Predictably Inaccurate - The prevalence and perils of bad data Deloitte 20170731
Predictably inaccurate: The prevalence and perils of bad big data
Deloitte Review, issue 21

John Lucker, Susan K. Hogan, Trevor Bischoff
July 31, 2017
Article Sections
​When big data contains bad data, it can lead to big problems for organizations that use that data to build and strengthen relationships with consumers. Here are some ways to manage the risks of relying too heavily—or too blindly—on big data sets.

Is our love affair with big data leading us astray?
We’re not that much smarter than we used to be, even though we have much more information—and that means the real skill now is learning how to pick out the useful information from all this noise.
—Nate Silver1

SOCIETY and businesses have fallen in love with big data. We can’t get enough: The more we collect, the more we want. Some companies hoard data, unsure of its value or unclear if or when it will be useful to them but, all the while, reticent to delete or not capture it for fear of missing out on potential future value. Stoking this appetite is the sheer growth in the volume, velocity, and variety of the data.

Most of all, many business leaders see high potential in a fourth V: value. Given our ability to access and (potentially) understand every move our current and potential customers make, coupled with access to their demographic, biographic, and psychographic data, it seems logical that we should be able to form a more intimate, meaningful relationship with them. Every data point should move the business at least one step closer to the customer.

Yet despite all the digital breadcrumbs, it turns out that marketers might know less about individual consumers than they think. The numbers don’t lie—or do they? What if much of this data is less accurate than we expect it to be?

Perils ranging from minor embarrassments to complete customer alienation may await businesses that increasingly depend on big data to guide business decisions and pursue micro-segmentation and micro-targeting marketing strategies. Specifically, overconfidence in the accuracy of both original and purchased data can lead to a false sense of security that can compromise these efforts to such an extent that it undermines the overall strategy.

This article explores the potential adverse consequences of our current love affair with big data. Evidence from our prior2 and current primary research, supported by secondary research, highlights the potential prevalence and types of inaccurate data from US-based data brokers, as well as the factors that might be causing these errors. The good news is that strategies and guardrails exist to help businesses improve the accuracy of their data sets as well as decrease the risks associated with overreliance on big data in general.
#data  #accuracy  #privacy  #analytics  #critique  #personalization  #Deloitte  #2017 
Tech fluency and the future of work Deloitte 20170731
Tech fluency: A foundation of future careers Deloitte 20170731
Tech fluency: A foundation of future careers Deloitte 20170731
Anthony Stephan, Martin Kamen, Catherine Bannister
July 31, 2017
Article Sections
​Technology permeates virtually all aspects of our lives—and our jobs. Without a strong foundation of knowledge about technology in the workplace, workers will likely find it harder and harder to contribute to enterprise value—and to grow professionally.
The language of technology
IN the 21st century, it’s often said, everycompany is a technology company. Across industry sectors, powerful technological forces—including mobile, cloud, analytics, and social collaboration—now drive business strategy, fuel new opportunities, and upend long-established markets.
Think about how technology-enabled possibilities that emerged over the past decade have transformed the way we work now, whether in a securities trading office in Manhattan, on a factory floor in Ohio, or in an automobile in Los Angeles that is part of a ridesharing network. In each, technology is both ubiquitous and foundational, enabling the communications, transactions, and operations that drive revenue and strategy.
Indeed, technology is integral to almost everyone’s daily work, and businesses increasingly rely on innovative applications to engage customers and partners, engineer new products and services, and identify business insights buried within mountains of data. And technology’s disruption of business models, markets, and career paths doesn’t end there: Cognitive computing, machine intelligence, and advanced robotics are poised to replace some traditional human employees and augment the skills and productivity of others.1
Analysts have written plenty about this phenomenon’s impact on enterprise technologists and their work within IT organizations. But what does it mean for non-IT workers? If every company is now a tech company, will business leaders, marketers, and HR professionals need to learn to write code in order to get ahead?
We wouldn’t go that far—though some have suggested that computer programming could be the next big blue-collar job opportunity.2 It does mean, however, that to engage in and contribute to a tech-driven business environment, to be able to quickly learn the next big emerging technology’s functions, and to grow professionally, all workers—from executives to interns—will need to learn much more about critical systems: their capabilities and adjacencies, their strategic and operational value, and the particular possibilities they enable.3 In other words, individuals will need to become tech fluent.
#tech  #analytics  #enteprise  #executives  #fluency 
Intelligent automation: A new era of innovation Deloitte 20140122
Intelligent automation: A new era of innovation

David Schatsky, Vikram Mahidhar
January 22, 2014
Intelligent automation—the combination of artificial intelligence and automation— is already helping companies transcend conventional performance tradeoffs to achieve unprecedented levels of efficiency and quality. Applications range from the routine to the revolutionary: from collecting, analyzing, and making decisions about textual information to guiding autonomous vehicles and advanced robots.

Intelligent automation—the combination of artificial intelligence and automation—is starting to change the way business is done in nearly every sector of the economy. Intelligent automation systems sense and synthesize vast amounts of information and can automate entire processes or workflows, learning and adapting as they go. Applications range from the routine to the revolutionary: from collecting, analyzing, and making decisions about textual information to guiding autonomous vehicles and advanced robots. It is already helping companies transcend conventional performance tradeoffs to achieve unprecedented levels of efficiency and quality.

References to artificial intelligence on have quadrupled since 2010.1
Since 2011, venture capital investment in ventures related to robotics and artificial intelligence has grown more than 70 percent per year, exceeding $600 million.2
An exchange-traded fund that tracks the global robotics and automation sector was listed on NASDAQ.3
Google acquired eight robotics start-ups in six months.4
Littler Mendelson, a major employment and labor law firm, has formed a practice group focused on robotics and personal enhancement technologies.5
Facebook acquired a speech recognition and machine translation company and is creating an artificial intelligence laboratory.6
Audi, BMW, Mercedes-Benz, Nissan, and Volvo plan to introduce autonomous vehicles.
IBM announced a $1 billion investment to commercialize its Watson cognitive computing technology.7
#automation  #ai  #rpa  #Deloitte  #2014 
Shantanu Mullick (@ShantanuMullick) | Twitter
Shantanu Mullick
@ShantanuMullick Follows you
Assistant Professor of marketing @TUEindhoven| Research on #bigdata health, nutrition, obesity and retailing| Teach and consult about marketing #analytics

Eindhoven, The Netherlands
Joined January 2012
#health  #marketing  #analytics  #academic  #TUEEindhoven 
3 days ago
Cognitive Computing and the Marketing Machine Webinar CMO Council 20170822
Cognitive Computing and the Marketing Machine
What It Is, What It Will Be, and Why We Should All Be Excited for the Future August 22, 2017 10:00 AM Pacific Time
we speak with Brady Fox, who leads strategy and execution for IBM's Watson Marketing and Predictive Analytics group, shares his views into the very definition of what cognitive computing actually is, what it is not, and how it is poised to fundamentally change how customers engage and how marketing teams work (and work together across the organization).
What It Is, What It Will Be, and Why We Should All Be Excited for the Future

Date: August 22, 2017
Time: 10:00 AM
Content Provided by the CMO Council and IBM

In a study conducted by the CMO Council in partnership with IBM, 42 percent of marketers surveyed indicated that their top digital experience goal in the coming year was to better connect the multitude of campaigns being executed across the organization into a comprehensive, connected customer experience that could drive engagement throughout the engagement lifecycle. The challenge is how they will get there when 56 percent of marketers admit that data for their organizations is in more of a state of collection than action, or data is only being used to measure past activities. How will marketers—who are struggling to turn data to insight and insight into action—be able to reach their goal of a connected, robust digital experience? That is, how will marketing achieve this goal along with all of the other engagement, experience and operational goals that they must reach? How can data stop being a challenge and start being the fuel for innovation and engagement?

Enter the new era of analytics and operations that look to “machines” to deliver smarter decisions and outcomes through amplified analytics and improved experiences. You have likely heard buzz around machine learning, artificial intelligence or cognitive computing. Perhaps you have thought that these terms are more buzz than reality—don’t they all just mean the same thing? If you are like the 24 percent of senior marketing leaders that the CMO Council surveyed, you are not 100-percent sure what cognitive computing is or what value it could bring to the organization.

To help answer some of these questions and to bring some much-needed definition to this evolving and exciting conversation, the CMO Council will be speaking with Brady Fox, who leads strategy and execution for North American Sales with IBM’s Watson Marketing and Predictive Analytics group. A veteran of digital transformation and customer engagement, Fox is a seasoned marketing and sales leader, having led engagements with IBM, Tealium, Adobe and Omniture. He will be sharing his views into the very definition of what cognitive computing actually is, what it is not, and how it is poised to fundamentally change how customers engage and how marketing teams work (and work together across the organization).

Join the CMO Council for this interactive webcast that will not only share thought leadership and insights, but also provide an opportunity to ask a key industry leader the big questions about a big trend that is taking marketing and businesses by storm.
#cognitivecomputing  #application  #marketing  #2017  #CMOCouncil 
3 days ago
Agile Innovation Bain 20160420
Agile Innovation
April 20, 2016 Bain Brief By Darrell K. Rigby, Steve Berez, Greg Caimi and Andrew Noble
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As a 21st-century Mark Twain might observe, everybody talks about innovation, but nobody does anything about it. Of course, that’s not quite right. Several path-breaking thinkers have come up with better ways to design new products. The problem is that anywhere from 70% to 90%1 of those new products continue to fail. Lacking a systematic, repeatable and fast-moving method for designing and developing innovations, companies find themselves struggling to keep up with market changes.

But there is hope. Agile methodologies have transformed the software industry over the past 25 or 30 years. Software development is an especially challenging form of innovation since technologies and customer demands seem to change at the speed of Moore’s law. Software is also playing an increasingly important role in nearly every element of business. (As they say, every company is now a software company, whether its executives know it or not.) So it’s worth looking into how the industry has changed and what lessons it might hold for innovation in the rest of the organization.

Darrell Rigby: Agile Innovation
Gregory Caimi: Agile Innovation
Bain's Darrell Rigby and Scrum's Jeff Sutherland on Agile Innovation
Design Principles for a Robust Operating Model
Bury the pilot project and build a hothouse
When we do, we find some remarkable facts. Agile isn’t just one more approach to creative thinking or iterative prototyping. Rather, it’s a well-developed holistic system engineered to overcome more than a dozen common barriers to successful innovation. It’s also highly effective. In tens of thousands of software development projects, Agile methods have boosted average success rates to 39% from 11%, a more than threefold improvement. In large, complex projects Agile’s success rate jumps to six times that of conventional methods.2
#innovation  #agile  #methods  #enterprise  #MVP  #Bain  #2016 
3 days ago
Darrell Rigby Agile Innovation Bain 20170714
Darrell Rigby: Agile Innovation
July 14, 2017 Bain video By Darrell Rigby
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What makes innovation programs succeed? Darrell Rigby, who leads Bain's Global Innovation practice, discusses how Agile innovation teams can help companies quickly pivot to take advantage of market opportunities.

