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What Autonomous Cars Mean For Insurance Companies

Robert Cordray

Silicon Valley thrives on being disruptive. So far, these startups have taken down the music industry, the movie rental business, and the print news industry. Now, they are in the process of speeding carchanging countless other bastions that were once considered traditional workplaces.

So what’s next? One of the big ideas being explored – and taken more seriously – is the concept of the driverless or autonomous vehicle. The implications for this technology are staggering, possibly even more so than the other revolutionary changes we’ve seen in tech over the past 10 to 20 years.

Now more than ever, executives in the insurance industry are the ones who need to be cautiously looking over their shoulders. When the technology completely matures for an autonomous vehicle that allows you take a nap on the way to work every day, insurance companies better have a new business model in place if they want to stay alive.

How the advent of the autonomous car will impact insurers

There will always be a need for insurance, no matter how advanced technology becomes. The possibility of an act of nature occurring and damaging property will always be present. But, these kinds of accidents are usually outliers when it comes to the insurance business for automobiles. The problem of human error on the roads is what inspired the concept of an autonomous car from the start, but this is also the very same thing insurance companies rely on to stay profitable.

Insurance premiums are charged based on the individual, their propensity for safe driving along with their driving history, and the vehicle they wish to insure. A car insurance comparison will show that these premiums differ slightly, but each of these premiums will incorporate this human element as a big factor for the final cost. So what happens when that element ceases to be present? This is a point of contention now among car manufacturers, regulators, and, especially, insurance companies. And opinions largely differ across these groups.

Why insurers should care now

Some insurance executives aren’t worried about this rise in automated driving, asserting that this change isn’t going to happen for years or possibly even decades. And they do have a point.

Driverless technologies are still only in their infancy, and it can be presumed that we’ll see iterations of them introduced in a piecemeal fashion. For example, in the past five years, some cars have introduced features like automated parallel parking and assisted cruise control options. So how long is it before the full package arrives? It could be years until something like Google’s driverless car is on sale at your local dealership. And even then there would be a huge amount of red tape, bureaucracy, and safety testing that would need to be done to help ensure these vehicles are actually safe for consumers.

Then, on top of everything already mentioned, the technology must be accepted by the public. It could reasonably take at least a decade for driverless cars to constitute a majority of cars on the road from the time they’re introduced in the marketplace. The adoption cycle for new technologies represents a bell curve, with only a few early adopters at first who are then followed by a constantly rising number of people until a majority is reached. Know any adults who don’t own a cell phone? These are the late adopters, and they’ll be the same people are the last to phase out their non-autonomous vehicles.

However, it would be a mistake for these same executives to dismiss this innovation so quickly, especially with the radical changes that technology has introduced to topple previously successful businesses virtually overnight. While this could take multiple decades, it’s still something that must be considered now so insurance companies can minimize future risk to profits, especially if it ends up happening sooner rather than later. Some thoughts on where profit could be made up include insuring the new components on these cars. Driverless cars will have a host of new sensors, cameras, and software to give them these new capabilities. A more expensive vehicle with more specialized parts inherently means a greater premium cost.

But, does this make up for the cost of losing out on the mistakes of human drivers? Probably not. Driverless cars mean fewer accidents by orders of magnitude less than the current statistics on automobile accidents. Undoubtedly, there will be bugs in the software and hardware with some of the first iterations of these vehicles, and there might even be the occasional bug even after driverless cars have been around for years. While this could mean big trouble and liability for automakers, filling the insurance gap by selling to automakers in place of consumers is still likely to yield less of an overall profit. For this dilemma, these companies are going to have to get a little more creative with the products and services they offer people.

Finally, it should be noted that driverless cars raise new issues that we likely haven’t even contemplated yet. There will be unforeseen circumstances that begin to appear as implementation begins. These are the areas insurance companies will likely be forced to focus on in order to maintain their current sizes. Otherwise, they’ll risk drawdowns that will most likely end up happening.

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In The Future, All Products Will Automatically Improve As People Use Them

Timo Elliott

This is the Tesla autopilot in action – and it’s a great analogy for the future of digital business.

As every Tesla car equipped with autopilot drives down the street, it’s using sensors and the Internet of Things to gather vast amounts of Big Data. And then it uses analytics, machine learning, and artificial intelligence to turn that data into a superior driving experience.

