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Health care bots are only as good as the data and doctors they learn from

 Health care bots are only as good as the data and doctors they learn from

The number of tech companies pursuing health care seems to have reached an all-time high: Google, Amazon, Apple, and IBM’s Watson all want to change health care using artificial intelligence. IBM has even rebranded its health offering as “Watson Health — Cognitive Healthcare Solutions.” Although technologies from these giants show great promise, the question of whether effective health care AI already exists or whether it is still a dream remains.

As a physician, I believe that in order to understand what is artificially intelligent in health care, you have to first define what it means to be intelligent in health care. Consider the Turing test, a point when a machine becomes indistinguishable from a human.

Joshua Batson, a writer for Wired magazine, has mused whether there is an alternative measurement to the Turing test, one where the machine doesn’t just seem like a person, but an intelligent person. Think of it this way: If you were to ask a random person about symptoms you experience, they’d likely reply “I have no idea. You should ask your doctor.” A bot supplying that response would certainly be indistinguishable from a human — but we expect a little more than that.

The challenge of health care AI

Health is hard, and that makes AI in health care especially hard. Interpretation, empathy, and knowledge all have unique challenges in health care AI.

To date, interpretation is where much of the technology investment has gone. Whether for touchscreen or voice recognition, natural language processing (NLP) has seen enormous investment including Amazon’s Comprehend, IBM’s Natural Language Understanding, and Google Cloud Natural Language. But even though there are plenty of health-specific interpretation challenges, interpretation challenges are really no greater in this particular sector than in other domains.

Similarly, while empathy needs to be particularly appropriate for the emotionally charged field of health care, bots are equally challenged trying to strike just the right tone for retail customer service, legal services, or childcare advice.

That leaves knowledge. The knowledge needed to be a successful conversational bot is where health care diverges greatly from other fields. We can divide that knowledge into two major categories: What do you know about the individual? And what do you know about medicine in general that will be most useful their individual case?

If a person is a diabetic and has high cholesterol, for example, then we know from existing data that the risks of having a heart attack are higher for that person and that aggressive blood sugar and diet control are effective in significantly lowering that risk. That combines with a general knowledge of medicine which says that multiple randomized controlled trials have found diabetics with uncontrolled blood sugars and high cholesterol to be twice as likely as others to have a cardiac event.

What is good enough?

There are two approaches to creating an algorithm that delivers a customized message. Humans can create it based on their domain knowledge, or computers can derive the algorithm based on patterns observed in data — i.e., machine learning. With a perfect profile and perfect domain knowledge, humans or machines could create the perfect algorithm. Combined with good interpretation and empathy you would have the ideal, artificially intelligent conversation. In other words, you’d have created the perfect doctor.

The problem comes when the profile or domain knowledge is less than perfect (which it always is), and then trying to determine when it is “good enough.”

The answer to “When is that knowledge good enough?” really comes down to the strength of your profile knowledge and the strength of your domain knowledge. While you can make up a shortfall in one with the other, inevitably, you’re left with something very human: a judgment call on when the profile and domain knowledge is sufficient.

Lucky for us, rich and structured health data is more prevalent than ever before, but making that data actionable takes a lot of informatics and computationally intensive processes that few companies are prepared for. As a result, many companies have turned to deriving that information through pattern analysis or machine learning. And where you have key gaps in your knowledge — like environmental data — you can simply ask the patient.

Companies looking for new “conversational AI” are filling these gaps in health care, beyond Alexa and Siri. Conversational AI can take our health care experience from a traditional, episodic one to a more insightful, collaborative, and continuous one. For example, conversational AI can build out consumer profiles from native clinical and consumer data to answer difficult questions very quickly, like “Is this person on heart medication?” or “Does this person have any medications that could complicate their condition?”

Not until recently has the technology been able to touch this in-depth and profile on-the-fly. It’s become that perfect doctor, knowing not only everything about your health history, but knowing how all of that connects to combinations of characteristics. Now, organizations are beginning to use that profile knowledge to derive engagement points to better characterize some of the “softer” attributes of an individual, like self-esteem, literacy, or other factors that will dictate their level of engagement.

Think about all of the knowledge that medical professionals have derived from centuries of research. In 2016 alone, Research America estimated, the U.S. spent $ 171.8 billion on medical research. But how do we capture all of that knowledge, and how could we use it in conversational systems? This lack of standardization is why we’ve developed so many rules-based or expert systems over the years.

It’s also why there’s a lot of new investment in deriving domain knowledge from large data sets. Google’s DeepMind partnership with the U.K.’s National Health Service is a great example. By combining their rich data on diagnoses, outcomes, medications, test results, and other information, Google’s DeepMind can use AI to derive patterns that will help it predict an individual’s outcome. But do we have to wait upon large, prospective data analyses to derive medical knowledge, or can we start with what we know today?

Putting data points to work

Expert-defined vs. machine-defined knowledge will have to be balanced in the near term. We must start with the structured data that is available, then ask what we don’t know so that we can derive additional knowledge from observed patterns. Domain knowledge should start with expert consensus in order to derive additional knowledge from observed patterns.

Knowing one particular data point about an individual can make the biggest difference in being able to read their situation. That’s when you’ll start getting questions that may make no sense whatsoever, but will make all the sense in the world to the machine. Imagine a conversation like this:

BOT: I noticed you were in Charlotte last week. By any chance, did you happen to eat at Larry’s Restaurant on 5th Street?

USER: Uh, yes, I did actually.

BOT: Well, that could explain your stomach problems. There has been a Salmonella outbreak reported from that location. I’ve ordered Amoxicillin and it should be to you shortly. Make sure to take it for the full 10 days. The drug Cipro is normally the first line therapy, but it would potentially interact badly with your Glyburide. I’ll check back in daily to see how you’re doing.

But while we wait for the detection of patterns by machines, the knowledge that is already out there should not be overlooked, even if it takes a lot of informatics and computations. I’d like to think the perfect AI doctor is just around the corner. But my guess is that those who take a “good enough” approach today will be the ones who get there first. After all, for so many people who don’t have access to adequate care today, and for all that we’re spending on health care, we don’t yet have a health care system that is “good enough.”

Dr. Phil Marshall is the cofounder and chief product officer at Conversa Health, a conversation platform for the health care sector.

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Data Socialization 101: What Is Data Socialization, and Why Should You Care?

Data socialization is one of the newest buzzwords in the world of data analytics and management. What does data socialization mean, and what can it do for you? Find out in this post.

Data Socialization 101 What Is Data Socialization and Why Should You Care banner Data Socialization 101: What Is Data Socialization, and Why Should You Care?

What Is Data Socialization?

