âThere is now a specific demand from companies saying, âWe want analytics professionals,â â said Nilakanta, who chairs ISUâs information systems department. âItâs easier now to put a label on it.â
While some technology companies have used data analytics for years, other industries are realizing the larger implications.
âGoogle started hiring, Facebook started hiring and then everybody figured out that we need to get into this game,â Nilakanta said.
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.
âEverybody is looking for these types of individuals,â Lillis said.
The bureau doesnât track specific âbig dataâ 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 âdeep analytical skillsâ 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.
âThe tradition is, you hire one or two Ph.Ds and you expect all kinds of brilliance to come out. Well, thatâs not sustainable,â he said. âYou need people to know how to think with data at every level of the organization, and thatâs what theyâre looking for.â
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 betweenâand 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 scientistsÂ to break the rules. A new paper by Morton Oskvold, a Norwegian scientist, will fan those flames, as it makesÂ 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 thisÂ 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 wereÂ 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 dataÂ in each, looking for images (or elements in images) that appeared more than once. In papers where these elementsÂ were found, Oskvold then dug deeper, also looking at other publications from the same authors to see if there wasÂ evidence of reused data.
The images Oskvold focused onÂ 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 startlingâa quarter of the papers showed identical images in two or more figures, a finding that was consistent across all three journals. However, once oneÂ 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.
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. OskvoldÂ calls the publication into doubt because it raises uncertainty about whether or not sufficient experiments were actually performedâit’s not enough toÂ 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 onceÂ might not be the most effective way of communicating aÂ researcher’s results;Â using subsets of an experiment to communicate specific pointsÂ may be more effective. In fact, there’s evidence of exactly this kind of duplication in one of Oskvold’s ownÂ 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).Â He didn’t hear back from any of the three journal editorial boards.
While we don’t think that the initial claimâa quarter of cancer research is fakeâis accurate, the fact that it’s closer to one in eight should still be troubling. AÂ lot of responsibility rests with the authors who write these papers, as well asÂ 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.
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”