That’s what digital business is all about – using the latest technologies to create better products and services. But Tesla is taking things to a whole new level. The data from every car is sent to headquarters and shared with every other car on the road. So your car knows what to look out for even if you’ve never been on that street before.

This means that Tesla has essentially turned itself into a massively parallel learning machine. The Tesla customer experience now improves automatically the more you drive and the more other people use the product.

In addition, the company is gathering detailed information that can be used for many of other business opportunities in the future. And that’s perhaps why Tesla is now the most valuable U.S. car company, eclipsing General Motors, even though GM makes over 100 times as many cars.

These types of self-improving products are now starting to take over the world. For example, AlphaGo’s algorithms shocked the experts last year by beating one of the world’s strongest Go champions. And it’s been steadily improving ever since – to the point where one Chinese Go master says it now “plays like a God.”

So imagine — what if your products and services automatically improved as more people used them?

New customer chatbots are a simple example of self-improving interfaces. Modeled on consumer services such as Siri and Alexa, these chatbots are poised to make all interactions with computers easier, from purchasing items online to working with internal business applications. You can simply ask things like “what colors are available?” or “what are the details of this order?” and the chatbot will respond. And because these services leverage machine learning, the quality of the responses will automatically improve over time and as more people use them.

And that’s just the start. Machine learning can now be embedded into every customer experience and operational process. For example, chemicals giant BASF was able to use machine learning on repetitive decisions in the finance function, improving invoice matching from 70% to 94% – and that score should rise as the algorithms master the remaining variations.

But there’s one key prerequisite for this vision: good data

Artificial intelligence works best when you have large amounts of high-quality training data, applied to a specific, clearly defined business problem. So the first step to introducing self-improving products and services to your customers is a single, consistent, governed view of all necessary information, no matter where it’s stored, inside or outside the organization.

The self-fulfilling cycle of data, AI, and better product experiences

More data means better artificial intelligence algorithms, which means better customer experiences, which means better customers, which means more data… The winners in digital business will be the ones who first unleash this virtuous circle of self-improving AI and “self-driving business.”

Autopilot Full Self-Driving Hardware (Neighborhood Short) from Tesla, Inc on Vimeo.

Develop a machine learning strategy that will change the basis of competition in your industry. Learn Why Machine Learning and Why Now?

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About Timo Elliott

Timo Elliott is an Innovation Evangelist for SAP and a passionate advocate of innovation, digital business, analytics, and artificial intelligence. He was the eighth employee of BusinessObjects and for the last 25 years he has worked closely with SAP customers around the world on new technology directions and their impact on real-world organizations. His articles have appeared in articles such as Harvard Business Review, Forbes, ZDNet, The Guardian, and Digitalist Magazine. He has worked in the UK, Hong Kong, New Zealand, and Silicon Valley, and currently lives in Paris, France. He has a degree in Econometrics and a patent in mobile analytics. 

McLaren Automotive: Racing Ahead With Real-Time Connected Intelligence

Richard Howells

McLaren Automotive’s entry-level 570S Coupé packs 562 horsepower that rockets the car from 0 to 60 mph in 2.9 seconds. But that’s nothing compared with the speed of the company’s real-time connected intelligence.

Based in Woking, England, McLaren designs and manufactures sports and luxury cars. Most are produced in-house at designated production facilities. And increasingly, the company relies on Internet of Things (IoT) technologies.

I caught up with Craig Charlton, CIO of McLaren Technology Group, in May at SAPPHIRE NOW, where we discussed McLaren’s IoT journey.

One strategy, four units, five transformers

McLaren is pursuing a single IT strategy: “to deliver core solutions, core platforms, and winning platforms.” But it needs to execute that strategy across four business units, each of which requires a different approach to IT:

  1. McLaren Automotive — Manufactures high-performance sports and luxury cars
  2. McLaren Racing — Races to win in Grands Prix and World Championships
  3. McLaren Applied Technologies — Applies advanced technologies and designs across markets as diverse as health and energy to achieve performance breakthroughs
  4. McLaren Commercial — Identifies and enriches partnerships to drive business success

The company is achieving its IT goals through its “Transformational Big Five:”

  1. Business platforms — Advanced business platforms support processes in each of McLaren’s four units.
  2. Cloud and mobility — With 2,800 of the company’s 3,400 employees on mobile devices, cloud is everywhere.
  3. Managed risk — By migrating from legacy systems, McLaren is reducing cybersecurity vulnerabilities and managing risk.
  4. People-centricity — IT is central to how McLaren’s people do business every day.
  5. Partners — McLaren has been co-innovating with SAP for more than 20 years.

