Comcast: How customer experience drives product development

Customer experience is one of those buzzwords that has come to mean anything, everything, and yet nothing at all. Although hype-mongers and tricksters have co-opted customer experience, in truth, the concept is profoundly important.

At its heart, customer experience means all touchpoints or interactions between an organization or brand and its customers. It’s a simple concept that is fraught with complexity.

Think about the touchpoints that a typical consumer or business buyer may have when interacting with a seller through the entire lifecycle or customer journey:

  • First, they research the product and company, seeking information directly from the brand but also from reviewers, friends, and other sources.
  • Having decided on a product or service, the consumer evaluates where to buy and may choose among product variants and configurations. Obviously, the nature of the purchase, whether consumer or enterprise, for example, dictates specifics of the actual transaction flow.
  • Following the purchase, the customer may need post-sales help with setup and initial use.
  • Eventually, that consumer may seek technical support or other forms of customer service.
  • At some point, the consumer may make another purchase, beginning the cycle again, or, hopefully, remain with the brand as a repeat buyer.

While the brand can control some of these steps directly — such as product features or technical support — other aspects of this broad customer journey are fully in the hands of consumers. No brand can control, for example, the discussions that consumers have among themselves outside of official company channels. Through its actions, the brand can influence, but not control, these conversations.

Customer experience is challenging because so many points of interaction must come together to create a positive impression in the customer’s mind. Product design, engineering, marketing, sales, support, and service all contribute to a buyer’s overall experience with a company or brand.

To dive into the complexities of customer experience, I asked the seven-time author of books on customer experience and digital transformation, Anurag Harsh, to share his thoughts. Harsh is the chief marketing officer at IPsoft [disclosure: a CXOTalk underwriter] that develops cognitive AI technology for customer service. He also co-founded the large publishing company, Ziff Davis, giving him a broad perspective on these issues:

Customer experience is not just a touchpoint or a series of interactions between a customer and a company, but a voyage. I call it a voyage because customer experience demands re-wiring a company’s systems, employees, and culture towards the sole benefit of the customer.

Companies must learn to view the world from the customer’s point of view and deploy resources around the customer’s needs, to build a company culture that screams “customer first.” This task is hard, especially in companies with large numbers of employees, because everyone must evolve together, speaking the same company language.

In customer support, for example, creating the right experience goes beyond conversations between a customer and support agent. You need a customer-first culture that performs well behind the scenes at the back-end. All this directly affects the quality of the agent’s response back to the customer.

Great experience happens when a customer-first culture meets the right back-end processes and technology, supported at every step by the organization’s leadership.

Because customer experience is profoundly important, I invited two of the world’s top practitioners to join me on episode 267 of the CXOTalk series of conversations with innovators.

Chris Satchell is executive vice president and chief product officer at Comcast, where is he responsible for the company’s product, design, and innovation teams. Comcast has annual revenue over $80 billion.

Brian Solis is one of the most well-known authors and analysts on digital transformation. He is a principal analyst at Altimeter Group, a Prophet company. Among his reports are the 2017 State of Digital Transformation and the Digital Change Agent’s Manifesto.

See also: Comcast: How AI, machine learning, DevOps, and a bit of hardware may make it a smart home platform

During this episode of CXOTalk, these experts discuss the subtle points of customer experience and present a framework for thinking about the problem. The conversation describes how Comcast uses customer experience as a reference point when developing new products and services.

Watch the entire conversation embedded at the top of this page and read a complete transcript at the CXOTalk site.

Here are edited highlights from the discussion:

What are the core issues around customer experience?

Brian Solis: If you look at the proper definition of customer experience, or employee experience for that matter, it is the sum of all engagements someone has with your organization throughout the entire journey and throughout the lifecycle. It’s not just about any one moment. It’s about how all those moments come together.

Chris Satchell: It’s about designing the entire journey. There’s a couple of things we focused on [when I worked] at Nike.

One was this idea of consumer brand business. Do what’s right for the customer first; then worry about the brand and the brand promise you make to the customer; then worry about the business. If you get the first two right, the third will come.

The second one was about thinking about the entire journey. Every touch point on that journey is an interaction with the customer. That can be positive or negative so that you could be building promoters all the way along or detractors all the way along.

You must think very broadly, and so you think way beyond when you’ve got a product installed, or you’ve got it in your home. You have to think, “How did I learn about it? How did I acquire it? How did I pay for it? How did it get to me? How did I install it?”

At Nike, we would think all the way to, “What is my interaction with an in-store athlete that was serving me?” because that is a great connection point with the company. Every point along a journey is your brand. You have to be authentic, and you have to serve the customer correctly there. That’s some of the things we brought here.

Then from my time at Xbox, again it’s a lot about delivering the very best experience, not settling, and never being content with what you’re providing, no matter how good it is, because you have to think in this consumer world about how good you think your experience is. There is somebody out there merrily raising the bar on you. It won’t have to be in your sector. It doesn’t have to be in your industry.

Brian Solis: I wrote a book a couple of years ago called X: The Experience When Business Meets Design. It explained how to think differently about innovation by taking a step back and thinking about design for a new generation of customers and employees that are not in alignment with today’s corporate policies, processes, and even just how we think and how we think about productization.

The consumer doesn’t care about all of the politics and BS that happen within the organization. They just want the experience to be personalized. They want it to be great. They want it to be intuitive, maybe transparent in many ways. Innovation is as much about products as it is about policies, processes, and how we even work as well. I think the biggest thing is just shifting mindsets.

I look at today’s — I call them — Generation C. They’re not millennials. They’re not Centennials. They’re not Generation X. They’re just anybody who lives a digital lifestyle. What they all share is this heightened bar for expectations and these new behaviors. They’re impatient. For example, we talk about the uberization or the consumerization of technology. When someone uses Uber, that becomes their standard of engagement. When someone uses an Apple product, that’s their standard — or Google Search.

This new level of experience is blurring the line regardless of products or services that they want that same sort of intuition, that same sort of clarity and cleanliness throughout the entire journey. Yet, organizations are built on these 50-, 60-year-old structures that have all those things apart.

Chris Satchell: If I’m a customer, my reaction is, “Your org structure is not my problem.” We used to talk about this back at Xbox about trying to paper over the cracks in our org structure so that the consumer didn’t see. You’ll see so many companies when you track that product portfolio, it matches the org structure, and you’ve got to fight so hard to take that mentality out, and you’ve got to find leaders who will be selfless and say, “Okay. Yes, I have this release vehicle, but I’m going to take functionality from somewhere else. I’m going to give up something in my release vehicle because it doesn’t make sense to the customer.”

How do you empower teams to create great customer experiences?

One of the things that we’ve done that’s helped empower the teams — and it’s going to sound boring, but it’s so important — is we have this quarterly planning process. It’s how we take our annual goals for our portfolio and break it into quarters. Every quarter what we do is, the products managers, they get with all their stakeholders, wherever they are, including user research and what they want to do, and they write. They say, “For my area, here is a one-page spec of what I want to do,” and it’s something that can be achieved in a quarter for their end of the product or their product.

