you
yeah good afternoon luckily luckily
there was a video in between Mike and
myself because that was I’m actually in
love with my Apple watch
it lets me not lose any call from my
wife I work for GE and we produce
locomotives as well as many other things
but we definitely do that and I’m going
to talk to you about what is the beauty
in between a frontier that is blurring
in between human beings and machines and
how they might turn into a positive spin
so yeah I’ll just try to give us that
honor I’m a CEO so chief information
officer what am I supposed to be doing
in 2017 that’s a fundamental question
that I have in front of me and then I’m
trying every single day to give the
right answer to Curley yes and my help
me so bit of a calibration here I’m not
a baby boomer there are a few baby baby
boomers in the room majority is Gen Y or
gen said but Gen X the one that I belong
to is the one that actually went through
this 120 yeah 1990 10 years there was
the moment where internet was growing we
were actually focusing more in the
availability of the information their
basic form there was not a lot of
research on user experience and actually
applications that that time sucked I
don’t know if anybody if you went
through Archie or gopher for University
kind of research the most far item from
the wiki that was mentioned before
anyhow that that is what was happening
what was 80 doing at the time well we
were connecting cables that’s in essence
what we were doing so we were plugging
computers making sure that telephone
worked the communication was there that
was in essence what falls under the name
of the ICT connecting cables ensuring
that communication was there one big
fear was the 2000 year there was a
change in the format of the date nobody
really knew what was about to happen but
it went well and there was a moment
where we started having a bit of
outsourcing so we decided to commoditize
as much as possible many of the IT
services and we all source that to
external suppliers and that was me I was
at the University we were studying logic
at the time going through
my PhD and thinking about yeah the
wonderful future the bright future in
front of me
ten years after internet was becoming
definitely more pervasive what it was
kind of everywhere and interestingly
enough consumers technology was getting
into the space and starting influencing
the way enterprises worked there was
also the time where cybersecurity was
getting a pretty high level in our
attention that was a time where we
started thinking about this
pervasiveness of the connectivity might
generate some issues if anybody tried
that and so cybersecurity became
absolutely popular there was also the
time where we started having some
initial and preliminary sort of
regulation about IT socks4 for came out
and IT was not really itne what was now
ICT anymore we thought we were capable
to analyze big processes and that’s the
moment we were started thinking about
big earpiece so Oracle si PE all of
those we thought that managing the
information was much more important than
connecting cables and they drove the
change from IT ICT into I am and guess
what that was me I was sitting in the
back room doing some sort of security
role and yeah no need whatsoever to be
reasonably well dressed
long beard nobody was looking at my fish
face so yep that’s how it worked ten
years it’s hard to recap everything in
just one page but I’m I’m just trying
ten years after so now what am i doing
yep Internet is everywhere with the pros
and the cons that comes with social
computing so the ability of mix and
match the behavior of the machine with
your social emotion and social behavior
is absolutely there and many systems are
out just to do that technology wise
cloud is there nobody is thinking about
anymore to build physical data center on
premise we just go cloud it costs less
it works better except it cetera
there’s virtualization we’re not
building any more physical systems
there’s a huge amount of information
that is out there there’s data is
probably the resource that we are not
missing at all there’s good data they
respect data but big data is absolutely
their analysis of the data and getting
into the analytics getting insights out
of the data is becoming one of the most
essential work that we need to do on a
daily basis there’s this idea of having
this pervasiveness of the connectivity
so that internet is really everywhere
there’s not a single device anymore that
is not talking again my Apple watch has
one bulb or whatever you guys have in
mind is capable to talk it’s just a
matter of putting that into the network
getting the information out of it and
use them cyber security is paramount now
in it’s a different cybersecurity with
respect to the one that we’re used to
and just one example to make this even
clearer if you think about car stealers
the car stealer that we’re not really
used to is the one that goes close to
the car break the glass get into the car
and drives the car somewhere else where
I know some sort of recycle can happen
there’s not anymore that particular way
so the true car stealer the most
powerful class car stealer that is in
front of us is a is a good hacker is
someone that can get into the machine
from remote and get the machine driving
on its own there’s the place where the
machine can be reused in a different
manner so it’s a different level of
cyber security that there has to be
taken into consideration and again usual
picture that’s just me so that’s the way
I am that’s why I probably dress the
same way I guess but anyhow that’s
that’s what I’m doing now now moving
forward there are three components that