Internet of (very fast) Things

But some of the most exciting IT at McLaren revolves around IoT. And as Craig explains, IoT is hardly new at McLaren. “We’ve been using IoT-type technology since 1993,” he says, “when we first put telemetry on our racing cars to analyze race performance.”

Today, at a typical race, the company has 150 to 300 car sensors tracking everything from tire pressure to brake wear to G-force. These sensors generate more than 100 GB of data every race weekend — producing 11.8 billion data points per season and 1080 race permutations in real time, so the race team can ask questions like, “How many times did Fernando Alonso pull 6G in the last race?” — and get the answer in two or three seconds.

“The data has truly transformed how we race,” Craig says. “Solutions like SAP HANA have allowed us to track billions of data points and look at historical data going back 24 years. In fact, we can analyze about 1 trillion data points.”

Fine-tuning race cars, transforming business models

What makes IoT mission-critical to McLaren is the ability to gain new insights to improve performance. By analyzing its Big Data, the company can identify nuggets that help it fine-tune its cars and be faster around the track.

But the company also expects to leverage real-time connected intelligence to improve the performance of its business. “IoT is going to change many organizations from being product-based to being service-based,” Craig predicts. “In the automotive industry, when we talk about autonomous cars, customers may be looking to buy a unit of travel rather than a car.”

For companies in the automotive and many other industries, business change is hardly slowing down. Real-time connected intelligence will help them stay ahead of the curve.

To learn more about McLaren’s IoT journey, watch Craig’s SAPPHIRE NOW presentation or listen to a one-on-one interview with Craig.

To see Craig and 50 other industry experts in person, attend SAP Leonardo Live, July 11 and 12 at the Kap Europa Congress Center in Frankfurt, Germany. The event will bring together a vibrant global community of up to 1,500 IoT, manufacturing, supply chain, R&D, and operations decision makers, influencers, analysts, and media. Learn firsthand from more than 50 SAP customer showcases how to connect IoT and core business processes to achieve digital transformation.

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About Richard Howells

Richard Howells is a Vice President at SAP responsible for the positioning, messaging, AR , PR and go-to market activities for the SAP Supply Chain solutions.

Heroes in the Race to Save Antibiotics

Dr. David Delaney, Joseph Miles, Walt Ellenberger, Saravana Chandran, and Stephanie Overby

Last August, a woman arrived at a Reno, Nevada, hospital and told the attending doctors that she had recently returned from an extended trip to India, where she had broken her right thighbone two years ago. The woman, who was in her 70s, had subsequently developed an infection in her thigh and hip for which she was hospitalized in India several times. The Reno doctors recognized that the infection was serious—and the visit to India, where antibiotic-resistant bacteria runs rampant, raised red flags.

When none of the 14 antibiotics the physicians used to treat the woman worked, they sent a sample of the bacterium to the U.S. Centers for Disease Control (CDC) for testing. The CDC confirmed the doctors’ worst fears: the woman had a class of microbe called carbapenem-resistant Enterobacteriaceae (CRE). Carbapenems are a powerful class of antibiotics used as last-resort treatment for multidrug-resistant infections. The CDC further found that, in this patient’s case, the pathogen was impervious to all 26 antibiotics approved by the U.S. Food and Drug Administration (FDA).

In other words, there was no cure.

This is just the latest alarming development signaling the end of the road for antibiotics as we know them. In September, the woman died from septic shock, in which an infection takes over and shuts down the body’s systems, according to the CDC’s Morbidity and Mortality Weekly Report.

Other antibiotic options, had they been available, might have saved the Nevada woman. But the solution to the larger problem won’t be a new drug. It will have to be an entirely new approach to the diagnosis of infectious disease, to the use of antibiotics, and to the monitoring of antimicrobial resistance (AMR)—all enabled by new technology.