In a nutshell, data socialization refers to the sharing of data and data analytics tools with all members of an organization. The key idea behind data socialization is to make data-driven insights available to everyone in a self-service fashion.

Another way of defining data socialization is to say that it involves the “democratization” of data. Whereas the typical business has traditionally assigned data analytics tasks to only a handful of employees who specialize in data management, the data socialization concept aims to involve everyone in the organization in collecting, managing, analyzing and reacting to data.

Why Does Data Socialization Matter?

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Data socialization is innovative because it helps businesses to double down on their ability to leverage data.

These days, most businesses collect huge troves of information, ranging from machine data (like Web server logs) to manually entered customer reports and everything in between.

Yet traditionally, the extent to which businesses have leveraged that data has been limited. As noted above, the ability to access and analyze business-critical data has typically been available only to a small team of data specialists. Unless data analytics or data management is an explicit part of your job title, you probably didn’t do much with data; instead, you relied on other people — the ones who specialized in data management — to collect and analyze your business’s data for you, then provide recommendations to you based on it.

From a business standpoint, this approach is not ideal, for two main reasons:

  1. When a business relies on only a small group of data specialists to process all of its data, those specialists are likely to become overwhelmed. It’s difficult for a small group to process an entire business’s data single-handedly and deliver relevant insights and recommendations to every business unit. This is especially true today, when the amount of data that organizations collect is larger than ever.
  2. In most cases, data specialists have a limited understanding of other parts of the business. Their ability to leverage data in ways that benefit other business units is therefore limited, too.

Data socialization aims to solve these challenges by placing data and data analytics tools directly in the hands of the people who can use them as part of their jobs.

For example, if you work in marketing, data socialization means that you can collect and analyze data related to marketing campaigns yourself, rather than depending on data specialists to perform that task for you. Because you know your business’s marketing needs better than anyone who does not specialize in marketing, you are better positioned than the rest of your organization to derive relevant insights from that data.

Similarly, a customer service specialist can benefit from data socialization by being able to access and analyze information related to each of the customers he or she supports.

Data socialization does not mean, by the way, that data specialists have no role to play in data socialization. They remain the experts, and they oversee the tools and processes that enable other parts of the organization to perform data self-service. But they are no longer solely responsible for data management.

bigstock  173111750 600x Data Socialization 101: What Is Data Socialization, and Why Should You Care?

Best Practices for Data Socialization

When you want to empower everyone in your organization with the ability to manage and interpret data, you need to approach data management somewhat differently than you would when only data specialists are involved in the process.

Most obviously, you need data management tools that enable self-service without requiring a great deal of expertise. This might seem difficult to achieve, but in fact, data integration and analytics are simpler today than they once were. Even your non-technical employees will likely be able to work with data much more effectively using modern data management tools than you might expect.

That said, the ability to deliver a streamlined data experience is important for enabling data socialization. By streamlined, I mean providing a data analytics process that is free of complex technical kinks. For example, you should not expect your non-data-specialist employees to be able to perform complex data transformation or data integration tasks. Nor should they be expected to clean up low quality data sets.

Instead, you need to provide them with data that is readily usable. Providing tools that enable them to visualize data easily is also important.


In today’s data-driven world, everyone in the business stands to benefit from being able to access and interpret data that is relevant to his or her role within the organization. By embracing data socialization, businesses can make data analytics more efficient and faster, reduce the burden they place on their data specialists and provide more relevant data-driven insights to employees who stand to gain the most from them.

Make sure to download our eBook, “The New Rules for Your Data Landscape“, and take a look at the rules that are transforming the relationship between business and IT.

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Rabbit Takes Care Of Dog

 Rabbit Takes Care Of Dog

Animals that care for one another.

“Bunny grooms his dog friend..”
Image courtesy of https://imgur.com/gallery/nEm7tsM.


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Digitalist Flash Briefing: Your Duty Of Care And The Increase In Traveler Concerns

For nerds, the weeks right before finals are a Cinderella moment. Suddenly they’re stars. Pocket protectors are fashionable; people find their jokes a whole lot funnier; Dungeons & Dragons sounds cool.

Many CIOs are enjoying this kind of moment now, as companies everywhere face the business equivalent of a final exam for a vital class they have managed to mostly avoid so far: digital transformation.

But as always, there is a limit to nerdy magic. No matter how helpful CIOs try to be, their classmates still won’t pass if they don’t learn the material. With IT increasingly central to every business—from the customer experience to the offering to the business model itself—we all need to start thinking like CIOs.

Pass the digital transformation exam, and you probably have a bright future ahead. A recent SAP-Oxford Economics study of 3,100 organizations in a variety of industries across 17 countries found that the companies that have taken the lead in digital transformation earn higher profits and revenues and have more competitive differentiation than their peers. They also expect 23% more revenue growth from their digital initiatives over the next two years—an estimate 2.5 to 4 times larger than the average company’s.

But the market is grading on a steep curve: this same SAP-Oxford study found that only 3% have completed some degree of digital transformation across their organization. Other surveys also suggest that most companies won’t be graduating anytime soon: in one recent survey of 450 heads of digital transformation for enterprises in the United States, United Kingdom, France, and Germany by technology company Couchbase, 90% agreed that most digital projects fail to meet expectations and deliver only incremental improvements. Worse: over half (54%) believe that organizations that don’t succeed with their transformation project will fail or be absorbed by a savvier competitor within four years.

Companies that are making the grade understand that unlike earlier technical advances, digital transformation doesn’t just support the business, it’s the future of the business. That’s why 60% of digital leading companies have entrusted the leadership of their transformation to their CIO, and that’s why experts say businesspeople must do more than have a vague understanding of the technology. They must also master a way of thinking and looking at business challenges that is unfamiliar to most people outside the IT department.

In other words, if you don’t think like a CIO yet, now is a very good time to learn.

However, given that you probably don’t have a spare 15 years to learn what your CIO knows, we asked the experts what makes CIO thinking distinctive. Here are the top eight mind hacks.

1. Think in Systems

Q118 Feature3 img1 Jump Digitalist Flash Briefing: Your Duty Of Care And The Increase In Traveler ConcernsA lot of businesspeople are used to seeing their organization as a series of loosely joined silos. But in the world of digital business, everything is part of a larger system.

CIOs have known for a long time that smart processes win. Whether they were installing enterprise resource planning systems or working with the business to imagine the customer’s journey, they always had to think in holistic ways that crossed traditional departmental, functional, and operational boundaries.

Unlike other business leaders, CIOs spend their careers looking across systems. Why did our supply chain go down? How can we support this new business initiative beyond a single department or function? Now supported by end-to-end process methodologies such as design thinking, good CIOs have developed a way of looking at the company that can lead to radical simplifications that can reduce cost and improve performance at the same time.

