Technology Vision 2015—
Outcome Economy
Podcast Transcript
ELISE: Welcome to the Accenture
Technology Innovation Podcast. I’m
Elise Cornille and I’m joined here
today with Steven Tiell, who’s one of
the authors of the Accenture
Technology Vision’s 2015 trends.
We just released these in the
market, and I’m excited to have you
here, Steven.
STEVEN: Thank you, Elise, it’s good
to be here.
ELISE: Thanks. So your trend is
about the outcome economy. What
is this? Talk to me about what you
mean when you say outcome
economy.
STEVEN: That’s a great question,
and we’ve seen it bantered around
for quite a number of years. I think
one of the first instances was a
professor at Harvard who talked
about drills, and people don’t really
want drills, they want holes in
things, and that’s the outcome that
they’re looking for, and we’re seeing
the same thing in businesses. And
what businesses want is not to buy
another router or switch, they want
to be able to count how many
people are connected to their
network, where they’re at within
their store and so forth, they’re
looking for those outcomes.
ELISE: So what’s driving this? You
know, as you say, I remember when
you and I first talked about this trend
months and months ago I said oh,
Steven, outcomes, we’ve been
talking about outcomes for years,
you know, what’s different about
what we’re saying now, that’s what
we were saying about the drill years
ago. What’s changed?
STEVEN: Well, technology and its
processing capabilities are moving
further and further toward the edge,
closer to where customers are
actually engaging with the brand
and engaging with the real world.
And we’re seeing that same ability
kind of transform our capacity to do
metrics on all of that, so really
understand those measures of value
by which customers define success.
And in doing so, we find that we’re
able to measure those things close
to the customer by using hardware.
A lot of times this means sensors,
but other instances it can also mean,
you know, just having that feedback
loop, having that understanding of
what’s happening all the way at the
edge of your network, and knowing
how the customer’s using that
information to really build their brand
and understand their customers
better to really make that profit. And
so we see those customers
demanding the technology that gives
them the intelligence to do that.
ELISE: Talk a little bit more about
this idea of an edge of a network.
You know, when you say that I
visually, literally think almost a click.
Is that how we should be looking at
this?
STEVEN: Sort of, but that hard stop
is getting fuzzier. So Cisco calls it
fog computing, right, with that ability
to do processing at the edge of
networks. And what that means is
really, you know, every company has
the furthest extent at which its data
reaches. And if you think about it in
the context of that, it’s a lot easier to
understand. If I’m a, you know, small
bodega on the corner, my edge is
within my store, but if I’m a large
multinational company, my edge can
expand to every country in the world,
it can expand to customer’s mobile
devices, it can expand to machine
equipment, so we’re seeing the
industrial internet of things being
mechanized, we’re starting to see
locomotives have sensors in them
that are reporting back to a central
control that are getting more rich
information than what they ever had
before. And that same is true of
heavy machinery, the same is true,
you know, last year’s Vision we
talked about GE’s jet engines and
their ability to stream, basically tweet
information on how they’re doing in
real time so that mechanics on the
ground have that information.
Where you’re moving that edge of
your network to, GE never had
sensors in their jet engines before,
and then they added those, and
their edge expanded and their ability
of what they could do as a company
with that expanded edge and the
intelligence on the edge really gives
them advantages that are leaps and
bounds above where they were
before.
ELISE: There’s a lot of good
examples in your chapter that you
wrote, and one of the most
compelling, I love is how you kicked
off the chapter, and I’d love for you
to talk to us a little bit more about
this favorite line. How much would
you pay to laugh out loud? So I’m
not going to read it here, but give us
this example. I mean, I think it’s a
real human example of what
outcome is, how much would you
pay to laugh out loud?
STEVEN: The tough thing is really
getting the outcome economy
message through. And I think that
example does a really terrific job of
saying, look, if you’re going to laugh,
there’s a value associated with that,
and if you’re coming to my comedy
club, you know, you’re coming here
to laugh, you’re not coming here to
have a miserable time. And if you
have a miserable time, as the owner
of the club, I don’t want your money.
