Latest / Private Equity Data Guy / How Data Strategy Transforms Business Decision-Making w/ Nick White

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How Data Strategy Transforms Business Decision-Making w/ Nick White · Private Equity Data Guy

0:00
There's,
0:00
oh,
0:00
there's
0:00
fancy
0:01
app
0:01
people
0:02
and
0:02
then
0:02
there's,
0:03
hey,
0:03
data
0:04
people.
0:04
It's
0:04
like,
0:05
no,
0:05
you're,
0:05
you're
0:05
actually
0:06
building
0:06
an
0:06
experience
0:07
for
0:07
somebody.
0:08
So
0:08
be
0:09
certain
0:09
that
0:09
it's
0:09
right
0:10
and
0:10
explainable
0:11
and
0:11
be
0:11
certain
0:11
that
0:12
it
0:12
fits
0:12
in
0:12
and
0:13
it's
0:13
pleasant
0:13
to
0:14
use.
0:14
It's
0:15
important.
0:15
Saying
0:16
AI
0:16
hallucinates
0:17
is
0:17
a
0:17
really
0:18
easy
0:18
way
0:18
to,
0:19
you
0:19
know,
0:19
devolve
0:20
yourself
0:20
of
0:20
any
0:20
ownership.
0:21
Well,
0:21
that's
0:21
nothing
0:21
to
0:21
do
0:22
with
0:22
humanity
0:23
or
0:23
me.
0:23
Like,
0:23
it's
0:23
reflecting
0:24
us
0:24
back
0:24
at
0:25
us
0:25
and
0:25
it's,
0:25
it's,
0:26
you
0:26
know,
0:26
we
0:26
need
0:26
to
0:26
remember
0:27
that
0:27
sometimes
0:28
things
0:28
are
0:28
going.
0:28
To
0:29
push
0:29
to
0:29
the
0:29
end.
0:30
When
0:30
I
0:30
saw
0:30
what
0:31
the
0:31
power
0:31
of
0:31
what
0:31
AI
0:32
could
0:32
do
0:32
is
0:32
like,
0:33
data
0:34
people
0:34
are
0:34
either
0:34
going
0:35
to
0:35
have
0:35
to
0:35
be
0:35
hardcore
0:37
data
0:37
engineering,
0:38
like
0:39
doing
0:39
things
0:39
that
0:41
ETL
0:41
like
0:42
that
0:42
is
0:42
the
0:42
hard
0:42
work,
0:43
or
0:43
they
0:43
have
0:44
to
0:44
be
0:44
hardcore
0:45
business
0:46
facing.
0:47
There's
0:47
no
0:48
more
0:48
delicate
0:48
geniuses
0:49
allowed.
0:50
Scale
0:51
your
0:51
business
0:51
with
0:51
data.
0:52
A
0:52
podcast
0:53
where
0:53
masters,
0:54
virtuosos,
0:54
titans
0:55
and
0:55
leaders
0:56
reveal
0:56
how
0:56
they
0:56
turn
0:56
data
0:57
chaos
0:57
pimmic
0:58
into
0:58
powerful
0:59
scalable
0:59
insights.
1:00
No
1:01
theory,
1:01
no
1:02
jargon,
1:02
just
1:03
real
1:03
world
1:03
strategies
1:04
that
1:04
actually
1:04
work.
1:05
Whether
1:05
you're
1:05
managing
1:06
billions
1:06
in
1:06
marketing
1:07
spend
1:07
or
1:07
building
1:08
the
1:08
foundation
1:08
for
1:08
your
1:09
first
1:09
startup,
1:09
you're
1:10
in
1:10
the
1:10
right
1:10
place.
1:11
Discover
1:11
what's
1:12
truly
1:12
possible
1:12
with
1:13
the
1:13
right
1:13
skills,
1:14
leadership
1:14
and
1:14
approach.
1:15
Come
1:16
join
1:16
the
1:16
party
1:16
and
1:16
let's
1:17
scale
1:17
your
1:17
business
1:17
with
1:18
data.
1:19
All
1:19
right,
1:19
so
1:20
today
1:20
I'm
1:20
joined
1:20
by
1:21
Nick
1:21
White,
1:21
a
1:21
data
1:21
strategy
1:22
leader
1:22
with
1:22
over
1:22
20
1:23
years
1:23
of
1:23
experience
1:24
transforming
1:24
how
1:25
organizations
1:25
leverage
1:25
their
1:26
information
1:26
assets.
1:27
Nick's
1:27
impressive
1:28
career
1:28
has
1:28
taken
1:28
him
1:29
through
1:29
leadership
1:29
roles
1:29
at
1:30
companies
1:30
like
1:30
Nike,
1:31
where
1:31
he
1:31
revolutionized
1:32
their
1:32
approach
1:33
to
1:33
KPI
1:33
alignment,
1:34
and
1:34
Kinnan
1:34
Carta,
1:35
where
1:35
he
1:35
led
1:36
data
1:36
strategy
1:36
initiatives
1:37
across
1:37
multiple
1:38
industries.
1:39
What
1:39
makes
1:39
Nick's
1:40
perspective
1:40
particularly
1:41
valuable
1:41
is
1:42
his
1:42
ability
1:42
to
1:42
bridge
1:43
the
1:43
technical
1:43
aspects
1:44
of
1:44
data
1:44
with
1:44
the
1:44
human
1:45
elements
1:45
of
1:45
decision
1:46
making.
1:46
And
1:47
in
1:47
today's
1:47
conversation,
1:48
we're
1:48
going
1:48
to
1:48
explore
1:48
how
1:49
organizations
1:49
can
1:50
move
1:50
beyond
1:50
simply
1:51
collecting
1:51
data
1:52
to
1:52
actually
1:52
using
1:53
it
1:53
to
1:53
drive
1:53
meaningful
1:54
outcomes.
1:55
Also,
1:55
the
1:55
evolution
1:56
of
1:56
AI
1:56
implementation
1:57
and
1:57
Nick's
1:57
framework
1:58
for
1:58
decision
1:58
intelligence
1:59
that
1:59
helps
1:59
businesses
2:00
focus
2:00
on
2:00
what
2:01
truly
2:01
matters.
2:02
Nick,
2:02
welcome
2:03
to
2:03
the
2:03
show.
2:03
It's
2:03
great
2:03
to
2:03
have
2:03
you.
2:04
Oh,
2:04
thanks
2:04
for
2:05
those
2:05
dice
2:05
words.
2:05
Good
2:06
to
2:06
be
2:06
here.
2:06
Graham.
2:06
I
2:07
mentioned
2:07
there
2:07
in
2:07
the
2:07
intro,
2:07
like,
2:08
this
2:08
big
2:08
gap
2:08
between
2:09
collecting
2:10
data
2:10
and
2:10
making
2:11
it
2:11
useful.
2:12
So
2:13
just
2:13
how
2:14
big
2:14
is
2:14
that
2:14
gap
2:14
and
2:14
how
2:15
much
2:15
is
2:15
in
2:15
between
2:15
it?
2:16
Oh,
2:16
it's
2:16
ginormous,
2:17
to
2:17
use
2:17
the
2:17
technical
2:18
term.
2:19
I
2:19
think
2:19
for
2:20
years
2:20
and
2:20
years
2:20
it's
2:21
been
2:21
collecting
2:22
data
2:22
and
2:23
visualizing
2:24
data.
2:25
But
2:25
how
2:25
many
2:26
decisions
2:27
are
2:27
actually
2:28
being
2:28
impacted
2:29
positively
2:30
with
2:31
data?
2:31
When
2:31
you're
2:31
in
2:32
the
2:32
belly
2:32
of
2:32
some
2:33
beasts
2:33
and
2:33
you
2:33
see
2:34
how
2:34
they
2:34
work,
2:34
you
2:34
kind
2:35
of
2:35
go,
2:35
oh,
2:36
well,
2:37
you
2:37
guys
2:37
make
2:38
money
2:38
despite
2:40
not
2:40
really
2:40
having
2:41
an
2:41
idea
2:41
if
2:42
your
2:42
decisions
2:42
are
2:42
correct
2:43
or
2:43
not.
2:43
Right.
2:44
So
2:44
I
2:44
think
2:44
there's
2:45
a
2:45
huge
2:45
gap.
2:45
I
2:46
think
2:46
people,
2:47
you
2:47
know,
2:47
either
2:48
look
2:48
at
2:48
data
2:49
with
2:49
a
2:49
capital
2:49
D
2:50
as
2:50
gospel,
2:51
or
2:52
they
2:52
kind
2:52
of
2:53
look
2:54
for
2:54
the
2:54
data
2:55
to
2:55
go
2:56
reinforce
2:57
their
2:58
inherent
2:58
biases
2:59
on
2:59
what
2:59
they
2:59
think
2:59
they
3:00
should
3:00
do.
3:01
So
3:01
I
3:01
think
3:02
the
3:02
gap's
3:02
there.
3:02
I
3:02
mean,
3:03
the
3:03
only
3:03
gap
3:04
that
3:04
isn't
3:04
there
3:04
is
3:04
the
3:05
amount
3:05
of
3:05
money
3:05
people
3:05
are
3:06
spending
3:06
working
3:07
with
3:07
data.
3:07
Yeah,
3:08
that's
3:08
for
3:08
sure.
3:08
And
3:10
the
3:10
other
3:10
one
3:10
I'd
3:10
add
3:10
on,
3:11
that
3:11
I
3:11
think
3:11
we
3:11
spoke
3:11
about
3:11
before
3:12
was
3:12
like,
3:12
beyond
3:13
the
3:14
biases,
3:14
maybe
3:14
the
3:14
unconscious
3:15
biases,
3:15
there
3:16
are
3:16
sometimes
3:16
these
3:16
conscious
3:17
biases
3:17
of,
3:17
like,
3:18
I
3:18
have
3:18
a
3:18
idea
3:19
in
3:19
my
3:19
mind
3:19
of
3:20
what
3:20
story
3:21
I
3:21
want
3:21
the
3:21
data
3:22
to
3:22
tell
3:22
me,
3:22
so
3:23
therefore
3:23
I'm
3:23
going
3:23
to
3:24
present
3:24
it
3:24
and
3:24
cut
3:25
it
3:25
and
3:25
slice
3:25
it
3:25
and
3:26
manipulate
3:26
it
3:27
or
3:27
even
3:27
change
3:28
the
3:28
collection
3:28
of
3:28
it
3:29
to
3:29
match
3:29
that
3:29
story
3:30
that
3:30
I
3:30
have
3:30
in
3:30
my
3:30
mind.
3:31
Yeah,
3:31
I
3:31
mean,
3:32
that's
3:32
a
3:32
huge
3:32
thing.
3:33
And
3:33
quite
3:33
frankly,
3:34
the
3:34
reason
3:34
why,
3:36
in
3:36
my
3:36
recent
3:37
career
3:37
here,
3:37
I've
3:38
focused
3:38
on
3:39
this
3:39
idea
3:39
of
3:39
decision
3:40
intelligence
3:40
and
3:41
decision
3:41
science.
3:42
And
3:43
one
3:43
of
3:43
the
3:43
leaders
3:43
in
3:44
that
3:44
space,
3:44
Cassie
3:45
K.
3:46
Who
3:46
was
3:46
at
3:46
Google,
3:47
I
3:48
mean,
3:48
she
3:49
has
3:49
said
3:49
the
3:49
big
3:50
question
3:50
for
3:50
data
3:50
professionals
3:51
is,
3:52
what
3:53
would
3:53
change
3:53
your
3:53
mind
3:54
if
3:54
I
3:54
could
3:54
tell
3:55
you
3:55
this
3:56
piece
3:56
of
3:56
data?
3:56
Like,
3:57
if
3:57
you're
3:58
able
3:58
to
3:58
do
3:58
that
3:58
and
3:58
actually
3:59
have
3:59
that
3:59
impact
4:00
on
4:00
what
4:00
somebody's
4:01
already
4:01
preset
4:02
to
4:02
do,
4:03
that's
4:04
huge.
4:05
But
4:05
it's
4:06
really
4:06
hard.
4:07
What
4:07
would
4:07
you
4:07
do
4:07
differently
4:08
if
4:08
this
4:08
measure
4:08
was
4:08
different?
4:09
Right.
4:09
I
4:09
was
4:09
writing
4:10
about
4:10
that
4:10
this
4:11
week.
4:12
I
4:12
call
4:12
it
4:12
the
4:13
mythical
4:13
all
4:13
green
4:13
dashboard.
4:14
And
4:14
I've
4:14
seen
4:14
it
4:14
at
4:14
many
4:15
different
4:15
companies
4:15
that
4:16
I've
4:16
gone
4:16
to.
4:17
And
4:17
there's
4:18
nothing
4:18
a
4:18
senior
4:19
executive
4:19
active
4:19
likes
4:20
more
4:20
than
4:20
to
4:20
show
4:20
up
4:20
to
4:20
the
4:20
monthly
4:21
meeting
4:21
and
4:21
see
4:21
the
4:22
dashboard
4:22
all
4:22
green.
4:23
My
4:23
initial
4:23
response
4:24
in
4:25
advance
4:25
of
4:25
a
4:25
meeting
4:25
like
4:25
that
4:26
would
4:26
be,
4:26
well,
4:26
we
4:26
can
4:26
cancel
4:27
the
4:27
meeting
4:27
then,
4:27
right,
4:28
because
4:28
everything's
4:28
green,
4:29
everything's
4:30
rosy.
4:30
But
4:30
of
4:30
course,
4:31
you
4:31
take
4:31
away
4:31
that
4:31
exposure
4:32
to
4:32
the
4:32
team,
4:33
to
4:33
the
4:33
senior
4:33
person,
4:34
and
4:34
suddenly
4:34
they're
4:34
not
4:34
so
4:35
keen
4:35
to
4:35
cancel
4:36
it.
4:36
So
4:36
that's
4:37
a
4:37
technique
4:37
I've
4:37
used
4:37
before
4:38
of
4:38
like,
4:38
hey,
4:39
we're
4:40
going
4:40
to
4:40
prioritize
4:41
the
4:41
conversation
4:42
and
4:42
the
4:42
agenda
4:43
around
4:44
whichever
4:44
work
4:44
streams
4:45
or
4:45
projects
4:45
or
4:46
whatever
4:46
it
4:46
might
4:46
be.
4:46
Those
4:47
that
4:47
are
4:47
yellow
4:48
are
4:48
those
4:48
that
4:48
are
4:48
red
4:49
and
4:49
those
4:49
that
4:49
actually
4:49
need
4:50
the
4:50
leadership
4:51
to
4:51
try
4:51
and
4:51
get
4:52
people
4:52
to
4:52
think
4:53
differently
4:53
about
4:53
it.
4:53
But
4:54
that's
4:54
at
4:54
the
4:54
root
4:54
of
4:54
it,
4:55
right?
4:55
It's
4:55
a
4:56
well
4:56
designed
4:56
dashboard,
4:57
should
4:57
drive
4:57
a
4:58
different
4:58
action
4:58
or
4:59
should
4:59
create
5:00
strategy
5:01
in
5:01
some,
5:02
in,
5:03
in
5:03
some
5:03
way
5:03
or
5:03
another.
5:04
Right?
5:04
Yeah.
5:05
I
5:05
mean,
5:05
this
5:05
could
5:05
have
5:06
been
5:06
an
5:06
email,
5:07
you
5:07
know
5:07
that
5:07
that's
5:08
kind
5:08
of
5:08
what
5:08
you're
5:08
saying
5:08
to
5:08
them.
5:09
Right.
5:09
And
5:09
I
5:09
think
5:10
it
5:10
comes
5:10
down
5:11
to
5:11
how
5:11
people
5:11
are
5:12
constructing
5:12
KPIs.
5:13
And
5:14
we've
5:14
all
5:14
seen.
5:15
I
5:15
don't
5:15
know
5:15
if
5:15
you've
5:15
seen,
5:16
but
5:16
definitely
5:16
I've
5:16
seen
5:17
on
5:17
LinkedIn
5:18
and
5:18
other
5:18
social
5:19
media.
5:20
Just
5:21
like,
5:21
what
5:21
does
5:21
a
5:22
KPI
5:22
stand
5:23
for?
5:23
We
5:23
all
5:23
know.
5:24
Key
5:24
performance
5:24
indicator.
5:25
And
5:25
there's
5:25
like
5:26
the
5:26
live
5:26
Laugh
5:27
Love
5:27
version
5:27
of
5:28
that
5:28
which
5:28
is
5:28
like
5:29
keep
5:29
people
5:30
interested
5:31
and
5:31
all
5:31
that
5:31
stuff.
5:32
But
5:32
at
5:32
the
5:32
end
5:32
of
5:32
the
5:32
day,
5:33
what
5:33
I've
5:34
seen
5:34
is
5:34
KPIs
5:35
end
5:35
up
5:35
being
5:36
established
5:37
for
5:37
plausible
5:38
deniability
5:39
and
5:40
like
5:40
stolen
5:41
valor
5:41
type
5:41
of
5:41
things.
5:42
So
5:42
they're
5:43
either
5:43
too
5:43
big
5:44
or
5:44
too,
5:45
you
5:46
know,
5:46
manipulatable
5:47
to
5:48
really
5:48
have
5:49
an
5:49
impact.
5:49
Like
5:49
you
5:50
said,
5:50
like
5:50
there
5:50
should
5:51
be.
5:51
Where's
5:52
the
5:52
discussion?
5:53
Data
5:53
should
5:53
bring
5:54
up
5:54
a
5:54
friction
5:54
and
5:55
a
5:55
discussion
5:55
point
5:56
to
5:56
add
5:56
additional
5:57
context
5:58
to
5:58
make
5:58
a
5:58
different
5:58
decision
5:59
if
6:00
you're
6:00
doing
6:00
it
6:00
right.