It says, “Here’s all the teams’ help I need.” What we do is we have this process where we stack rank them. Then we plan, and we just plan from top to bottom, making sure that any higher priority thing, you know, it fills resources in first.

There are 2,000 people in my organization and another 8,000 people we work with. What you don’t want to have happen is you start off on something and then find out one of the constituent teams can’t deal with the capacity constraints for that quarter, and you can’t deliver anything.

We solved that problem, but importantly, it gives all your partners somewhere to go. When they say mid-quarter, “We’d like to go in this direction,” or, “We want to change what’s happening,” you say, “Great. Talk to your product manager. If they like the idea, they can bring it to the next planning.”

It’s a way that we have managed to bring quarterly agility to annual planning. What we do is we only schedule 50 percent of our capacity that way. We call it “directed.” We do 50 percent of what we call “trusted capacity” where we just say to the scrum teams, “Hey, work your backlog. Put on your backlog what you know that you need, what the customer needs. That’s your capacity to manage. Go manage it.” We work very hard to carve off part of their capacity that they could just use to do the right thing. It’s taken us a year and a half of constant effort to get that to work, but it has helped us take the 36 teams that we’re feeding into video and make them more agile and coordinate across them. It’s agile writ large at a very big scale.

How do you measure ROI?

Here’s a controversial statement. I think it is pointless measuring ROI below the portfolio level for a given line of business. I sometimes have some very spirited discussions with our finance team around this. The reason is, we’ve got all these projects. They’re feeding into the overall experience the consumer gets. Then the consumer, especially in our business, has got a subscription they’re holding because of that.

When somebody comes to me and says, “Well, we need to know exactly what it costs,” I go, “Why do you need to know what it costs? You know what the portfolio costs.”

They’re like, “Well, so we can plan ROI.” I’m like, “How on earth do you know what the return is? There is no way to untangle these variables. That is impossible. It’s mathematically impossible. We don’t have that precision.”

And so, I think one of the problems is when people start measuring ROI. Measure it at an appropriate level. The level I think is appropriate is: Here’s what we invest in a business, and here’s what that business returns. If you start looking at features, and you start looking at product extensions and all these other things, and saying, “Well, we need an ROI,”

I think you’re missing the point in the modern world. You need to look at total investment, total return. That clears up a lot of the mess if you can convince people of that. I find that a lot of organizations love, would much prefer, to be precisely incorrect than right, because it gives them a sense of, “Well, we must be on it because we got all these detailed numbers.”

Well, the detailed numbers are fiction. We don’t know how the customer will receive it. How many of us see ROI projections that pan out?

Now, large-cap scale investment and capital investment, that’s a different matter. You can plan that. But, when it comes to consumer-based products, I just don’t think, other than the line of business, you can plan it. The first one is, if you can, don’t get caught in the game of ROI for small things. Talk about portfolio ROI.

Then what we measure depends. You’ve got your vanity stats because you want to know your population and what your monthly actives are and your unique users. But beyond that, you must measure, one, what you think is important. If you’re in a moment of truth, you need to measure success across a moment of truth. Maybe you need to measure net promoter score on one side then the other.

That means you must run experiments, take people through a new experience and measure what their net promoter score was at the end versus the net promoter score of people on the old path.

We have this idea of relationship net promoter score, so RNPS, which is the long-term [of] how you feel. Then TNPS, which is, through a transaction, how did you feel? Then other than that it’s, you’ve got to come back to the product teams. It’s like any good data science. KPI is no different. What question do you need to answer? You have to think about the questions you need to answer and then plan for the data to answer those questions.

From a development perspective, it’s great to put the infrastructure in to be able to say, “I want real-time stats. I want batch stats. I’ve got these different things that I want to get back from my application to make it very easy for developers to instrument.” Whereas, product comes in and says, “Could you find these things out for me?” They’re like, “Yeah, that’s easy. I can just go and add that.”

Beyond that, it depends [on] what you’re trying to answer for that question. If you’ve got a funnel problem with, “Hey, how do I track from when somebody downloads an application, how many people go through, set up an account, and they watch that first video and go to the second video?” That’s very different than saying, “I want to understand the heat map of how somebody moves through our user experience.” We’d say in England, “Horses for courses,” but it is about understanding the question; design your data feeds and your data analysis for the answer.

How does data help customer experience?

Chris Satchell: We have huge amounts of data on everything, whether it’s our products. You can only vaguely imagine how much data our network produces. We use it in many ways. We use it operationally to keep the service running, to give customers a great service. We also use it, as I said, to answer product questions, to understand where we should go next in our products.

We have a very strong machine learning, artificial intelligence, and deep learning set of core teams here. We’re using that data to recognize new insights in our products and also to

create new product experiences that you can only do with those intelligent methods.

The same with operations, feeding data in and looking for that sort of pattern matching recognition and next action recommendation that you can only do by using very deep networks to recognize all this data coming in.

We’re starting to use data as a way to change how we operate and as a core of how we build and the functionality our products deliver. I think that’s going to become common to many companies. Data will become part of the product.

We’re finding that the algorithms that are available are becoming a commodity. You can get great data algorithms everywhere.

The actual technology frameworks — whether it’s MXNet, whether it’s TensorFlow — analysis and modeling frameworks are becoming a commodity. The real thing you have as a company is your data. The models you build with that data, that is your secret sauce. That is your gold.

We’re very focused on using our data effectively. It’s a question of capacity. We have infinite amounts of great questions and things we can do. It’s just sequencing them through product development, through product insights, through network operations, and customer experience to get the most valuable things done first.

I think we always talk about big data. Now we’re talking about AI and machine learning, but all of that — let’s just remember, they’re just tools. Without great people thinking great ideas, without being able to develop it, without being able to take the insights or the data and have the actuation loop to affect things, there’s no point collecting it.

I used to joke that what would happen in the big data world, you’d have a board of directors that says, “We need data.” Dutifully, the company would go off and gather huge amounts of data. Then they would say, “Well, nothing is happening,” and so they go, “Ah, we need more data!” So you get even bigger data.

Then you realize a little bit later, you’ve got no insights from it, so you start building the insight engine. You have this, like, huge first bit, and then it narrows to insights. Then still nothing happens.

Everybody is scratching their heads, and then you realize actuation. There was no pathway to take the results we had and change the world based on that. You want it to look more like a pipe where your insights match your analysis match your ability to actuate.

There is also a cultural element where you need to check your ego and say, “Wow. I’m surprised. I had an insight. My insight was wrong, but I’ve got a new insight. Let’s go drive that.” If you can get those to line up, you can start making a change in the org.

Brian Solis: [Chris just described] the biggest challenge I’ve seen with data. This is across the board. The challenges for any of this are human.