I only want your money if I’m giving
you that outcome that you’re looking
for, which is, laughter and having a
fun time. And so if you do that, then
I want to earn more money, but I’m
going to put it upward bound
because I still want to have a good
relationship with my customers. And
what they found is that ticket sales
and their revenue actually went up
quite a bit –
ELISE: ‘Cause they promised us,
right? Like this was what they were
going to deliver, it wasn’t just come
to our show, it was come and laugh,
correct?
STEVEN: That’s a great point. So
it’s, you know, you’re going to come
here, you’re going to have a good
time, and if you don’t, you’re not
going to owe us anything, and if you
do, we’ll charge you, accordingly.
And the public seemed receptive to
that.
ELISE: It’s just such a really
resonate example for me. When we
talk about outcomes, you know, I
think everybody intuitively
understands what they want to do it,
but the hard thing is figuring out
what those are, what are you going
to hang your hat on so to say, and
how are you going to get there. So
what’s an example of someone
doing this well?
STEVEN: Well, really, in the digital
business era there’s just a plethora
of companies we could use as
examples. I actually like to pull up a
level and look at kind of what are
the company ecosystems that we
can talk about? And so a great one
is agriculture, right? For years and
years we’ve seen a lot of
aggregation in that industry, the big
chemical giants having a lot of
influence, and they’re now able to
use that influence to build systems
that are able to not just predict what
the weather is going to be, but
actually use that information to tell
growers what type of crops are
going to be more profitable at
harvest time, get very finely tuned
insurance products that are tailor
made for each individual field, and
we can use the intelligence
throughout that supply chain, be it
sensors in the ground, sensors on
tractors, sensors from satellites, and
analytic capabilities, to really project
what the revenue is going to be at
harvest at the time when the grower
is actually planting seeds and
selecting the seeds to plant, which
is just miraculous.
ELISE: It’s like a new digital
Farmer’s Almanac, right? Like I don’t
want to throw away our copies of
that, but this is taking that
intelligence, like you said, up a level
and it’s actionable, as well. One of
the interesting things, too, that
occurs to me, especially in that
example, we’re talking about some
major impacts for the world.
Agriculture tells – we’re talking about
crop yields, we’re talking about
better quality, we’re talking ability to
feed more and more people. Beyond
the agriculture example, what other
kind of outcomes do you see people
staring to align around?
come in that were exposed to that
problem, have the maintenance
done, and it was a no hassle
situation, and safety was
paramount. Their ability to have
end-to-end control over the vehicles
that they sell has ushered in a new
level of safety.
The same is true in digital health. So
if we look at a healthcare provider,
Proteus Digital Health, and this is
one of my favorite examples
because their technology is just so
elegant. They have a microchip
that’s about the size of a half a grain
of rice that’s fully inert, so it just
passes through the body. And what
STEVEN: That’s a great question.
it can do is help patients and payers
Again, pulling up a level, if we look at and providers understand the full
a couple of industries, let’s take
adherence transparency, so our
automotive and healthcare. If you’re patients are actually taking
an automotive company, you can
medications when they’re supposed
say I want to maximize safety of my to be. And what they found is that
drivers. The way that that can be
the outcomes to patients increased
accomplished today through digital
significantly, and patients are having
technologies and looking at
healthier lives, payers are paying
optimizing for that outcome, a great
less money for the care because
example is Tesla Motors and what
they’re able to track compliance, so
they were able to do. They had two
if someone’s saying, oh, that drug
instances where batteries would
regime isn’t working on me, I can
catch on fire on freeways, huge
actually go back as a physician and
safety issue. And what they
say, oh, well, maybe it’s not working
uncovered through data analytics is, because you missed one every
basically, those were all cars with
couple of days or what not, so let’s
the air suspension system, the air
try to adhere to the schedule, let’s
suspension system was designed to go back and try it again –
lower at freeway speeds, and what
happened is that some road debris
ELISE: Versus going to a new
punctured the batteries causing a
medicine that may be more
fire, nobody had loss of life or
expensive?
serious injuries, but they wanted to
handle the problem really quick. And STEVEN: Exactly, exactly. And so in
so what they did was issue a new
vertically integrated healthcare
firmware release that didn’t allow the systems like Kaiser in the United
cars to go down as low, and they
States and National Institutes of
figured out another engineering
Health in the UK for instance, they
solution to it, which was improve
can really optimize the value that’s
undercarriage armor for the cars.
created from that type of technology.