Read the Bain Brief: Agile Innovation

Read the transcript below.

DARRELL RIGBY: If you study the successes and failures of innovation programs long enough, you'll find two pretty interesting things. The first is, sadly, 70% to 90% of those innovation programs will fail. But secondly, you will find—and this is really quite interesting—if you focus on those successes, you'll find that 2/3 of those successes pivot or adapt or change significantly from their original concept.

So, for example, YouTube was originally conceived to be an online video dating site. It just didn't work out very well. And so they learned that if they allowed those people to share all kinds of videos, then it turned out to be very successful. And that's what we're trying to do with Agile innovation teams.

We are trying to build teams that attack market opportunities the same way those successful start-ups do. And that means we're forming small, multidisciplinary, entrepreneurial, very adaptive teams. We're putting them right in the middle of corporate headquarters.

And then we're saying, take these complex tasks, break them into smaller components that you can attack on a modular basis, sequence those so that you are only working on the highest priorities 100% of the time, build rapid prototypes, get those in front of customers so that you can learn as quickly as possible what works and what doesn't work, and then adapt.

And what we're finding is those teams are remarkably successful. They tend to increase the success rates of innovations by 250% to 500%. We know that customers are more satisfied with the work of those teams, and we know that the teams themselves are happier and more productive. There just aren't that many opportunities for executives to improve all three of those things at the same time. They should find ways to do more of this.

Read the Bain Brief: Agile Innovation
#innovation  #agile  #enterprise  #mvp  #Bain 
3 days ago
How to Make Agile Work for the C-Suite HBR 20170719
How to Make Agile Work for the C-Suite
Eric GartonAndy Noble
JULY 19, 2017


HBR Guide to Performance Management

Accelerating Change: A PowerPoint Presentation Based on the Work of...

HBR Guide to Leading Teams Ebook + Tools
Many companies are attempting a radical — and often rapid — shift from hierarchical structures to more agile environments, in order to operate at the speed required by today’s competitive marketplace. Companies like ANZ, the Australian-based banking giant, have made explicit commitments to adopt agile principles, while others like Zappos, are on the bleeding edge of organizational transformation. Many stopping points exist along the continuum from hierarchy to holacracy. To successfully transform to a more agile enterprise, companies must make conscious choices about where and how to become agile. They have to decide where to adopt agile principles and mindsets, where to use agile problem-solving methodologies to dynamically address strategic and organizational challenges, and where to more formally deploy the full agile model, including self-managed teams.

At Bain & Company, we do not believe that companies should try to use agile methods everywhere. In many functional areas, such as plant maintenance, purchasing, sales calls, or accounting, more traditional structures and processes likely will deliver lower cost, more repeatable outcomes and more scalable organizations. Sorting through every function and every part of your company’s operating model to determine which parts of the agile playbook to adopt requires some deep thinking. It also means you have to figure out how to make the agile and traditional parts of your organization effectively operate with one another. This takes time.

There is, however, a no-regrets first move available to the leaders of organizations that are working through a complicated transition from a traditional to an agile enterprise, and that is to become agile at the top. Senior leadership teams that embrace agile do a few things differently. Based on our experience working with these teams, we recommend senior teams do the following if they want to become more agile >> more
#agile  #leadership  #advice  #executives  #innovation  #Bain 
3 days ago
Google buys Seattle health monitoring startup Senosis, bolstering digital health push 20170813
Exclusive: Google buys Seattle health monitoring startup Senosis, bolstering digital health push
BY JOHN COOK on August 13, 2017 at 4:39 pm
1 Comment Share  585 Tweet Share  2k Reddit Email
GeekWire Summit: Early-bird tickets here!

Computer scientist and electrical engineer Shwetak Patel. His most recent venture, Senosis, developed apps for smartphones that can help diagnose disease. (GeekWire Photo / Todd Bishop)
Shwetak Patel has struck again.

The University of Washington computer scientist has sold his newest Seattle startup company, Senosis Health, to Google, according to sources familiar with the deal.

It marks the latest acquisition for Patel, whose past startup ventures have landed in the hands of companies such as Belkin International and Sears.

Patel, who founded Senosis Health with four other clinicians, researchers and tech transfer experts from the University of Washington, won a MacArthur genius grant in 2011 and his past innovations have ranged from energy meters to air quality sensors.

With Senosis, Patel and his team of about a dozen engineers and physicians took on a bigger challenge: Turning smartphones into monitoring devices that collect health metrics to diagnose pulmonary function, hemoglobin counts and other critical health information.

The company’s apps – including SpiroSmart and SpiroCall, HemaApp and OsteoApp – were under review by the Food and Drug Administration earlier this year when GeekWire first wrote about the novel concept. At the time, Patel seemed especially bullish on the idea of using the enhanced cameras, accelerometers and microphones of modern-day smartphones as a new type of health care diagnostic tool.
#healthcare  #digitalhealth  #mhealth  #startup  #mobiledevice  #sensors  #Google  #UW 
3 days ago
Edward Boudrot | Optum LinkedIn
Edward Boudrot
Innovation Leader ►Market Strategy ♦ Product Strategy ♦ Human Centered Design ♦ Design Thinking
Optum Stanford University Graduate School of Business
Greater Boston Area 500+ 500+ connections

I love the creative spirit obsessed with possibility. I live for the rush of turning thought -an abstract idea- into something real, tangible, touchable, readable, effectual, profitable. I love to get things done! I have led high performing product teams in Fortune 500 companies and start-ups.

My passion is bringing new innovative experiences, products and solutions to market, resulting in leading companies. I execute relentlessly with a "get it done" attitude leveraging my "big idea" skills learned at Babson and years of experience successfully competing in emerging markets.

I am now with Optum, driving products and experiences that change peoples lives through better health and wellness. I founded the idea and process called "Fusion" that focuses on fusing Market research, Human centered design, Product Management, Experience management and Development practices together for more delivering faster more effective solutions to market. We have cut the time to market for developing solutions by an astounding 75%.

Specialties: Innovation, Human Centered Design, Product Management, Product Strategy, Market Strategy, Agile, Cloud based Solutions, Technology Marketing, Marketing, Competitive Analysis, Business Models,Go-to-Market Strategy, PLM, Product life cycle management, Healthcare, Internet, Cloud, P&L, Strategic Planning, Partnerships, Best Practices, Campaign Strategy, Marketing Communications, Branding, Channel Development, Training & Development, Market Research, Machine Learning, Artificial Intelligence, AI, ML
#innovation  #thoughtleader  #healthcare  #Optum  #enterprise  #exec 
3 days ago
Value Proposition Design Book
Value Proposition Design simplifies complex ideas into quickly readable illustrations with only the most practical, important details. The result? You'll learn more, in less time, and have fun along the way.
#innovation  #book  #valueproposition 
3 days ago
Mastering Value Propositions - Strategyzer
Mastering Value Propositions
An online course that will teach you how to better understand customers, and create value propositions that sell

Learn the Value Proposition Canvas methodology, used by millions business practitioners worldwide
Turn fluffy discussions into practical artifacts
Create a shared language around value propositions
Pairs with the Business Model Canvas methodology
#innovation  #valueproposition  #consulting  #firm  #mvp 
3 days ago
5 Ways Machine Learning Reinvents IT Root Cause Analysis 20151216
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5 Ways Machine Learning Reinvents IT Root Cause Analysis
by Rob Markovich | December 16, 2015 4:00 pm | 0 Comments

Rob Markovich, Chief Marketing Officer, Moogsoft
Rob Markovich, Chief Marketing Officer, Moogsoft
What do Google driverless cars and Stanford University autonomous helicopters have in common?

Both rely on machine learning technology to make sense of complex environments, while ensuring good decisions are made sooner. Machine learning’s ability to make good decisions faster in complex environments also can be applied to solve challenges in IT operations.

In today’s dynamic IT environments driven by virtualization, mobility, and cloud, application and infrastructure issues are popping up constantly. When an issue affecting service unfolds, there can be multiple underlying root causes that are simultaneously cascading across technology domains – apps, servers, storage, networks and, increasingly, private to public cloud hybrids.

The volume of IT telemetry (events, alerts, traps, messages) can be huge and overwhelming. With the assistance of machine learning, however, your IT production operations staff can make immediate sense of the potentially thousands of events generated across the environment. The ability to quickly see the signal from the noise, as well as to understand how the events are all related to a couple of situations, is known as IT situational awareness – something that all IT teams are looking to improve upon.
#ML  #analytics  #processimprovement  #reengineering  #rootcause 
4 days ago
Understanding the Promise and Pitfalls of Machine Learning 20151230
Understanding the Promise and Pitfalls of Machine Learning
by Gurjeet Singh   |   December 30, 2015 5:30 am   |   1 Comments

Gurjeet Singh, CEO, Ayasdi
Machine learning is generating a tremendous amount of attention these days from the press as well as the practitioners. And rightly so – machine learning is a transformative technology. But despite the references to the topic, the money raised from venture capitalists, and the spotlight that Google is bringing to the subject, machine learning is still poorly understood outside of a core group of highly technical leaders.

This has the effect of underestimating how transformative machine learning is going to be. It also has the effect of shielding business leaders from what they need to do to prepare for the era of machine learning.

Let’s discuss both sides of the sword – the promise and the pitfalls, starting with a definition.

Machine learning is a class of algorithms that can learn from and make predictions on data. Generally speaking, the more data, the better the outcome for machine learning techniques. Machine learning doesn’t require explicit rules to govern performance. It does not require manual construction of “if this, then that.” It will make that determination on its own, based on the data.

The transformative effect of machine learning, and why it is so important now, is a function of that fact that we are hitting trigger points across data, compute, and algorithmic sophistication.
#analytics  #ml  #challenges  #deployment  #Ayasdi  #GurjeetSingh 
4 days ago
Report: Adoption of Embedded Analytics Growing 20160727
Report: Adoption of Embedded Analytics Growing
by Mark Lockwood   |   July 27, 2016 5:30 am   |   0 Comments

Mark Lockwood, Director, Product Marketing, Logi Analytics
We rely on technology to help us make virtually every decision. Look at popular consumer applications like Amazon, Netflix, and Facebook: They all are centered on data. Amazon offers reviews to help us select the best product. Netflix uses our viewing history to recommend shows. Facebook adds content to our newsfeeds that it thinks we’ll enjoy.

Naturally, people now have come to crave a similarly seamless, data-centric user experience within their business applications. This “consumerization of technology” means people expect to be able to access the data they need when they need it, in the applications they are already using, to make business decisions on a daily basis. And they don’t want to have to go through time-consuming training to understand these new analytics tools. Can you imagine needing a training course to learn how to use Amazon?

There’s no denying that the analytics landscape is shifting, and new needs are driving major transformations. Chief among these is organizations’ need for self-service tools that will enable users with easy access to business data.

But even as organizations expect employees to use these tools to make data-driven decisions, and as users get the access to data they demand, many teams are still struggling with adoption. This may be due to one key difference between consumer applications and most business applications: where the analytics reside.

When you are looking at a product on Amazon, the reviews are embedded right on the page. When you check your Facebook app, your newsfeed is the first thing you see. Yet when you want to analyze business data, it’s often in a separate application all together. It’s not a seamless experience.

Organizations can ensure their users will actually use the tools they provide to make decisions by making the experience more like the consumer experience to which they have become accustomed – with embedded analytics.
#enterprise  #analytics  #process  #embedded  #operations 
4 days ago
The Next Logical Step Past Analytics Is Cognitive Computing Tom Davenport 20160223
The Next Logical Step Past Analytics Is Cognitive Computing
by Thomas H. Davenport | February 23, 2016 5:30 am | 4 Comments

Thomas Davenport
Thomas Davenport
Many people and companies seem to think of “cognitive computing” as an area separate from analytics. Most large organizations today have significant analytical initiatives underway, but they think of the cognitive space as being an exotic science project. One executive told me, “We have no desire to win Jeopardy,” an allusion, of course, to the IBM Watson project from 2011. But cognitive computing is not just about Watson, and it’s not an exotic science project.

In fact, I’d argue that cognitive computing is a logical extension of analytics work. It’s the next step for any organization that has been pursuing traditional analytics, i.e., analytical models driven by human hypotheses. Any organization that wishes to improve the speed and scale of its analytical activities should be exploring at least some cognitive capabilities now. Cognitive methods are a straightforward extension of previous analytical methods, and there are several reasons why they are better for many applications.

Most cognitive methods are, in fact, based on statistical models. Your organization may be doing “cognitive” work without even knowing it. Perhaps, for example, you are using some form of “machine learning,” which attempts to automatically improve the fit of models and “learns” its way to a better set of explanations or predictions. Machine learning often uses logistic regression, a statistical method that has been around since the 1930s. Automated fitting of models has been around only since about 1957, when Cornell researchers created the “perceptron.” That same invention was the beginning of neural networks as well, which are the basis of the “deep learning” approaches used by many cognitive applications today. So all of these cognitive approaches have deep roots in statistical approaches that are very familiar to analytical folks.
#cognitivecomputing  #data  #analytics  #enterprise  #applications  #outlook  #TomDavenport 
4 days ago
Effective Operational Analytics is About More than Analytics Tom Davenport 20160720
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Effective Operational Analytics is About More than Analytics
by Thomas H. Davenport | July 20, 2016 1:17 pm | 0 Comments

Thomas Davenport
Thomas Davenport
Many times when I speak with analytics managers or businesspeople interested in analytics, they tell me that performing some analytics on data is not the primary problem they have. “We have to get the analytics integrated with the process and the systems that support it,” they say. This issue, sometimes called “operational analytics,” is the most important factor in delivering business value from analytics. It’s also critical to delivering value from cognitive technologies – which, in my view, are just an extension of analytics anyway.

A quick aside: Someone who anticipated this issue early on was Bill Franks, the Chief Analytics Officer at Teradata. He published a book a couple of years ago called The Analytics Revolution, which is really about operational analytics. I wrote the foreword to the book, but the meat of the text the good advice about integrating analytics with the core business processes of your organization.

Three things make operational analytics tough, in my opinion. One is that to make it work, you have to integrate it with transactional or workflow systems. Two is that you often have to pull data from a variety of difficult places. And problem three is that embedding analytics within operational processes means that you have to change the behavior of the people who perform that process.

And if you are successful, you eventually will run into a fourth problem, which is that the embedded analytical models will have to be monitored over time to make sure they remain correct. But since that’s a second-order problem (you should be so lucky to have it), I won’t discuss it further here.
#analytics  #operations  #integration  #enterprise  #TomDavenport 
4 days ago
Rise of the Strategy Machines Tom Davenport IIA 20170228

While humans may be ahead of computers in the ability to create strategy today, we shouldn’t be complacent about our dominance.

“Within the next five years, how will technology change the practice of management in a way we have not yet witnessed?”

As a society, we are becoming increasingly comfortable with the idea that machines can make decisions and take actions on their own. We already have semi-autonomous vehicles, high-performing manufacturing robots, and automated decision making in insurance underwriting and bank credit. We have machines that can beat humans at virtually any game that can be programmed. Intelligent systems can recommend cancer cures and diabetes treatments. “Robotic process automation” can perform a wide variety of digital tasks.

What we don’t have yet, however, are machines for producing strategy. We still believe that humans are uniquely capable of making “big swing” strategic decisions. For example, we wouldn’t ask a computer to put together a new “mobility strategy” for a car company based on such trends as a decreased interest in driving among teens, the rise of ride-on-demand services like Uber and Lyft, and the likelihood of self-driving cars at some point in the future. We assume that the defined capabilities of algorithms are no match for the uncertainties, high-level issues, and problems that strategy often serves up.
#analytics  #applications  #IIA  #strategy 
4 days ago
See and Believe: Fast Analytics for Big Data and Small Webinar 20171004
See and Believe: Fast Analytics for Big Data and Small

To see the big picture, analysts need a view into the full spectrum of their business.
Whether big data or small, streaming or static, every relevant piece of information is important.
Piecing all of that together requires a visual analytics platform that consumes all data types, at scale.
And it must be fast enough to enable rapid-fire iteration and discovery, even for massive data sets.
Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature, as he explains why the unwieldy nature of modern data requires a concerted effort to analyze.
He’ll be briefed by Ruhollah Farchtchi who will demonstrate how Zoomdata achieves remarkable speed, and delivers rich visual analytics on data of all shapes and sizes. He’ll also explain how Zoomdata can be used to super-charge any number of business applications.
#bigdata  #analytics  #visualization  #webinar  #Bloor 
4 days ago
RT : So excited and proud of for being named to the list of fastest growing cos in America. 📈🎉…
Inc500  from twitter
4 days ago
Redesigning work in an era of cognitive technologies Deloitte 20150727
Redesigning work in an era of cognitive technologies Deloitte 20150727
David Schatsky, Jeff Schwartz
July 27, 2015
Cognitive technologies, a product of the field of artificial intelligence, can and will be used to eliminate jobs. But leaders face choices about how to apply cognitive technologies. These decisions will determine whether workers are marginalized or empowered, and whether their organizations are creating value or merely cutting costs.

Rapid progress in the field of artificial intelligence (AI) has provoked intensive debate about the implications of this trend for society. Some see a driver of economic growth and boundless opportunities to improve living standards. Others see existential threats ranging from killer robots to widespread technological unemployment. Though we believe the worst of the fears are overblown, cognitive technologies—the products of the field of AI—cannot be ignored. They are an emerging source of competitive advantage for businesses and are on their way to ubiquity at work and at home.
Artificial intelligence researchers have sought to develop techniques to enable computers to perform a wide range of tasks once thought to be solely the domain of humans, including playing games, recognizing faces and speech, making decisions under uncertainty, learning, and translating between languages. We distinguish between the field of artificial intelligence and the technologies that emanate from the field, which we call cognitive technologies. Commonly used cognitive technologies include machine learning, computer vision, speech recognition, natural language processing, and robotics.1
Listen to the related podcast, “Redesigning work in an era of cognitive technologies”
Over the next three to five years cognitive technologies likely will have a profound impact on work, workers, and organizations. These technologies can and will be used to eliminate jobs. But they will also make it possible to redesign work, creating new opportunities for workers and greater value for businesses and their customers. Business leaders should understand the four main automation choices and the cost and value strategies we describe. And they should tune their talent practices to attract and develop the skills, including creativity and emotional intelligence, which will become relatively more important in an era of cognitive technologies.
#cognitivecomputing  #automation  #work  #Deloitte  #DavidSchatsky  #2015 
5 days ago
Robotic process automation A path to the cognitive enterprise Deloitte 2016
Robotic process automation
A path to the cognitive enterprise
By David Schatsky, Craig Muraskin, and Kaushik Iyengar

COMPANIES are increasingly using software robots to perform routine business processes by mimicking the ways in which people interact with software applications. And the rapidly growing market for robot process automation (RPA) is already showing signs of an important emerging trend: Enterprises are beginning to employ RPA together with cognitive technologies such as speech recognition, natural language processing, and machine learning to automate perceptual and judgment-based tasks once reserved for humans. The integration of cognitive technologies and RPA is extending automation to new areas and can help companies become more efficient and agile as they move down the path of becoming fully digital businesses.


Leading RPA vendors are incorporating cognitive technologies such as natural language processing and machine learning into their offerings
#rpa  #overview  #applications  #cognitivecomputing  #Deloitte  #2016 
5 days ago
Applying cognitive tools to knowledge-based work Tom Davenport 20170731
The rise of cognitive work (re)design: Applying cognitive tools to knowledge-based work
Deloitte Review, issue 21

Tom Davenport
July 31, 2017
Article Sections
Cognitive technologies and business process reengineering could be a match made in heaven, but only if organizations do the work to redesign their processes with cognitive technologies' specific capabilities in mind.

Business process change for the cognitive era
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NEW automation and cognitive technologies present a unique opportunity to redesign knowledge-based work, but they likely won’t do so without a concerted effort to redesign work processes around their capabilities. In order to achieve the productivity and effectiveness benefits that these technologies offer, companies may need to adopt, or readopt, techniques from a variety of systematic approaches to process improvement and change. This time, however, they may want to take a synthetic approach to process change that is consistent with the unique capabilities of cognitive technology.

The rebirth of reengineering?
In the early 1990s, one of the most important management trends was “business process reengineering” (BPR).1 This set of ideas, which encouraged order-of-magnitude improvement in broad business processes, was widely advanced in best-selling books, and led to considerable activity among consulting firms. The primary drivers of the BPR movement were need to substantially improve productivity (in part because of a perceived threat from Japanese competitors) and a powerful new set of information technologies, such as enterprise resource planning (ERP) systems, direct connections between customers and suppliers, and the then-nascent Internet. BPR may have been the only process change approach that specifically addressed information technology as an enabler of innovation and improvement.

Some of the same opportunities and threats appear to be present today. Productivity growth in the United States has slowed for several years,2 and some prominent economists have proclaimed that information technologies have never fueled the productivity improvements of which they might be capable.3 As for threats, established firms’ primary perceived risks no longer come from large Japanese competitors, but from nimble start-ups in regions like Silicon Valley.

On the technology front, perhaps the most disruptive collection of tools is found in cognitive technologies, the contemporary term for artificial intelligence. This group of technologies, which includes deep and machine learning, natural language processing (NLP) and generation, robotic process automation (RPA), and older tools based on rule and recommendation engines, is currently capturing substantial attention as a source of business and workforce disruption. Perhaps, as in the earlier generation of process reengineering, this generation of technologies can become a driver of work transformation. Also, as in the 1990s, the desired transformation won’t take place with technology alone.

It may be time, then, for a renaissance of BPR—this time with a specific focus on cognitive technologies as an enabler of process change, and with a more synthetic approach to process change methods. The marriage seems a good match. Cognitive technologies need a set of management structures and best implementation practices to yield the benefits of which they are capable. BPR could use some updating to accommodate contemporary technologies, and an injection of new change techniques could make it a more effective methodology.

Most importantly, immediate opportunities for business improvement from cognitive technologies are likely not being realized because complementary process changes aren’t being designed and implemented. At one large bank, for example, NLP technology was used to extract payment terms from a large volume of vendor contracts. The terms were then compared to the amounts actually paid by the bank in a large number of invoices (from which the payment amounts had also been extracted with a different set of cognitive tools). The automated analysis identified tens of millions of dollars in contract/invoice mismatches, most of the value of which would accrue to the bank. But the value couldn’t be captured until the bank redesigned its processes to review the mismatches and approach vendors to negotiate recovery of inaccurate payments.

Another opportunity for cognitive work redesign may be in the thousands of projects underway today involving RPA.4 This technology makes it relatively easy to automate structured digital tasks that involve interaction with multiple information systems. But perhaps because of the ease of automating these tasks, very few organizations undertake a systematic effort to redesign the processes and underlying tasks before automating them. While RPA typically leads to substantial gains in efficiency, a process reengineering initiative might reveal substantially greater opportunities for efficiency and effectiveness.
#cognitivecomputing  #rpa  #applications  #TomDavenport  #Deloitte  #2017 
5 days ago
Cognitive technology course on AI’s business applications Deloitte
Cognitive technologies: The real opportunities for business
Massive Open Online Course

This complimentary business course is designed to help demystify artificial intelligence, provide an overview of a wide range of cognitive technologies, and offer a framework to help you understand the business implications of AI and cognitive technologies.

Artificial intelligence (AI) may sound like science fiction, but it is real, and becoming increasingly important to companies in every sector. The field of artificial intelligence has produced a wide variety of “cognitive technologies” that simulate human reasoning and perceptual skills, giving businesses entirely new capabilities and enabling organizations to break prevailing tradeoffs between speed, cost, and quality. Aimed at a general business audience, this course demystifies artificial intelligence, provides an overview of a wide range of cognitive technologies, and offers a framework to help you understand their business implications. Some experts have called artificial intelligence “more important than anything since the industrial revolution.” That makes this course essential for professionals working in business, operations, strategy, IT, and other disciplines. Throughout the course, participants will build a knowledge base on cognitive technologies to equip them to engage in discussions with colleagues, customers, and suppliers and help them shape cognitive technology strategy in their organization. You will explore many topics in this course including:

What is artificial intelligence, and how did the field attain the importance it has today?
What are cognitive technologies? Dig below the surface and gain a basic understanding of the technologies that simulate language understanding, seeing, and reasoning.
How will cognitive technologies impact my business? Explore the types of applications that cognitive technologies may be suited for, and discover a framework that can help you conceptualize whether and where you might deploy them in your organization.
What is the impact of cognitive technologies on work? Cut through the hype and understand what leaders and workers need to know now to prepare for the changes these technologies will bring to work, workers, and organizations.
What does the future hold for cognitive technologies? Learn about the technological, legal and ethical factors that may influence the evolution and further adoption of cognitive technologies.
The course consists of approximately 2.5 hours of video content divided into approximately six-minute mini lectures. Quizzes allow you to check and consolidate your knowledge. Peer-reviewed assignments can take you deeper and give you practical information to take back to your job.
#ai  #cognitivecomputing  #applications  #RPA  #Deloitte  #course  #A+ 
5 days ago
Clean Brain — Tanjo
Clean Brain
What if you could intelligently map all of the knowledge inside your organization into one place?

With Tanjo's Clean Brain, everyone in your organization has what they need at the tip of their fingers, and any new knowledge created is automatically organized. 

Auto-generated Taxonomy based on what knowledge is contained in your organization
New documents created by your employees are automatically filed into the appropriate place
Visually engaging, easy to use interface for your employees to find any information they need, customized based on your organization's specific needs

Never again have two groups working on the same problem, without knowing it, and without collaborating. In real-time, Tanjo's Clean Brain can alert users about new information available.

Employees can tell the Clean Brain what they are interested in and receive alerts about new information.
As users are creating documents or sending emails, the Clean Brain understands what content they are creating and will find useful information that has been previously created and alert the user of references and people.
How Do We Create A Clean Brain?
In order to create a Clean Brain for your organization, we work with your team to input all of the information that your organization needs and has created in the past to Your Clean Brain, so that it can learn everything about your organization that has happened in the past, and continue to evolve as your organization does. This mean that we input all of the documents that your employees have created, in whatever formats you have it available, as well as any external data sources that may be required by your organization (e.g., news forums, government databases).
#ai  #application  #enterprise  #knowledgemap  #taxonomy  #information  #NC 
5 days ago
Robotic process automation is killer app for cognitive computing 2016
Robotic process automation is killer app for cognitive computing
Cognitive capabilities could supercharge RPA efforts automating tasks that once required the judgment and perception of humans. But implementing these systems is not as simple as it sounds.

Stephanie Overby (CIO (US))
04 November, 2016 23:20





Robotic Process Automation (RPA) is an increasingly hot topic in the digital enterprise. Implementing software robots to perform routine business processes and eliminate inefficiencies is an attractive proposition for IT and business leaders. And providers of traditional IT and business process outsourcing facing potential loss of business to bots are themselves investing in these automation capabilities as well.

[ Related: Why automation doubles IT outsourcing cost savings ]

While the basic benefits of RPA are relatively straightforward, however, these emerging business process automation tools could also serve as en entry point for incorporating cognitive computing capabilities into the enterprise, says David Schatzky managing director with Deloitte.

By injecting RPA with cognitive computing power, companies can supercharge their automation efforts, says Schatzky, who analyzes the implications of emerging technology and other business trends. By combining RPA with cognitive technologies such as machine learning, speech recognition, and natural language processing, companies can automate higher-order tasks that in the past required the perceptual and judgment capabilities of humans.

Some leading RPA vendors are already combining forces with cognitive computing vendors. Blue Prism, for example, is working with IBM’s Watson team to bring cognitive capabilities to clients. And a recent Forrester report on RPA best practices advised companies to design their software robot systems to integrate with cognitive platforms.

[ Related: What companies need to know when considering automation ] talked to Schatzky about RPA adoption rates, the budding relationship between software robots and cognitive systems, the likelihood that the combination of the two will replace traditional outsourcing, and the three steps companies should take before implementing RPA on a wider scale.
#rpa  #adoption  #advice  #2016  #Deloitte 
5 days ago
Robotic process automation is killer app for cognitive computing 20161104
Robotic process automation is killer app for cognitive computing
Cognitive capabilities could supercharge RPA efforts automating tasks that once required the judgment and perception of humans. But implementing these systems is not as simple as it sounds.

Stephanie Overby (CIO (US))
04 November, 2016 23:20





We do see outsourcing providers themselves investing in RPA in order to capture the cost and business benefits to remain competitive and forestall the adoption of alternatives that don’t include them. In some cases, the business process outsourcing model will likely evolve. You’ve noted that a proof-of-concept RPA project may take as little as two weeks and a pilot could be up and running within a month or two. Sounds simple. Is it?

Schatzky: Some of these proofs of concept are intended to do just that, prove the concept. They explain what RPA is and show it working in practice. You have to go a fair way up the road to get to something that’s in production ready and delivering value. What are the most important steps companies need to take if they’re thinking about implementing RPA on a wider scale.

Schatzky: There are three issues that companies need to think about.

One is the level of standardization of the business process you want to automate. You have to understand the business processes you’re seeing to automate enough to determine if automatable as is or whether it makes send to redesign them a bit.

Sometime business processes performed by humans, who are adaptable and flexible, can be fairly unstandardized and full of exceptions. That’s not a problem for people, but is a problem for an automated tool that seeks to do this in a more repetitive way. Processes can be hard to automate as is and will need to be rationalized in order to take advantage of RPA.

The other thing is scalability. Once someone has proved the value of RPA in one particular business process or piece of a business process, the interest in expanding the use of it grows. But companies need to do more planning when they expand the use of RPA. They think about issues like how many software bots do we need to have and how they will manage secure access to systems the bots are interacting with. That requires more thought.
#rpa  #overview  #2016 
5 days ago
Artificial Intelligence Startups - AngelList
RT : Check out the 2,985 companies in Artificial Intelligence on AngelList
from twitter
5 days ago
“[To many in the U.S.] the tech world is just as distant as Hollywood or Wall Street”
from twitter
5 days ago
Aqua : Conversable
Automated responses in conversational interfaces are a valuable complement to customer care teams. AQUA responds instantly to answer questions and efficiently deliver the next generation customer service.

Much of what users want to know happens to be available in some form inside your enterprise already. AQUA’s powerful language processing allows us to recognize and respond appropriately to common queries. These responses are tuned over time with machine learning within the Conversable system.

Configure intent & response dataIdentify unresolved inquiriesTrain your AISuccess of inquiry responses over timeIntent analyticsInsights into inquiriesMachine learningQuestion analytics data
#faqs  #vendor  #ai  #ML  #application  #platform  #CX  #digital 
5 days ago
Conversable : More than basic bots
Conversable is the enterprise-class software-as-a-service (SaaS) platform for designing, building, and distributing AI-enhanced messaging and voice experiences across multiple platforms, including Facebook Messenger, Twitter, SMS, Amazon Echo, Google Home, and many others.
#bot  #enterprise  #vendror  #digital  #mobile  #platform  #conversationalagent  #CX 
5 days ago
AI Has Grown Up and Left Home 20131219
A.I. Has Grown Up and Left Home
It matters only that we think, not how we think.

DECEMBER 19, 2013
The history of Artificial Intelligence,” said my computer science professor on the first day of class, “is a history of failure.” This harsh judgment summed up 50 years of trying to get computers to think. Sure, they could crunch numbers a billion times faster in 2000 than they could in 1950, but computer science pioneer and genius Alan Turing had predicted in 1950 that machines would be thinking by 2000: Capable of human levels of creativity, problem solving, personality, and adaptive behavior. Maybe they wouldn’t be conscious (that question is for the philosophers), but they would have personalities and motivations, like Robbie the Robot or HAL 9000. Not only did we miss the deadline, but we don’t even seem to be close. And this is a double failure, because it also means that we don’t understand what thinking really is.

Our approach to thinking, from the early days of the computer era, focused on the question of how to represent the knowledge about which thoughts are thought, and the rules that operate on that knowledge. So when advances in technology made artificial intelligence a viable field in the 1940s and 1950s, researchers turned to formal symbolic processes. After all, it seemed easy to represent “There’s a cat on the mat” in terms of symbols and logic:

Literally translated, this reads as “there exists variable x and variable y such that x is a cat, y is a mat, and x is sitting on y.” Which is no doubt part of the puzzle. But does this get us close to understanding what it is to think that there is a cat sitting on the mat? The answer has turned out be “no,” in part because of those constants in the equation. “Cat,” “mat,” and “sitting” aren’t as simple as they seem. Stripping them of their relationship to real-world objects, and all of the complexity that entails, dooms the project of making anything resembling a human thought.
#ai  #history  #outlook  #evolution 
5 days ago
How Information Got Re-Invented 20170810
How Information Got Re-Invented
The story behind the birth of the information age.

AUGUST 10, 2017
With his marriage to Norma Levor over, Claude Shannon was a bachelor again, with no attachments, a small Greenwich Village apartment, and a demanding job. His evenings were mostly his own, and if there’s a moment in Shannon’s life when he was at his most freewheeling, this was it. He kept odd hours, played music too loud, and relished the New York jazz scene. He went out late for raucous dinners and dropped by the chess clubs in Washington Square Park. He rode the A train up to Harlem to dance the jitterbug and take in shows at the Apollo. He went swimming at a pool in the Village and played tennis at the courts along the Hudson River’s edge. Once, he tripped over the tennis net, fell hard, and had to be stitched up.

His home, on the third floor of 51 West Eleventh Street, was a small New York studio. “There was a bedroom on the way to the bathroom. It was old. It was a boardinghouse ... it was quite romantic,” recalled Maria Moulton, the downstairs neighbor. Perhaps somewhat predictably, Shannon’s space was a mess: dusty, disorganized, with the guts of a large music player he had taken apart strewn about on the center table. “In the winter it was cold, so he took an old piano he had and chopped it up and put it in the fireplace to get some heat.” His fridge was mostly empty, his record player and clarinet among the only prized possessions in the otherwise spartan space. Claude’s apartment faced the street. The same apartment building housed Claude Levi-Strauss, the great anthropologist. Later, Levi-Strauss would find that his work was influenced by the work of his former neighbor, though the two rarely interacted while under the same roof.

Though the building’s live-in super and housekeeper, Freddy, thought Shannon morose and a bit of a loner, Shannon did befriend and date his neighbor Maria. They met when the high volume of his music finally forced her to knock on his door; a friendship, and a romantic relationship, blossomed from her complaint.
#trends  #data  #digital  #applications  #read 
5 days ago
The Real Risks of AI 20170724
The Real Risks of AI
Augmenting our own abilities is the best possible defense

Start Highlighting

“There is a movie called ‘Arrival’ that I happened to work on as a science adviser. And it’s about communicating. I realized that the problem of communicating with extraterrestrials is bizarrely similar to the problem of communicating intentions to AIs. An AI is an example of an alien intelligence, so to speak.”
— Stephen Wolfram, CEO, Wolfram Research
Discussions today about the risks of AI often bark up the wrong tree and tend to be misleading.
This matters because AI is being invoked left and right. Basic misunderstandings of what AI is and isn’t are also creating unnecessary fear, and those fears in turn stifle adoption.
Confoundingly, by running away from AI or making false assumptions about its evolution, we potentially make our worst case scenario more likely, not less. We should also be as focused on understanding human intelligence as we are on AI.
We need to first get straight on the many different meanings of AI. I’ll move quickly.
One definition of AI, which is most frequently used in business and academic contexts, is a discipline of computer science that entails using computers in which the system (ie: a machine) learns from data. This definition is largely synonymous with machine learning and encompasses computationally complex tasks such as natural language processing, predictive analytics, pattern recognition, computer vision, robotics, and more.
Use cases for this first kind of AI include autonomous cars, robots, chatbots, trading systems, facial recognition, and virtual assistants. These use cases typically combine multiple computational tasks using machine learning to achieve seemingly miraculous results such as self-driving cars. Virtually every “AI” article is about this type of AI which will be referenced here as “VAI” for Vertical Artificial Intelligence.
The risk posed by VAI is not significant, but it does exist. The most likely scenario for VAI danger is faulty (ie: badly programmed, trained or managed) systems. Weapon systems are the worst-case scenario; they obviously have a much higher risk of killing humans by accident or in a large-scale way, compared to a domain like virtual assistants. To be clear, any apocalyptic scenario involving autonomous weapons systems would be initiated by humans.
#ai  #outlook  #critique  #challenges  #impact 
5 days ago
The End of Typing The Next Billion Mobile Users Will Rely on Video and Voice WSJ 20170807
The End of Typing: The Next Billion Mobile Users Will Rely on Video and Voice
Tech companies are rethinking products for the developing world, creating new winners and losers
Ilphaz Khan, 24, communicates via voice text with a family member on WhatsApp at a New Delhi railway station.
By Eric Bellman | Photographs by Karan Deep Singh/The Wall Street Journal
Aug. 7, 2017 10:27 a.m. ET

Link copied…
The internet’s global expansion is entering a new phase, and it looks decidedly unlike the last one.

Instead of typing searches and emails, a wave of newcomers—“the next billion,” the tech industry calls them—is avoiding text, using voice activation and communicating with images. They are a swath of the world’s less-educated, online for the first time thanks to low-end smartphones, cheap data plans and intuitive apps that let them navigate despite poor literacy.

Incumbent tech companies are finding they must rethink their products for these newcomers and face local competitors that have been quicker to figure them out. “We are seeing a new kind of internet user,” said Caesar Sengupta, who heads a group at Alphabet Inc.’s Google trying to adapt to the new wave. “The new users are very different from the first billion.”

The Internet's Next Big User Group
Inexpensive smartphones and data plans are powering the next phase of Internet growth. Photo/Video: Karan Deep Singh/The Wall Street Journal
A look at Megh Singh’s smartphone suggests how the next billion might determine a new set of winners and losers in tech.

Mr. Singh, 36, balances suitcases on his head in New Delhi, earning less than $8 a day as a porter in one of India’s biggest railway stations. He isn’t comfortable reading or using a keyboard. That doesn’t stop him from checking train schedules, messaging family and downloading movies.

“We don’t know anything about emails or even how to send one,” said Mr. Singh, who went online only in the past year. “But we are enjoying the internet to the fullest.”

Mr. Singh squatted under the station stairwell, whispering into his phone using speech recognition on the station’s free Wi-Fi. It is a simple affair, a Sony Corp. model with 4GB of storage, versus the 32GB that is typically considered minimal in the developed world.
#mobile  #speech  #recognition 
5 days ago
Google and Facebook could be fined billions under new law 20170808
Google and Facebook could be fined billions under new law
The UK will be able to fine companies 4% of their global turnover if they breach users' privacy under new laws.
05:28, UK,
Tuesday 08 August 2017
The government is introducing a new Data Protection Bill
Social media companies will have to delete information when asked

By Alexander J Martin, Technology Reporter

Google and Facebook could face fines stretching into billions of pounds if they breach users' privacy under a new law.

The fines are part of the Data Protection Bill which the Government is introducing to give citizens more control over their data.

It will place new requirements on companies about how they are allowed to hold and use data on ordinary citizens.

:: How much dirt do social networks have on you?

In the case of the most serious breaches of these rules, it allows the data regulator, the Information Commissioner's Office (ICO), to fine companies £17m or 4% of their global turnover, whichever is higher.

The fines for the largest companies which use individuals' data to sell advertisements, such as Google and Facebook, could stretch to billions of pounds.

Neil Brown, a solicitor at Decoded Legal, a law firm specialising in digital laws, told Sky News "it was unlikely that the regulator will go anywhere near the top level very quickly".

"Other corrective powers - including the power to ban a company from processing data - are likely to be the regulator's first port of call," Mr Brown said.

The proposal
#privacy  #regulation  #UK  #2017 
5 days ago
Li Jiang (@gsvpioneer) | Twitter
Li Jiang
The Global Silicon Valley Handbook is the exclusive guide to what you need to know to who you need to know in the innovation hotspots around the World.

Silicon Valley, USA…
Joined February 2012
#startups  #entrepreneurs  #vc  #investors  #advice 
5 days ago
Navigating Silicon Valley 20170803
Navigating Silicon Valley
Start Highlighting
Career Advice Talk for Elite Meet, August 3, 2017

I want to start by sharing my personal story. The first time I tried to move to Silicon Valley in 2011, I interviewed at a startup, yet despite my efforts, I didn’t get the job. That startup later went bankrupt. The second time I tried to go to a startup accelerator, didn’t work out either. Half a year later, they shut down too. So if any prospective employer is hesitant about hiring you, just tell them, “no worries, but you might just go bankrupt.”
Sorry, all kidding aside, this session will be a quick overview of how to navigate Silicon Valley. I have a quick presentation, but most of the juicy stuff will be in the Q&A with Dan.

Now, we live in an exponential era. Technological progress is happening exponentially. But human brains are terrible at understanding what exponential means.
#startup  #ecosystem  #investing  #mentoring  #vc  #advice 
5 days ago
Meet the Chinese Finance Giant That’s Secretly an AI Company 20170616
Meet the Chinese Finance Giant That’s Secretly an AI Company

The smartphone payments business Ant Financial is using computer vision, natural language processing, and mountains of data to reimagine banking, insurance, and more.

by Will Knight June 16, 2017

Ant Financial announces a developer initiative in 2016.
If you get into a car accident in China in the near future, you'll be able to pull out your smartphone, take a photo, and file an insurance claim with an AI system.

That system, from Ant Financial, will automatically decide how serious the ding was and process the claim accordingly with an insurer. It shows how the company—which already operates a hugely successful smartphone payments business in China—aims to upend many areas of personal finance using machine learning and AI.

The e-commerce giant Alibaba created Ant in 2014 to operate Alipay, a ubiquitous mobile payments service in China. If you have visited the country in recent years, then you have probably seen people paying for meals, taxi rides, and a whole lot more by scanning a code with the Alipay app. The system is far more popular than the wireless payments systems offered in the U.S. by Apple, Google, and others. The company boasts more than 450 million active users compared to about 12 million for Apple Pay.
#ai  #case  #fintech  #China  #2017  AI 
5 days ago
Aha! Product Management Software
ou need to know where you are going in order to get there. Put goals and initiatives first to link a “red thread of strategy” through your roadmap to the actual work.

Overview set vision.f938600b5579be36933ede3364197373
Vision defines your passion and soul. Dream big and define your view of the future.
Goals are what you are aiming for. Set objectives so you can track your progress.
Initiatives are workstreams. Create the key themes your teams will be working on.
#software  #productmanagement  #review  #pm  #features  #ValuePropositions 
5 days ago
Helge Hannisdal (@hhannis) | Twitter
Helge Hannisdal
@hhannis Follows you
Serial entrepreneur & investor #fintech #startups #nordicmade #growthhacking #roboadvisor #ai #wealthmanagement #quantfinance Founded @quantfol & @itslearning

Bergen, Norway
Joined May 2009
#ai  #expert  #fintech  #startup  #founder  #Norway  #2017  #A+  #readregularly 
5 days ago
Quantfolio - "AI in a box" for Wealth Managers
Quantfolio is a Bergen based Fintech company delivering “AI-in-a-box” components for banks & wealth managers with a digitial presence.

Our goal is to empower banks and WM’s with our AI investment components to provide their customers with anything from low cost savings portfolios to sophisticated investment strategies normally reserved for HNW individuals through quantitative hedgefunds.

Ultimately we aim to democratize wealth creation by offering sophisticated financial advice for EVERYONE through their local bank.

Our team consist of passionate and highly skilled individuals, ranging from serial tech-entrepreneurs, traders, quants and developers with PhDs within machine learning. Quantfolio is partly owned by Skandiabanken, Norway’s largest challenger bank, also our first customer.

Our HQ is based in Bergen with a quant team based in San Diego.
#ai  #vendor  #fintech  #Norway  #wealthmanagement 
5 days ago
Minding the Analytics Gap MIT Sloan Review 2017
Minding the Analytics Gap
With more access to useful data, companies are increasingly
using sophisticated analytical methods. That means there’s
often a gap between an organization’s capacity to produce
analytical results and its ability to apply them effectively to
business issues.
#analytics  #deployment  #application  #strategy  #ROI 
7 days ago
Health IT startups to watch in 2017 - A running list 20170808
Health IT startups to watch in 2017: A running list

The healthcare IT landscape that is always in flux, thanks to new approaches driven by entrepreneurs who are adept at shaking things up.
By Bernie MonegainAugust 08, 201710:08 AM

1 slide of 23
From companies created to work in best in today's new value-based healthcare system to precision medicine, data analytics and interoperability, you can count on a healthcare IT landscape that is always in flux, thanks to new approaches driven by entrepreneurs who are adept at shaking things up.

This gallery highlights some of the most promising new companies and the founders and CEOs who are making news in 2017. Healthcare IT News is keeping tabs on the most exciting and promising new ventures.

Check back often as we will be updating the collection regularly.
#healthcare  #startups  #digital  #clinical  #A+ 
7 days ago
Andrew Ng’s Next Trick - Training a Million AI Experts 201708
Andrew Ng’s Next Trick: Training a Million AI Experts

Millions of people should master deep learning, says a leading AI researcher and educator.

by Will Knight August 8, 2017

Andrew Ng, one of the world’s best-known artificial-intelligence experts, is launching an online effort to create millions more AI experts across a range of industries. Ng, an early pioneer in online learning, hopes his new deep-learning course on Coursera will train people to use the most powerful idea to have emerged in AI in recent years.

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AI experts have become some of the most sought-after and well-paid employees in today’s tech economy. Deep learning involves teaching a machine to perform a complex task using large amounts of data along with a large simulated neural network. The technique has typically required deep technical knowledge and expertise to master (see “10 Breakthrough Technologies 2013: Deep Learning”).

Ng left his post as chief scientist at the Chinese Internet company Baidu this March, and there has been widespread speculation about what his next move might be (see “Andrew Ng Is Leaving Baidu in Search of a Big New AI Mission”). He says the deep-learning course is the first of three projects he plans to launch through his new startup,

Before joining Baidu, Ng was a professor at Stanford and then the founder of Coursera, creating a machine-learning course that has attracted more than two million enrollees over the past few years. Several years ago, Ng was also the founding director of the Google Brain project, an effort to deploy deep learning across the company.

Ng spoke with MIT Technology Review senior editor Will Knight about his mission to rebuild the world using deep learning.
#ai  #deeplearning  #enterprise  #deployment  #training  #course  #AndrewNg  #2017 
8 days ago
The Financial World Wants to Open AI’s Black Boxes 20170413
The Financial World Wants to Open AI’s Black Boxes

Some of the most powerful machine-learning techniques work in mysterious ways, which is a problem if you need to explain a decision to customers.

by Will Knight April 13, 2017


Powerful machine-learning methods have taken the tech world by storm in recent years, vastly improving voice and image recognition, machine translation, and many other things.

Now these techniques are poised to upend countless other industries, including the world of finance. But progress may be stymied by a significant problem: it’s often impossible to explain how these “deep learning” algorithms reach a decision (see “The Dark Secret at the Heart of AI”).

Adam Wenchel, vice president of machine learning and data innovation at Capital One, says the company would like to use deep learning for all sorts of functions, including deciding who is granted a credit card. But it cannot do that because the law requires companies to explain the reason for any such decision to a prospective customer. Late last year Capital One created a research team, led by Wenchel, dedicated to finding ways of making these computer techniques more explainable.

“Our research is to ensure we can maintain that high bar for explainability as we push into these much more advanced, and inherently more opaque, models,” he says.
#ai  #model  #explainability  #transparency  #MITTR 
8 days ago
A business leader’s guide to agile McKinsey 201707
A business leader’s guide to agile
By Santiago Comella-Dorda, Krish Krishnakanthan, Jeff Maurone, and Gayatri Shenai
A business leader’s guide to agile
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Agile promises rapidly evolving software and substantial business benefits, but it requires new habits from everyone: from IT and from business partners.

Agile development has largely become synonymous with digitization: senior business leaders have realized that their companies cannot take full advantage of digital tools and technologies without having new, amped-up processes for managing them. The value of these processes is immense.

Senior executives need only look at two recent examples in the banking industry to understand what’s at stake: ING and South Africa’s Standard Bank have both incorporated digital technologies and agile ways of working into their operations, and both are achieving positive results. ING is releasing software features to its web and mobile sites every two or three weeks rather than five or six times a year. As a result, the company’s customer-satisfaction scores are up by multiple points. Standard Bank has improved the quality of its new mobile applications by finding and fixing potential bugs earlier in the software-development process—building more trust with employees and customers in the process.

What may be less clear to senior executives is the role they can play in jolting their own business units and IT organizations to break from the status quo and realize similar advantages. “This was one of the toughest challenges,” says Mike Murphy, CTO at Standard Bank. “A lot of staffers at the bank were comfortable with the ways things were. They didn’t want to change their daily routines. They were focused on simply getting the job done.”
#innovation  #agile  #enterprise  #benefits  #advice  #McKinsey  #2017 
8 days ago
Privitar lands $16M to fuel its 'privacy engineering company' 20170809
Privitar lands $16M to fuel its 'privacy engineering company'
schedule Aug 9, 2017 queue Save This
Jedidiah Bracy, CIPP/E, CIPP/US
For companies looking to leverage data to gain insights and value — and who isn't doing this right now? — the practice is fraught with privacy risk, especially with the EU General Data Protection Regulation around the corner. That means tech startups are busy designing and rolling out solutions for secondary use while respecting the confidentiality of data. Importantly, venture capital is funding some of these solutions. 

That's certainly the case with London-based tech startup Privitar. In July, the company announced it sealed $16 million in Series A funding to expand its privacy technology platform into the U.S. market, bringing its total funding thus far up to a hefty $21 million. 

"These are exciting times," Privitar CEO Jason du Preez explained during a phone conversation with Privacy Tech. "We're really creating what is almost a new segment in the space. There's a lot of education that goes into that process. The feedback we're getting from our clients is that the tech space is confusing right now. There are a lot of adjacent segments using the same language, particularly with regard to the GDPR." 
#startup  #data  #security  #privacy  #enterprise  #compliance  #anonymization  #2017 
9 days ago
QPID Health (@QPIDHealth) | Twitter
QPID Health
We help healthcare providers achieve quality goals with software that locates critical patient facts in EHRs. QPID Health is now part of eviCore healthcare.

Boston, MA
Joined January 2013
#healthcare  #cds  #priorauth  #EviCore 
10 days ago
Overuse 101 - Lown Institute
Overuse 101+SHARE
Here are some common questions about overuse:

What is overuse?
How do tests and treatments get overused?
How can overuse affect me?
How do we know overuse is a problem?
Isn’t undertreatment the most important problem in the American health care system?
What can I do about overuse?
Where can I learn more?
#healthcare  #overuse  #overprescribing  #waste  #spending 
10 days ago
HealthLeaders Media
HealthLeaders Media, a division of BLR, is a multi-platform media company dedicated to meeting the business information needs of healthcare executives and professionals. We offer an extensive and integrated series of leadership publications, products, research, and events devoted to the business of healthcare:

HealthLeaders magazine
HealthLeaders Daily News & Analysis and 10 weekly e-newsletters
Monthly Intelligence Reports powered by the HealthLeaders Media Council
Exchange events for healthcare executives
Executive Roundtables
HLM Live virtual events and expert webcasts
California HealthFax
#healthcare  #healthinsurance  #newsource  #A+  #readregularly 
10 days ago
Prior Authorization and Utilization Management Reform Principles AMA
Prior Authorization and Utilization Management Reform Principles AMA

Patient-centered care has emerged as a major common goal across the health care industry. By empowering patients to play an active role in their care and assume a pivotal role in developing an individualized treatment plan to meet their health care needs, this care model can increase patients’ satisfaction with provided services and ultimately improve treatment quality and outcomes.

Yet despite these clear advantages to adopting patient-centered care, health care providers and patients often face significant obstacles in putting this concept into practice. Utilization management programs, such as prior authorization and step therapy, can create significant barriers for patients by delaying the start or continuation of necessary treatment and negatively affecting patient health outcomes. The very manual, time-consuming processes used in these programs burden providers (physician practices, pharmacies and hospitals) and divert valuable resources away from direct patient care. However, health plans and benefit managers contend that utilization management programs are employed to control costs and ensure appropriate treatment.
#priorauth  #industry  #guidelines  #recommendations  #AMA 
10 days ago
Prior Authorization One of Several Key Topics Impacting the Healthcare Landscape to be Discussed at WEDI 2017 20170426
Prior Authorization One of Several Key Topics Impacting the Healthcare Landscape to be Discussed at WEDI 2017

May 15-18 conference will bring healthcare leaders together across payers, providers, government regulators and industry vendors to discuss the ever-evolving healthcare environment

RESTON, Va. — April 26, 2017 — As the call for prior authorization reform continues to make headlines and drive high-level conversations among healthcare industry stakeholders, the discussion surrounding the topic will continue at WEDI 2017- Driving Solutions in a Changing Healthcare Environment – taking place May 15-18, 2017 in Los Angeles, Calif. Hosted by the Workgroup for Electronic Data Interchange (WEDI), the nation’s leading nonprofit authority on the use of health IT to create efficiencies in healthcare information exchange and a trusted advisor to the U.S. Department of Health and Human Services (HHS), the event will feature two main sessions focusing on prior authorization.

WEDI President and CEO Charles W. Stellar, who recently joined a panel session at the 2017 HIMSS Annual Convention and Exhibition to discuss prior authorization pain points and how healthcare organizations need to engage in dialogue in order to help lessen payer and provider frustrations, will deliver remarks at the first prior authorization session taking place on Wednesday, May 17 while executives from Aetna and the American Medical Association will present at the latter.

“With a change in HHS leadership and the call for prior authorization reform sweeping our industry, we are, I believe, at a time where dialogue surrounding prior authorization can and should be restored between payers and providers in order to come to a solution that benefits all parties without compromising patient care,” said Stellar. “Attending a WEDI conference session, like the one on prior authorization, provides individuals with the opportunity to actively participate in high-level discussions shaping the healthcare landscape through direct access to industry influencers. We’re looking forward to continuing this important dialogue between key industry stakeholders at WEDI’s National Conference.”