You’re working against a lengthy career of experiences that are behind every executive or decision-maker. They got to that role of where they are because they’ve made great decisions along the way. Those decisions have fortified their experiences and have validated their beliefs and their perspectives. When you’re trying to challenge convention, data only reinforces what you want to see or what you expect to see.

Being a data storyteller and having common language [means] getting data to tell the story of what is happening based on assumptions that will challenge convention. That’s the art.

Final thoughts on innovation?

Brian Solis: I often talk about the difference between iteration and innovation. Many companies think they’re innovative, but they’re actually being iterative, which I describe as doing the same thing, but better, whereas innovation is doing new things that create new value.

I look at the Comcast or the Xfinity remote as sort of this metaphor for the two. Buttons are iterative: backlit keys, dedicated buttons. Then the voice, the whole infrastructure for voice was innovative.

Chris Satchell: It’s a continuum, so I think small iteration is just micro innovation. You need innovation that’s small. You need innovation that’s medium where you’re expanding products. You need innovations doing completely new things, and you have people dedicated across that time continuum.

CXOTalk brings together the most world’s top business and government leaders for in-depth conversations on digital disruption, AI, innovation, and related topics. Be sure to watch our many episodes! Thumbnail image Creative Commons from Pixabay.

(Cross-posted @ ZDNet | Beyond IT Failure Blog)

Artificial intelligence: McKinsey talks workforce, training, and AI ethics

As I talk with the many extraordinary guests on CXOTalk, an interview discussion forum that brings together the most innovative thinkers in the world, three key business aspects of artificial intelligence have emerged.

First, AI is a vague umbrella concept that ties together data and a set of technologies, such as pattern recognition and other techniques, that emulate human learning and intelligence. The term “artificial intelligence” is an imprecise marketing or presentation phrase used for the sake of convenience. Business buyers should dig deeper to understand the technologies that make the most sense for their organizations.

Second, few companies have deployed AI at scale. There are lots of prototypes and proofs of concept, but AI is still new and experimental for most organizations. For example, a recent survey by SAS states “AI adoption still in early stages.”

Third, be skeptical of vendor claims. Technology companies are still trying to figure out where AI can improve their products and the processes they automate. Many vendors have bought AI startups to gain expertise and fill gaps.

The bottom line for enterprise buyers: learn the tech, question your vendors, and plan for AI by developing data science talent in-house now. Talent scarcity is a big issue today.


McKinsey Global Institute (MGI) is one of the foremost research organizations in the world on how AI will affect organizations and their workforce. McKinsey’s research combines quantitative analysis with extensive on-the-ground interviews of executives and business operators. As a result, the material they produce is insightful and useful. Two recent reports focus on the business value of AI and the impact of automation and demographics on work and the economy.

One of the partners leading McKinsey Global Institute’s work on the impact of AI and related technologies is Dr. Michael Chui, who is one of the most articulate people I know on these topics.

Chui’s comments are clear and rooted in solid research, making him a natural participant in the CXOTalk series of conversations with the most innovative leaders in the world. On episode 268 of CXOTalk, I spoke with Michael Chui about AI, business, ethics, policy, and economics.

Chui makes a couple of key points that I want to highlight. First, the success of an organization’s efforts to adopt AI is based strongly on its overall digital maturity. Companies with active programs of digital transformation will be more likely to make progress with AI initiatives. From my perspective, we can think of AI initiatives as extensions of digital transformation – rethinking culture, mindset, and business model — rather than isolated technology projects.

Second, start thinking now about how you will train your workforce as AI changes jobs and frees up labor to be re-deployed. Chui says that mass redeployment of labor will likely be one of the “grand challenges” we face.

The in-depth conversation lasts 45-minutes and is an important document describing how one of the world’s top AI business researchers views the issues today. You can watch the entire conversation and read a complete transcript of episode 268 at the CXOTalk site.

Here is an edited summary transcript pulled from the long discussion:

Tell us about McKinsey Global Institute?

McKinsey & Company is a global management consulting firm. The McKinsey Global Institute is part of McKinsey. It’s an investment by our group of global partners around the world to do research, quite frankly, on topics that matter. We’ve been around for over 25 years as part of McKinsey [and], for most of that time, have done work on productivity, country competitiveness, labor markets, [and] capital markets.

For the past few years, we’ve added another research leg, which is around the impact of long-term technology trends. We’ve looked at data and analytics. We’ve looked at open data. We’ve looked at Internet of Things. Increasingly, now we’re looking at artificial intelligence, robotics, and automation technologies and their potential impact on business, society, and jobs and employment more generally.

How do you define artificial intelligence?

You could go for hours debating it. We describe it as using machines to do cognitive work, to do the work that comes about primarily because of our brains. But, as it turns out, even from my graduate research studies, we know that not all our intelligence is just trapped in our brains. It’s also part of our bodies, et cetera. And so, we understand that, in many cases, artificial intelligence itself might enter the physical world and be things like robotics and autonomous vehicles, et cetera. But, it has to do with intelligence and then the machines that instantiate it.

What are some of the important conclusions of your research?

The potential for these technologies that we call artificial intelligence is huge. They affect potentially every sector, potentially every function. One reason for that is a lot of the potential applications of AI are extensions of the work that people had already started in data and analytics. And so, we’ve been looking at nearly 500 different use cases of artificial intelligence across every sector, across every function.

Sometimes what we say is, these traditional analytic methods, whether it’s regression or what have you, gets you this much impact. But, when you could add the multidimensionality of additional data or these additional deep learning techniques, you could increase, for instance, forecast accuracy or increased OEE or decreased waste, a number of these things, which these use cases allow us to do. You could think of AI as just being another turbocharged tool for your analytical toolkit. I think that’s one broad finding, which is that there is almost no part of the business that this couldn’t affect.

Another piece, though, is we’ve been surveying thousands of different executives in companies all around the world. My colleagues who serve clients on these topics also have very direct contact with people who are thinking about or are using AI. One of the things that we know now, as we sit in December 2017, as we’re talking, is that it’s very early. While there’s huge potential for improving economics, both in the top line and bottom line, only a very small percentage of companies have either deployed AI at scale or within core business processes.

Now, that’s changing every day as more companies develop this capability, learn more about the technology, and they can embed it within the processes of an organization, which in some cases is the hardest thing to do. We’re just very early on this learning curve. It’s a steep learning curve, but we’re early. There is so much potential, but we’re early.

What are common threads among the industries you have studied?

There are many industries where much of their value gets driven by their customer interactions. If you’re a retail company, if you’re a consumer package company, et cetera, it might make more sense to look at the value of AI and those types of functions. On the other hand, if your operational effectiveness drives you, if you’re in the business of manufacturing, delivering and shipping products, for instance, if you’re in logistics, then perhaps those operational needs [take precedence]. I think those are, at least at the top level, one way to think about it.

Another common thread that we have found is the following, which is, I think often you discover a technology which has a potentially transformative impact. You say, “Gosh, isn’t there a shortcut? Can’t I just jump and use that to compete?”