They are also used to thinking beyond temporal boundaries. “This idea that the power of technology doubles every two years means that as you’re planning ahead you can’t think in terms of a linear process, you have to think in terms of huge jumps,” says Jay Ferro, CIO of TransPerfect, a New York–based global translation firm.

No wonder the SAP-Oxford transformation study found that one of the values transformational leaders shared was a tendency to look beyond silos and view the digital transformation as a company-wide initiative.

This will come in handy because in digital transformation, not only do business processes evolve but the company’s entire value proposition changes, says Jeanne Ross, principal research scientist at the Center for Information Systems Research at the Massachusetts Institute of Technology (MIT). “It either already has or it’s going to, because digital technologies make things possible that weren’t possible before,” she explains.

2. Work in Diverse Teams

When it comes to large projects, CIOs have always needed input from a diverse collection of businesspeople to be successful. The best have developed ways to convince and cajole reluctant participants to come to the table. They seek out technology enthusiasts in the business and those who are respected by their peers to help build passion and commitment among the halfhearted.

Digital transformation amps up the urgency for building diverse teams even further. “A small, focused group simply won’t have the same breadth of perspective as a team that includes a salesperson and a service person and a development person, as well as an IT person,” says Ross.

At Lenovo, the global technology giant, many of these cross-functional teams become so used to working together that it’s hard to tell where each member originally belonged: “You can’t tell who is business or IT; you can’t tell who is product, IT, or design,” says the company’s CIO, Arthur Hu.

One interesting corollary of this trend toward broader teamwork is that talent is a priority among digital leaders: they spend more on training their employees and partners than ordinary companies, as well as on hiring the people they need, according to the SAP-Oxford Economics survey. They’re also already being rewarded for their faith in their teams: 71% of leaders say that their successful digital transformation has made it easier for them to attract and retain talent, and 64% say that their employees are now more engaged than they were before the transformation.

3. Become a Consultant

Good CIOs have long needed to be internal consultants to the business. Ever since technology moved out of the glasshouse and onto employees’ desks, CIOs have not only needed a deep understanding of the goals of a given project but also to make sure that the project didn’t stray from those goals, even after the businesspeople who had ordered the project went back to their day jobs. “Businesspeople didn’t really need to get into the details of what IT was really doing,” recalls Ferro. “They just had a set of demands and said, ‘Hey, IT, go do that.’”

But that was then. Now software has become so integral to the business that nobody can afford to walk away. Businesspeople must join the ranks of the IT consultants. “If you’re building a house, you don’t just disappear for six months and come back and go, ‘Oh, it looks pretty good,’” says Ferro. “You’re on that work site constantly and all of a sudden you’re looking at something, going, ‘Well, that looked really good on the blueprint, not sure it makes sense in reality. Let’s move that over six feet.’ Or, ‘I don’t know if I like that anymore.’ It’s really not much different in application development or for IT or technical projects, where on paper it looked really good and three weeks in, in that second sprint, you’re going, ‘Oh, now that I look at it, that’s really stupid.’”

4. Learn Horizontal Leadership

CIOs have always needed the ability to educate and influence other leaders that they don’t directly control. For major IT projects to be successful, they need other leaders to contribute budget, time, and resources from multiple areas of the business.

It’s a kind of horizontal leadership that will become critical for businesspeople to acquire in digital transformation. “The leadership role becomes one much more of coaching others across the organization—encouraging people to be creative, making sure everybody knows how to use data well,” Ross says.

In this team-based environment, having all the answers becomes less important. “It used to be that the best business executives and leaders had the best answers. Today that is no longer the case,” observes Gary Cokins, a technology consultant who focuses on analytics-based performance management. “Increasingly, it’s the executives and leaders who ask the best questions. There is too much volatility and uncertainty for them to rely on their intuition or past experiences.”

Many experts expect this trend to continue as the confluence of automation and data keeps chipping away at the organizational pyramid. “Hierarchical, command-and-control leadership will become obsolete,” says Edward Hess, professor of business administration and Batten executive-in-residence at the Darden School of Business at the University of Virginia. “Flatter, distributive leadership via teams will become the dominant structure.”

Q118 Feature3 img3 rock Digitalist Flash Briefing: Your Duty Of Care And The Increase In Traveler Concerns5. Understand Process Design

When business processes were simpler, IT could analyze the process and improve it without input from the business. But today many processes are triggered on the fly by the customer, making a seamless customer experience more difficult to build without the benefit of a larger, multifunctional team. In a highly digitalized organization like Amazon, which releases thousands of new software programs each year, IT can no longer do it all.

While businesspeople aren’t expected to start coding, their involvement in process design is crucial. One of the techniques that many organizations have adopted to help IT and businesspeople visualize business processes together is design thinking (for more on design thinking techniques, see “A Cult of Creation“).

Customers aren’t the only ones who benefit from better processes. Among the 100 companies the SAP-Oxford Economics researchers have identified as digital leaders, two-thirds say that they are making their employees’ lives easier by eliminating process roadblocks that interfere with their ability to do their jobs. Ninety percent of leaders surveyed expect to see value from these projects in the next two years alone.

6. Learn to Keep Learning

The ability to learn and keep learning has been a part of IT from the start. Since the first mainframes in the 1950s, technologists have understood that they need to keep reinventing themselves and their skills to adapt to the changes around them.

Now that’s starting to become part of other job descriptions too. Many companies are investing in teaching their employees new digital skills. One South American auto products company, for example, has created a custom-education institute that trained 20,000 employees and partner-employees in 2016. In addition to training current staff, many leading digital companies are also hiring new employees and creating new roles, such as a chief robotics officer, to support their digital transformation efforts.

Nicolas van Zeebroeck, professor of information systems and digital business innovation at the Solvay Brussels School of Economics and Management at the Free University of Brussels, says that he expects the ability to learn quickly will remain crucial. “If I had to think of one critical skill,” he explains, “I would have to say it’s the ability to learn and keep learning—the ability to challenge the status quo and question what you take for granted.”

7. Fail Smarter

Traditionally, CIOs tended to be good at thinking through tests that would allow the company to experiment with new technology without risking the entire network.

This is another unfamiliar skill that smart managers are trying to pick up. “There’s a lot of trial and error in the best companies right now,” notes MIT’s Ross. But there’s a catch, she adds. “Most companies aren’t designed for trial and error—they’re trying to avoid an error,” she says.

Q118 Feature3 img4 fail Digitalist Flash Briefing: Your Duty Of Care And The Increase In Traveler ConcernsTo learn how to do it better, take your lead from IT, where many people have already learned to work in small, innovative teams that use agile development principles, advises Ross.