And once they issued that, it wasn’t
even a recall, it was, we’re going to
ELISE: So last year in the vision we
put back the capabilities that were
said, hardware is back, and, oh, by
originally there, and please make an the way, it never really went away,
appointment to come in and get the
right? This year you’re taking that to
new undercarriage armor. A
a different level, and I’d like to first
beautiful way to handle it, and what
start off by asking you something
they saw was that people would
that you put forward in the chapter
as well, what exactly is meant by
hardware? So when we say
hardware, what are you talking
about here?
STEVEN: Yeah, good question,
especially with last year, and last
year we were talking about
hardware as hyper scale systems,
these are very large complex
systems which really five companies
in the world possess today. And
then we were talking about hyper
scale capabilities which appliances
allow everyone else to have access
to today so you can get those kind
of real time insights that are really
valuable to businesses. And what
we’re talking about this year with
hardware is that hardware at the
edge, it’s the small devices, it’s the
sensors, it’s the custom equipment,
it’s the wearable technologies –
ELISE: Internet of things?
STEVEN: Exactly, it’s the internet of
things, and it’s the smaller things
that offer those, that create the data
that those hyper scale systems are
then processing and adding to
create insights.
ELISE: And you write that
hardware’s now more approachable,
I think you also mean accessible,
and what’s behind that? Is this cost,
is this just more appetite for
hardware? You know, where – I ask
because the world is very dazzled
by software. We’ve had the phrasing
that software is eating the world,
etc., apps are all the rage, but
you’re putting a different thing here,
you’re saying pay attention to
hardware.
STEVEN: Absolutely. So many
years ago, Marc Andreessen,
founder of Netscape and now a
renowned venture capitalist, said,
hardware’s hard, and it’s very
descriptive of what it meant to
actually produce a piece of
hardware. I remember 10 years ago,
to produce a single remote control
was somewhere in the order of
magnitude of hundreds of thousands
of dollars to produce a single remote
control. And so by hardware
becoming more approachable, what
we mean there is, really, it’s
becoming cheaper, it’s becoming
easier to do, you know, 3D printing
gives us this great capability to do
rapid prototyping. You can make
sure something gets right before you
actually make a tool or dye to create
mass quantities of it. And we also
see, you know, prices coming down
extraordinarily. So, for example,
Broadcom released the Wiced
Sense Development kit – it includes
five MEMS and MEMS are the micro
electro mechanical sensors. This is
basically kind of analog technology
taken digital, how you sense
something in the real world and turn
it into those bits that get analyzed
later. So they have five MEMS on
this thing. It’s a gyroscope,
accelerometer, pressure, humidity
and temperature sensor, all in one
package at a retail price of $20.00.
And we’re looking at opportunities
where you can put sensors in light
bulbs and each of those sensors are
less than twenty-five cents. You
know, when it gets to that kind of
price level, the ability to embed
those types of sensors in pretty
much anything is just abundant.
it’s also getting smaller, you can put
it in more places. It’s getting more
energy efficient than ever before, so
you can put it somewhere in a
product that needs to last two or five
years, and we’re seeing those
timelines extend pretty regularly.
You know, I’m hoping to see battery
capacity take on Moore’s law here
within the next couple years in
networks that don’t need to be
serviced or replaced on a regular
basis, so it’s cheap to maintain and
you get really rich and valuable data
out of that.
ELISE: So I want to ask another
question that is actually something
that’s a passion of both of ours, and
it’s related to the Vision overall and
also to this chapter. We talked a lot,
you know, about data ethics, you
know, and the importance and the
rising importance of this. You know,
can you give our listeners a little bit
of the sense of what our initial
thoughts are on this type of vision
and some of the things that you’re
exploring?