The discussion surrounding prior authorization is not new to WEDI as a workgroup was formed last year to evaluate barriers, business cases, current workflows, and return-on-investment related to prior authorization. Co-chaired by the American Medical Association, who recently released a survey of practicing physicians on the topic, and a payer organization, the group looks to foster enhanced collaboration between payers and providers and support prior authorization processes being performed with a focus on increased value, automation, and efficiency. With prior authorization reform becoming an industry focus, WEDI announced during the HIMSS17 session that they will facilitate a Prior Authorization Council which will focus on bringing payers and providers together to not only discuss reform but bring feedback on the topic to industry change agents who can help implement the Council’s recommendations.
10 days ago
Making Better Decisions at the Point of Care EviCore 20170605
Making Better Decisions at the Point of Care
Monday, June 05, 2017
​Today, physicians and healthcare providers are asked to do more than ever, in less time. They are treating more patients and have less time to spend with each. At the same time, they must meet requirements for quality reporting, keep up with changing evidence-based treatment protocols, and do their best to prevent malpractice and liability litigation.

For many physicians, the practice of “defensive medicine" — including ordering redundant tests — has become the norm. In 2012, the Institute of Medicine found that unnecessary medical tests accounted for $750 billion, or about 30 percent, of all healthcare spending in 2009.

To better cope with these challenges, a growing number of physicians are using clinical decision support (CDS) tools, designed to help them access evidence-based data to improve diagnosis and treatment at the point of care. The HITECH Act required that clinics and hospitals include CDS features in their electronic health records (EHRs), giving the use of CDS a jumpstart. Recent reviews find that the number of CDS tools, whether standalone or part of an EHR or physician-ordering system, is increasing, and their quality is improving.

The Agency for Healthcare Research and Quality (AHRQ) has been closely studying CDS use and effectiveness. In 2016, the agency set up a CDS learning network to help accelerate development of CDS tools and ensure that knowledge gained from patient- centered outcomes research is incorporated into such development.
#healthcare  #priorauth  #cds  #pointofcare  #Evicore  #2017 
10 days ago
Tracing the Path and Significance of Prior Authorization evicore 20170103
Tracing the Path and Significance of Prior Authorization
Tuesday, January 03, 2017
​Health insurers require prior authorization (PA) for certain healthcare services, treatment plans, prescription drugs, and/or durable medical equipment before a patient can receive them. The determination focuses on whether the service, drug, or product is medically necessary. It does not guarantee that the insurer will cover the cost.

The PA process—and research into its effectiveness—dates back more than 25 years, when states began analyzing how to curtail drug costs within the Medicaid program. The Omnibus Budget Reconciliation Act of 1990 included PA among explicit provisions for limiting drug coverage and cutting costs. The legislation also required the Health and Human Services Secretary to study the impact of PA programs on beneficiary and provider access to prescription drugs as well as on program costs, and to make recommendations for PA reforms if needed.

Prior authorization is also known as prospective review and falls under the purview of a set of utilization management activities that are governed typically by the states department of insurance. Health plans or insurance companies employ PA programs with significant oversight and monitoring by those same state insurance departments.

A costly proposition
According to a study​ published in 2009 in Health Affairs, based on 2006 American Medical Association (AMA) data, the estimated national cost to physician practices of interactions with health plans (including compliance with PA requirements and pharmaceutical formularies) totaled between $23 billion and $31 billion per year. On average, primary-care physicians spent a little over an hour weekly on PA. Nursing staff spent more than 13 hours per physician per week on authorizations, far exceeding nurses' other types of health-plan interactions. In comparison, physicians and staff devoted only a “small fraction" of this time to providing quality data to health plans or to reviewing practice-specific quality data generated by plans, according to the study.

In June 2011, an AMA white paper stated that “intensely manual" prior authorization programs had placed excessive administrative burdens on physicians and payers. The paper called for automation/ standardization of PA across payers through a process that could be integrated within the respective workflows of physician practice management and payer administrative systems.

Where PA is headed
#priorauth  #history  #cost  #impact  #outlook  #EviCore  #2017 
10 days ago
We Survived Spreadsheets, and We’ll Survive AI 20170802
We Survived Spreadsheets, and We’ll Survive AI
History shows technology fuels new kinds of jobs in addition to the ones it renders obsolete
A robot inspected a power system in Chuzhou, China, last month.
A robot inspected a power system in Chuzhou, China, last month. PHOTO: SONG WEIXING/SIPA ASIA/ZUMA PRESS

By Greg Ip
Updated Aug. 2, 2017 11:47 a.m. ET
Whether truck drivers or marketing executives, all workers consider intelligence intrinsic to how they do their jobs. No wonder the rise of “artificial intelligence” is uniquely terrifying. From Stephen Hawking to Elon Musk, we are told almost daily our jobs will soon be done more cheaply by AI.

Yet AI is too amorphous a label to actually convey anything useful about what, precisely, it’s supposed to displace. Instead, think of it as a technology that does one thing particularly well: predictions. Such as, will that mark on the X-ray prove to be a tumor? Is the object in the road a paper bag or a child? Which headline will get the most readers to click on an article?

Treating prediction as an input into an economic process makes it much easier to map AI’s impact. History and economics show that when an input such as energy, communication or calculation becomes cheaper, we find many more uses for it. Some jobs become superfluous, but others more valuable, and brand new ones spring into existence. Why should AI be different?

Back in the 1860s, the British economist William Stanley Jevons noticed that when more-efficient steam engines reduced the coal needed to generate power, steam power became more widespread and coal consumption rose. More recently, a Massachusetts Institute of Technology-led study found that as semiconductor manufacturers squeezed more computing power out of each unit of silicon, the demand for computing power shot up, and silicon consumption rose.

The “Jevons paradox” is true of information-based inputs, not just materials like coal and silicon. Until the 1980s, manipulating large quantities of data—for example, calculating how higher interest rates changed a company’s future profits—was time-consuming and error-prone. Then along came personal computers and spreadsheet programs VisiCalc in 1979, Lotus 1-2-3 in 1983 and Microsoft Excel a few years later. Suddenly, you could change one number—say, this year’s rent—and instantly recalculate costs, revenues and profits years into the future. This simplified routine bookkeeping while making many tasks possible, such as modeling alternate scenarios.
#ai  #impact  #optimistic 
10 days ago
RT : The future of Machine Learning from . Highlighting Program Synthesis. An exciting passion area for Lux.…
from twitter
11 days ago
"The European Artificial Intelligence Landscape" 400+ companies (report) ASGARD
AI  from twitter
11 days ago
The goal of CloudMedx is to combine the latest machine learning, natural language processing and a clinical ontology to provide real time insights at the point of care. All data - clinical and non clinical, structured and unstructured - may be taken into account with the goal to improve outcomes
#healthcare  #analytics  #startup  #LuxCapital  #CloudMedX  #NLP 
11 days ago
This AI Software {CloudMedx} Aims To Conquer Heart Disease 20161017
This AI Software {CloudMedx} Aims To Conquer Heart Disease
by Barb Darrow, October 17, 2016
Improving healthcare is a critical application for big data analytics and artificial intelligence (AI). Think of all that available patient information—from medical records, health monitoring devices, drug trials, genetics databases—there is no dearth of data. What is often lacking is a way to aggregate that trove of information and sort through it in a way that makes it useful.

If that can be accomplished, patient outcomes could be improved. Given the incentives insurance companies have to keep people healthy (i.e. out of the hospital) and prevent former patients from being readmitted, the stakes are high. If all that data could be used to make intelligent predictions about where a particular person is headed in terms of health, that could be a huge deal.

The advent of electronic health records, mandated over the last few years, has helped with patient care, but it’s not enough. “Now at least you can access data, but it’s still hard to make sense out of it,” Ashish Atreja, chief technology innovation and engagement officer for medicine at Mount Sinai’s Icahn School of Medicine told Fortune. “The health ecosystem lacks tools that can be used generally to make sense of that data.”

“A patient may spend one to two hours in an outpatient facility in a year, but then there are 5,000 waking hours spent outside the hospital, which is where outcomes can be affected. What we need is one tool to check on them at home,” he noted.

Towards this end, New York’s Mount Sinai Hospital plans to use CloudMedx Clinical AI Platform to help craft care for people who have—or might develop—congestive heart failure (CHF), a condition affecting an estimated 5 million Americans. CloudMedx is already working with Sutter Physician Services, a Sacramento-based healthcare organization to apply its AI services to patient care.
#healthcare  #analytics  #chronic  #ai  #application  #CloudMedX 
11 days ago
Artificial Intelligence Uses EHRs as Smart Analytics Tools 20170131
Artificial Intelligence Uses EHRs as Smart Analytics Tools
CloudMedX CEO, Tashfeen Suleman explains the value of natural language processing and machine learning for EHR analytics.

Source: Thinkstock

By Elizabeth O'Dowd

January 31, 2017 - As artificial intelligence (AI) continues to grow in health IT infrastructure, vendors are finding ways to develop AI solutions to improve patient care using the data collected by connected medical devices.

CloudMedX CEO Tashfeen Suleman developed his AI solution to improve clinician workflows by turning electronic health records (EHRs) into smart predictive tools, making doctors more accurate in decision metrics.

Healthcare organizations are challenged by the limitations of EHRs and the inability to truly leverage EHR data between the structured and unstructured data entered by a clinician. The use of natural language processing (NLP) and machine learning (ML) are playing a major role in EHR healthcare analytics.

Suleman began CloudMedX to prevent important patient details from being overlooked or lost in the data.

“EHRs haven't been on the market for long,” Suleman told “They started with the Affordable Care Act and the intent is that all healthcare organizations adopt EHRs. EHRs have a 70 percent adoption rate, meaning 30 percent are still on paper charts. The process of digitization has started but that process is no older than maybe seven to 10 years.”
#healthcare  #analytics  #unstructureddata  #NLP  #CloudMedX  #2017 
11 days ago
CloudMedx Releases New Risk Adjustment Module for Large Healthcare Providers and Physician Groups 20170214
CloudMedx Releases New Risk Adjustment Module for Large Healthcare Providers and Physician Groups
PALO ALTO, CA--(Marketwired - Feb 14, 2017) - CloudMedx, a Health AI analytics company, today announced its release of risk adjustment module as part of its Clinical AI Platform. For larger healthcare providers, physician practices, and systems they will now be able to leverage their unstructured data to automate their risk adjustments for high risk patients. For payers, they can now get highly accurate risk scores to assess the overall health of their risk pool, ensure that they are reimbursed appropriately by the Centers for Medicare & Medicaid Services (CMS) based on how sick their members actually are, and know whether they will receive transfer payments or pay them out.
Risk adjustment is a tool that allows payers to be reimbursed based on health status or "actuarial risk" of a patient population. It is designed to pay health plans more precisely for the anticipated health costs of a patient population by adjusting payments based on demographics and health status.1 These attributes are used to generate a risk score -- a measure of a patient's cost to a plan. As approximately 80% of the Medicare population has at least one chronic condition2, the clinical and financial impact of missing data is high, given the risk associated with an elderly population. Therefore, CloudMedx risk adjustment provides a quick snapshot of patients' risks to payers and providers that are regularly affected by overlooked data.
#healthcare  #analytics  #AI  #clinicial  #diagnostic  #riskadjustment 
11 days ago
Tap: Unlocking the Mobile Economy Anindya Ghose 201704
Tap: Unlocking the Mobile Economy (MIT Press) Hardcover – April 17, 2017
by Anindya Ghose (Author)
5.0 out of 5 stars 35 customer reviews

Consumers create a data trail by tapping their phones; businesses can tap into this trail to harness the power of the more than three trillion dollar mobile economy. According to Anindya Ghose, a global authority on the mobile economy, this two-way exchange can benefit both customers and businesses. In Tap, Ghose welcomes us to the mobile economy of smartphones, smarter companies, and value-seeking consumers.