For example, business managers must learn how to think in terms of a minimum viable product: build a simple version of what you have in mind, test it, and if it works start building. You don’t build the whole thing at once anymore.… It’s really important to build things incrementally,” Ross says.

Flexibility and the ability to capitalize on accidental discoveries during experimentation are more important than having a concrete project plan, says Ross. At Spotify, the music service, and CarMax, the used-car retailer, change is driven not from the center but from small teams that have developed something new. “The thing you have to get comfortable with is not having the formalized plan that we would have traditionally relied on, because as soon as you insist on that, you limit your ability to keep learning,” Ross warns.

8. Understand the True Cost—and Speed—of Data

Gut instincts have never had much to do with being a CIO; now they should have less to do with being an ordinary manager as well, as data becomes more important.

As part of that calculation, businesspeople must have the ability to analyze the value of the data that they seek. “You’ll need to apply a pinch of knowledge salt to your data,” advises Solvay’s van Zeebroeck. “What really matters is the ability not just to tap into data but to see what is behind the data. Is it a fair representation? Is it impartial?”

Increasingly, businesspeople will need to do their analysis in real time, just as CIOs have always had to manage live systems and processes. Moving toward real-time reports and away from paper-based decisions increases accuracy and effectiveness—and leaves less time for long meetings and PowerPoint presentations (let us all rejoice).

Not Every CIO Is Ready

Of course, not all CIOs are ready for these changes. Just as high school has a lot of false positives—genius nerds who turn out to be merely nearsighted—so there are many CIOs who aren’t good role models for transformation.

Success as a CIO these days requires more than delivering near-perfect uptime, says Lenovo’s Hu. You need to be able to understand the business as well. Some CIOs simply don’t have all the business skills that are needed to succeed in the transformation. Others lack the internal clout: a 2016 KPMG study found that only 34% of CIOs report directly to the CEO.

This lack of a strategic perspective is holding back digital transformation at many organizations. They approach digital transformation as a cool, one-off project: we’re going to put this new mobile app in place and we’re done. But that’s not a systematic approach; it’s an island of innovation that doesn’t join up with the other islands of innovation. In the longer term, this kind of development creates more problems than it fixes.

Such organizations are not building in the capacity for change; they’re trying to get away with just doing it once rather than thinking about how they’re going to use digitalization as a means to constantly experiment and become a better company over the long term.

Q118 Feature3 img6 CIOready Digitalist Flash Briefing: Your Duty Of Care And The Increase In Traveler ConcernsAs a result, in some companies, the most interesting tech developments are happening despite IT, not because of it. “There’s an alarming digital divide within many companies. Marketers are developing nimble software to give customers an engaging, personalized experience, while IT departments remain focused on the legacy infrastructure. The front and back ends aren’t working together, resulting in appealing web sites and apps that don’t quite deliver,” writes George Colony, founder, chairman, and CEO of Forrester Research, in the MIT Sloan Management Review.

Thanks to cloud computing and easier development tools, many departments are developing on their own, without IT’s support. These days, anybody with a credit card can do it.

Traditionally, IT departments looked askance at these kinds of do-it-yourself shadow IT programs, but that’s changing. Ferro, for one, says that it’s better to look at those teams not as rogue groups but as people who are trying to help. “It’s less about ‘Hey, something’s escaped,’ and more about ‘No, we just actually grew our capacity and grew our ability to innovate,’” he explains.

“I don’t like the term ‘shadow IT,’” agrees Lenovo’s Hu. “I think it’s an artifact of a very traditional CIO team. If you think of it as shadow IT, you’re out of step with reality,” he says.

The reality today is that a company needs both a strong IT department and strong digital capacities outside its IT department. If the relationship is good, the CIO and IT become valuable allies in helping businesspeople add digital capabilities without disrupting or duplicating existing IT infrastructure.

If a company already has strong digital capacities, it should be able to move forward quickly, according to Ross. But many companies are still playing catch-up and aren’t even ready to begin transforming, as the SAP-Oxford Economics survey shows.

For enterprises where business and IT are unable to get their collective act together, Ross predicts that the next few years will be rough. “I think these companies ought to panic,” she says. D!

About the Authors

Thomas Saueressig is Chief Information Officer at SAP.

Timo Elliott is an Innovation Evangelist at SAP.

Sam Yen is Chief Design Officer at SAP and Managing Director of SAP Labs.

Bennett Voyles is a Berlin-based business writer.

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The 3 most valuable applications of AI in health care

 The 3 most valuable applications of AI in health care

Artificial intelligence could prove to be a self-running growth engine for the health care sector in the not-so-distant future.

A recent report from Accenture analyzed the “near-term value” of AI applications in health care to determine how the potential impact of the technology stacks up against the upfront costs of implementation. Results from the report estimated that AI applications in health care could save up to $ 150 billion annually for the U.S. health care economy by 2026.

The report focused on 10 AI applications with potential for near-term impact in medicine and analyzed each application to derive an associated estimated value. Researchers considered the impact of each application, likelihood of adoption, and value to the health economy in their evaluation.

Here are the top three AI applications with the greatest value potential in health care, according to the report’s findings.

1. Robot-assisted surgery: Estimated value of $ 40 billion

Robotic surgeries are considered “minimally invasive” surgeries – meaning practitioners replace large incisions with a series of quarter-inch incisions and utilize miniaturized surgical instruments.

Cognitive surgical robotics combines information from actual surgical experiences to improve surgical techniques. In this type of procedure, medical teams integrate the data from pre-op medical records with real-time operating metrics to improve surgical outcomes. The technique enhances a physician’s instrument precision and can lead to a 21 percent reduction in a patient’s length of hospital stay post operation.

The da Vinci technique allows surgeons to perform a range of complex procedures with greater flexibility and control in comparison to conventional techniques. Considered to be the world’s most advanced surgical robot, the da Vinci’s robotic limbs have surgical instruments attached and provide a high-definition, magnified, 3-D view of the surgical site. A surgeon controls the machine’s arms from a seat at a computer console near the operating table. This allows the surgeon to successfully perform surgeries in tight spaces and reduce the margin for error.

Also under the physician’s control is HeartLander – a miniature mobile robot that can enter the chest through an incision below the sternum. It reduces the damage required to access the heart and allows the use of a single device for performing stable and localized sensing, mapping, and treatment over the entire surface of the heart. In addition to administering the therapy, the robot adheres to the epicardial surface of the heart and can autonomously navigate to the directed location.

2. Virtual nursing assistants: Estimated value of $ 20 billion

Virtual nursing assistants could help achieve a reduction in unnecessary hospital visits and lessen the burden on medical professionals. According to Syneos Health Communications, 64 percent of patients reported they would be comfortable with AI virtual nurse assistants, listing the benefits of 24/7 access to answers and support, round-the-clock monitoring, and the ability to get quick answers to questions regarding medications.