I want to drive you through number one
the data how important that is how
available that is in a few examples
about big companies that are making out
of the data their business so I think
you know them all it’s interesting to
see the way the biggest real estate in
hotel company is made of they own no
single flat or apartment that the the
rent
they’re just managing the information
flow they’re just
putting you in touch with the guy that
has that particular Flair that fits your
needs in that particular city that
particular wick in that company is just
worth 20 billions 20 billions it’s just
huge it’s the number that we never will
never have you done you know in our
hands second example is Apple it’s a
it’s a producer of many different things
and the majority the majority of us
thinks Apple is making money out of the
hardware they produced this wonderful
phone iPod computer solo than to do this
in China it costs not a lot of money but
it’s definitely get sold for the value
that the brand comes with the reality is
that the biggest chunk of the money
Apple is making comes not from the
hardware that they produce but from the
app from the application economy that
they set up so they created this
ecosystem where applications can be
exposed and bought by by the users and
the beauty is that they produce just a
few of them the majority of the
applications that are out in the Apple
market is just something that someone
else is creating and posting there they
just put some controls on it to be sure
that they are not the buggy but they’re
not making them all again it’s a 18
billion those two combined do not even
get to the volume that you burr is
making that’s a 50 plus and again the
beauty is that they own no car there’s
not a single car that is owned by uber
but the what they do is a again putting
you in contact with a guy that can drive
you to that particular place of a
particular time and the user experience
is just gorgeous it’s just wonderful so
data is there and data will be there
even more and like it or not this is
what its gonna happen there’s not a
single device that is not going to be
equipped with that ability to speak and
talk it goes from meters it goes to
lightbulbs it goes to yeah industrial
equipment and again pros and cons one
easy pros is think about the saving that
you can get by having a city where the
lights pop up when the
in it but I guess there’s nobody that is
passing through that particular road
you’re just off that’s a huge saving but
at the same time it ensures that if
there’s anybody there lights are on and
that clearly reduces the possibility of
a car crash so who’s gonna win in this
pace
clearly the company that will be able to
master the data to the highest level
possible and the company that will be
able to get the most effective insights
out of the data so one example a couple
examples that come from the company that
I work for think about willing be
willing to produce power for a community
for a city for whomever is gonna consume
it and think about the feather you might
be mixing and matching different sources
of power so electricity can come from
the Sun can come from the wind can come
from the seat I can come from many
different sources and think about having
the possibility to not only know how the
pitman work but also know how the
weather is going to be in the I don’t
know coming to of hours and based on
that you know that there’s gonna wind
there’s gonna be wind and the Sun is not
going to be there there is going to be
cloud and use that information to tune
which are the machines that you’re going
to use so that you will produce in the
right amount of energy that is needed
you’re not produce anything more you
will not have to stalk you have
inventory of electricity and you’ll
pollute less I think it’s probably it’s
a good example of what information can
be used for example number two this is a
this is an aircraft engine so again we
produce but the example actually applies
to your car or to your motorbike or your
cycle whatever and at least it
definitely applies to me if my car is
able to tell me yeah you better go to
the to the center to the repair center
because there’s something that needs to
happen needs to be done on the on the
engine or you better go there because
the oil is dirty you need to change it
or maybe the pressure of a flat of a
yeah of a
year is reducing so I don’t know if it
is flat or not but it can be flat in
white so you better go again knowing
what is the remaining life of each each
and every individual part of your engine
drives a better maintenance cycle and
clearly reduce the costs out of it
in the efficiency gravel now all of this
combined just for one just one second
would you bet on the feather this gives
you one percent productivity increase
whatever the productivity is might be
cast out might be efficiency gain might
be less pollution might be whatever you
want so 1% I think everybody would bet
on it it’s not it’s not a huge
percentage you probably can get there
but if you use that one percent on the
volumes that we are talking about and I
just focus on one the the airline fuel
consumption that 1% is a huge amount so
airline fuel consumption 1% less that’s
what they starts into it’s a thirty
billion saving on a nearly basis so
again small isn’t something becomes a
huge cost our efficiency gain it clearly
puts you in a much better shape and much
better position with respect to the
competition so item number one use of
the data
I don’t number two machines machines
assets so whatever we have in our hands
so few prediction here Gartner is a is a
consultancy firm that is fairly famous