6:01
Stolen
6:01
valor
6:02
sounds
6:02
like
6:02
a
6:02
fascinating
6:03
phrase.
6:03
Like,
6:03
can
6:03
you
6:03
tell
6:03
me
6:03
a
6:04
bit
6:04
more,
6:04
dive
6:04
a
6:04
bit
6:04
more
6:05
into
6:05
that.
6:05
Well,
6:06
I
6:06
have
6:06
to
6:06
thank
6:06
my
6:06
wife
6:07
for
6:07
watching
6:07
all
6:07
these
6:08
date
6:08
lines
6:08
and
6:08
they
6:09
talk
6:09
about,
6:09
oh,
6:09
this
6:10
guy
6:10
who
6:10
murdered
6:10
somebody
6:11
had
6:11
stolen
6:12
valor.
6:12
It's
6:12
kind
6:13
of
6:13
when
6:13
you
6:13
in
6:14
the
6:14
armed
6:14
forces,
6:15
it's
6:16
like,
6:16
well,
6:16
you're
6:17
saying
6:17
you
6:17
did
6:17
something
6:18
and
6:18
you
6:18
didn't
6:18
do
6:18
it,
6:19
like
6:19
you
6:19
were
6:19
not
6:19
a
6:19
hero.
6:20
And
6:21
I
6:22
kind
6:22
of
6:22
look
6:22
at
6:22
some
6:22
KPIs
6:23
that
6:23
way
6:23
where
6:24
there's
6:24
this
6:25
KPI
6:25
that's
6:26
huge
6:27
and
6:28
it
6:28
depends
6:29
on
6:29
six
6:30
or
6:30
seven
6:30
different
6:31
business
6:32
units
6:33
kind
6:33
of
6:33
doing
6:34
some
6:34
of
6:34
the
6:34
right
6:35
things
6:35
and
6:36
a
6:36
lot
6:36
to
6:36
do
6:36
with
6:37
luck.
6:37
And
6:38
then
6:38
somebody
6:38
goes,
6:39
that's
6:39
my
6:39
KPI.
6:40
I
6:41
won
6:41
that.
6:42
And
6:42
I
6:43
think
6:43
what
6:43
happens
6:44
is
6:44
you
6:44
lose
6:45
people
6:45
because
6:46
you
6:46
go,
6:46
well,
6:47
for
6:47
that
6:48
to
6:48
happen,
6:49
the
6:49
product
6:50
had
6:50
to
6:50
be
6:50
good,
6:51
it
6:51
had
6:51
to
6:51
be
6:51
delivered
6:52
well,
6:52
we
6:53
had
6:53
to
6:53
buy
6:53
the
6:53
right
6:53
amount
6:54
of
6:54
it.
6:55
It
6:55
had
6:55
to
6:55
be
6:55
merchandised
6:56
correctly.
6:57
You
6:57
know,
6:57
there
6:57
was
6:57
a
6:57
lot
6:58
that
6:58
happened
6:58
and
6:59
all
6:59
you
6:59
do
6:59
is
7:00
move
7:00
product.
7:01
So
7:01
thank
7:01
you
7:01
for
7:01
moving
7:02
product.
7:03
But
7:03
I
7:03
bet
7:03
there's
7:04
a
7:04
KPI
7:04
that
7:05
more
7:05
is
7:06
derivative
7:07
of
7:07
that,
7:07
you
7:08
know,
7:08
larger
7:08
KPI
7:09
that
7:10
gives
7:10
you
7:11
real
7:11
time
7:11
feedback
7:12
on
7:12
how
7:12
well
7:12
did
7:13
you
7:13
contribute
7:13
to
7:14
that
7:14
big
7:14
one.
7:15
That's
7:16
a
7:16
fascinating
7:16
one.
7:16
And
7:17
especially
7:17
if
7:17
I
7:17
think
7:17
about
7:17
it
7:18
in
7:18
the
7:18
context
7:18
of
7:18
something
7:19
that
7:19
I've
7:19
spoken
7:19
about
7:19
a
7:19
lot,
7:20
which
7:20
is
7:20
the
7:20
way
7:21
that
7:22
performance
7:22
is
7:23
measured
7:23
and
7:23
managed
7:24
within
7:25
particularly
7:25
larger
7:25
corporate
7:26
environments.
7:26
It's
7:27
a
7:27
zero
7:27
sum
7:27
game.
7:28
There's
7:28
a
7:28
normalized
7:28
distribution
7:29
curve.
7:29
Not
7:29
everyone
7:30
can
7:30
win,
7:30
some
7:30
people
7:31
have
7:31
to
7:31
lose.
7:31
And
7:32
those
7:32
are
7:32
the
7:32
rules.
7:33
So
7:33
given
7:34
that
7:34
context
7:34
and
7:35
that
7:35
setting,
7:35
it
7:35
almost
7:36
becomes
7:36
understandable
7:37
to
7:37
me
7:37
why
7:37
stolen
7:38
valor
7:38
would
7:38
exist.
7:38
There's
7:39
a
7:39
clear
7:39
value
7:39
value
7:40
from
7:40
stolen
7:40
valor
7:41
in
7:42
that
7:42
particular
7:44
scenario.
7:45
It
7:45
also
7:46
then
7:46
creates
7:46
this
7:47
behavior
7:47
that
7:47
I've
7:47
seen
7:47
multiple
7:48
times
7:48
when
7:48
you're
7:49
at
7:49
the
7:49
start
7:49
of
7:49
a
7:49
project
7:50
or
7:50
you're
7:50
building
7:50
a
7:50
new
7:50
process,
7:51
or
7:51
you're
7:52
standing
7:52
up
7:52
a
7:52
new
7:52
team.
7:53
Why
7:53
people
7:54
get
7:54
so
7:55
emotional
7:55
around
7:56
where
7:56
we
7:56
set
7:57
the
7:57
KPIs
7:57
and
7:58
what
7:58
red,
7:58
yellow
7:58
and
7:58
green
7:59
actually
7:59
means
7:59
because
8:00
they're
8:00
concerned
8:00
about
8:00
how
8:00
that
8:01
might
8:01
reflect
8:02
back
8:02
on
8:02
them.
8:02
Right.
8:03
You're
8:03
right.
8:03
These
8:04
are
8:04
judgments
8:04
of
8:05
good
8:05
or
8:05
bad,
8:06
not
8:06
answering
8:06
a
8:07
question
8:07
if
8:07
we
8:08
did
8:08
something
8:08
right
8:08
or
8:09
wrong.
8:10
And
8:10
that's
8:10
inherently
8:11
where
8:11
it
8:12
loses
8:12
its
8:12
steam.
8:13
And
8:13
it
8:13
is
8:13
a
8:13
very
8:15
political
8:16
thing
8:16
within
8:17
a
8:17
corporation
8:18
to
8:18
have
8:18
a
8:19
KPI
8:19
that
8:19
you
8:19
can
8:20
deliver
8:20
at
8:21
because,
8:22
you
8:22
know,
8:22
anymore
8:23
we're,
8:24
you
8:24
know,
8:24
and
8:24
we
8:24
do
8:24
want
8:25
to
8:25
put
8:25
some
8:25
system
8:26
and
8:26
science
8:26
against
8:27
art,
8:27
but
8:28
anymore
8:28
when
8:28
you're
8:29
just
8:29
looking
8:29
at,
8:29
oh,
8:30
did
8:30
you
8:30
check
8:30
off
8:31
this
8:31
KPI?
8:31
I'll
8:32
never
8:32
forget
8:32
I
8:33
was
8:33
a
8:33
pricing
8:34
strategist
8:35
earlier
8:35
in
8:35
my
8:36
career
8:37
and
8:37
we
8:37
were
8:37
setting
8:38
all
8:38
these
8:39
kind
8:39
of
8:40
ambitious
8:40
goals
8:41
and
8:41
okay,
8:41
we're
8:42
going
8:42
to
8:42
have
8:42
percent
8:42
increase
8:43
of
8:43
price
8:44
year
8:44
over
8:44
year.
8:46
And
8:47
I
8:47
was
8:47
like,
8:47
I
8:47
don't
8:48
know
8:48
if
8:48
I
8:48
can
8:48
do
8:48
that.
8:49
I'm
8:49
actually
8:49
looking
8:50
at
8:50
my
8:50
book
8:50
of
8:50
business
8:51
that
8:51
I
8:51
have
8:51
to
8:52
manage
8:52
and
8:53
there
8:54
are
8:54
some
8:54
egregious
8:55
prices
8:56
already
8:57
there
8:57
and
8:58
I
8:58
don't
8:58
feel
8:59
good
8:59
about,
9:00
you
9:01
know,
9:02
raising
9:02
them
9:03
now.
9:03
Luckily
9:04
I
9:04
worked
9:04
with
9:04
a
9:05
very
9:05
thoughtful
9:05
guy
9:06
and
9:06
a
9:06
thoughtful
9:07
VP
9:07
and
9:08
executive
9:08
that
9:09
said,
9:09
yeah,
9:09
you
9:10
know
9:10
what,
9:10
like
9:11
we
9:11
have
9:11
to
9:11
do
9:12
the
9:12
right
9:12
thing,
9:13
but
9:14
that
9:14
should
9:15
not
9:15
be
9:15
the
9:15
exception.
9:16
The
9:17
right
9:17
thing
9:17
for
9:17
the
9:18
business
9:18
should
9:19
be
9:19
what
9:20
we're
9:20
measuring,
9:21
not
9:21
an
9:21
exception
9:22
to
9:22
what
9:22
we're
9:23
measuring.
9:23
Right.
9:23
Which
9:24
it
9:24
seems
9:25
like
9:25
there's
9:25
a
9:25
lot
9:25
of
9:26
that.
9:26
And
9:26
then,
9:26
you
9:26
know,
9:27
if
9:27
you
9:27
get
9:28
somebody
9:28
who
9:30
doesn't
9:30
like,
9:30
you
9:31
know,
9:32
the
9:32
shape
9:32
of
9:33
your
9:33
jib
9:33
or
9:33
whatever
9:34
it
9:34
is.
9:34
Right.
9:35
It
9:35
could
9:35
be
9:36
used
9:36
as
9:36
a
9:36
negative.
9:36
So
9:37
I
9:37
think
9:37
it's,
9:38
it
9:38
feels
9:38
like
9:38
it's
9:39
kind
9:39
of
9:39
lost
9:40
its
9:40
place.
9:41
I
9:41
think
9:41
people
9:41
are
9:42
trying,
9:42
but
9:43
we're
9:43
still
9:43
getting
9:44
caught
9:44
up
9:44
in
9:45
protecting,
9:46
I
9:46
mean,
9:46
Maslow's
9:47
hierarchy
9:47
of
9:47
needs.
9:48
You
9:48
know,
9:49
you
9:49
have
9:49
to
9:49
meet
9:49
your
9:49
need,
9:50
your
9:50
needs
9:50
first
9:51
before
9:51
you're
9:52
self
9:52
actualizing.
9:53
So
9:53
there's
9:53
just
9:53
a
9:54
lot
9:54
of
9:54
stuff
9:55
pent
9:55
up
9:55
and
9:56
something
9:56
as
9:56
simple
9:56
as
9:57
a
9:57
KPI
9:58
today.
9:59
Yeah.
9:59
And
10:00
that
10:00
hierarchy
10:00
of
10:00
needs
10:00
in
10:01
those
10:01
corporate
10:01
environments
10:01
that
10:02
we've
10:02
talked
10:02
about,
10:02
like
10:03
always,
10:03
number
10:04
one
10:04
is
10:04
that,
10:04
is
10:05
that
10:05
job
10:05
security
10:06
or
10:06
illusion
10:07
of
10:07
job
10:07
security,
10:08
at
10:08
least
10:08
as
10:08
I
10:08
would
10:08
like
10:08
to
10:09
call
10:09
it
10:09
number
10:10
one
10:10
before,
10:10
like,
10:11
can
10:11
I
10:11
add
10:11
incredible
10:11
value
10:12
to
10:12
this
10:12
business?
10:12
Is
10:13
can
10:13
I
10:13
retain
10:14
my
10:14
job
10:14
right?
10:15
And
10:15
what
10:16
do
10:16
I
10:16
need
10:16
to
10:16
do
10:17
to
10:17
prove
10:17
that
10:18
my
10:18
performance
10:18
has
10:18
been
10:19
sufficient
10:19
in
10:20
order
10:20
to
10:20
meet
10:20
the
10:20
expectations
10:21
and,
10:21
you
10:22
know,
10:22
dodge
10:22
this
10:23
rotation
10:24
of
10:24
the
10:24
swinging
10:24
axe
10:25
that
10:25
comes
10:25
around
10:25
every
10:26
six
10:26
months
10:26
or
10:26
every
10:27
or
10:27
every
10:27
12
10:27
months,
10:28
depending
10:28
on
10:29
where
10:29
you're
10:29
at.
10:29
And
10:30
not
10:30
only
10:30
does
10:30
it
10:30
kill
10:30
KPIs
10:31
and
10:32
data,
10:32
but
10:32
it
10:32
stifles
10:33
innovation
10:33
and
10:34
it
10:34
hugely
10:35
disincentivizes
10:36
risk
10:37
taking
10:37
100%.
10:38
And
10:38
I
10:38
just,
10:39
I
10:39
keep
10:40
going
10:40
back
10:40
to
10:40
this
10:40
idea.
10:41
Like
10:41
we're
10:41
talking
10:42
about
10:42
this
10:42
very
10:42
simple
10:43
thing
10:43
called
10:43
a
10:43
KPI.
10:45
And
10:46
I
10:46
mean,
10:46
I
10:46
don't
10:46
know
10:46
about
10:47
you,
10:47
but
10:47
most
10:48
of
10:48
the
10:48
places
10:48
I've
10:49
seen
10:49
KPIs
10:50
are
10:50
at
10:50
executive
10:52
level.
10:53
What,
10:53
what
10:53
about
10:54
those
10:54
operational
10:54
KPIs?
10:55
Like,
10:55
how
10:56
are
10:56
we,
10:57
how
10:57
are
10:57
we
10:57
empowering
10:58
somebody
10:59
to
10:59
have
10:59
that
10:59
sense
11:00
of
11:00
security
11:01
by
11:01
tracking
11:01
to
11:02
a
11:02
KPI
11:02
that's
11:03
meaningful
11:03
to
11:03
them
11:04
and
11:04
it
11:05
can
11:05
be
11:05
derivative.
11:06
It
11:06
doesn't
11:06
have
11:06
to.
11:07
Look,
11:07
things
11:08
in
11:08
life
11:08
are
11:09
not
11:09
directly
11:10
like,
11:10
it's
11:11
not
11:11
cause
11:11
and
11:11
effect
11:12
all
11:12
the
11:12
time.
11:13
Sometimes
11:14
correlation
11:14
is
11:15
good
11:15
enough,
11:16
but
11:16
you
11:16
got
11:16
to
11:17
have
11:17
some
11:17
sort
11:17
of
11:17
effort
11:18
to
11:18
empower
11:20
an
11:20
employee
11:21
to
11:22
feel
11:22
like
11:22
they
11:22
can
11:22
tell
11:23
their
11:23
story
11:23
on
11:24
how
11:24
they've
11:24
actually
11:25
impacted
11:26
or
11:27
took
11:27
a
11:27
really
11:28
good
11:28
chance
11:28
at
11:28
trying
11:29
to
11:29
positively
11:29
impact
11:30
a
11:30
KPI
11:31
at
11:31
an
11:32
executive
11:33
level
11:33
by
11:34
decomposing
11:35
it
11:35
into
11:35
something
11:35
that.
11:36
Here's
11:36
a
11:36
KPI
11:37
that
11:37
you
11:38
should
11:38
use
11:38
as
11:39
a
11:39
litmus
11:39
test
11:40
of
11:40
should
11:41
I
11:41
make
11:41
a
11:41
different
11:41
decision
11:42
and
11:43
then
11:43
tell
11:43
that
11:44
story
11:45
as
11:45
you
11:46
go
11:46
through
11:46
your
11:46
reviews
11:47
and
11:47
how
11:47
it
11:47
might
11:47
have
11:47
impacted?
11:48
Like,
11:48
I
11:48
took
11:49
this
11:49
action
11:49
when
11:49
I
11:50
knew
11:50
I
11:50
should
11:50
have.
11:51
That's
11:52
all
11:52
you
11:52
can
11:52
do.
11:53
I
11:53
think
11:53
ambitiously
11:54
you
11:54
go,
11:54
well,
11:55
if
11:56
I
11:56
can
11:56
raise
11:56
my
11:57
prices
11:57
3%,
11:59
then
12:00
maybe
12:00
my
12:00
margins
12:01
are
12:01
3%
12:02
more
12:02
or
12:02
more.
12:03
Right?
12:03
Like
12:04
pricing,
12:05
you
12:05
know,
12:05
driving
12:06
demand
12:06
and
12:07
driving
12:07
pricing
12:08
up.
12:08
The
12:09
pricing
12:09
lever
12:09
is
12:10
a
12:10
huge
12:10
thing
12:11
everybody
12:11
uses
12:12
now.
12:13
Sometimes
12:13
it
12:14
makes
12:14
sense,
12:14
other
12:14
times
12:15
it's
12:15
kind
12:15
of
12:15
a
12:16
lazy,
12:16
easy
12:16
way
12:17
to
12:17
make
12:17
more
12:17
money.
12:18
I
12:18
think
12:19
really
12:20
the
12:20
ambition
12:20
there
12:21
is
12:22
to
12:22
go,
12:23
we
12:24
want
12:24
to
12:24
increase
12:24
margins.
12:26
That
12:26
was
12:26
the
12:26
real.