San Francisco-based virtual nurse assistant, Sensely, recently raised $ 8 million in Series B funding to deploy fleets of AI-powered nurse avatars to clinics and patients. The key goals of the technology are to keep patients and care providers in communication between office visits and prevent hospital readmission. Sensely’s most commonly referenced nurse is Molly, which uses a proprietary classification engine and listens and responds to users.

Care Angel’s virtual nurse assistant, Angel is another good example for this category. The bot enables wellness checks through voice and AI to drive better medical outcomes at a lower cost. It is able to manage, monitor, and communicate using unique insights and real-time notifications.

3. Administrative workflow assistance: Estimated value of $ 18 billion

Automation of administrative workflow ensures that care providers prioritize urgent matters and can also help doctors, nurses, and assistants save time on routine tasks. Some applications of AI on the administrative end of health care include voice-to-text transcriptions that automate non-patient care activities like writing chart notes, prescribing medications, and ordering tests.

An example of this comes from Nuance. The company provides AI-powered solutions that rely on machine learning to help health care providers cut documentation time and improve reporting quality. Computer-assisted physician documentation (CAPD) like this provides real-time clinical documentation guidance that helps providers ensure their patients receive an accurate clinical history and consistent recommendations.

Another example of this is a five-year agreement between IBM and Cleveland Clinic that aims to transform clinical care and administrative operations. The collaboration uses Watson and other advanced technologies to mine big data and help physicians provide a more personalized and efficient treatment experience. Watson’s natural language processing capabilities allow care providers to quickly and accurately analyze thousands of medical papers to provide improved patient care and reduce operational costs.

John Hopkins Hospital made a similar move in its partnership with GE Healthcare Camden Group. This initiative aims to improve patient care and efficiency via the adoption of hospital command centers equipped with predictive analytics. The strategy will help health care professionals make quick and informed decisions for operational tasks like scheduling bed assignments and managing requests for unit assistance.

Bottom line

While advancements like those mentioned in this article will leave little room for human error and boost overall outcomes and consumer trust, there still remain reservations on AI’s practical applicability in health care. Patients and caregivers fear that lack of human oversight and the potential for machine errors can lead to mismanagement of health. Among many concerns, data privacy remains one of the biggest challenges to health care which may rely heavily on AI.

Despite concerns, AI’s future in health care is inevitable and if this report provides any indication of its impact, the potential benefits might just outweigh the risks.

Deena Zaidi is a Seattle-based contributor for financial websites like TheStreet, Seeking Alpha, Truthout, Economy Watch, and icrunchdata.

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Big Data – VentureBeat

Reducing Health Care Fraud, Waste & Abuse: How Much Is Enough?

Health Care Fraud Waste and Abuse Reducing Health Care Fraud, Waste & Abuse: How Much Is Enough?

Every day I see headline-worthy stories of convictions and legal settlements for fraud and abuse in health care around the world. Many health care payers’ SIUs or forensics teams spend their days “grinding it out” in the fight against health care fraud, waste and abuse — they may only aspire to these kinds of headline results.

So how does a health care payer measure success in payment integrity? How much is enough? Each payer tends to have their own point of view.

Here are four examples of “How much is enough?”

  • Some years ago, a payer came to us with a simple goal. With just one employee in their SIU shop and very simple tools, they had suffered a significant fraud loss while their one SIU employee was away during the Christmas – New Year’s holiday. In response to this loss, their Board of Directors defined “How much is enough?” as “Let’s not have that happen again.”
  • Historically, RFPs for administration of government-funded health care insurance programs in the United States have included a requirement that the administrator fight fraud. Since this kind of administration contract is often awarded to the bidder with the lowest price, it’s possible that a small, low cost SIU shop which produces any kind of result will be “enough.”
  • We did a one-off project for a payer that unexpectedly identified significant fraud and abuse committed by an extremely powerful provider. For this payer, “enough” did not include addressing the behavior of this powerful provider.
  • We have a client in a country that is publicly agonizing over a culture of corruption which permeates every industry, including health care. For this payer, “enough” will probably be the day that they are recognized as having achieved a level of payment integrity which defies that norm. They are on their way!

So how do you set your goals in the fight against losses to fraud, waste and abuse? How much is enough?

Whether your goal is to achieve structural change, or your goal is incremental improvement, we recommend that you do the following things.

  • Recognize that payment integrity includes attention to a range of types of losses, not just fraud, but also to waste (unintentional or unnecessary payment) and abuse (manipulation).
  • Fraud is traditionally addressed by the SIU or forensics. Engage your stakeholders to mitigate losses to waste and abuse.
  • Quantify your losses with a project that validates the integrity of your historical paid claims so that you can establish a baseline of losses which is relevant to your business.
  • Establish achievable goals for measurable results that impact your organization’s bottom line.
  • Be prepared to balance your goals and the needs of your stakeholders. Challenge your stakeholders to make the case for their interests in measurable terms.
  • Be prepared for change, because the people who inappropriately take your money are entrepreneurial.
  • Benchmark your losses and your results against industry best practices, for celebration of success and to support your case for the resources that you need to achieve your goals.

In my next blog on health care fraud, waste and abuse, I’ll ask: “Who Do You Task with Responsibility for Payment Integrity?” I welcome your comments.

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Football fans don't care about sports. Wait, what?

wi fi image 6 interior of levis stadium Football fans don't care about sports. Wait, what?

Video: IBM provides ultimate fan experience at Atlanta’s Mercedes-Benz stadium

Hello everyone! Happy New Year! I promised you that this year, now that my book is more or less out of the way beyond what is mostly cleanup, I would provide you with some fresh thinking and some great content from me and a host of guests — plus some exciting announcements.

Because I’m preparing a post that includes announcements about the CRM Watchlist changes for 2018 to 2019 and the rules of the Emergence Maturity Index battle royale, I’m starting the new year with a guest post from a really interesting guy who is positing a really interesting idea — one that as a set of principals and practices I have been a firm adherent of for many years.

Alex Slawsby goes by the title of director of innovation for Embraer, which, as all the road warriors among you know, is one of the largest producers of jets in the world. But prior to this job, Alex worked for the Sports Innovation Lab, putting forward the ideas of Clayton Christensen (who he also worked for) on disruption and innovation. I met him at the SEAT conference he is referencing in this post, and I have to say, I was thoroughly impressed. He and I are both strong advocates of Service Design and Service Dominant Logic — which, at its absolute simplest, means that products aren’t where the value is; the outcomes they produce and the tasks they support are where the value is. Value in Use is what it is often called. Alex is one of the most articulate people I have known in how he is able to explain, describe, and actually build plans and programs around these concepts. Sports is an area he not only is passionate about but has been successful in — and he is a helluva guy. So, Alex, start the new year right — and right at the time of the College Football championship game and shortly before the Superbowl, get us all going in 2018.