But that new technology is not being implemented fast enough to prevent what former CDC director Tom Frieden has nicknamed nightmare bacteria. And the nightmare is becoming scarier by the year. A 2014 British study calculated that 700,000 people die globally each year because of AMR. By 2050, the global cost of antibiotic resistance could grow to 10 million deaths and US$100 trillion a year, according to a 2014 estimate. And the rate of AMR is growing exponentially, thanks to the speed with which humans serving as hosts for these nasty bugs can move among healthcare facilities—or countries. In the United States, for example, CRE had been seen only in North Carolina in 2000; today it’s nationwide.

Abuse and overuse of antibiotics in healthcare and livestock production have enabled bacteria to both mutate and acquire resistant genes from other organisms, resulting in truly pan-drug resistant organisms. As ever-more powerful superbugs continue to proliferate, we are potentially facing the deadliest and most costly human-made catastrophe in modern times.

“Without urgent, coordinated action by many stakeholders, the world is headed for a post-antibiotic era, in which common infections and minor injuries which have been treatable for decades can once again kill,” said Dr. Keiji Fukuda, assistant director-general for health security for the World Health Organization (WHO).

Even if new antibiotics could solve the problem, there are obstacles to their development. For one thing, antibiotics have complex molecular structures, which slows the discovery process. Further, they aren’t terribly lucrative for pharmaceutical manufacturers: public health concerns call for new antimicrobials to be financially accessible to patients and used conservatively precisely because of the AMR issue, which reduces the financial incentives to create new compounds. The last entirely new class of antibiotic was introduced 30 year ago. Finally, bacteria will develop resistance to new antibiotics as well if we don’t adopt new approaches to using them.

Technology can play the lead role in heading off this disaster. Vast amounts of data from multiple sources are required for better decision making at all points in the process, from tracking or predicting antibiotic-resistant disease outbreaks to speeding the potential discovery of new antibiotic compounds. However, microbes will quickly adapt and resist new medications, too, if we don’t also employ systems that help doctors diagnose and treat infection in a more targeted and judicious way.

Indeed, digital tools can help in all four actions that the CDC recommends for combating AMR: preventing infections and their spread, tracking resistance patterns, improving antibiotic use, and developing new diagnostics and treatment.

Meanwhile, individuals who understand both the complexities of AMR and the value of technologies like machine learning, human-computer interaction (HCI), and mobile applications are working to develop and advocate for solutions that could save millions of lives.

Keeping an Eye Out for Outbreaks

Like others who are leading the fight against AMR, Dr. Steven Solomon has no illusions about the difficulty of the challenge. “It is the single most complex problem in all of medicine and public health—far outpacing the complexity and the difficulty of any other problem that we face,” says Solomon, who is a global health consultant and former director of the CDC’s Office of Antimicrobial Resistance.

Solomon wants to take the battle against AMR beyond the laboratory. In his view, surveillance—tracking and analyzing various data on AMR—is critical, particularly given how quickly and widely it spreads. But surveillance efforts are currently fraught with shortcomings. The available data is fragmented and often not comparable. Hospitals fail to collect the representative samples necessary for surveillance analytics, collecting data only on those patients who experience resistance and not on those who get better. Laboratories use a wide variety of testing methods, and reporting is not always consistent or complete.

Surveillance can serve as an early warning system. But weaknesses in these systems have caused public health officials to consistently underestimate the impact of AMR in loss of lives and financial costs. That’s why improving surveillance must be a top priority, says Solomon, who previously served as chair of the U.S. Federal Interagency Task Force on AMR and has been tracking the advance of AMR since he joined the U.S. Public Health Service in 1981.

A Collaborative Diagnosis

Ineffective surveillance has also contributed to huge growth in the use of antibiotics when they aren’t warranted. Strong patient demand and financial incentives for prescribing physicians are blamed for antibiotics abuse in China. India has become the largest consumer of antibiotics on the planet, in part because they are prescribed or sold for diarrheal diseases and upper respiratory infections for which they have limited value. And many countries allow individuals to purchase antibiotics over the counter, exacerbating misuse and overuse.

In the United States, antibiotics are improperly prescribed 50% of the time, according to CDC estimates. One study of adult patients visiting U.S. doctors to treat respiratory problems found that more than two-thirds of antibiotics were prescribed for conditions that were not infections at all or for infections caused by viruses—for which an antibiotic would do nothing. That’s 27 million courses of antibiotics wasted a year—just for respiratory problems—in the United States alone.