12:27
But
12:28
there
12:28
shouldn't
12:29
be
12:29
this
12:29
philosophical
12:32
wheel
12:33
in
12:33
my
12:33
head
12:33
going
12:34
to
12:34
go,
12:34
oh,
12:35
you
12:36
really
12:36
just
12:36
care
12:37
about
12:38
increasing
12:38
margins.
12:39
Makes
12:39
complete
12:40
sense.
12:41
But
12:41
also
12:42
I
12:42
would
12:42
go
12:43
back
12:43
and
12:43
I
12:44
didn't
12:44
say
12:44
this
12:44
back
12:45
then,
12:45
but
12:46
I've
12:46
taken
12:47
this
12:47
forward
12:47
and
12:48
I,
12:48
you
12:48
know,
12:48
like,
12:48
oh,
12:49
I
12:49
wish
12:49
I
12:49
would
12:49
have
12:49
Said
12:50
that.
12:50
But,
12:51
you
12:52
know,
12:53
3%
12:54
of
12:54
$0
12:54
is
12:55
$0.
12:56
So
12:56
one
12:56
of
12:56
the
12:56
things
12:57
for
12:57
me
12:57
is
12:57
like,
12:58
there's
12:58
inflation,
12:59
there's
12:59
cost
13:00
of
13:00
living
13:00
increases.
13:01
There
13:01
are
13:01
these
13:01
things
13:02
that
13:02
happen,
13:03
you
13:03
know,
13:03
but
13:04
once
13:05
again,
13:06
in
13:07
the
13:07
best
13:07
of
13:07
times,
13:08
you're
13:08
going
13:08
to
13:08
be
13:09
able
13:09
to
13:10
have
13:11
something,
13:11
have
13:12
a
13:12
margin,
13:13
have
13:13
price
13:14
increases
13:14
above
13:15
that.
13:15
Other
13:15
times,
13:15
you
13:16
got
13:16
to
13:16
be
13:16
below
13:16
it.
13:17
The
13:17
main
13:17
thing
13:18
is
13:18
that
13:18
you
13:19
are
13:20
working
13:20
with
13:20
customers
13:21
to
13:22
make
13:22
sure
13:22
that
13:23
you're
13:24
keeping
13:24
them,
13:24
because
13:25
that
13:25
is
13:27
price
13:27
increases
13:28
that
13:28
lose
13:28
you
13:29
customers
13:29
that
13:29
are
13:30
good
13:30
is
13:31
a
13:31
bad
13:31
idea.
13:32
Dad
13:32
used
13:32
to
13:33
say
13:33
to
13:33
me
13:33
in
13:34
situations
13:35
like
13:35
that,
13:36
the
13:37
operation
13:37
was
13:37
a
13:37
success,
13:38
but
13:38
the
13:38
patient
13:39
is
13:39
dead.
13:39
Yeah,
13:40
great,
13:41
great
13:41
job,
13:41
but
13:42
it
13:42
doesn't
13:42
matter.
13:43
Yeah,
13:44
absolutely.
13:44
How
13:45
do
13:45
you
13:45
think
13:45
that
13:45
would
13:45
have
13:45
played
13:46
out
13:46
if
13:46
you
13:46
had.
13:46
You
13:47
say
13:47
you
13:47
wish
13:47
you
13:47
had
13:47
said
13:47
that
13:47
at
13:48
the
13:48
time.
13:48
How
13:48
do
13:48
you
13:48
think
13:48
it
13:48
would
13:48
have
13:49
played
13:49
out
13:49
if
13:50
you
13:50
had
13:50
come
13:50
forward
13:51
and
13:51
said
13:51
it?
13:52
I
13:52
think
13:52
my
13:52
VP
13:53
would
13:53
have
13:54
laughed
13:55
and
13:55
liked
13:55
it
13:55
because
13:56
I
13:56
talk
13:57
about
13:57
one
13:57
of
13:57
his
13:57
quotes.
13:58
I'll
13:58
never
13:58
forget
13:58
this.
13:59
Cause
13:59
I
13:59
was.
14:00
I
14:00
think
14:00
I
14:00
was
14:00
finally
14:01
becoming
14:01
an
14:01
adult
14:02
when
14:02
I
14:02
had
14:02
this
14:03
job.
14:03
And
14:03
one
14:03
of
14:03
the
14:04
things
14:04
he
14:04
said
14:04
is,
14:04
nick,
14:05
hope's
14:05
not
14:05
a
14:05
strategy.
14:06
I'm
14:06
like,
14:06
got
14:07
it.
14:07
You
14:08
know,
14:08
because
14:08
it
14:08
isn't.
14:08
And
14:09
I
14:09
use
14:09
that
14:09
all
14:09
the
14:10
time.
14:10
So
14:10
I
14:10
think
14:10
he
14:11
would
14:11
actually
14:11
liked
14:12
it
14:12
and
14:12
said,
14:13
you
14:13
know,
14:13
and
14:14
that's
14:14
why
14:14
I
14:14
didn't
14:14
get
14:14
pushback.
14:15
I
14:15
think
14:16
overall
14:17
he
14:17
knew,
14:17
hey,
14:18
my
14:19
pricing
14:19
team's
14:20
goal
14:20
here
14:20
is
14:21
to
14:21
increase
14:22
by
14:23
3%.
14:24
You
14:24
know,
14:24
but
14:25
price
14:26
is
14:26
a
14:26
part
14:27
of
14:27
revenue
14:27
and
14:28
it
14:28
impacts
14:29
margin.
14:29
So
14:30
he
14:30
was
14:30
also
14:32
pretty
14:33
open
14:33
to
14:33
all
14:34
of
14:34
that.
14:34
So
14:35
I
14:35
think
14:35
you
14:35
would
14:35
have
14:35
liked
14:36
it
14:36
because
14:36
he
14:36
gave
14:36
me
14:37
that
14:37
gem
14:37
about
14:38
hope
14:38
is
14:38
not
14:39
a
14:39
strategy,
14:39
which
14:40
I've
14:40
used
14:41
so
14:41
much
14:41
as
14:41
a
14:41
product
14:42
manager
14:42
and
14:43
strategist.
14:44
Yeah,
14:44
I
14:45
learned
14:45
that
14:45
one
14:45
the
14:45
hard
14:45
way
14:45
as
14:46
well.
14:47
I
14:47
think
14:47
I
14:47
put
14:47
the
14:47
word
14:48
hope
14:48
or
14:48
hopefully
14:48
or
14:49
ever
14:49
in
14:49
a
14:50
leadership
14:51
presentation
14:51
deck
14:52
that
14:52
I
14:52
was
14:52
doing
14:52
and
14:52
had
14:53
the
14:53
same
14:53
quote
14:53
thrown
14:53
at
14:54
me
14:54
just
14:55
by
14:55
virtue
14:56
of
14:56
me
14:56
including.
14:56
It
14:57
basically
14:57
undermined
14:57
the
14:58
credibility
14:58
of
14:58
the
14:58
whole
14:58
deck
14:59
because
14:59
I'd
14:59
put
14:59
hope
14:59
in
14:59
there
15:00
and,
15:00
you
15:00
know,
15:01
removed
15:01
that
15:01
certainty
15:02
from
15:02
the
15:02
recommendation,
15:03
even
15:03
though
15:03
I
15:03
was
15:03
actually
15:03
certain
15:04
about
15:04
the
15:04
recommendation.
15:05
I
15:05
think,
15:06
you
15:06
know,
15:06
those
15:06
environments
15:06
we
15:07
talked
15:07
about
15:07
before
15:07
lead
15:08
you
15:08
to
15:08
want
15:09
to
15:09
hedge
15:09
sometimes,
15:10
right.
15:10
And
15:10
you
15:10
have
15:11
to
15:11
bracket
15:11
it.
15:11
Right.
15:12
Everything
15:12
is
15:12
about
15:12
bracketing
15:13
it.
15:13
So
15:14
you
15:14
could.
15:15
You
15:15
could
15:15
go
15:15
like,
15:16
hey,
15:17
like
15:17
this
15:17
is
15:18
one
15:18
thing
15:18
you
15:18
could
15:18
do.
15:19
And
15:19
I
15:19
know
15:19
your
15:20
recommendations
15:20
are
15:21
good,
15:21
so
15:22
it
15:22
was
15:22
the
15:22
right
15:23
thing.
15:23
But
15:24
probably
15:24
in,
15:25
you
15:26
know,
15:26
in
15:26
the
15:26
background
15:27
of
15:27
your
15:27
mind
15:27
as
15:27
you're
15:27
delivering
15:28
it
15:28
or
15:28
typing
15:29
out
15:29
the
15:29
deck,
15:29
right?
15:29
You're
15:30
like,
15:30
well,
15:31
if
15:31
this
15:31
person
15:32
does
15:32
this,
15:32
if
15:33
they
15:33
actually
15:33
stick
15:33
to
15:33
it,
15:34
if,
15:34
if,
15:35
if
15:35
they're
15:36
ifs,
15:36
right.
15:37
So
15:37
those
15:38
become
15:38
assumptions,
15:39
those
15:39
become
15:39
risks.
15:41
And
15:41
we
15:41
can
15:41
give,
15:42
you
15:42
know,
15:42
our
15:43
best
15:43
guess,
15:43
like,
15:44
and
15:44
maybe
15:45
be
15:45
wrong,
15:45
but
15:45
it's
15:46
based
15:46
on
15:46
the
15:46
information
15:47
we
15:47
have.
15:48
So
15:48
hope
15:49
is
15:49
definitely
15:49
not
15:49
a
15:50
strategy.
15:50
But
15:52
like
15:52
I
15:53
said,
15:53
something
15:54
happening
15:55
and
15:56
you
15:56
doing
15:56
something
15:57
like
15:57
a
15:57
correlation
15:58
that
15:58
doesn't
15:59
correlate,
16:00
that's
16:01
also
16:01
terrible.
16:02
And
16:02
there's
16:03
people
16:03
running
16:03
around
16:04
with
16:04
a
16:04
bunch
16:04
of
16:04
bravado,
16:06
very
16:06
little
16:06
data,
16:08
and
16:08
kind
16:09
of
16:09
getting
16:09
away
16:09
with
16:09
it.
16:10
So
16:10
like
16:10
I
16:11
said,
16:11
there's
16:11
something
16:12
in
16:12
between.
16:12
Hey,
16:13
I'm
16:13
very
16:14
humble.
16:14
Maybe
16:14
this
16:15
will
16:15
do
16:15
it.
16:15
I
16:16
hope
16:16
it
16:16
works.
16:17
And
16:17
this
16:18
is
16:18
the
16:18
answer.
16:20
It's
16:20
two
16:20
sides
16:20
of
16:21
a
16:21
coin.
16:21
If
16:21
we
16:21
can
16:21
just
16:22
keep
16:22
that
16:22
coin
16:22
vertical,
16:23
we'll
16:23
be
16:23
okay.
16:24
Lies,
16:24
Damn.
16:25
Lies
16:25
and
16:25
statistics,
16:26
right?
16:26
That's
16:26
right.
16:27
I
16:27
think
16:27
you
16:27
could
16:27
take
16:28
any
16:29
standard
16:30
scatter
16:31
plot
16:31
and
16:32
basically
16:32
draw
16:32
a
16:32
straight
16:33
line
16:33
in
16:33
any
16:34
direction
16:34
and
16:34
angle
16:34
through
16:35
it
16:35
and
16:35
then
16:35
try
16:35
and
16:35
claim
16:36
correlation
16:36
from
16:36
it.
16:36
If
16:37
you
16:37
make
16:37
that
16:37
line
16:37
thick
16:37
and
16:38
black
16:38
enough,
16:39
it
16:39
can
16:39
look
16:40
convincing,
16:41
whatever
16:41
you're
16:41
trying
16:42
to
16:42
say.
16:42
We
16:42
talked
16:43
about,
16:44
you
16:44
know,
16:44
some
16:44
of
16:45
the
16:46
underlying
16:47
difficulties
16:48
and
16:48
contradictions
16:49
that
16:49
exist
16:50
in
16:50
the.
16:50
The
16:50
world
16:51
of
16:51
work.
16:52
And
16:52
the
16:52
phrase
16:52
you
16:52
used
16:53
was
16:53
you
16:53
were.
16:54
You
16:54
were
16:54
becoming
16:54
a.
16:55
An
16:55
adult.
16:55
And
16:55
I
16:55
think
16:56
it
16:56
resonated
16:56
with
16:56
me
16:57
in
16:57
terms
16:57
of,
16:57
like,
16:59
you
16:59
join
16:59
the
16:59
world
16:59
of
17:00
work
17:00
out
17:00
of
17:00
school,
17:00
and
17:00
then
17:00
you
17:01
basically
17:01
have
17:01
to
17:01
start
17:01
again
17:02
and
17:03
become
17:03
again
17:04
and
17:04
mature
17:04
again.
17:05
Can
17:05
you
17:05
tell
17:05
me
17:05
a
17:05
bit
17:05
more
17:06
about,
17:06
like,
17:06
what
17:06
you
17:07
meant
17:07
by
17:07
becoming
17:07
an
17:08
adult
17:08
and
17:08
what
17:08
that
17:09
process
17:09
was?
17:10
So
17:10
my
17:10
undergrad's
17:11
in
17:11
journalism,
17:12
photojournalism
17:13
specifically.
17:14
So
17:14
that
17:14
was
17:15
super
17:15
duper
17:16
helpful
17:16
as
17:16
I
17:16
went
17:17
into
17:18
things
17:18
like
17:18
pricing
17:19
strategy
17:19
and
17:20
data.
17:21
Yeah,
17:21
look,
17:22
every
17:22
organization
17:23
has
17:23
its
17:23
own
17:23
ecosystem.
17:26
When
17:26
I
17:26
mentor
17:27
and
17:27
manage
17:28
people
17:28
that
17:28
are
17:29
just
17:29
coming
17:29
into
17:29
the
17:30
work
17:30
world,
17:31
I
17:31
just
17:31
try
17:32
to
17:32
give
17:32
them,
17:32
like,
17:33
it's
17:33
not
17:33
so
17:34
much
17:35
whether
17:35
you're
17:36
right
17:36
or
17:36
not,
17:37
it's
17:37
how
17:38
you're
17:38
right.
17:38
So
17:39
something
17:40
I
17:40
learned
17:40
early
17:40
on
17:41
in
17:41
my
17:41
career,
17:42
there's
17:42
a
17:42
reason
17:43
why
17:43
I'm
17:44
in
17:44
data,
17:45
and
17:45
it's
17:46
because
17:46
I'm
17:46
kind
17:47
of
17:47
good
17:47
at
17:47
making
17:48
logical
17:48
connections
17:49
and
17:49
have
17:49
a
17:49
pretty
17:50
strong
17:50
background
17:50
in
17:51
math.
17:51
And
17:52
inherently,
17:52
I'm
17:53
there.
17:53
It
17:53
doesn't
17:54
matter
17:54
if
17:54
I
17:54
can't
17:55
make
17:56
somebody
17:56
else
17:56
understand
17:57
it,
17:57
who
17:57
needs
17:57
to
17:57
understand
17:58
it.
17:59
Right.
17:59
And
18:00
I
18:00
always
18:00
go
18:01
back
18:01
to
18:01
this
18:01
idea
18:02
of,
18:02
like,
18:03
people
18:03
want
18:03
to
18:03
watch
18:04
House
18:04
M.D.
18:05
they
18:06
don't
18:06
want
18:06
to
18:06
work
18:07
with
18:07
Dr.
18:07
House.
18:08
It's.
18:08
You
18:09
can't
18:09
Run.
18:10
So
18:10
I
18:10
think
18:10
for
18:11
me,
18:11
I
18:11
was
18:11
learning,
18:12
hey,
18:13
I
18:14
have
18:14
some
18:14
talent
18:15
here.
18:15
Whereas
18:16
before
18:16
I
18:16
felt
18:17
like
18:17
an
18:17
imposter.
18:18
But
18:18
also
18:19
I
18:20
don't
18:20
have
18:20
to
18:20
be
18:20
a
18:21
jackass
18:21
about
18:22
it
18:22
when
18:22
I'm
18:23
ahead
18:23
because
18:24
it
18:24
doesn't
18:24
matter.
18:25
Like
18:26
organizations
18:27
are
18:27
like
18:28
ships
18:28
or
18:29
boats.
18:29
The
18:30
bigger
18:30
the
18:30
one,
18:30
the
18:30
harder
18:31
it
18:31
is
18:31
to
18:31
turn.
18:32
So
18:33
that,
18:33
that's
18:33
where
18:34
I
18:34
kind
18:34
of
18:34
was
18:35
starting
18:35
to
18:35
learn
18:36
that
18:36
this
18:36
isn't
18:37
a
18:37
side
18:37
gig.
18:37
This
18:37
is
18:38
how
18:38
you
18:38
make
18:38
your
18:38
living.
18:39
And
18:39
if
18:39
you
18:39
want
18:39
to
18:39
make
18:39
a
18:40
good
18:40
living
18:40
in
18:40
corporate
18:41
America,
18:41
you
18:41
need
18:41
to
18:42
be
18:42
able
18:42
to
18:43
understand
18:44
how
18:44
to
18:45
bring
18:45
people
18:45
along
18:46
with
18:46
you,
18:46
even
18:46
if
18:47
you're
18:47
right
18:47
and
18:48
be
18:48
open
18:48
to
18:48
learning
18:49
and
18:49
being
18:49
wrong.
18:50
And
18:50
that's
18:50
kind
18:50
of
18:50
what
18:51
happened
18:51
in
18:51
that
18:51
space
18:52
where
18:52
I
18:53
heard
18:53
these
18:53
hope
18:53
is
18:54
not
18:54
a
18:54
strategy
18:55
stuff
18:55
and
18:55
just
18:55
kind
18:56
of
18:56
leaning
18:56
into
18:56
what
18:57
I
18:57
was
18:57
getting
18:57
good
18:57
at.