Your venue, sir.

Over the last decade, the NFL has enjoyed tremendous success. In 2007, League revenue surpassed $ 6 billion. In 2016, League revenue surpassed $ 13 billion. Television commercials during NFL games now cost more than twice what they did 10 years ago.

At the end of 2006, the NFL expanded its schedule to include a few Thursday night contests. During this season, there were games every Sunday, Monday, and Thursday.

TechRepublic: How the NFL and its stadiums became leaders in Wi-Fi, monetizing apps, and customer experience

Success without humility frequently leads to contempt. The NFL clearly had a target on its back, even before it began to face a wide range of on-the-field and off-the-field challenges. During this season, there seemed to be nothing the sports media enjoyed more than publishing pictures of half-empty stadiums at kickoff. While the League and its owners frequently push back using selective statistics, NFL Commissioner Roger Goodell recently conceded that attendance is down.

Having spent many years working for Professor Clayton Christensen, guiding corporations, and their leaders through disruptive innovation, I now apply his Jobs-to-be-Done theory (a lens through which to identify disruptive threats and opportunities) in my daily life. If you are not familiar with the theory, the following quote by Theodore Levitt (once a professor at Harvard Business School as well) nails it in a nutshell: “People don’t want to buy a quarter-inch drill, they want a quarter-inch hole.”

The point is that we don’t really care about the drill, we care about the outcome — the quarter-inch hole — that the drill helps us achieve. “Help me make a quarter-inch hole” is the Job-to-be-Done. The drill is the solution we “hire” to get that job done. Now, if something else comes along — a dropper of wood-eating liquid, say — that helps us more satisfactorily (less noise, dust, and cost) get that job done, we might ditch that drill in two seconds.

Given that drill company executives probably spend their time trying to one-up their fellow drill company executives, they probably wouldn’t recognize that “liquid competitor” until we were all ordering from a different section on Amazon. And, as Professor Christensen’s most famous theory states, that’s exactly why Blockbuster, Kodak, Sears, and others were disrupted. And this might be why the NFL has an “attendance problem” — other things are beginning to get fan jobs done more effectively.

Also: NFL star Fleener to help VR tech firm detect concussions

To begin assessing whether the NFL is truly in danger of getting disrupted, let me first describe an “Ah ha!” moment that I had this past summer. Just before noon on July 18, I was sitting in a packed meeting room at the Atlanta Marriott Marquis, surrounded by athletic directors, team technology leaders, and venue managers. The session, one of many smaller discussions at the SEAT Atlanta conference, was titled “Connected Smart Cities and The Future Stadium.”

Over the last several years, the “smart stadium” has become all the rage in sports. Owners fear that the at-home sports watching experience will get so good at satisfying fan Jobs-to-be-Done that fans will decide to stay home. And so owners are investing heavily in stadium technology to improve the in-venue experience, which includes addressing major fan frustrations such as slow-moving traffic and long concession and bathroom lines.

For half an hour, the directors, leaders, and managers seated around me that day discussed many technology solutions (IoT, mobile, social, analytics, etc) and how they might address such frustrations. A common theme emerged early on: There is no shortage of technology solutions out there (e.g. fan experience applications with wayfinding features, stadium display boards showing line length, in-seat concession delivery), but they all cost a lot of money and no one really knows what kind of ROI they deliver.

Also: NFL bets on AWS for machine learning

I began to wonder if there was another way to tackle the problem. I thought to myself, what if, for a moment, we took lines for granted? What if we just accepted that there would always be lines of cars and people? Could we make those lines less frustrating?

Sitting at that round table, I had my “Ah ha!” moment. As a fan, we “hire” sports to get jobs done like “Excite me,” “Feel part of something much bigger than me,” “Bond with my family,” and “Distract me from the stress of my daily life.” Essentially, we don’t care about the stadium or the sporting event itself (drills), we care about what the stadium and the sporting event help us achieve (jobs).

Now, if we, as fans, can only achieve our desired emotional and social outcomes while sitting in our seats, then, of course, we’ll find frustrating lots of slow-moving cars or people keeping us from those seats (particularly if we’re also spending hundreds of dollars on tickets and concessions as well).

This insight led me to make a comment toward the end of the discussion. I wondered out loud that as a group, perhaps we should not only be thinking about how we can use technology to reduce lines but how we might use technology to make those lines inherently less frustrating as well.

Also: How to watch the NFL on the internet in 2017

Later, after I returned to my room, I searched online to find examples where people or companies had solved this problem. I quickly found the following quote from a June 9 Washington Postarticle describing Disney World’s new “Pandora: The World of Avatar” experience (emphasis mine):

You’ll wait even longer for Flight of Passage, but the payoff is greater and the line easier to endure. That’s because Disney has reached new heights in the design of the queue, which succeeds as an attraction unto itself. Essentially, they’ve built a mountain-hiking path that switchbacks up and up through a fantastical watershed. You ant-line past waterfalls and cliff walls, eventually entering a network of painted caves with ample (and how!) time to decipher the ancient history of the Na’vi. The tunnels give way to an abandoned RDA bunker and then (not there yet!) to a lab much like Weaver’s HQ in the movie, including a floating Na’vi avatar bubbling and breathing in a tank. Even when the line moved ahead I lingered here, which is akin to sitting on the plane for bit after landing in Australia.

Disney has long known that lines are one of — if not the greatest — barrier to guest satisfaction. To tackle this problem, Disney first replaced long straight lines with now common “switchback” lines that made the wait seem shorter and added shelters around the lines to make waiting more bearable.

Over the years, Disney then worked to make the line experience part of the attraction itself to help guests satisfy their enjoyment Jobs-to-be-Done before they even set foot on the ride. I’d say a guest preferring to stand in line a bit longer constitutes success.

The question this raises for sports business leaders is intriguing: How can we help fans sitting in traffic or standing in lines achieve at least some of the in-seat payoff?

We’ve all had the experience of sitting in slow-moving stadium traffic. What if, while stuck in our cars, we could receive live, targeted programming through a fan experience mobile app? Dedicated announcers could describe to us in detail what’s happening in the stadium, pipe through crowd noise, provide frequent traffic, parking, and weather updates, and even take questions from drivers and call out specific license plates to win prizes.

We’ve also all had the experience of standing in slow-moving concession lines. What if the lines themselves were elevated for a view of the field or passed by video walls displaying a line-only, high-definition, field-level “photographer’s eye” view of the action? What if, while standing in line, we could be randomly selected to appear on the jumbotron or accrue greater loyalty points and rewards if the line moves more slowly than expected?