in the IT space and they have this idea
of making forecasts and there’s two
forecast on this page one is for
tomorrow and the other one is for the
day after tomorrow so extremely close to
us in what you see in the two is that
there’s a there’s a percentage that is
changing in between the ratio between
human beings and machines and
particularly what you what you what you
see happening tomorrow 2018 that the
percentage of human beings that are
going to be managing human beings is
going to reduce and you’ll see more and
more human beings whose boss’s image
or a process interesting interesting the
fact that information that can be used
to drive efficiency in the human beings
goes through those equipment that can
talk
Fitbit as one but I mean healthy devices
health devices that can be where is
actually something that will be more and
more popular the one that I want to
focus on is in 2020
what Gartner says is that the majority
of the information that is going to be
in our hands is going to be used to in a
way change or reduce or destroy
processing products that have been built
in the last ten years so in two years
from now we’ll use a majority of the
information to change the way we have
been working the last 10 years that’s
massive change that’s a massive change
that means that the cycle time for
products and process gets shorter and
shorter and again those those changes
would definitely change the way big
enterprises work it is not anything that
is just in gardeners mind and being
where we are I thought I had to come
with some sort of academic reference
that you guys can go through so 2013
it’s well-established work it goes back
three years and this is in essence well
the article says mm-hmm
it is based on the US market but it’s
probably applicable everywhere there’s a
decent chunk there’s a 47% of companies
within the u.s. that sees high
probability in automation in particular
working spaces and segments and so the
way you have to read it left to right I
well low probability up too high in the
band the color bands actually tells you
which are the segments of the activity
industrial activity that is becoming
more and more that can become more and
more automated and you see office and
administration
finances there but also services so the
way you’ll be able to keep your
equipment running is going to be
automatic smore and more and sales
commercials I mean having information
about what customers want is clearly
going to drive the way that particular
guys will work so there’s a high
probability that this happens being
particularly Eggman’s there’s an impact
on jobs there’s an impact on jobs and
what we see happening what we’ll see
happening in the in the coming future is
that the machines will drive behavior of
the human beings more and more and the
machine will tell if one activity has to
be performing one way or in another and
more and more the concept of the
knowledge worker is changing and the
beauty will have more time and we’ll use
that time in added value activity so
everything that is a routine airy kind
of activity is going to go this
particular way human beings are going to
be used for much more added value
contribution Third Point so item number
one data item number two assets and
machines
I don’t number three is people so what
was he happening more and more is that
this idea of the consumer technology
getting into the enterprise is becoming
absolutely popular there’s nobody that
wants to work in a company that is
working different with respect to the
consumer stuff that they use at home so
being the social computing being this
wonderful computers being this immediate
answer to the request we need to have a
company that adapts to what the consumer
technology looks like so this digital
DNA is getting into the big enterprises
and actually or is is forcing a change
there how does that reflect onto the IT
well IT is probably within a company
it’s a best position function to drive
this change because we know machines we
know technology in that’s my view IT
guys are usually well equipped from a
brain standpoint so the point is we need
to be pervasive we need to know we need
to have intimacy with our with our with
our customers and using that we can
actually drive a proud
strategy more than we did in the past so
what is what is the goal for a cio what
is the goal for a CEO in 2017 we talk
about data so we need to have in Nice
and work in ecosystem to grab
information whatever the information
comes from and that’s the Internet of
Things platform you can pick I mean
there are many out there we produce one
but there’s many out we need to collect
the information we need to have the
information store effectively in what we
call data lay cloud there many mantra
words that you can use but information
has to be stored in a useable manner has
to be achievable and understandable
number three you need to start
developing analytics and that is going
to give you the right insights about how
to use effectively that particular data
number four I would have made as much as
possible now with this for recipe well
with this recipe metaphor items what is
coming out is a new professional role
that is what it’s called
chief automation officer that is
probably what we need to be effective in
using the data now back in back to the
original question so having said that
there’s a there’s probably the fourth
industrial revolution that is happening
there is going to be based on data and
data use is going to be based on people
it’s going to be based on asset
automation is going to actually mix and
match all of those the goal of a CIO in
2017 is actually vanished
thank you