18:58
Being
18:58
right
18:59
gracefully
18:59
and
18:59
being
18:59
wrong
19:00
gracefully.
19:00
There's
19:00
a
19:00
corporate
19:01
version
19:01
of
19:01
what
19:01
we
19:02
get
19:02
taught
19:02
on
19:03
the
19:03
athletic
19:03
fields
19:04
of
19:04
high
19:05
school,
19:05
right?
19:05
Winning
19:06
and
19:06
winning
19:06
and
19:07
both
19:08
winning
19:08
and
19:08
losing
19:08
gracefully.
19:10
The
19:10
length,
19:10
by
19:11
the
19:11
way,
19:11
the
19:11
photojournalism
19:12
is
19:12
an
19:13
interesting
19:13
thing
19:13
because.
19:14
And
19:15
it
19:15
might
19:15
just
19:15
be
19:15
the
19:15
angle
19:15
that
19:16
I'm
19:16
looking
19:16
at
19:16
it
19:16
from,
19:16
because
19:16
I
19:17
used
19:17
to
19:17
be
19:17
a
19:17
photographer
19:18
myself,
19:18
but
19:20
we
19:20
should
19:20
have
19:21
another
19:21
conversation
19:21
about
19:21
that
19:22
and
19:22
why
19:23
SLRs
19:23
only
19:24
stay
19:24
open
19:24
for
19:24
30
19:24
seconds.
19:25
I
19:25
learned
19:26
a
19:26
really
19:26
interesting
19:27
reason
19:27
why
19:27
that,
19:27
why
19:28
that
19:28
happens.
19:28
It's
19:28
for
19:28
tax
19:29
purposes.
19:29
An
19:30
SLR
19:30
video
19:30
shutter,
19:31
shutter,
19:31
30
19:32
minutes.
19:32
Anyway,
19:34
photojournalism
19:36
and
19:36
business
19:37
intelligence,
19:37
like
19:38
telling
19:38
a
19:39
story
19:39
with
19:39
a
19:39
visual,
19:40
right?
19:40
You've
19:41
seen
19:41
that
19:41
connection
19:41
before,
19:42
surely?
19:42
Yes.
19:43
Yeah.
19:43
Like
19:44
as
19:44
I
19:44
got,
19:45
you
19:45
know,
19:45
I
19:45
think
19:46
you
19:46
end
19:46
up
19:46
at
19:46
a
19:47
certain
19:47
point,
19:47
you
19:47
know,
19:48
and
19:48
we're,
19:48
we're
19:49
of
19:49
similar
19:49
ages
19:50
where
19:50
you
19:50
go,
19:51
well,
19:51
okay,
19:52
how
19:52
the
19:52
heck
19:52
did
19:52
I
19:53
get
19:53
here?
19:53
And
19:54
you
19:54
look
19:54
back
19:54
and
19:55
you
19:55
go,
19:55
oh,
19:56
like,
19:57
you
19:57
know,
19:57
working
19:58
in
19:58
data,
19:58
telling
19:59
stories,
19:59
using
20:00
visuals.
20:02
These
20:03
are
20:03
all
20:03
things
20:04
that
20:04
I'm
20:04
doing.
20:05
And
20:06
you
20:06
know,
20:06
you
20:06
teach
20:07
yourself
20:07
SQL
20:08
along
20:08
the
20:08
way,
20:09
you
20:09
know,
20:09
you
20:10
teach
20:10
yourself
20:10
how
20:10
to
20:11
use
20:11
the
20:11
different
20:11
platforms.
20:13
So
20:13
yeah,
20:13
I
20:13
mean,
20:14
it
20:14
was
20:14
right
20:15
there,
20:15
but
20:15
I
20:16
had
20:16
no
20:16
idea,
20:16
you
20:16
know,
20:17
and
20:17
that's
20:17
why
20:17
I
20:18
go.
20:18
I
20:19
ran
20:19
away
20:19
from
20:20
computers
20:21
when
20:21
I
20:21
was
20:22
going
20:22
to
20:22
college
20:23
because
20:24
I
20:24
had
20:24
spent
20:25
so
20:25
much,
20:25
you
20:25
know,
20:25
I
20:26
had
20:26
done
20:26
some
20:26
programming
20:28
in
20:28
high
20:28
school.
20:29
We
20:29
had
20:29
a
20:29
program
20:30
and
20:30
I
20:30
was
20:30
in
20:30
the
20:30
program
20:32
and
20:32
I
20:32
just
20:33
kind
20:33
of,
20:33
I
20:33
didn't
20:34
like
20:34
it.
20:35
So
20:35
I
20:35
ran
20:35
away
20:36
from
20:36
it
20:36
only
20:37
to
20:37
kind
20:37
of
20:37
circle
20:38
back
20:38
into
20:38
more
20:38
of
20:39
a
20:39
technology
20:39
focused
20:40
career.
20:41
And
20:41
that
20:41
just
20:41
goes
20:41
to
20:42
prove
20:42
like
20:43
data
20:44
insights,
20:45
analytics,
20:46
if
20:46
you
20:47
want
20:47
to
20:47
extend
20:47
it
20:47
to
20:47
AI,
20:48
it's
20:49
really
20:49
about
20:49
storytelling
20:50
and
20:50
Communicating
20:51
and
20:53
defining
20:54
logical,
20:56
explainable
20:57
connections
20:58
between
20:58
things.
21:00
It's
21:00
not
21:01
technology.
21:01
It
21:02
isn't.
21:02
I'd
21:03
love
21:03
that
21:03
you
21:03
share
21:03
that
21:03
story.
21:04
I've
21:04
had
21:04
a
21:04
couple
21:04
of
21:04
conversations
21:05
with
21:05
people
21:06
in
21:06
the
21:07
last
21:07
few
21:07
weeks
21:08
who,
21:08
similar
21:09
to
21:09
you,
21:09
early,
21:10
got
21:10
scared
21:10
off
21:10
and
21:10
like,
21:10
well,
21:10
this
21:11
is
21:11
not
21:11
the
21:11
field
21:11
for
21:11
me.
21:12
And
21:12
ended
21:12
up
21:12
coming
21:13
back
21:13
to
21:13
it.
21:13
Like
21:13
the.
21:14
Call
21:15
it
21:15
fate
21:15
or
21:15
destiny
21:15
or
21:16
call
21:16
it
21:16
like,
21:16
people's
21:17
natural
21:17
strengths
21:17
will
21:17
always
21:18
pull
21:18
them
21:18
to
21:18
the
21:18
right
21:19
place
21:19
in
21:20
the
21:20
end
21:20
as
21:20
they
21:21
open
21:21
their
21:21
mind
21:21
and.
21:22
And
21:23
progress
21:24
through
21:24
their
21:24
careers.
21:25
What
21:25
was
21:25
it
21:25
about
21:25
the
21:26
technical
21:26
that.
21:27
That
21:27
scared
21:27
you
21:27
off
21:27
and
21:27
made
21:28
you
21:28
run
21:28
away?
21:28
Was
21:28
it
21:29
the.
21:29
Do
21:29
you
21:29
think
21:30
it
21:30
was
21:30
the
21:30
way
21:30
it
21:30
was
21:30
taught?
21:31
Do
21:31
you
21:31
think
21:31
it
21:31
was,
21:32
you
21:33
know,
21:33
coding
21:33
and
21:34
the
21:34
ones
21:35
and
21:35
zeros
21:35
and
21:35
starting
21:36
from
21:36
the
21:36
bottom
21:36
up
21:36
is
21:36
just
21:37
not
21:37
the
21:37
right
21:37
and
21:37
enticing
21:38
way
21:38
to
21:38
approach
21:38
it.
21:38
Like,
21:39
what
21:39
was
21:39
it
21:39
that.
21:39
That
21:40
scared
21:40
you
21:40
off?
21:40
Yeah,
21:41
coding.
21:41
I
21:41
mean,
21:42
I
21:42
wish
21:42
I
21:42
could
21:43
describe.
21:44
We
21:44
would
21:44
go
21:44
to
21:45
this
21:45
place
21:45
called
21:45
the
21:46
Career
21:46
Prep
21:46
center,
21:47
and
21:48
we
21:48
had
21:48
this
21:48
big
21:49
room,
21:50
you
21:50
know,
21:50
a
21:50
bunch
21:51
of
21:51
old
21:51
computers
21:52
hooked
21:52
up
21:52
to
21:52
an
21:53
as
21:53
400.
21:54
We
21:55
had,
21:55
you
21:56
know,
21:57
we.
21:58
We
21:58
had
21:58
chart
21:59
paper
21:59
to
21:59
do,
22:00
you
22:00
know,
22:01
our
22:01
flow
22:01
charts.
22:02
And
22:03
I
22:04
just
22:04
could
22:04
care
22:04
less.
22:05
Like,
22:06
I
22:06
was
22:06
like,
22:07
cool,
22:08
it
22:08
does
22:08
that.
22:09
But
22:10
I
22:10
didn't.
22:11
It
22:11
didn't
22:11
resonate
22:12
with
22:12
me.
22:12
I
22:12
was
22:12
like,
22:13
I
22:13
can't
22:13
spend
22:14
my
22:15
life
22:16
doing
22:16
that.
22:17
I
22:17
just
22:17
couldn't
22:18
do
22:18
it.
22:20
So
22:20
it
22:20
was
22:20
like
22:20
just
22:20
a
22:21
combination.
22:21
But,
22:22
you
22:23
know,
22:24
I
22:24
hate
22:24
coding.
22:25
You
22:25
know,
22:26
I
22:26
just.
22:27
Not
22:27
for
22:27
me.
22:28
But
22:28
do
22:28
I
22:28
love
22:29
solving
22:30
a
22:30
problem?
22:31
Yeah,
22:31
I
22:31
love
22:31
solving
22:32
a
22:32
problem.
22:32
And
22:33
I.
22:34
I
22:34
don't
22:34
mind,
22:35
you
22:35
know,
22:36
learning
22:36
what
22:36
I
22:37
need
22:37
to
22:37
from
22:37
a
22:37
technology
22:38
standpoint
22:39
to
22:39
do
22:39
that,
22:39
but
22:39
for
22:40
that,
22:40
like,
22:42
I
22:42
don't.
22:43
I
22:43
don't
22:43
know
22:43
how
22:43
to
22:43
describe
22:44
it.
22:44
It's
22:44
just
22:44
like,
22:45
you
22:45
know,
22:46
I'm
22:46
a
22:46
golfer,
22:47
right?
22:47
And
22:49
you
22:49
could
22:49
have
22:50
the
22:50
most
22:50
beautiful
22:50
swing
22:51
and
22:51
hit
22:52
the
22:52
ball
22:52
well
22:53
and
22:53
score
22:54
like.
22:56
Or
22:56
you
22:57
could
22:57
score
22:57
well.
22:58
And
22:59
for
22:59
me,
22:59
the
22:59
idea
23:00
was,
23:00
how
23:01
do
23:01
I
23:01
score
23:02
well
23:02
versus
23:03
have
23:03
a
23:03
beautiful
23:03
swing?
23:04
As
23:04
it
23:04
relates
23:05
to
23:05
working
23:06
with
23:06
data
23:06
and
23:06
in
23:06
these
23:07
constructs.
23:08
This
23:08
is
23:08
the
23:08
whole
23:08
fascinating
23:09
tangent.
23:09
We
23:09
can
23:09
go
23:09
off
23:09
down
23:10
for
23:10
a
23:10
long
23:10
time
23:11
because
23:11
I
23:11
also
23:11
play
23:12
golf,
23:12
and
23:13
I
23:13
also
23:14
have
23:14
improved
23:14
a
23:14
lot
23:15
in
23:15
the
23:15
past
23:15
few
23:15
years,
23:16
and
23:16
I
23:17
put
23:17
data
23:17
down
23:17
to
23:17
one
23:17
of
23:18
that.
23:18
So
23:19
as
23:20
a
23:20
certified
23:20
data
23:21
nerd,
23:22
I
23:22
use
23:22
the
23:22
Archos
23:23
Bluetooth
23:23
sensors
23:25
on
23:25
my
23:25
club.
23:26
Oh,
23:26
wow.
23:26
And
23:27
it
23:27
knocked
23:27
like
23:27
three
23:27
or
23:27
four
23:28
shots
23:28
off
23:28
my
23:28
handicap.
23:29
And.
23:29
And
23:29
the
23:29
reason
23:30
why
23:30
is
23:30
the
23:30
difference
23:31
between
23:31
what's
23:31
actually
23:31
happening
23:31
and
23:32
what's
23:32
happening
23:32
in
23:32
your
23:32
head,
23:33
right?
23:33
So,
23:33
you
23:33
know,
23:33
you
23:33
go
23:33
to
23:34
the
23:34
the
23:34
bag
23:34
and
23:34
you
23:34
pull
23:34
your
23:35
seven
23:35
iron
23:35
out
23:35
or
23:35
whatever
23:36
else
23:36
and
23:36
you
23:36
think,
23:36
oh,
23:37
I
23:37
hit
23:37
this
23:38
however
23:39
many.
23:39
What
23:39
do
23:39
I
23:39
used
23:39
to
23:39
tell
23:39
myself,
23:40
oh,
23:40
I
23:40
hit
23:40
this
23:40
like
23:41
nearly
23:41
160.
23:42
Turns
23:42
out
23:43
that
23:43
I
23:43
don't.
23:44
It
23:44
turns
23:44
out
23:44
that,
23:45
like,
23:45
if
23:45
I
23:45
hit
23:45
my
23:45
seven
23:46
iron
23:46
off
23:46
the
23:46
middle
23:47
with
23:47
a
23:47
perfect
23:47
swing
23:47
with
23:47
the
23:48
proper
23:48
weight
23:48
transfer
23:48
and
23:49
all
23:49
of
23:49
that
23:49
good
23:49
stuff,
23:49
then
23:49
I
23:50
can
23:50
hit
23:50
it
23:50
160,
23:51
60.
23:52
But
23:52
actually,
23:52
on
23:52
average
23:53
I
23:53
was
23:53
hitting
23:53
it
23:53
151
23:54
or
23:54
149.
23:55
So
23:55
it
23:55
works
23:55
by
23:56
Bluetooth.
23:56
It
23:56
measures
23:56
where
23:57
you
23:57
are,
23:57
it
23:57
knows
23:57
which
23:57
centers
23:58
on
23:58
each
23:58
club.
23:58
So
23:59
it
23:59
basically
23:59
it
24:00
tracks
24:00
at
24:00
a
24:00
basic
24:01
level
24:01
how
24:01
far
24:01
you're
24:02
hitting
24:02
each
24:02
club.
24:03
You
24:03
can
24:04
put
24:04
more
24:04
data
24:05
in
24:05
about
24:05
putting
24:05
and
24:05
whatever
24:06
else
24:06
on
24:06
greens
24:06
hit,
24:07
but
24:07
like,
24:07
it
24:07
will
24:07
just
24:07
sit
24:08
in
24:08
your
24:08
pocket
24:08
and
24:08
do
24:08
this
24:10
unobtrusively
24:11
without
24:11
you
24:11
having
24:11
to
24:12
do
24:12
anything.
24:12
Because
24:12
personally
24:12
I
24:13
found
24:13
like,
24:13
the
24:13
putting
24:14
stats,
24:14
like
24:14
you
24:14
have
24:14
to.
24:15
Here's
24:16
the
24:16
data
24:16
governance
24:17
data
24:17
quality
24:17
piece.
24:18
My
24:18
data
24:18
quality
24:18
is
24:18
lower,
24:19
but,
24:19
like,
24:19
I
24:19
don't
24:19
want
24:19
to
24:19
have
24:19
to
24:20
get
24:20
my
24:20
mobile
24:20
phone
24:20
out
24:20
on
24:21
every
24:21
green
24:21
and,
24:21
like,
24:22
chart
24:22
where
24:22
I
24:22
took
24:22
my
24:22
putts
24:23
from.
24:23
I
24:23
mean,
24:23
like,
24:23
especially
24:23
if
24:24
I'm
24:24
just
24:24
three
24:24
put
24:24
it.
24:24
The
24:25
last
24:25
thing
24:25
I
24:25
want
24:25
to
24:25
do
24:25
is
24:25
like
24:25
draw
24:26
three
24:26
lines
24:26
on
24:26
this.
24:27
You
24:27
know,
24:28
it's
24:28
not
24:28
what
24:28
I
24:28
want
24:28
to
24:29
do,
24:29
but
24:29
the
24:29
single
24:30
most
24:30
interesting
24:31
stat
24:31
that
24:31
I
24:32
took
24:32
away
24:32
is,
24:32
was
24:33
the,
24:34
the
24:34
GIR
24:35
thing.
24:35
And
24:35
I
24:36
thought,
24:36
oh,
24:36
it'll
24:36
get,
24:36
it'll
24:37
give
24:37
me
24:37
my
24:37
greens
24:37
and
24:37
regulation
24:38
percentage.
24:39
But
24:39
actually
24:40
what
24:40
it
24:40
did,
24:40
as
24:40
well
24:40
as
24:41
it
24:41
showing
24:41
me
24:41
Whatever
24:42
it
24:42
was,
24:42
42%
24:43
greens
24:43
and
24:43
regulation,
24:44
it
24:45
showed
24:45
me,
24:45
like,
24:46
where
24:46
I
24:46
missed
24:46
the
24:46
green.
24:47
And
24:47
it
24:47
was
24:47
like
24:47
I
24:48
missed
24:48
left
24:49
like
24:49
10%.
24:50
I
24:50
miss
24:51
right
24:52
about
24:52
8%.
24:53
I
24:54
miss
24:54
short
24:54
2%.