And even in countries where there are national guidelines for prescribing antibiotics, those guidelines aren’t always followed. A study published in medical journal Family Practice showed that Swedish doctors, both those trained in Sweden and those trained abroad, inconsistently followed rules for prescribing antibiotics.

Solomon strongly believes that, worldwide, doctors need to expand their use of technology in their offices or at the bedside to guide them through a more rational approach to antibiotic use. Doctors have traditionally been reluctant to adopt digital technologies, but Solomon thinks that the AMR crisis could change that. New digital tools could help doctors and hospitals integrate guidelines for optimal antibiotic prescribing into their everyday treatment routines.

“Human-computer interactions are critical, as the amount of information available on antibiotic resistance far exceeds the ability of humans to process it,” says Solomon. “It offers the possibility of greatly enhancing the utility of computer-assisted physician order entry (CPOE), combined with clinical decision support.” Healthcare facilities could embed relevant information and protocols at the point of care, guiding the physician through diagnosis and prescription and, as a byproduct, facilitating the collection and reporting of antibiotic use.

Cincinnati Children’s Hospital’s antibiotic stewardship division has deployed a software program that gathers information from electronic medical records, order entries, computerized laboratory and pathology reports, and more. The system measures baseline antimicrobial use, dosing, duration, costs, and use patterns. It also analyzes bacteria and trends in their susceptibilities and helps with clinical decision making and prescription choices. The goal, says Dr. David Haslam, who heads the program, is to decrease the use of “big gun” super antibiotics in favor of more targeted treatment.

While this approach is not yet widespread, there is consensus that incorporating such clinical-decision support into electronic health records will help improve quality of care, contain costs, and reduce overtreatment in healthcare overall—not just in AMR. A 2013 randomized clinical trial finds that doctors who used decision-support tools were significantly less likely to order antibiotics than those in the control group and prescribed 50% fewer broad-spectrum antibiotics.

Putting mobile devices into doctors’ hands could also help them accept decision support, believes Solomon. Last summer, Scotland’s National Health Service developed an antimicrobial companion app to give practitioners nationwide mobile access to clinical guidance, as well as an audit tool to support boards in gathering data for local and national use.

“The immediacy and the consistency of the input to physicians at the time of ordering antibiotics may significantly help address the problem of overprescribing in ways that less-immediate interventions have failed to do,” Solomon says. In addition, handheld devices with so-called lab-on-a-chip  technology could be used to test clinical specimens at the bedside and transmit the data across cellular or satellite networks in areas where infrastructure is more limited.

Artificial intelligence (AI) and machine learning can also become invaluable technology collaborators to help doctors more precisely diagnose and treat infection. In such a system, “the physician and the AI program are really ‘co-prescribing,’” says Solomon. “The AI can handle so much more information than the physician and make recommendations that can incorporate more input on the type of infection, the patient’s physiologic status and history, and resistance patterns of recent isolates in that ward, in that hospital, and in the community.”

Speed Is Everything

Growing bacteria in a dish has never appealed to Dr. James Davis, a computational biologist with joint appointments at Argonne National Laboratory and the University of Chicago Computation Institute. The first of a growing breed of computational biologists, Davis chose a PhD advisor in 2004 who was steeped in bioinformatics technology “because you could see that things were starting to change,” he says. He was one of the first in his microbiology department to submit a completely “dry” dissertation—that is, one that was all digital with nothing grown in a lab.

Upon graduation, Davis wanted to see if it was possible to predict whether an organism would be susceptible or resistant to a given antibiotic, leading him to explore the potential of machine learning to predict AMR.

As the availability of cheap computing power has gone up and the cost of genome sequencing has gone down, it has become possible to sequence a pathogen sample in order to detect its AMR resistance mechanisms. This could allow doctors to identify the nature of an infection in minutes instead of hours or days, says Davis.

Davis is part of a team creating a giant database of bacterial genomes with AMR metadata for the Pathosystems Resource Integration Center (PATRIC), funded by the U.S. National Institute of Allergy and Infectious Diseases to collect data on priority pathogens, such as tuberculosis and gonorrhea.