yeah good afternoon luckily luckily
there was a video in between Mike and
myself because that was I’m actually in
love with my Apple watch
it lets me not lose any call from my
wife I work for GE and we produce
locomotives as well as many other things
but we definitely do that and I’m going
to talk to you about what is the beauty
in between a frontier that is blurring
in between human beings and machines and
how they might turn into a positive spin
so yeah I’ll just try to give us that
honor I’m a CEO so chief information
officer what am I supposed to be doing
in 2017 that’s a fundamental question
that I have in front of me and then I’m
trying every single day to give the
right answer to Curley yes and my help
me so bit of a calibration here I’m not
a baby boomer there are a few baby baby
boomers in the room majority is Gen Y or
gen said but Gen X the one that I belong
to is the one that actually went through
this 120 yeah 1990 10 years there was
the moment where internet was growing we
were actually focusing more in the
availability of the information their
basic form there was not a lot of
research on user experience and actually
applications that that time sucked I
don’t know if anybody if you went
through Archie or gopher for University
kind of research the most far item from
the wiki that was mentioned before
anyhow that that is what was happening
what was 80 doing at the time well we
were connecting cables that’s in essence
what we were doing so we were plugging
computers making sure that telephone
worked the communication was there that
was in essence what falls under the name
of the ICT connecting cables ensuring
that communication was there one big
fear was the 2000 year there was a
change in the format of the date nobody
really knew what was about to happen but
it went well and there was a moment
where we started having a bit of
outsourcing so we decided to commoditize
as much as possible many of the IT
services and we all source that to
external suppliers and that was me I was
at the University we were studying logic
at the time going through
my PhD and thinking about yeah the
wonderful future the bright future in
front of me
ten years after internet was becoming
definitely more pervasive what it was
kind of everywhere and interestingly
enough consumers technology was getting
into the space and starting influencing
the way enterprises worked there was
also the time where cybersecurity was
getting a pretty high level in our
attention that was a time where we
started thinking about this
pervasiveness of the connectivity might
generate some issues if anybody tried
that and so cybersecurity became
absolutely popular there was also the
time where we started having some
initial and preliminary sort of
regulation about IT socks4 for came out
and IT was not really itne what was now
ICT anymore we thought we were capable
to analyze big processes and that’s the
moment we were started thinking about
big earpiece so Oracle si PE all of
those we thought that managing the
information was much more important than
connecting cables and they drove the
change from IT ICT into I am and guess
what that was me I was sitting in the
back room doing some sort of security
role and yeah no need whatsoever to be
reasonably well dressed
long beard nobody was looking at my fish
face so yep that’s how it worked ten
years it’s hard to recap everything in
just one page but I’m I’m just trying
ten years after so now what am i doing
yep Internet is everywhere with the pros
and the cons that comes with social
computing so the ability of mix and
match the behavior of the machine with
your social emotion and social behavior
is absolutely there and many systems are
out just to do that technology wise
cloud is there nobody is thinking about
anymore to build physical data center on
premise we just go cloud it costs less
it works better except it cetera
there’s virtualization we’re not
building any more physical systems
there’s a huge amount of information
that is out there there’s data is
probably the resource that we are not
missing at all there’s good data they
respect data but big data is absolutely
their analysis of the data and getting
into the analytics getting insights out
of the data is becoming one of the most
essential work that we need to do on a
daily basis there’s this idea of having
this pervasiveness of the connectivity
so that internet is really everywhere
there’s not a single device anymore that
is not talking again my Apple watch has
one bulb or whatever you guys have in
mind is capable to talk it’s just a
matter of putting that into the network
getting the information out of it and
use them cyber security is paramount now
in it’s a different cybersecurity with
respect to the one that we’re used to
and just one example to make this even
clearer if you think about car stealers
the car stealer that we’re not really
used to is the one that goes close to
the car break the glass get into the car
and drives the car somewhere else where
I know some sort of recycle can happen
there’s not anymore that particular way
so the true car stealer the most
powerful class car stealer that is in
front of us is a is a good hacker is
someone that can get into the machine
from remote and get the machine driving
on its own there’s the place where the
machine can be reused in a different
manner so it’s a different level of
cyber security that there has to be
taken into consideration and again usual
picture that’s just me so that’s the way
I am that’s why I probably dress the
same way I guess but anyhow that’s
that’s what I’m doing now now moving
forward there are three components that