24:55
And
24:56
I
24:56
missed
24:56
long
24:57
story,
24:57
I
24:57
missed
24:57
long
24:58
2%
24:58
or
24:59
1%
24:59
long
25:00
and
25:00
then
25:01
80
25:01
something,
25:01
70
25:02
something
25:02
percent,
25:02
whatever's
25:03
left
25:03
of
25:03
that
25:04
was
25:04
all
25:04
short.
25:04
And
25:05
I
25:05
was
25:05
telling
25:05
myself
25:06
in
25:06
my
25:06
head
25:07
that
25:07
what,
25:08
what
25:08
that
25:09
my
25:09
average
25:09
was
25:09
actually
25:12
in
25:12
truth
25:12
what
25:12
my
25:13
best
25:13
was
25:13
right
25:14
on
25:14
the
25:14
end
25:14
of
25:14
the
25:14
standard
25:15
deviation
25:15
curve.
25:15
And
25:16
it
25:16
knocked
25:16
like
25:16
three
25:16
or
25:16
four
25:16
shorts
25:17
off
25:17
my
25:17
handicap
25:17
because
25:18
I
25:18
just
25:18
stared
25:18
it
25:18
in
25:18
the
25:18
face
25:19
50
25:19
rounds
25:19
later.
25:19
And
25:19
I
25:20
was
25:20
like,
25:20
well,
25:20
I
25:20
guess
25:20
I
25:21
can't
25:21
deny
25:21
that
25:21
I'm
25:21
fooling
25:22
myself.
25:22
An
25:22
awesome
25:23
book.
25:24
It's
25:24
something
25:24
called
25:24
the
25:25
Four
25:25
Foundations.
25:26
I'll
25:26
send
25:26
it
25:26
to
25:26
you
25:26
afterwards.
25:27
That
25:27
took
25:27
an
25:27
additional
25:28
two
25:28
shots
25:28
off
25:29
my
25:29
game.
25:29
And
25:30
that
25:30
was
25:30
all
25:30
about
25:30
just
25:30
how
25:31
you
25:31
think
25:31
about
25:31
and
25:32
how
25:32
you
25:32
recover
25:32
from
25:33
your
25:34
game.
25:34
And
25:34
the
25:35
three.
25:36
It
25:36
encourages
25:36
you
25:37
to
25:37
split
25:37
your
25:37
game
25:37
into
25:37
Three
25:37
pieces
25:38
off
25:38
the
25:39
tee
25:39
and
25:40
then
25:41
moving
25:42
the
25:42
ball
25:43
forward,
25:44
sort
25:44
of
25:44
approach
25:45
and
25:45
then
25:45
short
25:45
game.
25:46
And
25:46
it
25:46
said,
25:47
hey,
25:47
like,
25:47
what's
25:47
your
25:47
handicap,
25:48
Nick?
25:48
Like,
25:48
where
25:48
are
25:48
you?
25:49
I'm
25:50
in
25:50
nine
25:50
right
25:50
now.
25:51
That
25:51
person
25:52
are
25:52
perfect
25:52
about
25:52
the
25:52
same.
25:53
I'm.
25:53
Yeah,
25:53
I'm
25:54
9.0.
25:54
Literally
25:55
just
25:56
from
25:56
8.0.
25:57
We're
25:57
almost
25:57
exactly
25:57
the
25:58
same.
25:59
So
25:59
for
25:59
someone
26:00
who
26:00
was
26:00
a
26:00
10
26:00
handicap,
26:01
it
26:01
said
26:02
off
26:02
the
26:02
tee,
26:03
approach,
26:03
short
26:04
game.
26:04
On
26:05
your
26:05
average
26:05
round,
26:06
one
26:06
of
26:06
those
26:06
three
26:07
things
26:07
is
26:07
going
26:07
to
26:07
be
26:07
going
26:07
well
26:08
for
26:08
you,
26:09
right?
26:11
You
26:11
will
26:11
feel
26:11
like
26:11
you've
26:12
had
26:12
a
26:12
great
26:12
round.
26:12
Two
26:12
out
26:13
of
26:13
those
26:13
three
26:13
things
26:13
have
26:14
gone
26:14
well
26:14
for
26:14
you,
26:16
approximately.
26:17
Like
26:17
one.
26:17
And
26:18
this
26:18
is
26:18
for
26:18
a
26:18
10
26:18
handicapper.
26:19
One
26:19
in
26:19
100
26:20
rounds
26:20
you'll
26:21
do
26:21
all
26:21
three
26:21
of
26:21
those
26:22
things
26:22
well.
26:23
And
26:23
so
26:23
many
26:24
handicappers
26:24
put
26:25
themselves
26:25
in
26:25
this
26:25
mental
26:26
prison
26:26
because
26:26
they
26:27
don't
26:27
equip
26:27
themselves
26:27
with
26:28
this
26:28
data
26:28
and
26:28
they
26:28
don't
26:28
understand
26:29
what
26:29
their
26:29
baseline
26:29
is.
26:30
And
26:30
like,
26:30
you
26:30
could
26:31
be
26:31
hitting
26:31
really
26:31
well
26:31
off
26:31
the
26:32
tee
26:32
and,
26:32
and
26:32
your
26:33
approach
26:33
in
26:33
your
26:33
short
26:33
game's
26:34
gone
26:34
to
26:34
pot.
26:35
And
26:36
then
26:36
you
26:36
talk
26:36
yourself
26:36
out
26:37
of
26:37
the
26:37
second
26:37
half
26:37
of
26:37
the
26:37
round
26:38
of
26:38
like,
26:38
I'm
26:38
not
26:38
playing
26:38
well,
26:39
blah,
26:39
blah,
26:39
blah,
26:39
blah.
26:40
When
26:40
actually
26:40
in
26:40
truth,
26:40
you're
26:41
having
26:41
one
26:41
of
26:41
your
26:41
better
26:41
rounds.
26:42
You
26:42
just
26:42
don't
26:42
realize
26:43
it
26:43
because
26:43
you're
26:43
letting
26:43
yourself
26:44
mentally
26:44
get
26:44
sucked
26:45
in
26:45
because
26:45
you're
26:45
not
26:45
looking
26:45
at
26:46
the
26:46
data
26:46
right.
26:46
And
26:48
it's,
26:48
it's
26:48
a
26:49
really
26:49
powerful
26:51
example
26:52
of,
26:52
of,
26:52
of
26:52
just
26:53
how
26:53
cool
26:53
it
26:53
could
26:53
be.
26:53
Anyway,
26:54
I'll
26:54
show
26:54
this.
26:54
I'll
26:54
share
26:54
all
26:55
that
26:55
stuff
26:55
with
26:55
you
26:55
afterwards.
26:56
I'll
26:56
show
26:56
this
26:56
book.
26:57
I
26:57
think
26:57
the
26:57
book's
26:57
a
26:58
fantastic
26:58
book.
26:58
I'll
26:59
do
26:59
a
26:59
sly
26:59
segue
27:00
from
27:00
golf
27:00
into
27:01
Nike
27:02
and
27:03
like,
27:03
all
27:03
the
27:04
time
27:04
you
27:04
spent
27:04
working
27:04
there.
27:05
In
27:05
data,
27:05
obviously
27:06
there's
27:06
confidentiality
27:07
and
27:07
I'm
27:07
not
27:07
asking
27:08
you
27:08
to
27:08
give
27:08
away
27:08
any
27:09
company
27:09
secrets,
27:09
but
27:10
as
27:10
I
27:10
think
27:10
of
27:11
companies
27:11
who
27:11
I'd
27:12
love
27:12
to
27:12
see
27:12
the
27:12
insides
27:13
of
27:13
their
27:13
data,
27:14
like,
27:14
Nike
27:14
is
27:15
certainly
27:15
one
27:15
of
27:15
them
27:15
and
27:15
not
27:16
just
27:16
because
27:16
of
27:16
the
27:16
six
27:16
pairs
27:16
of
27:17
Jordans
27:17
I
27:17
got
27:17
sitting
27:17
in
27:17
that
27:18
cupboard
27:18
next.
27:19
Next
27:19
to
27:19
me.
27:19
Just
27:19
seems
27:20
like
27:20
such
27:20
a
27:20
cool
27:20
place
27:20
to
27:20
work.
27:21
Is
27:21
there
27:21
anything
27:21
you
27:22
can
27:22
share
27:22
about
27:22
the
27:22
data
27:22
landscape
27:23
that
27:23
might
27:23
surprise
27:23
people
27:24
about
27:24
Nike
27:25
or
27:25
something?
27:26
And
27:26
something
27:26
interesting.
27:27
It's
27:27
interesting
27:28
because
27:29
Nike
27:29
was
27:30
founded
27:30
and
27:31
they
27:31
used
27:31
to
27:31
sell
27:33
shoes
27:34
out
27:34
of
27:34
the
27:34
back
27:34
of
27:35
like
27:35
this
27:36
VW
27:36
bus
27:37
type
27:37
of
27:37
thing.
27:38
Really?
27:39
Yeah,
27:39
there's
27:40
something
27:40
about
27:41
the
27:42
DNA
27:43
and
27:43
accepting
27:44
who
27:44
you
27:45
are.
27:45
And
27:45
they
27:45
used
27:45
to
27:45
talk
27:46
about
27:46
Nike
27:46
DNA
27:47
all
27:47
the
27:47
time.
27:48
And
27:48
of
27:48
course,
27:49
you
27:49
know,
27:49
like,
27:50
it
27:50
can
27:50
be
27:51
a
27:51
little
27:51
much,
27:51
but
27:52
I
27:53
think
27:53
what's
27:53
interesting
27:54
is
27:54
that
27:55
the
27:55
people
27:55
who
27:56
came
27:56
through,
27:57
who
27:57
understood
27:58
what
27:58
the
27:58
company
27:58
was
27:59
and
27:59
the
28:00
company,
28:01
like,
28:01
look,
28:01
the
28:02
company
28:02
is
28:02
not
28:03
a
28:03
shoe
28:03
company.
28:04
It's
28:04
a
28:04
sports
28:05
marketing
28:05
company.
28:06
You
28:06
know,
28:06
and
28:07
they
28:07
do
28:07
make
28:08
pretty
28:08
nice
28:08
shoes.
28:10
They
28:10
have
28:11
kind
28:11
of,
28:12
you
28:12
know,
28:13
a
28:13
monopoly
28:13
in,
28:14
in
28:14
the
28:14
space.
28:15
Right.
28:15
Like
28:15
with
28:15
Adidas
28:16
being
28:17
way
28:17
below
28:18
them
28:18
and
28:18
then
28:19
you
28:19
have
28:19
kind
28:19
of
28:19
these
28:19
bit
28:20
players
28:20
like
28:20
Puma
28:21
and
28:21
New
28:21
Balance
28:21
and
28:21
others.
28:22
Right.
28:22
But
28:22
that
28:22
was
28:23
before
28:23
the
28:23
badly
28:24
judged
28:24
chips
28:25
they
28:25
put
28:25
on
28:25
Kanye.
28:26
Yes,
28:27
of
28:27
course.
28:28
Yeah,
28:29
yeah,
28:30
we'll
28:30
see
28:30
what
28:30
Bad
28:31
Bunny
28:31
does
28:31
for
28:31
them.
28:32
I
28:32
think.
28:33
I
28:33
think
28:33
the
28:33
interesting
28:34
thing
28:34
with
28:35
them
28:35
is
28:35
that
28:37
the
28:38
more
28:38
that
28:38
they
28:38
leaned
28:39
into
28:40
what
28:40
they
28:40
were
28:40
really
28:41
good
28:41
at,
28:42
the
28:42
better
28:43
they
28:43
did.
28:43
And
28:44
that
28:44
never
28:44
changed.
28:45
So.
28:45
Right.
28:46
You
28:46
know,
28:47
this
28:47
idea
28:48
of,
28:49
you
28:49
know,
28:50
Nike
28:50
is
28:50
a
28:50
premium
28:51
brand,
28:51
well,
28:52
they,
28:52
they
28:52
kind
28:53
of
28:53
knew
28:53
they
28:53
could
28:53
go
28:54
across
28:54
the
28:54
board.
28:56
There
28:56
was
28:56
an
28:56
interesting
28:57
study
28:57
when
28:58
I
28:58
was
28:58
there
28:59
where,
29:00
you
29:00
know,
29:01
oh,
29:01
why
29:01
did
29:01
this
29:01
shoe.
29:02
We
29:02
did
29:02
this
29:02
shoe
29:03
last
29:03
year.
29:03
It
29:04
was
29:04
gangbusters.
29:05
What
29:06
happened?
29:07
Like,
29:07
why.
29:07
Why
29:08
are
29:08
we
29:08
liquidating
29:09
all
29:09
of
29:09
this?
29:09
And
29:09
these
29:10
are
29:10
all
29:10
data
29:10
issues,
29:10
right?
29:11
We're
29:11
looking
29:11
at
29:11
it.
29:12
We're
29:12
looking
29:13
at
29:13
prices,
29:14
we're
29:14
looking
29:14
at
29:15
profit.
29:16
And
29:17
the
29:17
one
29:18
thing
29:18
that
29:18
they
29:19
pulled
29:19
in
29:19
the
29:19
second
29:19
year
29:20
was
29:20
the
29:20
marketing
29:21
budget.
29:22
And
29:22
that
29:22
just
29:23
went
29:23
to,
29:23
like,
29:24
when
29:24
you
29:25
talk,
29:25
when
29:25
we
29:25
tell
29:26
stories
29:26
and
29:26
we
29:26
know
29:27
things
29:28
anecdotally,
29:29
there's
29:30
usually
29:30
some
29:30
truth
29:31
there.
29:32
Like
29:32
I
29:32
said,
29:33
they're
29:33
a
29:33
sports
29:34
marketing
29:36
company
29:37
and
29:38
the
29:38
more
29:38
they
29:38
lean
29:38
into
29:39
that's
29:39
what
29:39
they
29:39
are,
29:40
the
29:40
better
29:40
they
29:40
do.
29:41
And
29:41
it
29:41
was
29:41
just
29:42
interesting
29:42
to
29:42
see
29:43
version
29:44
one
29:44
of
29:44
this
29:44
shoe
29:45
and
29:45
version
29:45
two
29:46
of
29:46
this
29:46
shoe
29:47
be
29:47
completely
29:48
different.
29:48
And
29:48
it
29:48
all
29:49
came
29:49
down
29:49
to
29:50
how
29:50
much
29:50
marketing
29:51
bucks
29:51
they
29:52
put
29:52
into
29:52
it.
29:52
Like,
29:53
it
29:53
was
29:53
really
29:54
interesting.
29:55
And
29:55
then
29:55
you
29:55
have
29:55
like
29:56
these
29:56
evergreen
29:56
brands
29:57
like
29:57
Jordan,
29:58
and
29:58
Jordan's
29:59
going
29:59
to
29:59
finally
29:59
get
29:59
into
30:00
golf
30:00
full
30:01
time,
30:01
it
30:01
looks
30:01
like.
30:02
But,
30:03
you
30:03
know,
30:04
you
30:04
could
30:04
just.
30:05
You're
30:05
printing
30:05
money
30:06
with
30:06
those.
30:07
You
30:07
print
30:08
money
30:08
with
30:08
those
30:08
limited
30:09
edition,
30:09
like,
30:10
it's
30:10
just.
30:11
But
30:11
you
30:11
got
30:11
to
30:11
keep
30:12
a
30:12
limited
30:12
edition.
30:13
Yeah.
30:14
So
30:14
it's
30:15
just
30:15
kind
30:15
of
30:15
interesting
30:16
to
30:16
see
30:17
through
30:17
that
30:17
study
30:18
of
30:18
this
30:18
one
30:19
shoe
30:19
from
30:19
one
30:20
season
30:20
to
30:20
the
30:21
next.
30:22
The
30:22
marketing
30:23
budget
30:24
and
30:25
them
30:25
being
30:25
a
30:25
terrific
30:26
marketing
30:26
engine.
30:29
It
30:29
was
30:29
night
30:29
and
30:29
day,
30:30
the
30:30
way
30:30
this
30:30
thing
30:31
performed.
30:32
That's
30:32
amazing.
30:33
There's
30:33
a
30:34
couple
30:34
of
30:34
things
30:34
stick
30:34
in
30:34
my
30:34
mind.
30:35
Travis
30:35
Scott
30:36
is
30:36
the
30:36
obvious
30:36
one
30:37
that
30:37
comes
30:38
to
30:38
mind.
30:39
I've
30:39
never
30:39
taking
30:40
the
30:40
plunge
30:41
and
30:42
press
30:42
the
30:42
button
30:43
on
30:43
the,
30:43
on
30:44
the
30:44
sneaker
30:44
websites
30:44
to
30:45
get
30:45
one
30:45
of
30:45
them.
30:45
Mainly
30:45
because
30:46
I'm
30:46
worried
30:46
that,
30:46
like,
30:47
someone
30:47
will,
30:48
like,
30:48
mug
30:49
me
30:49
in
30:49
the
30:49
street,
30:50
take
30:50
them
30:50
off
30:50
my
30:50
feet.
30:51
I
30:51
don't,
30:51
you
30:52
know,
30:52
a
30:52
pair
30:52
of
30:52
shoes
30:52
that
30:52
I
30:52
don't
30:53
think
30:53
I
30:53
would
30:53
ever
30:53
wear
30:53
outside
30:54
is
30:55
for
30:55
fear
30:55
of
30:55
having
30:55
them
30:56
removed
30:56
from
30:56
me
30:56
is,
30:57
is
30:57
not
30:57
something
30:57
I
30:58
wanted.
30:58
But
30:58
the,
30:59
the
30:59
thing
30:59
that
30:59
sticks
30:59
in
30:59
my
30:59
mind
31:00
was
31:00
very
31:01
earlier
31:01
on
31:01
in
31:01
my
31:01
career,
31:02
while
31:02
I
31:02
was
31:02
still
31:02
becoming
31:03
an
31:03
adult,
31:04
where
31:04
I
31:04
was
31:04
learning
31:04
about
31:05
human
31:05
centered
31:05
design.