Because we need large training sets of data for AI, in fact, we’ve discovered a high correlation between sectors and individual companies that are further along on their digitization journey — the ability to use digital within their core processes to improve process effectiveness. There’s a high correlation between that and being ready for AI.

One of the other common threads we’ve discovered is, it’s quite difficult to accelerate past your digitization journey. You need to be on the digital journey to enable yourself to be ready for AI. I think that’s another finding.

If you want to accelerate your potential impact with AI, you need to accelerate your move along the digital journey.

What will the impact of AI be on workforce issues?

Some of the potential impacts are for these technologies to automate activities that we currently pay people to do in the economy.

We looked at individual activities, not just occupation, so 2,000 different activities we pay people to do in the global economy. Half the time people spend being paid at work is on activities that theoretically we could automate by adapting technologies that exist today. That sounds scary, right? That’s a large percentage, but we’re not predicting 50% unemployment tomorrow partly because it takes real time. It takes real time to develop the technology.

You need a positive business case. Technologies tend to be expensive when they are first developed, whether it’s a self-driving car or an artificial intelligence algorithm. That cost declines thanks to Moore’s law. You need to net that out against the cost of human labor, and that’s different around the world.

In any case, 50% of the world’s activities potentially might not be automated for another 40 years, so 2055. Although, we have a scenario which is 20 years earlier and a scenario that’s 20 years later. We do know that increasingly activities we pay people to do will be automated.

The question then is, will there be enough demand for human labor, even net of the things that might be automated? Our report from last month suggests yes.

If you look different potential catalysts — whether increasing prosperity around the world; another billion people entering the consuming class in the next couple of decades; whether you’re talking about aging, which is a troubling thing because we have [fewer] workers, but on the other hand it drives the need for healthcare. We have roles for people to develop and deploy the technologies themselves.

Hopefully, we’re going to see increased investment in infrastructure to help the consuming class, but also fix and improve the infrastructure we have. We’ll see changes in energy mix and efficiency, and potentially even a lot of what’s currently unpaid work in the economy that’s many times done by women at home, whether it’s childcare, cooking and cleaning, increasingly enter the market.

If you look at all of those things together, and then even your net against those the activities that AI and robotics might do, we still see plenty of work for people to do, enough to offset the effects of automation.

The broad question, though, is, if you think mass unemployment isn’t going to be the problem, mass redeployment might be the problem. As much as we want the education system to get better, it works fairly well.

We think that potentially the great grand challenge for the next couple of decades is, how do we retrain millions of workers who technology will displace? We need them to keep working to have economic growth and yet, at scale, retraining of people past their first two decades of life is something that I dare say we haven’t completely solved yet. That’s something on which we badly need to work.

Should business leaders start to think about worker retraining now or is it still too early?

This demands some immediate attention. It’s not necessarily because things are going to happen overnight, particularly with AI. But, if we think about automation technologies more broadly, then, in fact, we are starting to see these things, whether it’s robotic process automation, whether it’s physical automation from a manufacturing plant, in logistics, or in a distribution center. These technologies are coming into play today.

While we’ve described this as a multi-decade trend, which will take time in macro, it will happen quickly for individuals. It will happen quickly for individual workers. It also takes time to understand retraining. We described this as a grand challenge. Usually, grand challenges aren’t solved overnight, and so I do think business leaders engaging on this question about retraining their workforce on a continuous basis at scale is something that is a question that ought to be top of their mind when they’re starting to think about their workforce strategy.

The idea of universal basic income sometimes comes up in these discussions?

This idea of a universal basic income, guaranteed minimum income, et cetera, is capturing a lot of currency. I sit in San Francisco here and, as it turns out, there are a lot of people talking about it there. There are lots of arguments for it.

One of those arguments is if we think the machines are going to take everybody’s job and we’re going to have mass unemployment, we need to make sure that everybody has enough income so that they, in fact, can feed themselves and feed their families. I think that justification or that rationale for universal basic income gives up too early because that assumes mass unemployment. In fact, what we say is we do need mass redeployment, not mass unemployment, just to make sure that we have enough economic growth going forward.

Our point of view is that we’ve looked at the past 50 years of economic growth. Half of that has come about because of more people working. Because of aging, we’re going to lose a lot of that. One way to think about it is we just don’t have enough workers. We need all the AIs, robots, et cetera working, plus we need people working to have economic growth. Again, if you think UBI is based on the fact that we’re going to have mass unemployment, I think you’ve given up already and, in fact, you need to move.

The other thing that I think is also helpful, again, as we modeled out the potential impact of AI and other technologies, plus these additional drivers, we might continue to see this increasing income dispersion or income inequality. You might ask, “Look. We just need to make sure that people get paid enough.” Well, then again, if you want to look at it from a public policy standpoint, maybe you could target the types of subsidies such as the Earned Income Tax Credit, which both incent work as well as provide additional income to people. I think, thinking through all of those possibilities.

Now, that said, UBI for a place that’s a developing country, again, it might put a floor in place that allows people to have a lot more freedom regarding what they’re able to do in their job. But, in a developed country, both because of the expense, as well as the fact that it isn’t targeted towards trying to get people working, I think it’s challenging for that reason. That said, the overall point, another overall point that we found from history, which we hope will continue is, while we don’t think everyone can completely stop working, the working week has declined, on average, by double-digit percentages over a matter of decades and centuries.

Hopefully, we all can have more time for leisure. By the way, leisure drives new activities, new occupations. That’s something else we need to do. We need to continue to generate new activities and occupations. Hopefully, the workweek will continue to decrease over time. At least, for the foreseeable future, we don’t see it going to zero.

What about changing demographics?

Demographics is interesting and includes a number of powerful factors. Again, we cover some of this in the report we published last month. First of all, countries vary greatly in their demographics. For many countries, they’re aging, and that exacerbates this question; we don’t have enough workers to continue the economic growth that we’ve enjoyed for so many years. The reason why we have better lives than our parents and our parents had better lives than our grandparents, et cetera, is because of this economic growth and half of it coming from more people working.

Germany’s workforce is declining. Japan’s workforce is declining. China, with a population of a billion and a half people, their workforce either is or, depending on who you ask, will shortly begin declining. Those are countries which simply don’t have enough workers to underpin economic growth. Again, one of the implications of that is AI and robotics can be some of the workers, can fill in for that gap regarding just numbers of people who are available to work.

That said, there are other countries like India, countries on the African continent, et cetera, which are very young, and their demographic pyramid looks very different. We’re concerned at some point about the fact, well, gosh, what if automation AI, these technologies, come into play just as they need to create even more jobs? That’s absolutely true in India, for instance; another 150 million people need jobs going forward.

We modeled out all of these potential drivers of additional demand. By the way, we picked seven of them. We know that there are more, so even our modeling is limited. Particularly in those countries which tend to be young, those are countries also which tend to have high aspirations for their economic growth. They start relatively low on the GDP per capita scale. As a result, that will generate lots of demand for human labor, as well as robotics and artificial intelligence. Even in those countries, we see the potential for lots of work as well, work to be done.