Also: 3 things you can learn from the NFL about digital transformation

While some of these ideas are likely more viable than others, the broader objective here is to develop solutions that nail fan Jobs-to-be-Done — the Jobs for which fans “hire” the in-seat experience — even when the fan can’t be in their seat. It’s what Disney accomplished when it made the Flight of Passage line nearly as compelling a “drill” as the attraction itself. It might be time to balance trying to make lines go away with trying to make lines less frustrating.

This brings me back to the NFL and its attendance problem. If fans aren’t coming to NFL stadiums, it’s because other things are getting their jobs done more effectively. Owners can invest a lot of money in new smart stadiums, but few fans will not continue to “hire” the in-venue experience just because it includes the largest jumbotron or the fastest Wi-Fi. It is much more likely that fans will continue to come if owners invest in the things that reduce fan experience friction and truly nail fan emotional and social Jobs-to-be-Done.

This is true for the whole sport as well. When games only took place on Sunday and Monday and the in-venue sports consumption experience was far superior to any other, few things had the power to nail fan “Excite me” or “Distract me from the stress of my daily life” Jobs-to-be-Done like the NFL. While the NFL has become a revenue juggernaut over the last decade, it is now far more of a commodity frequently in the headlines for reasons which clearly diminish its value as a “drill” to fans.

Also: Robots get NFL tryout: Steelers experiment with robotic tackling dummies

As you think about the implications of this for the NFL and its future, think about what it might mean for you and your organization as well. Here are a few questions to consider:

1. What are your customers really buying? Peter Drucker once famously said, “The customer rarely buys what the company thinks it’s selling.” Combine this with the Levitt quote above and you might just see the, well, Matrix. It may be difficult for those in the industry to consider, but sports fans are not buying tickets to sporting events. They are buying tickets to outcomes, to Jobs-to-be-Done satisfaction. They are really “outcomes fans” as we all are in all areas of our lives. We don’t buy milk at the supermarket. We buy the outcomes (quench my thirst, improve my nutrition, and support my child’s good health) that milk helps us achieve. You don’t really care about this blog post. You care about what this blog post will do for you (ideally, if I’m lucky, entertain you, provoke your thoughts, and maybe help you work differently). It is very important that those in the sports industry — and leaders in any industry — consider carefully this question and its counter-intuitive answers, particularly for the following reasons…

2. Who are your real competitors? If drill company executives realize they are selling holes and not drills, they are more likely to see threats that look nothing like other drills. They are also more likely to see opportunities (“How else could we help our customers create holes?”) they would otherwise miss. If those team owners realize they are selling outcomes and not sports, they might realize that they are competing with all kinds of things that help consumers achieve the same outcomes. So, yes, while the NFL is competing with MLB for viewers when their seasons overlap, it’s also competing with very different DNA’d things like eSports, Twitch, Netflix, Amazon Prime Video, Facebook, Snapchat, Instagram, and drone racing. When they view the business of football through a Jobs-to-be-Done lens, NFL owners might understand just why divisiveness and controversy so powerfully destroy the sport’s value to consumers. And those same owners — and leaders in any industry — might also discover new ways to double down on delivering what truly matters.

3. What is your traffic? Your concession lines? When organizations realize they are selling outcomes, they see opportunities and threats very differently. When organizations take the time to see their products and services and experiences through the eyes of customers and to do that faithfully (i.e. holding back “the customer doesn’t know what they’re talking about” bias), they often see friction they’ve ignored or dismissed, friction which could be deadly in a day when customers can identify alternative solutions and switch in the blink of an eye. As I’ve argued here, if owners don’t find ways to reduce the friction inherent to the live in-stadium experience (and realize that “bigger is better” doesn’t work), fans will flee more quickly than they can imagine. The same goes for leaders in any industry — if you don’t double-down on maximizing value to your customers (always seeking to increase the “Jobs-to-be-Done crushing” benefits of your solution while reducing friction), they will not be your customers for very long.

4. Finally, when should it be “Day 2?” The answer: Never. For years, Jeff Bezos has held close the belief that organizations on their first day are hungry, lean, and paranoid (homage to Andy Grove) when it comes to delivering customer delight. But on their “second day,” they lose their edge, even slightly. Earlier this year, Bezos was asked by an employee what Day 2 looks like. His response, “Day 2 is stasis. Followed by irrelevance. Followed by excruciating, painful decline. Followed by death. And that is why it is always Day 1.”

To paraphrase something Professor Christensen once wrote, the more successful an organization, the harder it is for the organization to change. Across professional sports leagues, Day 2 came and went a long time ago. A victim of its own success, the NFL is in fact approaching day 35,600 and it shows. In other industries, organizations like the NFL that prioritize their own products over their own customers come and go in the blink of an eye. Sports team owners — and leaders in any industry — must not take for granted that “if they build it, customers will come.”

To sustainably lead, much less survive, it is imperative that organizations and their leaders stay very close to their customers’ Jobs-to-be-Done and prioritize doing what it takes to continue to satisfy those jobs. While the NFL has enjoyed tremendous revenue growth over the last decade, there are signs that such growth has masked a widening fundamental — and potentially disruptive — gap between what it is selling and what its fans want to buy.


Thanks Alex. Thoughts, people?

OK, until next week (or so) when we launch with the announcements and some thinking on the subject of 2018 and where we will be same time next year. See ya.

Previous and related coverage

Verizon, NFL strike deal to bring game streaming to any mobile network

Verizon’s acquisition of Yahoo gives the mobile carrier a massive platform to stream NFL games, starting in January 2018.

Microsoft, NFL extend Surface sideline deal to another season

Microsoft Surface will continue to be ‘the official tablet of the NFL’.

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Engagement Insights Easily Delivers Marketing Metrics You Care About

blog title engagement insights 351x200 Engagement Insights Easily Delivers Marketing Metrics You Care About

The modern marketer is flooded with data. Website data. Pipeline data. Email data. Social  Media data. Views. Likes. Comments. Clicks. Cart abandonment. Conversions. There are vanity metrics, marketing metrics, and business metrics.

It can be – and often is – overwhelming. As Act-On blogger Pam Neely recently reported, “53% of marketing executives feel ‘overwhelmed’ by the amount of data produced by their marketing technologies.”

At Act-On, we believe marketers should quickly be able to access the key engagement analytics they and the executive team care about. That’s why we’ve developed Engagement Insights, an easy-to-use templated approach to measuring marketing performance using tools you’re already using – Google Sheets or Excel.