31:05
And
31:07
you
31:07
know
31:07
what,
31:08
marketing
31:08
is
31:08
at
31:08
its
31:09
root,
31:09
right?
31:09
And
31:09
it's
31:10
about
31:10
telling
31:10
a,
31:10
it's
31:10
about
31:11
telling
31:11
a
31:11
story,
31:11
it's
31:11
about
31:11
addressing
31:12
a
31:12
problem.
31:13
It's
31:13
a
31:13
game
31:13
of
31:13
emotions
31:14
rather
31:14
than
31:15
a
31:15
game
31:15
of
31:15
data.
31:16
And
31:16
the
31:17
example
31:17
they
31:18
use
31:18
was
31:18
the,
31:20
the
31:20
marketing
31:20
video
31:21
that
31:21
Casey
31:21
Neistat
31:22
did,
31:22
the
31:22
make
31:22
it
31:23
count
31:24
video.
31:24
And
31:25
I
31:25
think
31:25
he
31:25
was
31:26
an
31:26
up
31:26
and
31:26
coming
31:26
advertiser.
31:27
And
31:28
they
31:29
said
31:29
he
31:29
has
31:29
a,
31:29
here's
31:30
a
31:30
bunch
31:30
of
31:30
money.
31:30
And
31:30
he
31:30
just
31:31
spent
31:31
it
31:31
like
31:31
flying
31:32
him
31:32
and
31:32
his
31:32
buddy
31:32
around
31:33
the
31:33
world
31:33
and
31:34
like
31:34
filming
31:35
him
31:35
running
31:35
in
31:35
lots
31:35
of
31:35
different
31:36
places.
31:36
It
31:36
was,
31:38
it
31:38
was
31:38
just
31:39
so
31:40
kind
31:40
of
31:40
ahead
31:40
of
31:41
its
31:41
time,
31:41
both
31:41
in
31:41
terms
31:42
of
31:42
how
31:42
you
31:42
do
31:42
it
31:42
and
31:43
then
31:43
they
31:43
were
31:43
able
31:44
to
31:44
spin
31:44
it
31:44
into
31:44
a
31:44
really,
31:45
really
31:45
cool
31:46
campaign.
31:47
I
31:47
worked
31:47
with
31:47
a
31:47
ton
31:47
of
31:48
designers
31:48
at
31:48
Kinnankarta.
31:49
I
31:49
got
31:50
very,
31:50
very
31:51
comfortable
31:52
and
31:53
things
31:53
started
31:54
clicking
31:54
when
31:54
I
31:55
heard
31:55
this
31:55
theory.
31:56
And
31:56
the
31:56
best
31:56
way
31:56
to
31:57
hear
31:57
these
31:57
theories
31:57
is
31:58
when
31:58
you
31:59
get
31:59
involved.
31:59
And
32:00
I
32:00
always
32:00
think
32:00
about,
32:01
whenever
32:01
I
32:02
present
32:02
things,
32:02
I
32:02
talk
32:03
about
32:03
the
32:04
front
32:04
stage
32:05
and
32:05
the
32:05
backstage
32:06
and
32:07
how
32:07
important
32:07
those
32:08
two
32:08
places
32:08
are
32:09
and
32:09
how
32:10
you
32:11
can't
32:11
really
32:12
put
32:12
on
32:13
a
32:13
good
32:13
play
32:13
without
32:14
the
32:14
backstage
32:15
having
32:15
something
32:16
together.
32:16
Yeah,
32:17
it's
32:17
going
32:18
to
32:18
be
32:18
a
32:18
mess.
32:18
Right.
32:19
And
32:19
you
32:19
also
32:20
don't
32:20
want
32:20
to
32:20
build
32:21
all
32:21
the
32:21
things
32:21
for
32:22
every
32:22
play
32:22
you're
32:23
going
32:23
to
32:23
do
32:24
before
32:25
you
32:25
actually
32:25
do
32:26
a
32:26
play
32:26
and
32:26
get
32:27
some
32:27
revenue
32:27
coming
32:28
in.
32:28
And
32:28
I
32:28
just
32:28
feel
32:29
like,
32:30
you
32:30
know,
32:31
it
32:31
gets
32:31
used
32:31
in
32:32
the
32:32
design
32:33
space,
32:33
but
32:34
everybody
32:35
is
32:35
designing
32:35
things
32:36
when
32:36
we're
32:36
doing,
32:37
like,
32:38
take
32:38
that
32:38
visual
32:39
element
32:39
away
32:40
of
32:40
dashboarding.
32:41
The
32:41
number
32:41
I
32:42
put
32:42
in
32:42
front
32:42
of
32:42
you
32:43
is
32:43
important.
32:44
That's
32:44
in
32:45
the
32:45
front
32:45
stage.
32:46
The
32:46
impact
32:46
it
32:47
has,
32:47
how
32:47
does
32:48
it
32:48
flow
32:48
into,
32:50
you
32:50
know,
32:51
the
32:51
process
32:51
and
32:52
decisions
32:52
you're
32:52
making?
32:53
Like
32:53
those
32:53
are
32:53
all
32:54
important
32:54
front
32:54
stage
32:55
things.
32:56
But
32:56
the
32:57
etl,
32:58
the
32:58
acquisition,
32:59
the
32:59
cleansing,
33:00
the
33:00
quality,
33:01
the
33:01
aggregating,
33:02
the
33:02
calculating,
33:04
those
33:04
are
33:04
backstage
33:05
things.
33:05
Now
33:05
you
33:06
need
33:06
both
33:06
of
33:06
them.
33:06
And
33:07
I
33:07
have
33:08
this
33:08
kind
33:08
of
33:08
Ron
33:08
Swanson
33:09
pyramid
33:09
of
33:10
greatness,
33:10
which
33:10
everybody
33:11
has
33:11
one
33:11
of
33:11
these.
33:11
Right.
33:12
Where.
33:12
Yep.
33:13
Take
33:13
a
33:13
thin
33:13
slice
33:14
of
33:14
value
33:14
and
33:16
kind
33:16
of
33:16
build
33:16
the
33:16
plane
33:17
while
33:17
you
33:17
fly
33:17
it.
33:19
Whatever
33:20
metaphor
33:20
you
33:21
want
33:21
to
33:21
use.
33:21
Right.
33:21
But
33:22
being
33:23
very,
33:23
very
33:24
conscious
33:25
of,
33:26
you're
33:27
building
33:27
data
33:28
products,
33:29
therefore
33:30
you
33:30
need
33:30
to
33:31
have
33:31
a
33:32
good
33:32
Front
33:32
stage
33:33
with
33:33
the
33:33
solid
33:33
backstage
33:34
that
33:34
helps
33:34
you
33:35
increase
33:36
speed
33:36
to
33:36
value
33:37
over
33:37
time.
33:38
That
33:38
is
33:38
the
33:38
story
33:38
I've
33:39
been
33:39
telling
33:39
for,
33:40
you
33:40
know,
33:41
in
33:41
one
33:41
way
33:42
or
33:42
another.
33:42
Last
33:43
20
33:43
years
33:43
I
33:44
told
33:44
my,
33:45
my
33:45
old
33:45
boss,
33:46
hey,
33:46
I've
33:47
been
33:47
working
33:47
on
33:47
the
33:47
same
33:47
deck
33:48
for
33:48
20
33:48
years.
33:49
It
33:49
just
33:49
keeps
33:49
evolving
33:50
as
33:50
I
33:50
learn
33:50
things
33:51
and
33:51
I
33:51
know
33:51
things.
33:52
But,
33:52
but
33:53
I
33:53
think
33:53
it's
33:54
so
33:54
important
33:54
you
33:54
bring
33:55
up
33:55
service
33:55
design,
33:56
especially
33:56
in
33:57
data.
33:58
I
33:58
think
33:59
there's,
33:59
oh,
33:59
there's
34:00
fancy
34:00
app
34:00
people
34:01
and
34:01
then
34:01
there's
34:02
hey,
34:02
data
34:03
people.
34:03
It's
34:03
like,
34:04
no,
34:04
you're
34:04
actually
34:05
building
34:05
an
34:05
experience
34:06
for
34:06
somebody.
34:07
So
34:07
be
34:08
certain
34:08
that
34:08
it's
34:08
right
34:09
and
34:09
explainable
34:10
and
34:10
be
34:10
certain
34:10
that
34:11
it
34:11
fits
34:11
in
34:11
and
34:12
it's
34:12
pleasant
34:12
to
34:13
use.
34:13
It's
34:14
important.
34:14
Yeah.
34:16
Similar
34:17
to
34:17
you
34:17
was
34:18
lucky
34:19
enough
34:19
when
34:19
I
34:19
was
34:19
at
34:20
Capital
34:20
One
34:21
to
34:22
meet
34:22
someone
34:22
who
34:22
took
34:22
me
34:23
under
34:23
her
34:23
wing
34:23
and
34:24
trained
34:24
me
34:24
in
34:24
the
34:25
ways
34:25
of
34:25
design
34:25
thinking.
34:26
And
34:26
she
34:26
had
34:26
a
34:26
background
34:27
from.
34:28
Hi,
34:28
Jennifer,
34:29
if
34:29
you're
34:29
listening,
34:29
from
34:30
Stanford
34:30
D
34:30
school
34:31
and
34:32
just
34:32
transformed
34:33
the
34:33
way
34:33
I
34:33
look
34:33
at
34:33
everything,
34:34
including
34:36
from
34:36
a
34:36
data
34:37
perspective.
34:37
So
34:37
I
34:37
used
34:38
to
34:38
be
34:38
so
34:38
caught
34:38
up
34:39
in.
34:39
You
34:39
don't
34:40
understand
34:40
all
34:40
the
34:40
hard
34:40
work
34:41
that
34:41
goes
34:41
into
34:42
this
34:42
stuff.
34:43
And
34:43
you'd
34:44
start
34:44
a
34:44
new
34:44
data
34:45
transformation
34:45
or
34:45
whatever
34:45
it
34:46
might
34:46
be,
34:46
and
34:47
your
34:47
clients
34:47
or
34:47
your
34:48
customers,
34:48
internal,
34:48
external,
34:49
whatever
34:49
it
34:49
is,
34:49
always
34:49
get
34:50
excited
34:50
and
34:50
be
34:50
like,
34:50
when,
34:51
when
34:51
can
34:51
we
34:51
see
34:51
the
34:51
dashboard?
34:53
And
34:53
a
34:53
previous
34:54
version
34:54
would
34:54
me,
34:55
with
34:55
me
34:55
would
34:55
say,
34:56
we
34:56
are
34:56
so
34:57
far
34:57
away
34:57
from
34:57
that.
34:58
Like
34:58
you
34:58
have
34:58
no
34:59
idea
34:59
that
35:00
basically
35:00
like
35:00
the
35:01
building,
35:01
picking
35:01
a
35:01
BI
35:02
tool
35:02
and
35:02
building
35:02
a
35:02
dashboard,
35:03
like
35:03
that's
35:03
the
35:03
5%
35:04
on
35:04
the,
35:04
on,
35:05
on
35:05
the
35:05
end
35:05
of
35:05
this,
35:05
like
35:06
the
35:06
95%
35:06
of
35:07
work
35:07
and
35:07
all
35:07
the
35:07
stuff
35:07
that
35:08
you
35:08
just
35:08
mentioned,
35:08
Nick,
35:08
about
35:09
the,
35:09
the,
35:10
the
35:10
cleansing,
35:11
the
35:11
quality,
35:11
the
35:12
sourcing,
35:12
the
35:13
governance,
35:15
the
35:15
quality
35:16
checking,
35:16
all
35:16
of
35:16
that
35:17
good
35:17
stuff
35:17
that
35:17
we
35:18
know
35:18
happens
35:18
in
35:18
behind.
35:19
But
35:20
as
35:20
I've
35:21
continued
35:21
through
35:21
my
35:22
career
35:22
and
35:22
had
35:22
those
35:22
influences
35:23
and
35:23
now
35:24
with
35:24
my
35:24
own
35:24
company,
35:26
unmistakably,
35:26
you
35:27
still
35:27
have
35:27
to
35:27
go
35:27
on
35:27
that
35:27
journey.
35:28
But
35:29
every
35:29
engagement
35:29
I
35:30
always
35:30
start
35:30
with
35:30
what
35:31
is
35:31
the
35:31
quickest
35:31
way
35:31
I
35:31
can
35:32
deliver
35:32
rni?
35:32
What
35:33
is
35:33
the
35:33
quickest
35:33
way
35:33
I
35:33
can
35:34
make
35:34
this?
35:35
Again,
35:35
to
35:35
use
35:35
Jennifer's
35:36
words,
35:36
how
35:36
can
35:36
you
35:36
make
35:36
it
35:37
real?
35:37
How
35:37
can
35:37
you
35:38
prototype
35:39
something
35:39
and
35:39
make
35:39
it
35:39
real
35:39
as
35:40
quickly
35:40
as
35:40
possible
35:41
so
35:41
that
35:41
even
35:41
if
35:41
it's
35:42
just
35:42
the
35:42
glimmer
35:42
of
35:42
the
35:42
impact,
35:43
as
35:43
you
35:43
said,
35:43
a
35:43
thin
35:44
slice
35:44
of
35:44
the
35:44
impact,
35:45
like
35:45
you
35:45
can
35:46
transport
35:47
people
35:47
to
35:47
a
35:47
future
35:48
of
35:48
where
35:48
you'll
35:48
be
35:49
once
35:49
you've
35:50
finished
35:51
this
35:51
work.
35:52
Yeah.
35:52
And
35:52
I
35:52
mean
35:53
once
35:54
AI
35:54
takes
35:55
over
35:55
the
35:55
world
35:55
and
35:56
there's
35:56
less,
35:57
it's
35:58
less
35:58
Heavy
35:58
handed
35:59
to
35:59
do
35:59
the
35:59
backstage
36:00
stuff,
36:00
which
36:00
we're
36:01
already
36:01
kind
36:01
of
36:02
seen
36:02
a
36:02
little
36:02
bit
36:02
of.
36:03
Right.
36:04
Some
36:04
of
36:04
these
36:04
new
36:05
platforms
36:05
and
36:05
as
36:05
they
36:06
get
36:06
AI
36:06
correct,
36:07
it's
36:07
going
36:07
to
36:07
be
36:07
even
36:09
easier
36:09
to
36:09
do
36:09
some
36:10
of
36:10
this
36:10
stuff.
36:11
It's
36:11
going
36:11
to
36:11
be
36:12
all
36:12
right.
36:12
Do
36:13
you
36:13
want
36:13
to
36:13
be.
36:14
I
36:15
always
36:15
said
36:16
things
36:16
are
36:17
going
36:17
to
36:17
push
36:17
to
36:17
the
36:18
end
36:18
when
36:18
I
36:18
saw
36:19
what
36:19
the
36:19
power
36:19
of
36:20
what
36:20
AI
36:20
could
36:20
do
36:20
is
36:21
like.
36:22
Data
36:22
people
36:22
are
36:23
either
36:23
going
36:23
to
36:23
have
36:23
to
36:23
be
36:24
hardcore
36:25
data
36:26
engineering,
36:27
like
36:27
doing
36:27
things
36:28
that
36:29
ETL
36:30
like
36:30
that
36:30
is
36:30
the
36:31
hard
36:31
work,
36:31
or
36:32
they're
36:32
going
36:32
to
36:32
have
36:32
to
36:32
be
36:33
hardcore
36:34
business
36:34
facing.
36:35
There's
36:36
no
36:36
more
36:36
delicate
36:37
geniuses
36:38
allowed
36:39
because
36:40
the
36:41
entry
36:41
is
36:42
coming
36:42
down.
36:43
And
36:43
I
36:43
keep
36:43
saying,
36:44
you
36:44
know,
36:46
as
36:46
I've
36:47
worked
36:47
with
36:47
people,
36:48
especially
36:48
in
36:49
more
36:49
AI
36:50
applications,
36:52
you
36:52
know,
36:52
data
36:53
scientists
36:53
aren't
36:53
the
36:53
right
36:54
answer
36:55
and
36:56
developers
36:57
and
36:57
engineers
36:57
are
36:57
not
36:58
the
36:58
right
36:58
answer.
36:58
It's
36:59
something
36:59
in
36:59
between.
37:00
It's
37:01
like
37:01
a
37:01
cloud
37:01
engineer
37:02
who
37:02
understands
37:03
data
37:03
structures
37:04
but
37:04
also
37:04
understands
37:05
the
37:05
front
37:05
end.
37:06
And
37:08
that's
37:08
what's
37:09
happening.
37:09
So
37:10
that
37:10
design
37:10
thinking,
37:12
you're
37:12
always
37:12
going
37:13
to
37:13
attach
37:13
yourself
37:13
to
37:14
value.
37:14
You're
37:15
always
37:15
going
37:15
to
37:15
be
37:15
able
37:16
to
37:16
figure
37:16
out
37:16
a
37:16
way
37:17
to
37:17
satisfy
37:18
your
37:18
clients.
37:18
I
37:18
think
37:19
if
37:19
you're
37:19
focused
37:20
on
37:21
how
37:21
do
37:21
I
37:21
deliver
37:22
you
37:22
value
37:23
and
37:23
how
37:23
do
37:23
I
37:23
tell
37:23
you
37:24
how
37:24
to
37:24
scale
37:24
this
37:24
value?
37:25
Because
37:25
those
37:25
are
37:26
the
37:26
two
37:26
sides
37:26
we
37:26
need
37:27
to
37:27
do
37:27
and
37:28
we
37:28
don't
37:28
want
37:28
to
37:28
sit
37:28
there
37:28
forever
37:29
and
37:31
boil
37:31
the
37:31
ocean.