Again, that comes back to the question of retraining and education. Can we get people into those jobs? Then, can you deploy those technologies in a way because, as I said before, AI and robotics require an underpinning of moving on the digital journey? Even those countries, which are developing and young, will need to move on the digital journey for them to take advantage of these other technologies and improve their productivity while they’re generating new jobs for people as well.

What advice do you have for established organizations?

Number one is, dedicate some time and resources to understanding the technology and its potential. I mean I should have said they should read our report, but [laughter] but I’m not going to do the commercial. I think it is starting to understand what that potential is. Then I think the same sort of test and learn philosophy, which was effective in data and analytics broadly, I think that’s something which is true here, too.

Another thing I think, which is also true, is particularly for the technologies which are working well today around machine learning and deep learning. They’re based on having training sets, so data. I think being sophisticated about having a data strategy is important.

I had the opportunity to speak with Andrew Ng, for instance, who is one of the pioneers in deep learning and machine learning, overall. He talks about some of the leading companies in the deployment of AI, really spending time on these multiyear views of what data is important to be collected or have access to so they’ll be able to compete going forward, and they’re playing these multiyear. He describes them as multidimensional chess games to have access to the data which matters.

One of the largest challenges now is on the human talent side. We saw this with data scientists previously. Again, to a certain extent, we talked about many of the AI use cases being extensions of the analytics use cases. The analytics challenges about talent now are extended to the challenges around AI as well, and so huge amounts of war for talent regarding people who understand these technologies deeply.

Of course, that’s changing, too, as more and more people take advantage of online resources, enroll in classes, et cetera. Again, supply and demand are constantly evolving. Right now, demand is so high, and supply is relatively limited. One of the biggest challenges is just having people onboard who can do it.

CXOTalk brings together the most world’s top business and government leaders for in-depth conversations on digital disruption, AI, innovation, and related topics. Be sure to watch our many episodes! Thumbnail image Creative Commons from Pixabay.

(Cross-posted @ ZDNet | Beyond IT Failure Blog)

iPhone X and Note 8 compared: The ultimate guide for business users

There’s a battle raging out there between the Apple iPhone X and the Samsung Galaxy Note 8, with strong opinions on both sides. As a proud owner of both phones, I want to share my experience and declare a definitive winner.

Also read: iPhone X vs Galaxy Note 8: Which phone has better business specs?

By way of background, here are key points about this review. First, this commentary focuses on productivity features that matter to business people. If you care mostly about games or other specialized uses, this review will not match your needs. Second, over the years, I have owned many models of both iPhone and Android mobile devices, from phones to tablets, with no allegiance to either platform. Third, I bought the iPhone X while Samsung sent me the Note 8 for free to keep, so bear that in mind as you try to infer bias in either direction.

To figure out which phone is better for business people, I created a scoring framework based on the attributes below. You can see a summary table at the bottom of this article.

A word about scores – READ THIS. I assigned each feature or attribute a score ranging from 0 to 15. Higher score features mean that item is more important, useful, or innovative. For example, Samsung gets a + 1 for the headphone jack, because it’s nice but not that important. Apple gets a + 15 because the Face ID system is both great and important.

To calculate the result, I added up individual scores to create a single summary for each device. That summary reflects both specific features and their practical value to business people.

I ignored future promises, such as augmented reality, because they serve little practical function today. Most business people will not find much use for an animated poop animoji, such as Apple includes with iPhone X, for example.


From a hardware perspective, both phones are beautiful, truly worthy of modern flagship devices. Still, we need to make a choice so let’s compare.

Speed and performance. In practical use, both phones are “fast enough.” The iPhone X has a specialized processor for AI and augmented reality while the Note 8 has more RAM. But, honestly, does it matter? Talk to me about AI processors in the future when AI dominates our lives and AR is useful on a practical level. If you are playing games and trying to coax higher frame rates, then perhaps the differences are important, otherwise no.

Typical business users will find that both phones are great. Scores: Apple +10 and Samsung +10

Note 8 Iphone X speed and performance

Physical size. The iPhone X is smaller and fits more easily in a pocket, It’s also easier to use one-handed. With phones, smaller is better, but Samsung makes excellent use of that larger size to include a higher resolution screen. Physical reality means you must trade off overall phone size against screen resolution and screen size. Scores: Apple + 10 and Samsung + 10


Note 8 Iphone X physical size

Storage expansion. Unlike the iPhone X, the Note 8 offers the ability to use a second SIM card or add storage with a MicroSD card. Strike a blow in favor of Samsung. Scores: Apple + 0 and Samsung +3


Note 8 Iphone X storage expansion

Power connector. The Note 8 uses an industry standard USB-C power connector, while the iPhone has a Lightning connector. Apple has already made the switch to USB-C in its laptops but keeps the proprietary and expensive Lightning cable for the iPhone. If you have a new MacBook laptop and an iPhone, you must deal with two different power cables, and that just sucks when you travel. Scores: Apple + 0 and Samsung +2


Note 8 Iphone X power connector

Fast charging. Both support fast charging, which allows the phone to gain a fifty percent charge after being connected to the power supply for only thirty minutes. However, Samsung includes everything you need for a fast charge in the box, while Apple forces you to buy a special charger and cable as optional items. Seriously, Apple, did you have to cheap out on a $1,000 phone?? Scores: Apple + 0 and Samsung +2


Note 8 Iphone X fast charging

Stylus. The Note 8 has one while the iPhone X does not. If you want to use handwriting, the Note 8 is your choice. Scores: Apple + 0 and Samsung + 3


Note 8 Iphone X stylus

Camera. According to the definitive source for mobile camera testing, DXO Optics, both phones offer best in class cameras. Dig into the details, and you will find each phone has specific strengths and weaknesses, relative to the other, for still photography, telephoto use, and video. Scores: Apple + 10 and Samsung + 10


Note 8 Iphone X camera

Headphone jack. The Note 8 has a headphone jack while the iPhone X doesn’t, giving Samsung users more flexibility and choice. Scores: Apple + 0 and Samsung + 1


Note 8 Iphone X headphone jack

Hardware mute button. The Apple phone has a little switch on the left side that turns off audio; on the Samsung, muting audio requires you to turn on the phone, swipe down from the top, and press the software mute switch. Not a big deal, but the iPhone wins this one. Scores: Apple + 1 and Samsung + 0


Note 8 Iphone X hardware mute button


Security often requires trade-offs between convenience and protection. Both phones are secure, but Face ID on the iPhone X is a big winner and one of its most compelling features.

Access method – Apple Face ID vs. Samsung fingerprint sensor. To avoid the hassle of entering a password every time you use the phone, the Note 8 has a fingerprint sensor while the iPhone X uses its front-facing camera to recognize your face; Apple calls this Face ID.

Before the iPhone X arrived, I was skeptical that Face ID would work fearing it would be inaccurate and error-prone. I was wrong. In fact, Face ID offers a stunning level of convenience. Face ID is a convenient time-saver and is great. Although not perfect, for example, it can only store one person’s face, Face ID is one of the best features of the iPhone X.