Now, Act-On customers can gain real-time insight on how their audience is engaging with them. And those insights should not only be actionable and easily shareable with key stakeholders throughout your organization, but also drive optimization and improvements for your marketing programs.

With Act-On’s Engagement Insights – powered by Data Studio – marketers will be able to quickly have visibility into what they care about:

  • Email & Message: Measure key metrics across all email campaigns including number sent, opened and click thru rates
  • Forms: Know exactly what forms are converting visitors into leads and see trends over time
  • Landing Pages: Better understand what campaign landing pages are performing best, as well as see the engagement trends over time
  • Content Assets: Focus future activities by learning exactly what your audience is engaging with on your website, as well as when they are engaging

At Act-On, our mission is to empower marketers to do the best work of their careers. With Engagement Insights, we’re giving marketers the cypher to find meaningful signals from all the data being collected.

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AI – What Chief Compliance Officers Care About

AI Technology Compliance AI – What Chief Compliance Officers Care About

Arguably, there are more financial institutions located in the New York metropolitan area than anywhere else on the planet, so it was only fitting for a conference on AI, Technology Innovation & Compliance to be held in NYC – at the storied Princeton Club, no less. A few weeks ago I had the pleasure of speaking at this one-day conference, and found the attendees’ receptivity to artificial intelligence (AI), and creativity in applying it, to be inspiring and energizing. Here’s what I learned.

CCOs Want AI Choices

As you might expect, the Chief Compliance Officers (CCOs) attending the AI conference were extremely interested in applying artificial intelligence to their business, whether in the form of machine learning models, natural language processing or robotic process automation – or all three. These CCOs already had a good understanding of AI in the context of compliance, knowing that:

  • Working the sets of rules will not find “unknown unknowns”
  • They should take a risk-based approach in determining where and how to divert resources to AI-based methods in order to find the big breakthroughs.

All understood the importance of data, and how getting the data you need to provide to the AI system is job number one. Otherwise, it’s “garbage in, garbage out.” I also discussed how to provide governance around the single source of data, the importance of regular updating, and how to ensure permissible use and quality.

AI Should Explain Itself

Explainable AI (XAI) is a big topic of interest to me, and among the CCOs at the conference, there was an appreciation that AI needs to be explainable, particularly in the context of compliance with GDPR. The audience also recognized that their organizations need to layer in the right governance processes around model development, deployment, and monitoring––key steps in the journey toward XAI. I reviewed the current state of art of Explainable AI methods, and where their road leads to getting AI that is more grey-boxed.

Ethics and Safety Matter

In pretty much every AI conversation I have, ethics are the subject of lively discussion. The New York AI conference was no exception. The panel members and I talked about how any given AI system is not inherently ‘ethical’; it learns from the inputs it’s given. The modelers who build the AI system need to not pass sensitive data fields, and those same modelers need to examine if inadvertent biases are derived from the inputs in the training of the machine learning model.

Here, I was glad to be able to share some of the organizational learning FICO has accumulated over decades of work in developing analytic models for the FICO® Score, our fraud, anti-money laundering (AML) products and many others.

AI safety was another hot topic. I shared that although models will make mistakes and there needs to be a risk-based approach, machines are often better than human decision-making, such as autopilots on airplanes. Humans need to be there to step in if something is changing, to the degree that the AI system may not make an optimal decision. This could arise as a change in environment or data character.

In the end, an AI system will work with the data on which it has trained, and is trained to find patterns in it, but the model itself is not necessarily curious; the model is still constrained by the algorithm development, data posed in the problem, and the data it trains on.

Open Source Is Risky

Finally, the panel and I talked about AI software and development practices, including the risks of open source software and open source development platforms. I indicated that I am not a fan of open source, as it often leads to scientists using algorithms incorrectly, or relying on someone else’s implementation. Building an AI implementation from scratch, or from an open source development platform, gives data scientists more hands-on control over the quality of the algorithms, assumptions, and ultimately the AI model’s success in use.

I am honored to have been invited to participate in Compliance Week’s AI Innovation in Compliance conference. Catch me at my upcoming speaking events in the next month: The University of Edinburgh Credit Scoring and Credit Control XV Conference on August 30-September 1, and the Naval Air Systems Command Data Challenge Summit.

In between speaking gigs I’m leading FICO’s 100-strong analytics and AI development team, and commenting on Twitter @ScottZoldi. Follow me, thanks!

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What is a Citizen Developer, and Why Should I Care?

080317 1926 WhatisaCiti2 300x201 What is a Citizen Developer, and Why Should I Care?

There’s a term floating around more and more lately: Citizen Developer. While it’s on the cusp of becoming a more mainstream phrase, its meaning has yet to become widespread, and that’s a shame because it describes a person with enormous potential. A Citizen Developer has the capability to grow an organization’s ability to do business. So, let’s break down this new term down for you so you can learn what it takes to become one!

You probably already know what a citizen is, right? They look kind of like this:

080317 1926 WhatisaCiti1 What is a Citizen Developer, and Why Should I Care?

Citizens are people, plain and simple. And then, of course, there’s the developer:

080317 1926 WhatisaCiti2 What is a Citizen Developer, and Why Should I Care?

Developers are also citizens, but they know varying degrees of coding language and typically require caffeine to operate.

As you can see, caffeine aside, there’s one big distinction: developers have a more in-depth technical breadth which allows them to customize things, such as CRM for Microsoft Dynamics 365, in ways a non-technical person could not. Up until recently, Microsoft Dynamics 365 didn’t have much of an in-between – you either had to know code or live with your existing system. Enter the Citizen Developer; they look something like this:

080317 1926 WhatisaCiti3 What is a Citizen Developer, and Why Should I Care?

The Citizen Developer is an exciting new role because they don’t believe coding knowledge should limit an organization’s ability to drive success through Dynamics 365. As they work within their Dynamics 365 environment, they see ways to improve it. While technical knowledge of CRM for Dynamics 365 is still needed, in-depth coding knowledge is not required. A Citizen Developer has learned how to leverage the new tools available in Dynamics 365, which allow CRM admins and customizers to customize their environment with no code and low code solutions. They enable an organization to run more efficiently and smoothly and they bridge the gap between the non-technical and technical skillsets of a business.

We know you’re wondering: how does one become a Citizen Developer? You’re in luck! PowerObjects has just released its new Dynamics 365 University Fall Course Catalog, and with it, there is a brand new CRM Citizen Developer Training for Dynamics 365 training. This course will get you on your way to propelling your organizations to new heights. Whether you are starting to learn code or don’t have any interest in coding at all or if you’re looking to customize your Dynamics 365 environment with no code/low code solutions, this class is for you.

Happy Dynamics 365’ing!

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PowerObjects- Bringing Focus to Dynamics CRM