37:32
And
37:32
we
37:32
also
37:33
don't
37:33
want
37:33
to
37:33
build
37:33
a
37:33
house
37:33
without
37:34
pouring
37:34
a
37:34
foundation.
37:35
So
37:35
it's
37:35
like,
37:35
how
37:36
do
37:36
you
37:36
find
37:37
the
37:37
middle
37:37
part
37:38
of
37:38
that,
37:38
the
37:38
middle
37:39
ground?
37:39
And
37:39
it
37:39
should
37:40
all
37:40
get
37:40
faster.
37:41
And
37:41
also
37:41
the
37:41
other
37:41
really
37:42
empowering
37:42
thing
37:42
from
37:42
design
37:43
thinking
37:43
is
37:43
it
37:43
allows
37:44
you
37:44
to
37:44
jump
37:44
the
37:45
gap
37:45
between
37:46
what
37:47
the
37:47
client
37:47
or
37:47
customer
37:48
says
37:48
they
37:48
need
37:48
and
37:48
what
37:48
they
37:49
actually
37:49
need.
37:49
Right.
37:51
Which
37:51
is
37:52
all
37:53
of
37:53
those
37:53
wonderful
37:54
processes
37:54
and
37:54
structures
37:55
you
37:55
can
37:55
use
37:55
early
37:55
on
37:56
to
37:56
tease
37:57
that
37:57
stuff
37:57
out.
37:58
And
37:58
it
37:59
boils
37:59
back
37:59
down
38:00
to
38:00
where
38:00
we
38:01
started
38:01
the
38:01
conversation.
38:02
Like,
38:02
what
38:02
are
38:02
we
38:02
actually
38:03
trying
38:03
to
38:03
measure?
38:03
Like
38:04
what
38:04
is
38:04
going
38:04
to
38:04
make
38:05
a
38:05
difference
38:05
to
38:05
this
38:05
business
38:06
versus
38:07
like
38:07
what
38:07
are
38:07
we
38:08
currently
38:08
measuring
38:08
and
38:09
recognizing
38:09
those
38:09
two
38:10
things
38:11
that
38:11
those
38:11
two
38:12
things
38:12
might
38:12
be
38:13
very
38:14
different.
38:14
I'm
38:14
glad
38:14
you
38:14
opened
38:15
the
38:15
can
38:15
of
38:16
worms
38:16
on
38:16
AI.
38:17
I
38:17
don't
38:17
think
38:17
there's
38:17
any
38:17
conversation
38:18
that
38:18
happens
38:18
about
38:18
data
38:19
that
38:19
doesn't
38:19
you
38:20
have
38:20
to.
38:21
The
38:21
world
38:21
of
38:21
software
38:21
engineering
38:22
I've
38:22
seen
38:22
have
38:23
a,
38:23
you
38:23
know,
38:23
the
38:23
vibe
38:24
coding
38:24
thing
38:24
that's
38:24
all,
38:25
all
38:25
out
38:25
and
38:25
about
38:26
at
38:26
the
38:26
minute.
38:27
Something
38:27
I'm
38:27
quick
38:27
to
38:28
warn
38:28
people
38:28
of.
38:28
Like,
38:28
yes,
38:29
it's
38:29
perfectly
38:29
like
38:30
you
38:30
can
38:30
band
38:30
aid
38:31
pretty
38:31
much
38:31
any
38:31
platform
38:32
together.
38:32
You
38:32
can
38:32
Frankenstein
38:33
any
38:33
SaaS
38:34
platform
38:34
that
38:34
doesn't
38:34
exist
38:35
for
38:35
yourself.
38:35
Now,
38:36
if
38:36
you've
38:36
got
38:37
someone
38:37
who's
38:37
smart
38:37
enough
38:37
with
38:38
Zapier
38:38
and
38:39
make
38:39
which
38:39
are
38:39
not
38:39
that
38:40
hard
38:40
to
38:40
master
38:41
if
38:41
you
38:41
understand
38:42
the
38:42
logical
38:43
flow
38:43
yet
38:44
that
38:44
in
38:44
itself
38:44
is
38:45
a
38:45
very
38:45
unreliable
38:46
data
38:46
infrastructure.
38:46
Right.
38:47
If
38:47
you're
38:47
flowing
38:47
data
38:48
across
38:48
10
38:48
different
38:48
platforms
38:49
and
38:49
you
38:50
can
38:50
have
38:50
the
38:50
best
38:50
SaaS
38:50
platform
38:51
in
38:51
the
38:51
world
38:51
that
38:51
will
38:51
monitor
38:52
and
38:52
tell
38:52
you
38:52
what's
38:52
happening
38:53
and
38:53
how
38:53
those
38:53
data
38:53
flows
38:53
are
38:54
going.
38:54
But
38:54
if
38:55
they
38:55
break,
38:55
who's
38:56
going
38:56
to
38:56
fix
38:56
them?
38:56
What
38:57
do
38:57
you
38:57
think
38:57
some
38:57
of
38:58
the
38:59
best
38:59
impact
38:59
of
39:00
AI
39:00
will
39:00
be
39:00
on
39:01
the
39:01
data
39:02
world?
39:02
And
39:03
like,
39:03
what
39:03
are
39:03
you
39:03
most
39:04
scared
39:04
about?
39:04
Or
39:04
what
39:05
do
39:05
you
39:05
think
39:05
people
39:05
will
39:05
get
39:05
wrong?
39:06
Yeah,
39:06
I
39:07
think
39:07
the
39:07
best
39:08
impact
39:08
it
39:08
can
39:09
have,
39:09
which
39:09
I
39:09
have
39:09
not
39:10
seen
39:11
yet,
39:11
people
39:12
are
39:12
still
39:12
trying
39:12
to,
39:13
you
39:13
know,
39:14
do
39:14
things
39:14
without
39:15
fixing
39:15
their
39:15
data.
39:16
But
39:17
I
39:17
don't
39:17
know
39:17
about
39:18
you,
39:18
the
39:18
hardest
39:18
thing
39:19
that
39:19
it
39:20
feels
39:20
like
39:20
to
39:20
get
39:21
is
39:21
to
39:21
get
39:21
people
39:22
to
39:22
define
39:23
data
39:23
elements.
39:25
And
39:25
it's
39:25
probably
39:25
the
39:26
most
39:26
important
39:26
thing,
39:27
like,
39:27
what
39:28
is
39:28
this
39:28
thing?
39:29
What
39:29
is
39:29
it
39:29
not?
39:30
Now
39:30
I
39:30
know
39:30
how
39:31
to
39:31
use
39:31
it,
39:32
but
39:32
we
39:32
never.
39:33
We
39:33
just
39:33
kind
39:33
of
39:34
assume
39:34
things,
39:35
right?
39:35
So
39:36
if
39:36
we're
39:37
able
39:37
to
39:37
use
39:37
large
39:38
language
39:38
models
39:39
to
39:39
make
39:39
it
39:39
easier
39:40
to
39:40
have
39:41
people
39:41
define
39:42
their
39:42
data,
39:43
that
39:43
could
39:43
be
39:44
a
39:44
game
39:44
changer
39:45
in
39:45
data
39:45
governance.
39:46
Now,
39:47
nobody's
39:48
thinking
39:48
about
39:48
that,
39:49
but
39:49
that
39:49
allows
39:50
you
39:50
to
39:51
then
39:51
do
39:51
whatever
39:52
the
39:52
heck
39:52
you
39:52
want
39:52
with
39:52
it,
39:53
because
39:53
now
39:55
everybody
39:55
can
39:56
understand
39:56
you're
39:56
making
39:57
data
39:57
dictionaries,
39:58
you're
39:58
making
39:58
glossaries,
39:59
things
40:00
are
40:00
lovely.
40:01
We
40:01
all
40:01
know
40:01
what
40:02
to
40:02
do.
40:02
We
40:02
can
40:02
search.
40:04
It
40:04
could
40:04
be
40:05
a
40:05
game
40:05
changer.
40:06
Nobody's
40:06
focused
40:07
on
40:07
that
40:07
right
40:07
now.
40:08
Everybody's
40:09
just
40:09
trying
40:09
to
40:10
jam
40:10
a
40:10
chatbot
40:11
in
40:11
it.
40:12
And
40:12
I
40:12
think
40:13
what
40:13
people
40:13
are
40:14
finding
40:14
is
40:15
that.
40:16
Okay,
40:17
so,
40:17
yeah,
40:18
GPT4
40:19
kicks
40:19
ass.
40:19
Gemini,
40:20
let's
40:20
just.
40:21
I
40:21
don't
40:21
have
40:21
a
40:22
dog
40:22
in
40:22
the
40:22
race.
40:22
Right?
40:23
But
40:26
you're
40:26
using
40:26
a
40:26
black
40:27
box.
40:29
And
40:29
then
40:29
you
40:30
start
40:30
to
40:30
hear
40:30
this
40:30
term,
40:31
agenic
40:31
AI,
40:32
which
40:32
is
40:32
the
40:32
hot
40:32
new
40:33
one.
40:33
Like,
40:33
meet
40:34
the
40:34
old
40:34
boss,
40:34
same
40:34
as
40:35
the
40:35
new
40:35
boss,
40:35
right?
40:36
Gen
40:36
AI.
40:37
And
40:37
now
40:37
it's
40:37
agentic
40:38
AI.
40:40
And
40:41
what
40:41
they've
40:41
done
40:41
is
40:42
they've
40:42
just
40:43
shown
40:44
you
40:44
a
40:45
different
40:45
black
40:45
box,
40:46
which
40:46
is
40:46
the
40:47
agent
40:47
framework.
40:48
So
40:48
now
40:49
you
40:49
have
40:49
an
40:49
agent
40:50
framework
40:50
plugging
40:51
into
40:51
this
40:51
giant
40:52
model
40:53
that
40:53
has
40:54
read
40:55
the
40:55
Internet
40:56
and
40:56
you're
40:57
saying,
40:57
okay,
40:58
run
40:58
my
40:58
customer
40:59
service
40:59
business.
40:59
That's
41:00
crazy.
41:00
Yeah,
41:00
run
41:00
my
41:00
company.
41:01
And
41:01
then
41:02
you
41:02
get
41:02
to
41:02
the
41:02
point
41:02
where
41:03
it's
41:03
not
41:03
quite
41:03
working.
41:04
So
41:05
I
41:05
think
41:05
those
41:06
are
41:06
the
41:06
things.
41:07
I've
41:07
predicted
41:08
this
41:08
for
41:08
a
41:08
while,
41:09
but
41:09
I
41:10
do
41:11
think
41:11
there's
41:11
going
41:11
to
41:12
be
41:13
a
41:13
huge
41:13
snafu
41:14
for
41:15
a
41:15
very
41:15
big
41:15
company
41:16
using
41:17
this
41:17
technology.
41:18
And
41:18
it
41:18
just
41:19
going
41:19
off
41:20
the
41:20
rails.
41:20
Right.
41:21
That's
41:21
the
41:21
bad
41:21
thing.
41:22
I
41:22
hope
41:22
people
41:23
start
41:23
to
41:23
use
41:24
it
41:24
to
41:25
actually
41:26
improve
41:27
their
41:28
data
41:28
quality
41:28
and
41:29
understanding
41:29
through
41:30
automated
41:31
kind
41:31
of
41:31
definitions
41:32
and
41:32
stuff
41:32
of
41:33
that
41:33
nature.
41:33
Yeah,
41:34
you
41:34
heard
41:34
it
41:34
here
41:34
first.
41:35
Like,
41:35
we'll
41:35
play
41:35
that
41:35
clip
41:35
back
41:37
when.
41:37
A
41:37
couple
41:38
of
41:38
years
41:38
ago,
41:38
it
41:38
didn't
41:38
happen
41:39
yet.
41:39
I
41:39
was
41:39
like,
41:40
oh.
41:40
Or
41:40
they
41:40
kept
41:41
it
41:41
under
41:41
wraps.
41:41
So
41:41
I'm
41:42
like,
41:42
it's
41:42
gotta
41:43
happen,
41:43
though.
41:45
Third
41:45
time's
41:46
the
41:46
charm.
41:48
Pro
41:48
clock
41:49
is
41:49
right
41:49
twice
41:49
a
41:50
day.
41:50
Yeah.
41:51
It's
41:51
like
41:51
thinking
41:51
about
41:52
that.
41:52
Those
41:52
agentic
41:53
frameworks,
41:53
it
41:53
can
41:53
be
41:53
like,
41:54
if
41:54
the
41:54
data
41:54
is
41:54
not
41:54
correct
41:55
underneath
41:55
and
41:55
not
41:55
well
41:56
cared
41:56
for
41:56
and
41:56
well
41:56
governed
41:57
and
41:57
well
41:57
structured,
41:58
then
41:59
it's
41:59
the
41:59
black
41:59
box,
42:00
but
42:00
in
42:00
Christmas
42:00
wrapping
42:01
paper,
42:01
basically
42:02
the
42:02
agentic
42:03
AI.
42:03
And
42:04
again,
42:04
looping
42:05
back
42:05
to
42:06
where
42:06
we
42:06
started.
42:07
Like
42:07
any
42:07
good
42:07
comedian.
42:08
Like,
42:08
people
42:08
use
42:08
it
42:09
to
42:09
fulfill
42:10
the
42:10
stories
42:11
that
42:11
they
42:11
want
42:11
to
42:11
be
42:11
true
42:11
or
42:12
fulfill
42:12
their
42:12
biases.
42:13
And
42:13
they'll
42:13
invest
42:14
in,
42:15
you
42:15
know,
42:15
they'll
42:16
be
42:16
scared
42:16
of
42:16
AI.
42:17
And,
42:17
you
42:17
know,
42:18
I,
42:19
I
42:19
understand
42:19
people's
42:19
fear
42:20
about
42:20
it
42:20
for.
42:20
For
42:21
a
42:21
variety
42:21
of
42:21
different
42:21
reasons,
42:22
but
42:22
their
42:23
narrative
42:23
going
42:23
in
42:23
will
42:23
be,
42:24
it's
42:24
not
42:24
all
42:24
it
42:25
cracked
42:25
up
42:25
to
42:25
be.
42:26
And
42:26
then
42:26
they'll
42:26
stick
42:27
a
42:27
chat
42:27
bot
42:27
on
42:27
the
42:27
front
42:28
of
42:28
data
42:28
that's
42:28
not
42:28
very
42:29
well
42:29
cared
42:29
for.
42:29
They'll
42:30
type
42:30
a
42:30
question
42:30
and
42:30
it'll
42:31
give
42:31
them
42:31
the
42:31
wrong
42:31
answer
42:31
and
42:31
they'll
42:32
say,
42:32
therefore
42:32
my.
42:33
My
42:33
conclusion
42:34
is
42:34
that
42:34
AI
42:34
is
42:34
terrible.
42:36
Right.
42:36
And
42:37
my
42:37
conclusion
42:37
is
42:38
you
42:38
don't
42:38
know
42:38
how
42:38
AI
42:39
works.
42:39
Because
42:41
I
42:41
actually
42:41
get
42:42
pissed
42:42
when
42:42
I
42:42
hear
42:42
the
42:43
term
42:43
hallucination.
42:44
Because
42:45
I
42:45
go,
42:45
it
42:45
wasn't
42:46
hallucinating.
42:47
It
42:47
was
42:47
predicting
42:48
the
42:48
next
42:49
token,
42:49
which
42:50
it's
42:50
supposed
42:51
to
42:51
do.
42:51
You
42:52
were
42:52
the
42:52
one
42:53
that
42:53
was
42:53
putting
42:54
your
42:54
trust
42:54
into
42:54
it.
42:55
And
42:56
I
42:56
think
42:56
you're
42:56
right,
42:57
Graham.
42:57
Like,
42:57
we'll
42:58
see
42:58
people
43:00
misuse
43:01
this
43:01
technology.
43:01
Could
43:01
you,
43:03
you
43:03
know,
43:04
program
43:05
your
43:05
own
43:07
organizational
43:08
software?
43:09
Sure.
43:09
Like,
43:10
you
43:10
could
43:10
do
43:10
it,
43:11
but
43:11
if
43:11
you
43:12
don't
43:12
know
43:12
how
43:12
it's
43:12
supposed
43:13
to
43:13
work,
43:13
then
43:14
you
43:14
shouldn't
43:14
be
43:15
doing
43:15
that.
43:15
I
43:15
think
43:16
the
43:16
nature
43:16
of
43:16
work
43:17
changes.
43:17
The
43:17
work
43:18
doesn't
43:18
change.
43:19
It's
43:19
like,
43:20
I
43:20
think
43:21
we
43:21
will
43:21
get
43:21
to
43:21
a
43:22
point
43:22
where
43:22
a
43:22
very
43:23
good
43:23
data
43:24
person
43:24
can
43:25
tell
43:25
some
43:25
data
43:25
robots
43:26
or
43:26
agents
43:27
to
43:28
go
43:28
do
43:28
some
43:28
stuff
43:28
for
43:29
them.
43:29
But
43:30
always
43:30
checking
43:31
it
43:31
out.
43:31
Right.
43:33
It
43:34
doesn't
43:34
solve
43:34
for
43:34
that.
43:35
And
43:35
if
43:35
it
43:35
does,
43:35
we're
43:36
all
43:36
in
43:36
trouble.
43:37
I
43:37
mean,
43:38
I
43:38
see
43:38
this.
43:38
Even
43:39
when
43:39
you
43:39
train
43:39
a
43:39
custom
43:39
model,
43:41
you
43:41
think,
43:41
oh,
43:41
I've
43:42
covered
43:42
all
43:42
the
43:42
angles
43:42
and
43:42
you've
43:43
told
43:43
it
43:43
seven
43:43
or
43:43
eight
43:43
or
43:44
nine
43:44
constraints
43:44
even,
43:45
but
43:45
there's
43:45
always
43:46
90
43:46
constraints
43:47
that
43:47
you
43:47
haven't
43:47
taught
43:47
it
43:48
and
43:48
it
43:48
will
43:48
fall
43:48
through
43:48
the
43:48
gaps.