Because the current inability to identify microbes quickly is one of the biggest roadblocks to making an accurate diagnosis, the team’s work is critically important. The standard method for identifying drug resistance is to take a sample from a wound, blood, or urine and expose the resident bacteria to various antibiotics. If the bacterial colony continues to divide and thrive despite the presence of a normally effective drug, it indicates resistance. The process typically takes between 16 and 20 hours, itself an inordinate amount of time in matters of life and death. For certain strains of antibiotic-resistant tuberculosis, though, such testing can take a week. While physicians are waiting for test results, they often prescribe broad-spectrum antibiotics or make a best guess about what drug will work based on their knowledge of what’s happening in their hospital, “and in the meantime, you either get better,” says Davis, “or you don’t.”

At PATRIC, researchers are using machine-learning classifiers to identify regions of the genome involved in antibiotic resistance that could form the foundation for a “laboratory free” process for predicting resistance. Being able to identify the genetic mechanisms of AMR and predict the behavior of bacterial pathogens without petri dishes could inform clinical decision making and improve reaction time. Thus far, the researchers have developed machine-learning classifiers for identifying antibiotic resistance in Acinetobacter baumannii (a big player in hospital-acquired infection), methicillin-resistant Staphylococcus aureus (a.k.a. MRSA, a worldwide problem), and Streptococcus pneumoniae (a leading cause of bacterial meningitis), with accuracies ranging from 88% to 99%.

Houston Methodist Hospital, which uses the PATRIC database, is researching multidrug-resistant bacteria, specifically MRSA. Not only does resistance increase the cost of care, but people with MRSA are 64% more likely to die than people with a nonresistant form of the infection, according to WHO. Houston Methodist is investigating the molecular genetic causes of drug resistance in MRSA in order to identify new treatment approaches and help develop novel antimicrobial agents.

The Hunt for a New Class of Antibiotics

There are antibiotic-resistant bacteria, and then there’s Clostridium difficile—a.k.a. C. difficile—a bacterium that attacks the intestines even in young and healthy patients in hospitals after the use of antibiotics.

It is because of C. difficile that Dr. L. Clifford McDonald jumped into the AMR fight. The epidemiologist was finishing his work analyzing the spread of SARS in Toronto hospitals in 2004 when he turned his attention to C. difficile, convinced that the bacteria would become more common and more deadly. He was right, and today he’s at the forefront of treating the infection and preventing the spread of AMR as senior advisor for science and integrity in the CDC’s Division of Healthcare Quality Promotion. “[AMR] is an area that we’re funding heavily…insofar as the CDC budget can fund anything heavily,” says McDonald, whose group has awarded $14 million in contracts for innovative anti-AMR approaches.

Developing new antibiotics is a major part of the AMR battle. The majority of new antibiotics developed in recent years have been variations of existing drug classes. It’s been three decades since the last new class of antibiotics was introduced. Less than 5% of venture capital in pharmaceutical R&D is focused on antimicrobial development. A 2008 study found that less than 10% of the 167 antibiotics in development at the time had a new “mechanism of action” to deal with multidrug resistance. “The low-hanging fruit [of antibiotic development] has been picked,” noted a WHO report.

Researchers will have to dig much deeper to develop novel medicines. Machine learning could help drug developers sort through much larger data sets and go about the capital-intensive drug development process in a more prescriptive fashion, synthesizing those molecules most likely to have an impact.

McDonald believes that it will become easier to find new antibiotics if we gain a better understanding of the communities of bacteria living in each of us—as many as 1,000 different types of microbes live in our intestines, for example. Disruption to those microbial communities—our “microbiome”—can herald AMR. McDonald says that Big Data and machine learning will be needed to unlock our microbiomes, and that’s where much of the medical community’s investment is going.

He predicts that within five years, hospitals will take fecal samples or skin swabs and sequence the microorganisms in them as a kind of pulse check on antibiotic resistance. “Just doing the bioinformatics to sort out what’s there and the types of antibiotic resistance that might be in that microbiome is a Big Data challenge,” McDonald says. “The only way to make sense of it, going forward, will be advanced analytic techniques, which will no doubt include machine learning.”

Reducing Resistance on the Farm

Bringing information closer to where it’s needed could also help reduce agriculture’s contribution to the antibiotic resistance problem. Antibiotics are widely given to livestock to promote growth or prevent disease. In the United States, more kilograms of antibiotics are administered to animals than to people, according to data from the FDA.