I want to drive you through number one
the data how important that is how
available that is in a few examples
about big companies that are making out
of the data their business so I think
you know them all it’s interesting to
see the way the biggest real estate in
hotel company is made of they own no
single flat or apartment that the the
rent
they’re just managing the information
flow they’re just
putting you in touch with the guy that
has that particular Flair that fits your
needs in that particular city that
particular wick in that company is just
worth 20 billions 20 billions it’s just
huge it’s the number that we never will
never have you done you know in our
hands second example is Apple it’s a
it’s a producer of many different things
and the majority the majority of us
thinks Apple is making money out of the
hardware they produced this wonderful
phone iPod computer solo than to do this
in China it costs not a lot of money but
it’s definitely get sold for the value
that the brand comes with the reality is
that the biggest chunk of the money
Apple is making comes not from the
hardware that they produce but from the
app from the application economy that
they set up so they created this
ecosystem where applications can be
exposed and bought by by the users and
the beauty is that they produce just a
few of them the majority of the
applications that are out in the Apple
market is just something that someone
else is creating and posting there they
just put some controls on it to be sure
that they are not the buggy but they’re
not making them all again it’s a 18
billion those two combined do not even
get to the volume that you burr is
making that’s a 50 plus and again the
beauty is that they own no car there’s
not a single car that is owned by uber
but the what they do is a again putting
you in contact with a guy that can drive
you to that particular place of a
particular time and the user experience
is just gorgeous it’s just wonderful so
data is there and data will be there
even more and like it or not this is
what its gonna happen there’s not a
single device that is not going to be
equipped with that ability to speak and
talk it goes from meters it goes to
lightbulbs it goes to yeah industrial
equipment and again pros and cons one
easy pros is think about the saving that
you can get by having a city where the
lights pop up when the
in it but I guess there’s nobody that is
passing through that particular road
you’re just off that’s a huge saving but
at the same time it ensures that if
there’s anybody there lights are on and
that clearly reduces the possibility of
a car crash so who’s gonna win in this
pace
clearly the company that will be able to
master the data to the highest level
possible and the company that will be
able to get the most effective insights
out of the data so one example a couple
examples that come from the company that
I work for think about willing be
willing to produce power for a community
for a city for whomever is gonna consume
it and think about the feather you might
be mixing and matching different sources
of power so electricity can come from
the Sun can come from the wind can come
from the seat I can come from many
different sources and think about having
the possibility to not only know how the
pitman work but also know how the
weather is going to be in the I don’t
know coming to of hours and based on
that you know that there’s gonna wind
there’s gonna be wind and the Sun is not
going to be there there is going to be
cloud and use that information to tune
which are the machines that you’re going
to use so that you will produce in the
right amount of energy that is needed
you’re not produce anything more you
will not have to stalk you have
inventory of electricity and you’ll
pollute less I think it’s probably it’s
a good example of what information can
be used for example number two this is a
this is an aircraft engine so again we
produce but the example actually applies
to your car or to your motorbike or your
cycle whatever and at least it
definitely applies to me if my car is
able to tell me yeah you better go to
the to the center to the repair center
because there’s something that needs to
happen needs to be done on the on the
engine or you better go there because
the oil is dirty you need to change it
or maybe the pressure of a flat of a
yeah of a
year is reducing so I don’t know if it
is flat or not but it can be flat in
white so you better go again knowing
what is the remaining life of each each
and every individual part of your engine
drives a better maintenance cycle and
clearly reduce the costs out of it
in the efficiency gravel now all of this
combined just for one just one second
would you bet on the feather this gives
you one percent productivity increase
whatever the productivity is might be
cast out might be efficiency gain might
be less pollution might be whatever you
want so 1% I think everybody would bet
on it it’s not it’s not a huge
percentage you probably can get there
but if you use that one percent on the
volumes that we are talking about and I
just focus on one the the airline fuel
consumption that 1% is a huge amount so
airline fuel consumption 1% less that’s
what they starts into it’s a thirty
billion saving on a nearly basis so
again small isn’t something becomes a
huge cost our efficiency gain it clearly
puts you in a much better shape and much
better position with respect to the
competition so item number one use of
the data
I don’t number two machines machines
assets so whatever we have in our hands
so few prediction here Gartner is a is a
consultancy firm that is fairly famous
in the IT space and they have this idea
