Do you still use Yahoo? Do you still remember MySpace? Compaq? Kodak? The cases of startups with superior ideas dethroning well-established incumbents are legion. This is the beauty of “creative destruction” – the term coined by innovation prophet Joseph Schumpeter almost a century ago. Incumbents have to keep innovating, lest they be overtaken by a new, more creative competitor. Arguably, at least in sectors shaped by technical change, entrepreneurial innovation has kept markets competitive far better than antitrust legislation ever could. For decades, creative destruction ensured competitive markets and a constant stream of new innovation. But what if that is no longer the case?
The trouble is that the source of innovation is shifting – from human ingenuity to data-driven machine-learning. Google’s self-driving cars are getting better through the analysis of billions of data points collected as Google’s self-driving cars roam the street. IBM Watson detects skin cancer
In the Black Mirror episode Nosedive, the protagonist, Lacie, lives in a saccharine world of pleasantries in which every personal or business interaction is scored. Everything depends on the social score, and everyone is desperate to move up in the rankings. But the omnipresent rating game has one big catch: ranking up is incredibly hard, while ranking down is rapid and easy, like a free-fall.
Welcome to the reputation economy, where the individual social graph — the social data set about each person — determines one’s value in society, access to services, and employability. In this economy, reputation becomes currency.
The reputation economy is based on the simplistic, but effective star ratings system. Anyone who’s ever rated their Uber driver or Airbnb host has actively participated. But what happens when algorithms, rather than humans, determine an individual’s reputation score based on multiple data sources
A key reason for this is that senior managers fail to manage their data scientists properly. Many fail to focus the data science program, or they put data scientists in the wrong spots in the organization; others view the data science as a technical, not business, initiative; and still others underestimate how resistant their organizations are to change, and do not fully equip data scientists to change hearts and minds.
These missteps lower your chances of success and, in extreme cases, doom the effort from the very start. But by following the road map below, you can ensure that your data scientists are more productive, and
A new year has arrived, along with the usual air of optimism. Yet the 21st century is already shaping up to be a challenging one. From climate change to terrorism, the difficulties confronting policy makers are unprecedented in their variety, but also in their complexity. Our existing policy tool kit seems stale and outdated. Increasingly, it is clear, we need not only new solutions but also new methods for arriving at solutions.
Data, and new methods for organizations to collaborate in order to extract insights from data, is likely to become more central to meeting these challenges. We live in a quantified era. It is estimated that 90% of the world’s data was generated in the last two years — from which entirely new inferences can be extracted and applied to help address some of today’s most vexing problems.
In particular, the vast streams of data generated through social
Nearly five years ago, a show that followed the lives of inmates in a women’s prison shook up television. Orange Is the New Black became an unexpected hit, helping to put Netflix on the map as a creator of innovative original entertainment. The show pushed boundaries with its dark humor and diverse cast, becoming Netflix’s most-watched original series. Uzo Aduba has won two Emmys for her nuanced, empathetic portrayal of a black lesbian woman struggling with mental illness, a character rarely seen on mainstream television.
Orange Is the New Black isn’t just great television — it’s also an example of data-driven creativity in action. With the recent explosion of shows produced by Silicon Valley companies like Amazon, Hulu, and Netflix comes a fear that entertainment will increasingly be shaped by analysts crunching numbers rather than creatives following their artistic vision. Five years in, Netflix’s foray into original
AI is coming. That is what we heard throughout 2017 and will likely continue to hear throughout this year. For established businesses that are not Google or Facebook, a natural question to ask is: What have we got that is going to allow us to survive this transition?
In our experience, when business leaders ask this with respect to AI, the answer they are given is “data.” This view is confirmed by the business press. There are hundreds of articles claiming that “data is the new oil” — by which they mean it is a fuel that will drive the AI economy.