Drawing on his extensive research in the United States, Europe, and Asia, and on a variety of real-world examples from companies including Alibaba, China Mobile, Coke, Facebook, SK Telecom, Telefónica, and Travelocity, Ghose describes some intriguingly contradictory consumer behavior: people seek spontaneity, but they are predictable; they find advertising annoying, but they fear missing out; they value their privacy, but they increasingly use personal data as currency. When mobile advertising is done well, Ghose argues, the smartphone plays the role of a personal concierge -- a butler, not a stalker.

Ghose identifies nine forces that shape consumer behavior, including time, crowdedness, trajectory, and weather, and he examines these how these forces operate, separately and in combination. With Tap, he highlights the true influence mobile wields over shoppers, the behavioral and economic motivations behind that influence, and the lucrative opportunities it represents. In a world of artificial intelligence, augmented and virtual reality, wearable technologies, smart homes, and the Internet of Things, the future of the mobile economy seems limitless.
#mobile  #apps  #book  #academic  #AnindyaGhose  #NYU 
11 days ago
Implementing Behavioral Analytics to Drive Customer Value EY 201702
Robotic process automation: Technology where software or a “robot” is programmed to capture and interpret data for further utilization.

Smart automation: Automation that uses machine learning to continuously optimize processes and decisions.
Automation shaping the insurance proposition
The convergence of technological innovations creates opportunities for insurers to tailor customer experiences in ways that would not have been possible even a few years ago. One can only imagine the potential that will be realized as technology continues to advance. Insurers are increasingly using robotic process automation (RPA) to increase efficiency and consistency in core insurance processes. Adding analytics that enable the automated process to respond to customer behavior presents an opportunity to develop smart automation. Combining smart automation with advances in sensor technologies, including telematics and wearables, means that insurers can now analyze and respond to consumer behavior both in the virtual and physical world. The key is understanding the characteristics and behaviors of the customer.

Behavioral analytics as a critical enabler Behavioral analytics creates a unique value proposition for both insurance companies and their customers. The benefits can range from higher acquisition rates and customer retention to more aligned product portfolios and a reduction in fraudulent activities, as shown in the chart below. Moreover, behavioral analysis helps explain the “why,” “who” and “what.” Insurers increasingly understand “who” their customers are based on the characteristics. Age, sex, income and residence are all attributes that insurers capture. They also are able to track the interactions they have had with customers; for example, a customer bought a policy, contacted the call center and paid the bill on time. All of this information provides valuable insights.
#rpa  #ML  #analytics  #CX  #EY 
12 days ago
Get ready for robots EY 201705
Software Robotics, or Robotic Process Automation (RPA) promises to transform the cost, efficiency and quality of executing many of the back office and customer-facing processes that businesses rely on people to perform.

That’s the good news. But RPA is not without its challenges. We have delivered RPA projects across 20 countries and are often called upon to help companies when their first attempt failed. While RPA can transform the economics and service level of current manual operations, we have seen as many as 30 to 50% of initial RPA projects fail. This isn’t a reflection of the technology; there are many successful deployments. But there are some common mistakes that will often prevent an organization from delivering on the promise of RPA.

In order to best gain buy into RPA by senior stakeholders, we recommend that an RPA portfolio balances cost reduction with other value drivers such as service improvement, transformative services, improved regulatory response and growth. While delivering cost-savings is great, “headlinegrabbing” service improvements or showing entirely new and innovative digital services or products makes the senior stakeholders even more interested in making RPA happen.

As one of the largest RPA consultancies delivering programs globally to financial services organisations, EY is often called in to get RPA programs back on track. This is the first in a series of papers based on our practical experience and the lessons we have learned. In this paper, we examine the common issues that we see clients facing as they move forward with robotics projects. Subsequent papers will define robotics and explore its potential, how best to structure RPA programs and advanced robotics.
#rpa  #applications  #guidelines  #EY  #2017 
12 days ago
Top 10 Healthcare Mobile Apps Among Hospital, Health Systems 20170706
Top 10 Healthcare Mobile Apps Among Hospital, Health Systems
The top hospital and health system mobile apps offer interoperability and secure care coordination to enhance clinical communication and workflows.

Source: Thinkstock

By Thomas Beaton

July 06, 2017 - Providers are adopting the use of mHealth in the form of mobile apps in their pursuit of easing clinical communication between providers and patients as well as improve the management of hospital workflows.

Mobile apps allow providers to effectively streamline communication between patients, providers, and their caregivers and allows for 24/7 management of a patient’s condition along with the ability to personalize healthcare per patient.

Apps provide organizational incentives for adoption such as reduced costs in workflow management. Providers leverage mobiles apps as a secure platform to manage and access important healthcare data without compromising the security of data.

The top 10 healthcare mobile app vendors includes interoperable platforms, secure two-way messaging, and patient-provider interactivity based on data gathered from Definitive Healthcare. Apps and vendors are listed in alphabetical order.
#healtchare  #mHealth  #mobile  #apps  mHealth 
12 days ago
Triage is a healthcare technology company headquartered in Denver, Colorado. Founded in 2008 by two emergency room doctors, iTriage was acquired by Aetna, Inc. in 2011. Today, as a wholly owned subsidiary with approximately 100 employees, iTriage connects patients, providers and health plans through technology to deliver personalized data insights that empower people to take action on their healthcare. iTriage also partners with hospitals and health systems, accountable care organizations, retail clinics, health plans, and employers to improve population health.

Since launch, the free iTriage application has been downloaded more than 15 million times to iPhone®, iPad® and AndroidTM mobile devices. Millions of users engage with the application monthly, and iTriage consistently receives an average of 4.5 out of 5 star-ratings in the Apple and Google stores.
#healthcare  #mHealth  #Aetna  #engagement 
12 days ago
SidekickHealth is a social health game, engaging employees through entertaining health improvement and team building.
We help providers and employers deliver quality lifestyle change
programs that promote health and tackle chronic diseases.
#mHealth  #becon  #app  #startup  #Harvard 
12 days ago
RT : This one hits close to home. “Is Social Media The New Tobacco?” — 
from twitter
13 days ago
Is Social Media The New Tobacco? – NewCo Shift
RT : This one hits close to home. “Is Social Media The New Tobacco?” — 
from twitter
13 days ago
“Borrowed from Retail, Anthem’s Big Data Boost Member
Analytics  Engagement  from twitter
13 days ago
What Is a Robot, Really 20160322
What Is a Robot?
The question is more complicated than it seems.

RYGER / Shutterstock / Zak Bickel / The Atlantic
Share Tweet …

The year is 2016. Robots have infiltrated the human world. We built them, one by one, and now they are all around us. Soon there will be many more of them, working alone and in swarms. One is no larger than a single grain of rice, while another is larger than a prairie barn. These machines can be angular, flat, tubby, spindly, bulbous, and gangly. Not all of them have faces. Not all of them have bodies.

And yet they can do things once thought impossible for machine. They vacuum carpets, zip up winter coats, paint cars, organize warehouses, mix drinks, play beer pong, waltz across a school gymnasium, limp like wounded animals, write and publish stories, replicate abstract expressionist art, clean up nuclear waste, even dream.

Except, wait. Are these all really robots? What is a robot, anyway?

This has become an increasingly difficult question to answer. Yet it’s a crucial one. Ubiquitous computing and automation are occurring in tandem. Self-operating machines are permeating every dimension of society, so that humans find themselves interacting more frequently with robots than ever before—often without even realizing it. The human-machine relationship is rapidly evolving as a result. Humanity, and what it means to be a human, will be defined in part by the machines people design.

“We design these machines, and we have the ability to design them as our masters, or our partners, or our slaves,” said John Markoff, the author of Machines of Loving Grace, and a long-time technology reporter for The New York Times. “As we design these machines, what does it do to the human if we have a class of slaves which are not human but that we treat as human? We’re creating this world in which most of our interactions are with anthropomorphized proxies.”
#ai  #robots  #history  #TheAtlantic 
13 days ago
Borrowed from Retail, Anthem’s Big Data Analytics Boost Member Engagement 20170803
Borrowed from Retail, Anthem’s Big Data Analytics Boost Member Engagement
Machine learning, big data analytics, and a few insights from the retail industry are helping Anthem improve member engagement and detect fraud, waste, and abuse.

Source: Thinkstock

By Jennifer Bresnick

August 03, 2017 - Commercial insurance companies are facing innumerable challenges as internal and external changes continue to buffet the healthcare industry. 

As both leaders in the field of value-based care and for-profit entities subject to political and economic forces outside of their control, the nation’s largest payers must balance bold proactivity with prudence and shrewd decision-making.

Heavyweight payers like Anthem and its peers were among the first in the healthcare industry to successfully leverage big data analytics to give them insights into the behaviors, risks, and likely actions of their members. 

Unlike many physicians and hospitals, who still struggle to make sense of the business case for value-based care, payers have a very clear incentive for using all the data at their disposal to ensure their members stay as healthy as possible and their provider partners remain on the right side of the payment equation.
#healthcare  #analytics  #case  #bestpractices  #Anthem  #engagement  #2017 
13 days ago
How Two Brothers Turned Seven Lines of Code Into a $9.2 Billion Startup 20170801
How Two Brothers Turned Seven Lines of Code Into a $9.2 Billion Startup

Now, Stripe’s Patrick and John Collison are teaming with Amazon to grab even more control over the global flow of commerce.
By Ashlee Vance August 1, 2017, 12:00 PM EDT

Every day, Americans spend about $1.2 billion online. That figure has roughly doubled in the past five years, according to the Department of Commerce, and it’s likely to double again in the next five as the internet continues to devour traditional retail. So it might come as a surprise that the web’s financial infrastructure is old and slow. For years, the explosive growth of e-commerce has outpaced the underlying technology; companies wanting to set up shop have had to go to a bank, a payment processor, and “gateways” that handle connections between the two. This takes weeks, lots of people, and fee after fee. Much of the software that processes the trans­actions is decades old, and the more modern bits are written by banks, credit card companies, and financial middlemen, none of whom are exactly winning ­hackathons for elegant coding.
#startup  #case  #payments  #Stripe  #2017 
13 days ago
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