The fingerprint sensor on the Note 8 works extremely well and is fast. Some people have complained about its location, but frankly, that’s just whining so ignore the complainers. Although the Samsung fingerprint sensor does the job, Face ID is better. Scores: Apple + 15 and Samsung + 0


Note 8 Iphone X access method

Trust and safety. Both Samsung and Apple are large reputable companies offering sophisticated security to their customers, so this is not an issue on either side. Scores: Apple + 10 and Samsung + 10


Note 8 Iphone X trust safety


The screen matters and both phones offer the business person a great display, although there are differences.

Screen resolution and size. While both screens are outstanding, Samsung offers significantly higher resolution than does the iPhone X. When combined with the larger screen size, the Note 8 offers more territory for reading, checking out maps, and so on. However, the tradeoff, as mentioned earlier, is the larger overall phone size. Whether that makes a difference is purely a personal choice.

Although most reviewers would give higher screen resolution a higher score, in this case I have balanced resolution against larger phone size. In summary, both screens are excellent, so you need to decide based on personal preference. Scores: Apple + 10 and Samsung + 10


Note 8 Iphone X screen resolution

Screen quality. Both phones display bright, rich, colors that are fabulous. Scores: Apple + 10 and Samsung + 10


Note 8 Iphone X screen quality

Display calibration and accuracy. The Note 8 has several display modes, with techy names like adaptive, AMOLED cinema, and Basic. I assume those modes do something useful, but I just set the phone to adaptive, which seems the simplest, general-purpose choice. Set to adaptive, the display is beautiful, but whites seem slightly off, and colors pop unnaturally. I’m sure there are settings to address these issues, but stuff like this should work correctly out of the box.

For color accuracy, the iPhone X display is superior to the Note 8. Respected video diagnostics company, DisplayMate, tested the iPhone X, calling the screen:

… superbly accurate, high performance, and gorgeous display, with close to Text Book Perfect Calibration and Performance!!

The iPhone X is the most innovative and high-performance Smartphone display that we have ever tested.

Although the iPhone X has greater color accuracy, the average business person will not find that precision to be especially meaningful. However, if you are a professional photographer or fashion designer using the phone to show your work, then color accuracy is essential and this could be reason enough to choose the iPhone X. But, for us business people, it’s a minor benefit. Scores: Apple + 3 and Samsung + 0


Note 8 Iphone X display calibration

Always-on display. The Note 8 lets you keep a portion of the screen always powered on to show the weather, notifications and other information. It’s a small, but nice, touch. Scores: Apple + 0 and Samsung + 1


Note 8 Iphone X always on display

Software and Ecosystem

Choosing the Apple or Android (Google) ecosystems is a core decision when selecting a phone.

iOS vs. Android. Five years ago, iOS would have easily won this contest. Today, Android is a mature operating system that many people prefer over iOS. I have experienced very few problems finding comparable apps on both platforms. At the same time, iOS still offers a smoother and more consistent experience than Android. Scores: Apple + 10 and Samsung + 8


Note 8 Iphone X ios android

Keyboard. This one is a very big deal. For those of us cursed with stubby fingers, the extra real estate of the Note 8 means larger keys that are far easier to use than those on the iPhone X. Whether this matters to you is another matter of personal preference (and finger size), but I find the Note 8 to be vastly superior when typing. Scores: Apple + 0 and Samsung + 10


Note 8 Iphone X keyboard

Score summary

To determine scores, I created the list of business features and then evaluated each phone based on the priority of those features. To my astonishment, the numbers came out almost identical in both cases.


Note 8 Iphone X score summary

What should you buy? The Note 8 score beats the iPhone X by one point, which means parity between the devices.

For most people, the decision comes down to three factors:

1. Size. Do you want a smaller phone or a larger screen?

2. Ecosystem. Do you prefer Android or iOS?

3. Access method. Face ID offers great convenience over a fingerprint sensor. How important is that to you?

Beyond these points, look at specific features to see what matters most to you. For example, if you are a professional photographer, the better screen calibration on the iPhone X could be your defining feature. On the other hand, if you have fat fingers or bad eyes, the Note 8 will be easier to use.


Note 8 Iphone X Score Table

Personally, I keep going back and forth because both phones are great but different.

(Cross-posted @ ZDNet | Beyond IT Failure Blog)

Oracle and cloud: Success demands a customer-centric culture

Because Oracle is one of the most important players in enterprise software, I asked three top industry analysts to join me on Episode 261 of the CXOTalk series of conversations with innovators. Read on to learn how these analysts view Oracle, its position in the market, and its relationship to customers.

Oracle is a 40-year old company with almost $40 billion in annual revenue. Having survived and prospered over the course of this lengthy period, Oracle has certainly proven its ability to adapt and evolve. The company built its reputation as the world’s premier database, eventually becoming an enterprise applications powerhouse. Today, Oracle is undertaking one of the most important transitions in its history: moving its product lines to the cloud.

As part of an all-out push to the cloud, which has created rapid growth and high margins for that part of the business, Oracle is rewriting its software to be a complete suite of cloud-ready products. This includes enterprise applications such as ERP and CRM, infrastructure-as-a-service in direct competition with Amazon Web Services, and the new Oracle autonomous database.

Becoming more responsive to customer relationships is a key part of this move to the cloud, however, the company’s history has not been all sweetness and light.

Just as cloud computing really began to take off, in 2009, Oracle’s founder, Larry Ellison, famously dismissed the cloud as “water vapor” (despite his being an early investor in NetSuite). Oracle also became known for using attack dog-style tactics during customer audits. As a result, Oracle’s reputation suffered among some analysts and customers.

Today, two primary factors help explain the renewed focus on customers that seems to be part of Oracle’s cloud strategy.

First, the impact of Mark Hurd as CEO. Hurd is a sales guy, which means he really does care about meeting customer needs. Even back in 2014, during a video interview, I speculated that Mark Hurd might create a “warmer, friendlier, happier Oracle.”

Second, and even more important, the cloud-based subscription business model forces software vendors to create a culture oriented toward customer satisfaction. Unlike the on-premises software model, cloud customers usually pay for usage over time, making customer adoption and retention core imperatives for any cloud company. Successful cloud vendors monitor a variety of metrics to ensure customers are happy with the software and therefore use it to the fullest extent possible.

The great panel for this CXOTalk episode discusses all these issues and much more. The group consists of:

  • Mike Fauscette, Chief Research Officer at G2 Crowd and previously Group Vice President at IDC
  • Liz Herbert, VP and Principal Analyst Serving Application Development & Delivery Professionals, at Forrester Research
  • Neil Ward-Dutton, co-founder and Research Director at MWD Associates, where he covers digital transformation, IT infrastructure, and related topics

You can watch our entire conversation in the video embedded above and see an edited summary below. You should also read the full transcript.

Where does Oracle fit in the technology industry today?