STEVEN: You know, data ethics is
becoming a conversation about risk.
And so many companies, you know,
now that we’re in the era of the
digital business, so many
companies are dependent upon
ELISE: Does this mean it’s a call to
massive amounts of data in order
action to enterprises to start buying
for their business to be profitable.
more hardware? We’ve gone
And so what we do with that data is
through different ways where there
really kind of critical. And so there’s
are different sourcing models for
a number of paths that we’re looking
whether it be talent or hardware or
at to explore further and provide
different kinds of services or
some recommendations to
products. Are we recommending that businesses about, and really how to
companies need to be investing in
manage that risk that they’re
more hardware in order to achieve
exposed to. And so this is
this outcome economy?
everything from kind of a universal
code of data ethics, something
STEVEN: Well, I think every situation that’s aspirational. We’re not going
is unique, and some answers will be, to be able to do all of the tenets of
yes, let’s make an investment in
the code today, but something that
developing our own hardware,
we can aspire to do. It’s about – we
others will be, let’s look at where we look at things from kind of three
can extend existing product lines
perspectives. You have data
and push our edge further out, and
acquisition, which is when someone
oftentimes that will mean hardware. shares it, when a sensor brings in
You know, it’s not just the fact that
data, and we have data sharing and
hardware’s getting cheaper,
analytics, this is whether I’m sharing
my data with a third party analytics
provider, or whether I’m providing
some value to that data as I hold
onto it before I get to that insight,
and then ultimately, how is that data
used, and whether it’s used to
provide a consumer with a coupon,
or whether it’s used to provide
business intelligence for how I’m
going to improve South American
operations for my international firm.
Those are really the three levels. So
the acquisition, the sharing and
analytics, and the use. And we want
to look at, you know, really, when is
it okay to share data? When is it
okay to sell data? What are the best
governance models that we have for
that? What is the common taxonomy
that we can use to talk about that
kind of stuff? There’s a whole slew
of things that we’re looking at. We
learn a lot from the medical
community in looking at things like
informed consent, and what does
that actually mean, what are the
implications of that consent when
you share the data and it could go to
all these other parties? Is there
metadata that we can attach that
actually carries forth that information
or the wishes of the discloser? Was
this obtained through public sector
use by sensors? Was this provided
by an individual? Is it personally
identifiable information and so forth?
But, ultimately, the concern that I
have for data ethics and proper care
is, if we look at a business context,
what’s the worst that can happen?
Maybe we disclose someone’s
information who didn’t want it to be
disclosed, and that is not right, but it
doesn’t result in life threatening
things, or, hopefully, it doesn’t. But if
we look at the humanitarian side of
things, if you’re in a war zone and
you have information about specific
people and they need to get disaster
supplies, or they need to get first
responder help, and so they
disclose their whereabouts and
that’s actually an enemy of the state,
that’s very valuable information and
it could lead to death. And so that’s
really what – that is the pinnacle of
what we care about, but how do we
do no harm through that data? And
through the humanitarian use is
really where you get those really
great use cases. And so we want
something that applies to both
humanitarian use of data and
corporate use of data, because,
ultimately, in some countries those
two will overlap and we want to
ensure people safety and security
and privacy throughout the entire
data supply chain.
ELISE: Thanks again for joining us.
It’s a pleasure to talk to you.
STEVEN: Oh, my pleasure. Thank
you.
ELISE: Well, thanks. Well thank you
all for tuning into our Accenture
Technology Innovation Podcast. We
will continue next time, moving onto
the next chapter within the
Technology Vision. In the meantime,
ELISE: So, Steven any final thoughts you can find our vision online at
as we close today’s session?
accenture.com/technologyvision.
Thanks for joining, we’ll talk to you
STEVEN: Well, I think, you know, to next time.
me, this is an exciting space, and it’s
really a refocus on the customer,
and what is the customer trying to
achieve? And in any kind of
organization, that’s always a good
exercise to go through. What are the
outcomes my customer wants? And
if I can deliver those outcomes, that’s
going to be a long-term customer for
me, and that’s really the big
message that we’re trying to
hammer home here.
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