One company has developed a rapid, on-farm diagnostics tool to provide livestock producers with more accurate disease detection to make more informed management and treatment decisions, which it says has demonstrated a 47% to 59% reduction in antibiotic usage. Such systems, combined with pressure or regulations to reduce antibiotic use in meat production, could also help turn the AMR tide.

Breaking Down Data Silos Is the First Step

Adding to the complexity of the fight against AMR is the structure and culture of the global healthcare system itself. Historically, healthcare has been a siloed industry, notorious for its scattered approach focused on transactions rather than healthy outcomes or the true value of treatment. There’s no definitive data on the impact of AMR worldwide; the best we can do is infer estimates from the information that does exist.

The biggest issue is the availability of good data to share through mobile solutions, to drive HCI clinical-decision support tools, and to feed supercomputers and machine-learning platforms. “We have a fragmented healthcare delivery system and therefore we have fragmented information. Getting these sources of data all into one place and then enabling them all to talk to each other has been problematic,” McDonald says.

Collecting, integrating, and sharing AMR-related data on a national and ultimately global scale will be necessary to better understand the issue. HCI and mobile tools can help doctors, hospitals, and public health authorities collect more information while advanced analytics, machine learning, and in-memory computing can enable them to analyze that data in close to real time. As a result, we’ll better understand patterns of resistance from the bedside to the community and up to national and international levels, says Solomon. The good news is that new technology capabilities like AI and new potential streams of data are coming online as an era of data sharing in healthcare is beginning to dawn, adds McDonald.

The ideal goal is a digitally enabled virtuous cycle of information and treatment that could save millions of dollars, lives, and perhaps even civilization if we can get there. D!

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.


About the Authors:

Dr. David Delaney is Chief Medical Officer for SAP.

Joseph Miles is Global Vice President, Life Sciences, for SAP.

Walt Ellenberger is Senior Director Business Development, Healthcare Transformation and Innovation, for SAP.

Saravana Chandran is Senior Director, Advanced Analytics, for SAP.

Stephanie Overby is an independent writer and editor focused on the intersection of business and technology.

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4 Traits Set Digital Leaders Apart From 97% Of The Competition

Vivek Bapat

Like the classic parable of the blind man and the elephant, it seems everyone has a unique take on digital transformation. Some equate digital transformation with emerging technologies, placing their bets on as the Internet of Things, machine learning, and artificial intelligence. Others see it as a way to increase efficiencies and change business processes to accelerate product to market. Some others think of it is a means of strategic differentiation, innovating new business models for serving and engaging their customers. Despite the range of viewpoints, many businesses are still challenged with pragmatically evolving digital in ways that are meaningful, industry-disruptive, and market-leading.

According to a recent study of more than 3,000 senior executives across 17 countries and regions, only a paltry three percent of businesses worldwide have successfully completed enterprise-wide digital transformation initiatives, even though 84% of C-level executives ranks such efforts as “critically important” to the fundamental sustenance of their business.

The most comprehensive global study of its kind, the SAP Center for Business Insight report “SAP Digital Transformation Executive Study: 4 Ways Leaders Set Themselves Apart,” in collaboration with Oxford Economics, identified the challenges, opportunities, value, and key technologies driving digital transformation. The findings specifically analyzed the performance of “digital leaders” – those who are connecting people, things, and businesses more intelligently, more effectively, and creating punctuated change faster than their less advanced rivals.

After analyzing the data, it was eye-opening to see that only three percent of companies (top 100) are successfully realizing their full potential through digital transformation. However, even more remarkable was that these leaders have four fundamental traits in common, regardless of their region of operation, their size, their organizational structure, or their industry.

We distilled these traits in the hope that others in the early stages of transformation or that are still struggling to find their bearings can embrace these principles in order to succeed. Ultimately I see these leaders as true ambidextrous organizations, managing evolutionary and revolutionary change simultaneously, willing to embrace innovation – not just on the edges of their business, but firmly into their core.

Here are the four traits that set these leaders apart from the rest:

Trait #1: They see digital transformation as truly transformational

An overwhelming majority (96%) of digital leaders view digital transformation as a core business goal that requires a unified digital mindset across the entire enterprise. But instead of allowing individual functions to change at their own pace, digital leaders prefer to evolve the organization to help ensure the success of their digital strategies.