of making forecasts and there’s two
forecast on this page one is for
tomorrow and the other one is for the
day after tomorrow so extremely close to
us in what you see in the two is that
there’s a there’s a percentage that is
changing in between the ratio between
human beings and machines and
particularly what you what you what you
see happening tomorrow 2018 that the
percentage of human beings that are
going to be managing human beings is
going to reduce and you’ll see more and
more human beings whose boss’s image
or a process interesting interesting the
fact that information that can be used
to drive efficiency in the human beings
goes through those equipment that can
talk
Fitbit as one but I mean healthy devices
health devices that can be where is
actually something that will be more and
more popular the one that I want to
focus on is in 2020
what Gartner says is that the majority
of the information that is going to be
in our hands is going to be used to in a
way change or reduce or destroy
processing products that have been built
in the last ten years so in two years
from now we’ll use a majority of the
information to change the way we have
been working the last 10 years that’s
massive change that’s a massive change
that means that the cycle time for
products and process gets shorter and
shorter and again those those changes
would definitely change the way big
enterprises work it is not anything that
is just in gardeners mind and being
where we are I thought I had to come
with some sort of academic reference
that you guys can go through so 2013
it’s well-established work it goes back
three years and this is in essence well
the article says mm-hmm
it is based on the US market but it’s
probably applicable everywhere there’s a
decent chunk there’s a 47% of companies
within the u.s. that sees high
probability in automation in particular
working spaces and segments and so the
way you have to read it left to right I
well low probability up too high in the
band the color bands actually tells you
which are the segments of the activity
industrial activity that is becoming
more and more that can become more and
more automated and you see office and
administration
finances there but also services so the
way you’ll be able to keep your
equipment running is going to be
automatic smore and more and sales
commercials I mean having information
about what customers want is clearly
going to drive the way that particular
guys will work so there’s a high
probability that this happens being
particularly Eggman’s there’s an impact
on jobs there’s an impact on jobs and
what we see happening what we’ll see
happening in the in the coming future is
that the machines will drive behavior of
the human beings more and more and the
machine will tell if one activity has to
be performing one way or in another and
more and more the concept of the
knowledge worker is changing and the
beauty will have more time and we’ll use
that time in added value activity so
everything that is a routine airy kind
of activity is going to go this
particular way human beings are going to
be used for much more added value
contribution Third Point so item number
one data item number two assets and
machines
I don’t number three is people so what
was he happening more and more is that
this idea of the consumer technology
getting into the enterprise is becoming
absolutely popular there’s nobody that
wants to work in a company that is
working different with respect to the
consumer stuff that they use at home so
being the social computing being this
wonderful computers being this immediate
answer to the request we need to have a
company that adapts to what the consumer
technology looks like so this digital
DNA is getting into the big enterprises
and actually or is is forcing a change
there how does that reflect onto the IT
well IT is probably within a company
it’s a best position function to drive
this change because we know machines we
know technology in that’s my view IT
guys are usually well equipped from a
brain standpoint so the point is we need
to be pervasive we need to know we need
to have intimacy with our with our with
our customers and using that we can
actually drive a proud
strategy more than we did in the past so
what is what is the goal for a cio what
is the goal for a CEO in 2017 we talk
about data so we need to have in Nice
and work in ecosystem to grab
information whatever the information
comes from and that’s the Internet of
Things platform you can pick I mean
there are many out there we produce one
but there’s many out we need to collect
the information we need to have the
information store effectively in what we
call data lay cloud there many mantra
words that you can use but information
has to be stored in a useable manner has
to be achievable and understandable
number three you need to start
developing analytics and that is going
to give you the right insights about how
to use effectively that particular data
number four I would have made as much as
possible now with this for recipe well
with this recipe metaphor items what is
coming out is a new professional role
that is what it’s called
chief automation officer that is
probably what we need to be effective in
using the data now back in back to the
original question so having said that
there’s a there’s probably the fourth
industrial revolution that is happening
there is going to be based on data and
data use is going to be based on people
it’s going to be based on asset
automation is going to actually mix and
match all of those the goal of a CIO in
2017 is actually vanished
thank you
Please follow and like us: