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Facebook's Graph Search And The Role Of Big Data

Luisa Ruppert

SearchWhen Facebook announced their newest product, the Facebook Graph Search, it sounded like the social network giant had created their own ‘Google’ enriched with personal recommendations from users’ acquaintances, friends and family.  It was a huge next step in leveraging the Big Data collected from over one billion users and a breakthrough in social and semantic search capability.

The Silicon Angle described it this way:

Graph Search, represents an intriguing marriage of Big Data and social networking that should allow people and businesses to connect in dozens of new and exciting ways.

Most people are not concerned or even aware of Internet giants like Google or Facebook collecting their personal data. They offer it willingly when registering for a give-away or a Facebook game application. Most people cannot even imagine how much companies profit from information as trivial as what you had for breakfast.

Few could comprehend the connections algorithms that process our data make out of the status updates and pictures posted. If they knew, it would most likely trigger a huge uproar.

The first concerns about data privacy were raised shortly after the launch of Graph Search.  Facebook had to face a flood of negative user comments.

Some of us already had the honor of testing the new function; a sample of feedback from the first wave of users includes: “Addictive”, “brings out the urge to get insights from family and friends”, “a future as a second Linked”, “it has a lot of potential” and simply “creepy”.

You can join the waitlist to get notified when you are able to use it here.

Facebook unveiled its Graph Search as their third pillar, the other two being the News Feed and the Timeline. The company makes it look like it is a feature created for the user’s benefit and to make the whole Facebook experience cooler and hipper. However, hidden from the general public lies a vast amount of information users provide with their likes, status updates, photos and check-ins: Facebook’s Big Data.

While the search is currently in beta and only a fraction of the available data has been indexed, it will undoubtedly grow more powerful over time and the company’s ad sales figures will grow with it.  It provides a new opportunity for advertisers, similar to the Google model, and is described as a notable development in the big data universe.

One reason it is notable for us is that it presents access to our own ‘personal Big Data’.  Companies have leveraged huge amounts of data for a while but this is the first time the average Internet user like you and me gets access to its own little piece of big data: Our social media life with connections, favorite restaurants and vacation destinations that produces vast amounts of data.

Some Implications of this launch

  • It offers some advantages for users; for example, restaurant recommendations of trusted friends (see why we tend not to trust strangers) or when looking for like-minded people in the same city to play sports together
  • Brings Big Data to the average Internet user
  • Another attack on Google: Sources report that if the search function will be extended in the future it will be synced with Microsoft’s Bing
  • It raises privacy and data security issues

Like other product launches there are both negative and positive reactions. In order to maintain its market leadership Facebook had to come up with an innovation in location-based services which could also be linked to the flopped Facebook deals hopefully, giving it a second chance.

Actually seeing the vast amount of personal data Facebook has access to should make the consumer more aware of what kind of information they share on the Internet. Let’s see what comes of it once it has been rolled out globally.

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About Luisa Ruppert

I am a recent graduate of International Management from Germany and have been working for SAP as an intern since April 2011 in Galway, Ireland and since March 2012 in New York. I am interested in social media, marketing, advertising, current affairs, technology news, politics and photography. In my spare time I love going to Broadway shows or a good movie as well as strolling around this exciting city.

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Letting Transparency And Insights Lead The Way

Mohammed Karzoun

Excellence in governance is hinged on exceeding service delivery expectations of citizens while balancing fiscal health and overall efficiencies. A transparent and connected government that uses insights to guide decision-making fosters trust and helps governments achieve efficiencies across financial, social, and economic aspects of governance. 

Today’s information age is no longer about sole proprietorship of information and data. The more information is shared, the more value it adds. Sharing information represents transparency and encourages trust by offering ample opportunity for citizens to scrutinize and understand the rationale behind public service offerings.

Further, analyzing data yields insights, helping the government make informed policy decisions and step up against corruption, fraud, and lacunae in functioning. To achieve this effectively, I feel data is key to driving excellence in governance. Data, captured in real time and shared, enables a transparent and accountable environment and delivers significant insights for improved governance. 

Insights help governments deliver the customer promise

Citizens today are very demanding of governments, expecting open access to information, transparency of transactions, seamless interactions, and fulfillment of needs. The last decade has witnessed a greater focus on reforms in the public sector aimed at improving service delivery and enhancing the customer promise. A critical result of these reforms has been the necessity to treat citizens like customers and place their needs at the core of every decision, from formulating strategies to designing policies and execution.

Governments need to re-engineer their processes and use data-driven governance to enable this customer-centricity. And for this, technology can be a key enabler because the development of customer-centric models calls for customer insights—an evaluation of customers’ wants and needs, improving the customer experience and minimizing risks associated with service delivery.

Business forensics can help optimize government resource planning

Customer feedback can generate significant insights to help governments deliver on the customer promise. By understanding the needs of its citizens and putting in place mechanisms to address those needs or problems, a government sets the tone for achieving excellence.

Insights are not only necessary to deliver efficiencies to citizens. To achieve a fiscal balance and internal efficiencies, the governments’ interaction with its extended customer base of employees, suppliers, and partners also needs to be managed and resources optimized. Business forensics also deliver these capabilities to governments by helping generate insights needed to optimize the governments’ financial resources and react to economic changes.

I believe that SAP’s transformative technologies that leverage real-time analytics provide governments with real-time data and flexible reporting tools to gain deeper insights into areas of improvement across departments. This helps identify and minimize risk and ensure compliance.

Transparency drives accountability as silos give way to a connected government

Transparency syncs governmental objectives with project objectives. A transparent and clear understanding of governance strategy helps create this sync while balancing resource priorities to ensure that outcomes are timely and effective. This also helps the governments see the big picture and drill down into details for instant decision-making while having single version of the truth.

Transparency requires that department silos give way to a connected government. An integrated and coordinated governance model not only avoids duplication of efforts and resources but also eliminates redundancies in storing information and utilizing resources.

A connected government boasts better communication and coordination across departments, increases ministry-wide transparency, and enhances accountability across a decentralized environment. It holds to reason that government entities and departments that are accountable tend to be more agile, flexible, and efficient.

I feel an important enabler for this transparency and accountability can be setting service standards. By putting in place specific mechanisms to measure KPI’s and evaluate service levels, governments can become more responsive and achieve citizen happiness.

Keeping it real: Why real-time information is the cornerstone of transparency and insights

People no longer want to be told about what happened in the past and how; they seek real-time information. Real-time information and real-time analytics enrich both the government and its citizens with data, which leads to transparency from the perspective of citizens and converts into insights from the government’s point of view.

This open government partnership implies that the decision-makers can anticipate the needs of citizens and the needs of the economy on a real-time basis and make decisions on policies accordingly and on the fly. Such decisions increase reliability and consistency of financial and operational information.

Real-time governance enabled by technology provides the governments entities and their leadership with unprecedented visibility into the workings of the various departments, increasing accountability and enhancing opportunities for collaboration across functions. It allows governments to answer key questions in the moment, such as: What is happening now? Why is it happening? What will happen? Governments can move to the era of predictive analytics by collecting and relating their objectives in real time to make informative decisions for better future planning.

In conclusion, changes in citizen needs and expectations are a continuous process, and a radical one-time reform cannot suffice. Governments need to leverage technology to implement a mechanism that continually monitors insights and develops an institutional culture of improvement. At the same time, encouraging a data-driven transparent service delivery model can position governments well to achieve excellence by delivering on the customer promise.

For more on the role of technology in government, see A Duty Of Care In The Digital Government Era.

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Mohammed Karzoun

About Mohammed Karzoun

Mohammed Karzoun is the Industry Leader for Public Sector at SAP. He manages government, smart cities, healthcare, public security, defense, higher education, and postal services sectors across the United Arab Emirates and Oman. With 20 years of experience in primarily public sector transformation, Mohammed has been engaged with multiple government entities to help drive their strategies and digital transformation initiatives.

How Analytics Fits Into Your Next Digital Transformation

Eamon Ida

We hear a lot about digital transformation these days and the groundbreaking technologies that will lead to these transformations, such as blockchain, IoT (Internet of Things), ML (machine learning), AI (artificial intelligence), the cloud, and analytics, to name just a few. All it takes is a look at advertisements from various tech companies (and these days every company is a tech company) in Harvard Business Review, Wired, or the Wall Street Journal to see the examples in action.

Much of the message revolves around leveraging these new technologies to disrupt existing models and create new value. But many business leaders who are new to these technologies—though  not lacking in curiosity about their potential—wonder how they can leverage these different technologies. Can they be connected and surfaced together to create true digital innovation systems? By showcasing scenarios, we’ve shown how an analytics cloud platform helps solve these questions and connect technology together to make smarter decisions faster and at the speed of market changes.

Digital transformation and machine learning

Machine learning is one example. In scores of today’s business scenarios, algorithms are instantly suggesting intelligent decisions for use in more involved decision making or where many variables are present. But in many cases, we’re still at the point where humans need to approve the machine (or robot) decision by hand.

However, scheduling a predictive maintenance repair to one or more your vehicles in a fleet before damage occurs, changing the route of a vehicle in the supply chain to avoid traffic that will delay delivery, or detecting patterns in transactions before a fraud occurs—all of these are examples of intelligent decisions suggested by machine learning. (Read Trenitalia’s IoT-Enabled Dynamic Maintenance.)

These decision suggestions or approvals are best located in an analytics platform, where we can surface up not only the recommended decisions, but also contextual data from other sources (such as geographic or IoT information) to improve the quality of the decision.

Digital transformation and IoT

When we consider IoT data, the value of analytics becomes even clearer—sensor technology, for example, can produce data every second or, in many instances, milliseconds. With analytics, we’re able to surface up this sensor data, along with data from other sources, to a real-time dashboard that provides both real-time and actionable insights into sensor data and activity.

Digital transformation and cloud platforms

With the democratization of data and the inevitable transparency of business in today’s connected world, we need to consider investments in technology that allow us to provide these platforms to employees, customers, and vendors all around the world. This is where the beauty of a cloud platform, powered by an in-memory computing platform comes into play.

Using a cloud platform not only enables innovation at scale, but also the connection of disparate data sources and technologies together. Just look at companies in industries such as airlines, delivery, and healthcare (to name just a few). Disruptors abound that are building “cloud first” and are not just using analytics internally but are providing it to customers as a competitive advantage.

You, like many of the leaders we speak with, might be wondering, What are some examples of digital transformation where analytics and data have played a critical role? What are some of the biggest challenges or roadblocks to more pervasive and effective analytics, and what are some of the best practices you have seen in the use of analytics?

SAP recently completed a new paper with IDC on the Value of Analytics in Digital Transformation. In it, we answer all of these common questions and more with the help of industry experts.  Read the whitepaper today to learn how you and your organization can leverage the disruptive powers of analytics in your next digital transformation. And see this infographic for a bird’s-eye view of the key findings.

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Eamon Ida

About Eamon Ida

Eamon is a passionate, creative, and data-driven analytics product marketer at SAP. He has a special interest in technology and its role at the forefront of how we live and work together.

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.