If that is the case, then your company can consider itself lucky. You collected all this data, and then it turned out you were sitting on an oil reserve when AI happened to show up. But when you have that sort
From the perspective of an AI system, the college transition provides intriguing challenges and opportunities. A successful system must cope with individual idiosyncrasies and varied needs. For instance, after acceptance into college, students must navigate a host of well-defined but challenging tasks: completing financial aid applications, submitting a final high school transcript, obtaining immunizations, accepting student loans, and paying tuition, among others. Fail to support students on some of these tasks and many of them — particularly those from low-income backgrounds or those who would be the first in their families
Does a robot manage your money? For many of us, the answer is yes. Online and algorithmic investment and financial advice is easy to come by these days, usually under the moniker of “robo-advisor.” Startups such as Wealthfront, Personal Capital, and Betterment launched robo-advisors as industry disruptors, and incumbents, such as Schwab’s (Intelligent Advisor), Vanguard (Personal Advisor Services), Morgan Stanley and BlackRock have joined the fray with their own hybrid machine/advisor solutions. It’s clear that robo-advisors and AI play an important and growing role in the financial services industry, but a question remains. Will robo-advisors disrupt corporate capital allocation the same way they have personal capital allocation? And, will they shake up the trillion-dollar corporate consulting and advisory industry?
Robo-advisors, which were introduced in 2008, are steadily eating up market share from their human counterparts much the way that Amazon and Netflix
I have been using Uber since the day it went into service. It has been a godsend for me — I don’t drive and almost always have to go to the far corners of the city to see the medical practitioners who have to keep me ticking over past ten years. I would be remiss in saying that Uber is not a net addition to my creature comforts. And that is saying a lot because I have twisted and turned over its ethics, corporate ethos and constant crossing the line.
But lately, I have been thinking — if I (like other riders) am really the customer for the company? Its actions display a certain level of “taking riders for granted” attitude, that reminds me of cable companies, phone companies, and certain web giants. Uber is yet another example of a company, where we are merely the product. Continue reading "Why we (the riders) are not Uber’s customers"
How many times have you had to watch your company’s latest cybersecurity training video? An entire industry now exists to train us humans to be smarter in how we operate computers, and yet the number of cybersecurity incidents continues to rise. Are the hackers always one step ahead? Are we impossible to train? Or are we being taught the wrong lessons?
The human is indeed the weakest link in cybersecurity. But all too often organizations’ approach to mitigating that risk — other than taking the wise step of ensuring that they have the state-of-the art technological protection in place — is more training. It won’t suffice.
The U.S. armed forces and security agencies are a case in point. Should the military train its soldiers, sailors, generals, and admirals so they are less of a weak link for cybersecurity, as Admiral Sandy Winnefeld, the former vice chairman of the U.
In his book “Who Owns the Future?” digital iconoclast Jaron Lanier, who was named one of the 100 most influential people in the world by Time magazine and is now a Microsoft employee, once pointed out that people should own their online data profile and be compensated if they choose to share some of it.
It was a compelling argument. Until recently, our global economy had been based around two models of value exchange: the first based on the exchange of goods and services, and then later, the exchange of attention in the form of media and entertainment. Now the rise of digital technologies has added a third construct: data equity. This is data that comes through search engines, social media platforms, loyalty points and other digital transactions, such as dynamic cookies.
But as Lanier rightfully questioned — who ultimately owns this data? And could we reshape a digital
When Google was founded in 1998, its goal was to organize the world’s information. And for the most part, mission accomplished — but in 19 years the goal post has moved forward and indexing and usefully presenting information isn’t enough. As machine learning matures, it’s becoming feasible for the first time to actually summarize and contextualize the world’s… Read More
Increasingly, physicians’ every action and outcome is measured and reported. The data-gathering process can be frustrating, and many clinicians are growing skeptical of its clinical value. For them, outcomes measurement may seem like just another reimbursement requirement or process compliance task. However, measuring patient-reported outcomes (PROs) — patients’ own accounting of their symptoms, functional status, and quality of life — can and should be a clinical tool. In the past year, there has been a flurry of announcements by international organizations and governments declaring their commitment to making PROs a centerpiece of quality assessment. As outcomes-measurement programs move from individual hospital-led initiatives to large-scale, top-down efforts, it’s critical that clinicians are engaged in the change and understand the potential for PRO measurement to improve the care they provide.
Here we describe three examples of clinicians who are using outcomes measurement to improve clinical care. Communicating successes like these is a powerful way
Airlines are arguably more operationally complex, asset-intensive, and regulated than hospitals, yet the best performers are doing a better job by far than most hospitals at keeping costs low and make a decent profit while delivering what their customers expect. Southwest Airlines, for example, has figured out how to do well the two operational things that matter most: Keep more planes in the sky more often, and fill each of them up more, and more often, than anyone else. Similarly, winners in other complex, asset-intensive, service-based industries — Amazon, well-run airports, UPS, and FedEx — have figured out how to over-deliver on their promise while staying streamlined and affordable.
These examples are relevant to health care for two reasons.
First, hospital operations are in many ways like airline and airport operations and transportation services. There are many steps in the service operation (check-in, baggage, the security line, gates), high
Providing health care in rural regions presents unique challenges. For some patients, the closest doctor may be a three-hour drive. Clinicians seeking an expert consult may find there’s no appropriate specialist within 100 miles. And vast distance can hinder the dissemination of best practices and coordination of care. At Sanford Health, one of the largest rural health-care-delivery systems, we’ve tackled this challenge by leveraging an array of technologies to provide high-value care to a population of around 2 million, dispersed across 300,000 square miles in the Dakotas. We’ve adopted a single electronic medical record (EMR) platform, embraced telehealth technologies, developed enterprise-wide departments, and committed to data transparency.
EMR platform. So far, we have rolled out our integrated EMR platform to 45 hospitals and more than 300 clinics. Key to its success in rural care delivery is that we can rapidly disseminate common decision-support tools across the entire network. For
When it comes to creating a more data-and-analytics-driven workforce, many companies make the mistake of conflating analytics training with data adoption. While training is indeed critical, having an adoption plan in place is even more essential.
Any good adoption plan should focus on continual learning. This might include online or recorded refresher sessions; mentors; online resources for questions, feedback, and new ideas; or a certification process. It might even mean rethinking your organization’s structure or core technologies. Based on my experience, here are three ways leaders can shift a company culture from a one-and-done focus on “training” employees in analytics to an “always on” focus on analytics adoption:
Form competency centers. At a high level, a competency center is a collection of domain experts who are given a goal to improve agility, foster innovation, establish best practices, provide training (and mentoring), and be a communications engine. These centers should be “owned
Perhaps the single most important algorithmic distinction between “born digital” enterprises and legacy companies is not their people, data sets, or computational resources, but a clear real-time commitment to delivering accurate, actionable customer recommendations. Recommendation engines (or recommenders) force organizations to fundamentally rethink how to get greater value from their data while creating greater value for their customers. In other words, they’re a terrific medium and mechanism for transitioning traditional managements to platform perspectives.
“Build real recommendation engines fast” is my mission-critical recommendation to companies aspiring — or struggling — to creatively cross the digital divide. Use recommenders to make it easier to gain better insight into customers while they’re getting better information about you. Making recommendation an organizing principle for digital design distinguishes leaders from laggards.
Recommenders’ true genius comes from their opportunity to build virtuous business cycles: The more people use them, the more valuable
When thinking about practical applications for artificial intelligence in your business, it’s easy to assume that you need vast amounts of data to get started. AI is fueled by data, and so it only makes sense that the more data you have, the smarter your AI gets, right? Not exactly.
When it comes to extracting intelligence by applying AI to data, context matters. In other words, you can build the biggest data lake imaginable, but if you don’t know what you’re trying to find and you don’t have the right data to do it, then you’re not going to get where you want to go.
That’s because AI is not some magical black box that can ingest mountains of data and then just spit out results. AI is a huge set of technologies, each with a specific, fine-tuned purpose. Companies that can zero-in on the impact they want to see