The study found that 56% of these businesses regularly shift their organizational structure, which includes processes, partners, suppliers, and customers, compared to 10% of remaining companies. Plus, 70% actively bring lines of business together through cross-functional processes and technologies.

By creating a firm foundation for transformation, digital leaders are further widening the gap between themselves and their less advanced competitors as they innovate business models that can mitigate emerging risks and seize new opportunities quickly.

Trait #2: They focus on transforming customer-facing functions first

Although most companies believe technology, the pace of change, and growing global competition are the key global trends that will affect everything for years to come, digital leaders are expanding their frame of mind to consider the influence of customer empowerment. Executives who build a momentum of breakthrough innovation and industry transformation are the ones that are moving beyond the high stakes of the market to the activation of complete, end-to-end customer experiences.

In fact, 92% of digital leaders have established sophisticated digital transformation strategies and processes to drive transformational change in customer satisfaction and engagement, compared to 22% of their less mature counterparts. As a result, 70% have realized significant or transformational value from these efforts.

Trait #3: They create a virtuous cycle of digital talent

There’s little doubt that the competition for qualified talent is fierce. But for nearly three-quarters of companies that demonstrate digital-transformation leadership, it is easier to attract and retain talent because they are five times more likely to leverage digitization to change their talent management efforts.

The impact of their efforts goes beyond empowering recruiters to identify best-fit candidates, highlight risk factors and hiring errors, and predict long-term talent needs. Nearly half (48%) of digital leaders understand that they must invest heavily in the development of digital skills and technology to drive revenue, retain productive employees, and create new roles to keep up with their digital maturity over the next two years, compared to 30% of all surveyed executives.

Trait #4: They invest in next-generation technology using a bimodal architecture

A couple years ago, Peter Sondergaard, senior vice president at Gartner and global head of research, observed that “CIOs can’t transform their old IT organization into a digital startup, but they can turn it into a bi-modal IT organization. Forty-five percent of CIOs state they currently have a fast mode of operation, and we predict that 75% of IT organizations will be bimodal in some way by 2017.”

Based on the results of the SAP Center for Business Insight study, Sondergaard’s prediction was spot on. As digital leaders dive into advanced technologies, 72% are using a digital twin of the conventional IT organization to operate efficiently without disruption while refining innovative scenarios to resolve business challenges and integrate them to stay ahead of the competition. Unfortunately, only 30% of less advanced businesses embrace this view.

Working within this bimodal architecture is emboldening digital leaders to take on incredibly progressive technology. For example, the study found that 50% of these firms are using artificial intelligence and machine learning, compared to seven percent of all respondents. They are also leading the adoption curve of Big Data solutions and analytics (94% vs. 60%) and the Internet of Things (76% vs. 52%).

Digital leadership is a practice of balance, not pure digitization

Most executives understand that digital transformation is a critical driver of revenue growth, profitability, and business expansion. However, as digital leaders are proving, digital strategies must deliver a balance of organizational flexibility, forward-looking technology adoption, and bold change. And clearly, this approach is paying dividends for them. They are growing market share, increasing customer satisfaction, improving employee engagement, and, perhaps more important, achieving more profitability than ever before.

For any company looking to catch up to digital leaders, the conversation around digital transformation needs to change immediately to combat three deadly sins: Stop investing in one-off, isolated projects hidden in a single organization. Stop viewing IT as an enabler instead of a strategic partner. Stop walling off the rest of the business from siloed digital successes.

As our study shows, companies that treat their digital transformation as an all-encompassing, all-sharing, and all-knowing business imperative will be the ones that disrupt the competitive landscape and stay ahead of a constantly evolving economy.

Follow me on twitter @vivek_bapat 

For more insight on digital leaders, check out the SAP Center for Business Insight report, conducted in collaboration with Oxford Economics,SAP Digital Transformation Executive Study: 4 Ways Leaders Set Themselves Apart.”

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About Vivek Bapat

Vivek Bapat is the Senior Vice President, Global Head of Marketing Strategy and Thought Leadership, at SAP. He leads SAP's Global Marketing Strategy, Messaging, Positioning and related Thought Leadership initiatives.