Mike Fauscette: The industry itself has changed quite a bit over the last 20 years. In the 2000s, when we started to see a lot of consolidation, Oracle was one of the companies that led that charge of bringing other application companies in and expanding their application portfolio so that it became quite broad. Then, at some point, they started on this transition path building out solutions that would work in the cloud, all the way from the infrastructure and up the stack, all the way to applications.

For the last several years, they have focused on changing the relationship, changing the way they do business, in this ongoing subscription-based business around almost all the product lines that they sell.

Certainly, they still have a lot of legacy customers who have older applications that are still on-premises still, but the bulk of what they’re selling new now is cloud-based, from the database up through the application layer. It was definitely a company in transition, and they are pretty far down the path now.

Liz Herbert: They’re one of the largest technology suppliers, which sometimes means they’re not the fastest. They don’t necessarily take risks the way that some smaller and more startup-type companies do. For example, it’s well known that Oracle was a bit late to the overall cloud competition that we see looming large in today’s applications market, as well as platforms and infrastructure.

That said, when they invest, they go big. Something notable about Oracle is that, where they jump into a new area of customer demand, they put a significant amount of investment behind it. In fact, there’s something very unique about the company, which we haven’t talked much about yet. Because Larry Ellison owns such a substantial part of the overall company, they’re able to take decisions in a way that many other public companies of their size would not be able to. That’s had a strong influence on where they invest.

Cloud is an area where, though they were a bit late; they’re making significant investments. You can see that in the way they treat their salespeople, in the evolution of roles like customer success, as well as, of course, in the products showing where those investments are heading.

Then, similarly, we all see another wave of technology looming. Digital technologies like artificial intelligence and machine learning, automation, and Internet of Things. Oracle has been investing in that wave. Again, they weren’t the first, but they’ve certainly got deep pockets, and we see them put a lot of muscle behind that now.

Neil Ward-Dutton: On the platform side, Oracle’s strategy is defensive, but that’s not necessarily bad nor is it surprising. Its position and its strategy are fundamentally about realizing that the center of gravity for new investments, in the near future, is going to be cloud.

It’s all about being there when customers want them to be there, to minimize opportunities for customers to go anywhere else; to make sure they always have something that they can offer customers.

[Although] a defensive strategy, it makes sense when you consider that Oracle has 400,000 or 500,000 customers. It can do very a healthy business by just making sure that it takes its customers on the journey to the cloud and provides the services they need as they take that journey.

Also, Oracle has, for a long time, been pitching to mainstream, slightly conservative buyers. When Oracle talks about 6 Journeys to the Cloud, it’s really saying, “No matter how fast or how slow you want to go, we’ll be there for you, and we’ll hold your hand.” It is a trust and a safety message.

How has Oracle’s reputation among customers evolved?

Liz Herbert: I noticed a big shift this year at the Oracle OpenWorld event, putting that customer success story front and center, which is notable.

The shift to cloud means strategic partnering because, when you buy cloud, you’re not really buying features and functions. You’re buying a long-term partnership, in which you trust the vendor to invest in the features and functions you’re going to need in two years, three years, or four years. That’s a big shift from [on-premises software], when you would buy a large packaged software and use it for the next number of years, maybe doing upgrades here and there.

They’ve done a good job starting to change from a culture that made a lot of money selling software packages, that may cost tens of millions of dollars, to subscription-based or pay-as-you-go pricing, depending on what product you’re talking about. To do that, you need to renew deals, and you’re not going to do that if you’re not a [real] partner. That’s a market shift in their culture and in the types of roles that they’re prioritizing.

Neil Ward-Dutton: Oracle is changing its culture to focus on customer relationships and maintain those close relationships. Unless you have high renewal rates, you’re going to hurt yourself in the long-term.

We see a new wave of technologies enter people’s consciousness in leadership positions around AI, around robotics, around machine learning; all those things. Certainly, when you look at what it’s doing around chatbot-based channels, AI frameworks, machine learning, and even blockchain, it’s not just putting stickers on bits of paper. It’s pursuing these quite seriously and with quite a lot of thought. That’s an encouraging sign.

In the context of the platform business at OpenWorld, Oracle was not holding back. Oracle is pushing to go further than just good enough.I do think there’s a lot to be positive about.

Mike Fauscette: Technology in business [has become] much more of a competitive differentiator, a competitive advantage. Companies can leapfrog the competition by using technology and people in the right combinations.

We mentioned artificial intelligence, IoT, and blockchain. Those technologies are out there, and companies are starting to use them, but they don’t stand by themselves. They’re embedded in the digital infrastructure and platform and fiber of the company as it moves forward.

Having that [platform] is important. Customers may not use it yet, completely. They may not go there all the way yet, but they need to see that Oracle, Salesforce, or the others are a partner that is investing now and will continue to invest and evolve because technology and the use of that technology will evolve.

Digital business is a long journey, and businesses want a partner for that. I heard more this year than ever, from Oracle customers, that Oracle is stepping up to be that technology partner, as a part of the cultural and technical shift through which these customers are going.

Liz, say a few words about suite vs. best-of-breed software?

Liz Herbert: Oracle has made a significant investment towards cloud. Most of the core applications, like ERP, HCM, and CRM, supply chain, and other areas, they are now available in the cloud.

What’s notable about Oracle’s strategy as it relates to the applications moving to the cloud is they are a very comprehensive portfolio. While we might talk about giants in the cloud space, particularly pure plays, Oracle offers a very comprehensive suite available on the cloud.

A lot of the clients that I’m working with prefer fewer providers. Best-of-breed is certainly in fashion right now, but too much best-of-breed is a bad thing. There’s a cost of doing that regarding vendor management, overhead, and not getting great discounts because you’re buying small chunks of software from everybody.

CXOTalk brings together the most world’s top business and government leaders for in-depth conversations on digital disruption, AI, innovation, and related topics. Be sure to watch our many episodes! Disclosure: Oracle is consulting client.

(Cross-posted @ ZDNet | Beyond IT Failure Blog)

Data science: Feeding the all-seeing beast

Pervasive data science is becoming a core enabler of business innovation and competition. Given this importance, it’s worth examining the context of data science to consider its trajectory and future value.

In my view, primary challenges around data science for business leaders comes from three sources:

  1. Business people do not always understand the power and implications of what’s possible with data science and machine learning. The impact on business models, operations, and customers can be profound.
  2. Lack of available data to feed the machine learning beast. Without historical or other data, machine learning has little value. Aggregating useful data can be expensive and time-consuming.
  3. Insufficient talent and resources to create models and set up analyses that can best serve users and customers. Making effective use of data science demands a different kind of thought than traditional analytics; even a culture shift, so it’s hard for established organizations to adapt quickly.

On episode 259 of the CXOTalk series of conversations with innovators, I invited two experts to examine these issues and talk about data science in financial services and insurance.

The discussion included an outstanding exposition of the broader business context and value of data science, so we pulled it out as the short, standalone video embedded above.

My two guests for this episode are:

To learn more, check out the short video above and read the edited transcript below. You can also watch the entire episode and read the complete episode transcript over at the CXOTalk site.

Where does data science fit into the insurance industry?

Murli Buluswar: The core challenge for the insurance sector is similar to some of the financial services. In insurance, you’re trying to predict your cost of goods sold at the point of sale. Getting that right is absolutely critical in your ability to achieve margins down the road. Anything and everything that you can do to understand that at its core will give you a significant competitive advantage.

Machine intelligence is the collective experience of an institution manifested through data.

— Murli Buluswar

Now, if you zoom out from that problem statement, there are many similarities in insurance other industries around the role of data science and machine learning in augmenting human intelligence and making better decisions. More structured, granular, sophisticated, consistent decisions, in sales and marketing, as well as in pricing, underwriting, and in claims, which is a significant part of the fulfillment of the promise that insurance carriers make to their customers.

Michael Li: What we call data science today is part of a long history of the application of mathematics and computing to industry.

When I joined the industry, and I started my world in finance at Wall Street, back then we used to call these jobs quant roles. You would figure out how to trade in capital markets, make predictions about which way the stock price would move. I think what we’ve seen is that the tools and the technologies that we used there were then really adopted in Silicon Valley, really turbocharged, frankly made, actually, much more usable. Then the cost of computing made it so that you could apply this not just to a few select problems on Wall Street, but all over Main Street, all over the rest of the financial services industry.

Please elaborate on similarities of data science across insurance and other industries?

Murli Buluswar: The first big dissimilarity, so to speak, when comparing insurance to other sectors is that the role of the actuarial profession dates back to the early days when insurance was created as a sector. Analytics in insurance has largely been driven by the actuarial function, which brings nuanced competencies and capabilities that are relevant to insurance.

If you think about the broader role that data science could play today in insurance, you can fundamentally reshape human judgment when it comes to sales, when it comes to underwriting judgment. Even when it comes to claims through the lens of data and technology in ways that might not have been feasible 10, 12, 15 years ago.

Like many other sectors, in insurance, you’ve got a sales or distribution channel. You’ve got a product channel that is around pricing the product. Some of that is your cost of goods sold, and some of that is trying to understand the market’s appetite and the customers’ demands, or demand elasticity if you will.

Last, but not least, you’ve got the fulfillment of the promise of that very, very data rich. So, if you break down that value chain to its core elements, there are similarities to other sectors.

Now the difference could be that if you think about healthcare, for instance, healthcare is much more a transaction, data-rich industry perhaps compared to insurance. You’re engaging with customers on a very consistent basis, just as you are in financial services, in banking, and credit cards and such. The difference, perhaps, between insurance and these other sectors is you’re not necessarily as data-rich, as transaction-data rich, as other sectors. [However,] certainly getting your cost of goods sold right early on remains critical.

Michael Li: Right, but you see this with retail. You see this through the smartphone, and we were doing a lot of that when I was at Foursquare trying to make that retail brick and mortar experience a bit more digital through your smartphone. You see this all over the place.

I think that that’s going to be a major driver of consumer electronics; you’re going to see the need for companies to have data to drive interactions onto smartphones, tablets, and wearables.

What about the technical aspects?

Murli Buluswar: To build on what you just said, Michael, if you were to contextualize that to insurance, I see the big leap in innovation happening in the next two to three years around this notion of making more granular, real-time decisions with machine learning.

By defining data not just in the traditional internal structured terms, but thinking of it in four quadrants: internal / external on one dimension and structured / unstructured on the other dimension.

The ability to build machine learning algorithms on these platforms will reshape what humans do with decision-making and judgment and where models harmonize or balance human judgment with machine intelligence.

Often, people think of it as an either/or. But if you re-paraphrase machine intelligence as nothing but the collective experience of the institution manifested through data, it brings consistency and granularity to decision-making.

That’s not to say that it obviates the role of human judgment completely, but it is to say that that balance, that harmony should and will look dramatically different two years, three years from now than it has for the last decade and before that.

The next big step-change that I see for this sector as a whole is evolving from a predictor of risk to an actual risk partner that can mitigate outcomes through the power of real-time insights.

The most obvious example of that is the role that sensors can play in providing real-time feedback to drivers of vehicles in a way that hopefully reduces risky driving and mitigates the likelihood of accidents. To me, that is the true power of data science in insurance.

Not only does it mitigate accidents from happening, or adverse events from happening, but reduces the cost of insurance and expands the reach of protection to a much broader population, both in the developed and developing worlds. To me, that’s a beautiful thing if you think about society having a much higher level of financial protection across every aspect of our lives.

Michael Li: Think about what’s new in data science, that is, why is data science different from or how does data science expand upon things like the actuarial tradition, like statisticians, the quants of yore.

We’re no longer just using structured data, so it’s not just SQL queries anymore. It’s now semi-structured and unstructured data. How do you start handling things when they don’t come in nice tables that you can load into Excel or that you can put into SQL?

We are also in a world where data is much larger. You mentioned telematics. If you took a reading from every car every second, that’s a lot of numbers you’ve got to store, and that’s a very different paradigm for computation. You have to think about storing this data. How do you deal with data now that it’s stored across multiple computers? How do you think about computation in that context?

Then, of course, the last thing is real-time data. Analytics has historically been — you might call it — a batch process. Run it once; generate a report; show it to people; you’re done.

Now it’s a continuous process. You run it; you have to instantly find the latest trends; put that into production so you can intelligently adapt; and then do it again the next hour, the next minute. That’s where competition is driving you.

If you look at what Silicon Valley has been doing, it is very much your server is constantly learning from user behavior and adjusts how it interacts with users in a way that — to borrow an expression — delights the user. I think that we see that.

Traditional companies, non-tech-based companies, have to emulate that kind of level of customer service and satisfaction. A lot of that comes down to big data and having a team capable of understanding new and different kinds of data in a world that’s rapidly evolving.

Murli Buluswar: If you think about the historical definition of transactional data in healthcare and banking, we know that that’s been at the core of how they think about analytics. Traditionally, insurance has not had that version.

If you zoom out and define data in a much broader sense that includes images, audio, all sorts of unstructured data: insurance then has its own version, layered on top with IoT and such. Insurance has its own version of transactional data. The ability to harness that and dramatically change the cycle time of decision-making, as well as the granularity of decision-making, is where the goldmine is for insurance in the coming five years or so.

Share your advice on building a data science culture?

Michael Li: Two basic, first steps. First, get the data, collect it, [and] store it, what have you. Second, find the talent that’s necessary to deal with the data, manipulate the data, and come up with actionable insights from that data. If you can do both of those things, you will at least take the first few steps in the direction of building a data-driven culture.

CXOTalk brings together the world’s top business leaders for in-depth conversations on AI and innovation. Thumbnail image Creative Commons from FreeVector. com.

(Cross-posted @ ZDNet | Beyond IT Failure Blog)