43:49
And
43:50
again,
43:50
if
43:50
you
43:50
are
43:50
not
43:51
self
43:51
aware
43:52
enough
43:52
or
43:52
not
43:53
adult
43:54
enough
43:54
in
43:57
your
43:57
work
43:58
progression
43:58
or
43:58
your
43:58
work
43:58
maturity,
43:59
then
43:59
it's
43:59
very
44:00
to
44:00
blame
44:00
the
44:00
system
44:02
or
44:02
blame
44:04
whatever
44:04
went
44:04
wrong
44:05
underneath
44:05
it.
44:06
Maybe
44:07
we
44:07
should
44:07
get
44:07
together
44:07
and
44:07
do
44:07
a
44:07
white
44:08
paper
44:08
on
44:08
this,
44:08
Nick,
44:08
because
44:09
I
44:09
have
44:09
the
44:10
same
44:10
thing.
44:11
LLMs
44:12
are
44:12
not
44:12
a
44:12
database
44:13
of
44:13
every
44:14
fact
44:15
that
44:15
exists
44:16
and
44:16
we've
44:16
verified
44:17
all
44:17
these
44:17
facts
44:18
and
44:18
we
44:18
know
44:19
they're
44:19
all
44:19
true.
44:20
It's
44:20
not
44:20
like,
44:21
and
44:21
this
44:21
is
44:21
the
44:21
fundamental
44:24
paradigm
44:24
that
44:24
people
44:25
get
44:25
wrong
44:25
is
44:25
not
44:26
like
44:26
a
44:26
smarter
44:26
version
44:27
of
44:27
Google.
44:27
It
44:27
is
44:27
a
44:28
predictive
44:28
model
44:28
that
44:29
predicts
44:29
the
44:30
next,
44:31
the
44:31
next
44:31
word
44:32
or
44:32
series
44:32
of
44:32
words
44:32
or
44:33
whatever
44:33
it
44:33
is,
44:33
or
44:33
the
44:33
flow
44:33
of
44:34
words
44:34
that
44:34
comes
44:34
after
44:34
the
44:34
ones
44:35
that
44:35
you've
44:35
given
44:35
it
44:35
within
44:35
the
44:36
context
44:36
that
44:36
you've
44:36
supplied.
44:37
Right.
44:37
So
44:38
the
44:38
two
44:39
things
44:39
that
44:39
always
44:39
frustrate
44:40
me
44:40
about
44:40
AI,
44:40
and
44:41
that's
44:41
one
44:41
of
44:41
them,
44:42
a
44:43
good
44:43
way
44:43
to
44:43
illustrate
44:44
that
44:44
one,
44:44
by
44:44
the
44:44
way
44:45
that
44:45
I've
44:46
seen
44:46
is
44:46
if
44:46
you
44:46
ask
44:47
an
44:47
AI
44:47
for
44:47
some
44:47
quotes
44:48
from
44:48
a
44:48
famous
44:49
person,
44:50
it
44:50
will
44:50
give
44:50
you
44:50
a
44:50
series
44:51
of
44:51
quotes
44:51
that
44:51
very
44:51
plausibly
44:52
could
44:52
have
44:52
been
44:53
said
44:53
by
44:53
that
44:53
person,
44:54
but
44:54
probably
44:54
won't
44:55
because
44:56
it
44:56
is
44:56
guessing
44:57
based
44:57
on
44:57
what
44:57
it
44:58
knows
44:58
about
44:58
this
44:58
person's
44:58
language
44:59
structure
44:59
and
44:59
what
44:59
they
44:59
have
45:00
said
45:00
and
45:00
what
45:00
they've
45:00
written
45:01
and
45:01
whatever
45:01
else.
45:01
Here
45:02
are
45:02
some
45:02
quotes
45:02
that
45:02
they
45:03
might
45:03
have
45:03
said
45:04
rather
45:04
than
45:04
here
45:05
are
45:05
some
45:05
quotes
45:05
that
45:05
they
45:05
did
45:06
that
45:06
they
45:06
did
45:06
say.
45:06
So
45:07
that's
45:07
an
45:07
interesting
45:07
test.
45:08
It's
45:08
like
45:09
somebody
45:09
saying
45:10
an
45:10
idiom
45:10
backwards
45:11
or
45:12
you're
45:12
one
45:12
buddy
45:13
that
45:13
just
45:13
quotes
45:14
the
45:14
movie
45:14
wrong,
45:15
you
45:15
know.
45:15
Oh,
45:16
you
45:16
kind
45:16
of
45:16
got
45:16
the
45:17
gist
45:17
of
45:17
it,
45:17
but
45:18
it's
45:18
not
45:18
quite
45:18
there.
45:19
Yeah,
45:19
or
45:20
I
45:20
heard
45:20
this
45:20
somewhere
45:20
else.
45:20
Like
45:21
the
45:21
humans,
45:21
the
45:21
humans
45:22
do
45:22
this
45:23
if,
45:23
if
45:23
they've
45:24
got
45:24
a
45:24
quote
45:24
that
45:24
they
45:24
think
45:24
is
45:25
clever
45:25
and
45:25
they
45:25
don't
45:25
quite
45:26
know
45:26
who
45:26
to
45:26
attribute
45:26
it
45:27
to.
45:27
Like
45:27
they
45:27
always
45:27
say
45:27
it
45:28
was
45:28
Churchill,
45:30
one
45:30
of
45:30
the
45:30
most,
45:31
the
45:31
most
45:31
misquoted
45:32
people
45:32
ever.
45:32
Because
45:33
if
45:33
in
45:33
doubt,
45:33
I'll
45:34
say
45:34
Churchill.
45:34
That
45:34
sounds,
45:35
you
45:35
know,
45:35
authoritative.
45:37
I'll
45:38
use
45:38
Churchill.
45:38
But
45:39
that's
45:39
the
45:39
other
45:39
one
45:39
that
45:40
I
45:40
get,
45:40
you
45:40
know,
45:41
when
45:41
I
45:41
see
45:41
these
45:41
outrage
45:42
posts
45:42
about
45:43
AI
45:44
is
45:44
biased
45:44
or
45:44
AI
45:45
thinks
45:46
that
45:46
all
45:47
people
45:47
of
45:47
XYZ
45:48
group
45:48
do
45:48
XYZ
45:49
thing.
45:50
Not
45:50
to
45:51
boost
45:51
any
45:52
Netflix
45:52
series
45:52
in
45:52
particular,
45:53
but
45:53
that
45:53
is
45:53
the,
45:54
that
45:54
is
45:54
the
45:54
black
45:54
mirror
45:55
that
45:55
we're
45:55
looking
45:56
at
45:56
ourselves
45:56
like
45:57
it
45:57
is
45:57
reflecting
45:58
ourselves
45:58
and
45:59
our
45:59
work
45:59
and
45:59
our
46:00
collective
46:00
creation
46:00
as
46:01
humanity,
46:01
which
46:01
is
46:01
The
46:01
Internet,
46:03
you
46:03
know,
46:03
the
46:03
good,
46:04
the
46:04
bad,
46:04
and,
46:05
you
46:05
know,
46:05
the
46:05
ugly,
46:06
the
46:06
far
46:06
reaches
46:07
of
46:07
Reddit
46:07
and
46:08
TikTok
46:08
and
46:08
the
46:09
Dark
46:09
web,
46:09
like,
46:10
that's
46:10
humanity,
46:11
and
46:11
it
46:11
is
46:12
reflecting
46:13
that
46:13
back
46:14
at
46:14
us.
46:14
And
46:14
it's
46:15
very
46:15
easy
46:15
to,
46:16
like,
46:17
saying
46:17
AI
46:18
hallucinates
46:18
is
46:19
a
46:19
really
46:19
easy
46:19
way
46:20
to,
46:20
you
46:21
know,
46:21
devolve
46:22
yourself
46:22
of
46:22
any
46:22
ownership.
46:23
Well,
46:23
that's
46:23
nothing
46:23
to
46:23
do
46:23
with
46:24
humanity
46:24
or
46:25
me.
46:25
Like,
46:25
it's
46:25
reflecting
46:26
us
46:26
back
46:26
at
46:26
us.
46:26
And
46:26
it's,
46:27
it's,
46:27
it's,
46:27
you
46:28
know,
46:28
we
46:28
need
46:28
to
46:28
remember
46:28
that
46:29
sometimes.
46:30
And
46:30
we,
46:30
when
46:30
we
46:31
go,
46:31
you
46:32
know,
46:32
below
46:32
the
46:32
ocean
46:33
in
46:33
the
46:33
iceberg,
46:35
people
46:35
have
46:35
been
46:36
saying,
46:36
my
46:36
data
46:36
is
46:37
bad.
46:38
No,
46:38
it's
46:38
not.
46:39
Your
46:40
data
46:40
is
46:40
not
46:41
defined
46:41
well.
46:42
Right.
46:42
You
46:42
don't
46:43
understand
46:43
it.
46:44
And
46:44
the
46:45
people
46:45
trying
46:46
to
46:46
give
46:46
it
46:46
to
46:46
you
46:46
don't
46:47
understand
46:47
it.
46:47
That,
46:48
that's,
46:49
that's
46:49
the
46:50
answer.
46:50
The
46:50
answer
46:51
is
46:51
the
46:51
AI
46:51
doesn't
46:52
suck
46:52
and
46:53
my
46:53
data's
46:53
bad.
46:54
It's
46:54
like,
46:55
no,
46:56
you
46:56
don't
46:56
understand
46:57
the
46:57
data
46:57
because
46:58
just
46:58
like
46:59
AI
46:59
is
46:59
a
46:59
reflection
47:00
of
47:00
society
47:00
because
47:01
it
47:01
read
47:01
the
47:01
Internet,
47:03
data
47:04
is
47:04
a
47:04
reflection
47:05
of
47:05
process.
47:06
People
47:06
result.
47:07
Like,
47:07
it
47:08
doesn't
47:08
come
47:08
out
47:08
of
47:08
thin
47:09
air.
47:10
It's
47:10
just
47:10
digitized
47:11
information
47:12
from
47:12
something
47:13
that
47:13
happened
47:14
or
47:15
something
47:15
that
47:15
exists.
47:16
That's
47:17
all
47:17
it
47:17
is.
47:17
It's
47:18
really
47:18
important
47:18
to
47:19
remember
47:19
that.
47:19
And
47:19
it's
47:19
a
47:20
really
47:20
difficult
47:20
tightrope
47:21
to
47:22
walk
47:22
thinking
47:22
about
47:22
data
47:23
from
47:23
a
47:23
product
47:23
perspective,
47:24
because
47:25
you
47:25
know
47:25
as
47:25
well
47:25
as
47:25
I
47:26
do,
47:26
you
47:26
put
47:26
a
47:26
new
47:26
BI
47:27
product
47:27
out
47:27
there,
47:27
whatever
47:27
it
47:28
might
47:28
be,
47:28
a
47:28
dashboard
47:28
out
47:28
there,
47:29
like,
47:29
the
47:29
second
47:30
that
47:30
it's
47:30
demonstrably
47:31
wrong
47:32
in
47:32
something
47:32
that
47:32
it
47:33
says,
47:33
or
47:33
trust
47:33
the
47:34
roads
47:34
and
47:34
you
47:34
may
47:34
as
47:34
well
47:34
start
47:34
again.
47:35
And
47:37
folks
47:37
lose
47:38
trust
47:38
in
47:38
data
47:38
products
47:39
so
47:39
quickly
47:39
and
47:39
easily,
47:40
even
47:40
if,
47:41
you
47:41
know
47:42
they
47:43
are
47:43
the
47:43
problem
47:43
in
47:43
the
47:43
first
47:44
place.
47:44
And,
47:44
you
47:45
know,
47:45
CRMs
47:46
is
47:46
the
47:46
place
47:46
I
47:46
see
47:46
this
47:46
the
47:47
most
47:47
of,
47:47
like,
47:48
oh,
47:48
well,
47:48
I
47:48
don't
47:48
even
47:49
bother
47:49
with
47:49
the
47:49
CRM
47:50
data
47:50
because
47:50
I
47:50
don't
47:50
trust
47:51
that
47:51
it's
47:51
so
47:51
bad.
47:52
And
47:53
then
47:53
you
47:54
could
47:54
automate
47:54
the
47:54
crap
47:55
out
47:55
of
47:55
it,
47:55
you
47:55
could
47:55
connect
47:56
it,
47:56
you
47:56
could
47:56
dashboard
47:57
over
47:57
the
47:57
front
47:57
of
47:57
it.
47:57
But
47:57
at
47:58
the
47:58
end
47:58
of
47:58
the
47:58
day,
47:58
if
47:58
the
47:58
salespeople
47:59
aren't
47:59
following
47:59
the
47:59
process
47:59
and
48:00
putting
48:01
the
48:01
context
48:01
and
48:01
the
48:02
right
48:02
information
48:02
into
48:02
the
48:03
CRM
48:03
in
48:03
the
48:03
first
48:03
place,
48:04
then
48:04
of
48:04
course,
48:06
and
48:07
I
48:07
feel
48:08
it's
48:08
hard
48:08
to
48:08
feel
48:08
sympathy
48:09
for
48:10
behemoth
48:10
like
48:10
Salesforce
48:11
or
48:11
HubSpot,
48:12
for
48:12
example,
48:12
but
48:13
they
48:13
get
48:13
a
48:13
bad
48:13
rap
48:14
because
48:14
people
48:14
are
48:14
like,
48:14
oh,
48:14
it's
48:15
all
48:15
over
48:15
the
48:15
place
48:15
and
48:15
the
48:15
data's
48:16
bad,
48:16
when
48:16
actually
48:16
the
48:16
people
48:17
using
48:17
it
48:17
are
48:17
just
48:17
not
48:18
putting
48:18
the
48:18
stuff
48:18
in
48:18
Right
48:19
in
48:19
the
48:19
first
48:19
place.
48:20
Yeah.
48:20
I
48:20
mean,
48:21
they
48:21
should
48:21
only
48:21
be
48:21
in
48:22
trouble
48:22
for
48:22
selling
48:23
the
48:23
fact
48:23
that
48:23
they're
48:23
going
48:23
to
48:24
solve
48:24
everything
48:24
with
48:25
one
48:25
thing.
48:25
Right?
48:26
Like
48:26
that.
48:26
That's
48:27
the
48:27
only
48:27
problem
48:27
with
48:27
those
48:28
guys.
48:28
It's
48:28
like,
48:28
oh
48:29
yeah,
48:29
SAP,
48:29
we
48:30
got
48:30
master
48:30
data.
48:30
I
48:31
was
48:31
like,
48:31
no,
48:31
you
48:31
don't.
48:32
You
48:32
have
48:33
data
48:34
structured
48:34
to
48:35
work
48:35
with
48:35
SAP
48:36
and
48:36
whatever
48:36
you
48:37
installed
48:37
in
48:37
it.
48:38
Yes,
48:38
you
48:38
do.
48:39
Now,
48:39
does
48:39
that
48:40
fit
48:40
into
48:41
the
48:41
entire
48:42
product
48:42
life
48:42
cycle
48:43
within
48:43
your
48:44
company?
48:44
Probably
48:45
not.
48:46
So
48:46
now
48:46
people
48:46
are
48:46
doing
48:47
workarounds
48:47
and
48:48
all
48:48
sorts
48:48
of
48:48
other
48:48
stuff
48:49
and
48:49
does
48:50
that
48:50
need
48:50
to
48:50
be.
48:51
I
48:51
don't
48:51
want
48:51
to.
48:52
There
48:52
was
48:53
an
48:53
egregious
48:55
error
48:55
I
48:56
kept
48:56
seeing
48:56
at
48:57
a
48:57
former
48:57
company.
48:58
And
48:58
we
48:58
can
48:58
talk
48:58
about
48:59
this
48:59
after.
48:59
But
49:00
why
49:01
isn't
49:01
it
49:01
a
49:01
dropdown?
49:02
Why
49:03
is
49:03
it
49:03
a
49:03
free
49:03
form
49:04
text
49:04
field?
49:05
Why
49:05
can
49:06
anybody
49:06
put
49:07
whatever
49:07
they
49:07
want
49:08
in
49:08
that?
49:09
Why
49:09
isn't
49:09
there
49:09
a
49:10
process
49:10
like,
49:10
like
49:11
if
49:11
you
49:11
want
49:11
good
49:12
insights,
49:12
you
49:12
need
49:13
to
49:13
have
49:13
good
49:13
processes
49:14
to
49:14
create
49:15
good
49:15
data
49:15
and
49:15
understand
49:16
it,
49:16
to
49:16
use
49:16
it.
49:17
And
49:17
we
49:17
just,
49:18
we
49:18
forget
49:18
about
49:19
that.
49:20
Oh,
49:20
let's
49:20
just
49:20
work
49:21
with
49:21
what
49:21
we
49:21
want.
49:21
It's
49:22
like,
49:22
well,
49:23
less
49:23
is
49:24
more.
49:24
Sometimes
49:25
less
49:25
good
49:25
data,
49:27
more
49:27
insights,
49:28
less
49:29
data.
49:29
Sciencey
49:30
stuff
49:30
like,
49:31
let's
49:31
just
49:31
not
49:31
machine
49:32
learn
49:32
something
49:32
when
49:33
we
49:33
can't
49:33
even
49:33
answer
49:33
the
49:34
simple
49:34
question.
49:35
We're
49:35
not
49:35
to
49:35
that
49:36
point.
49:36
Right.
49:38
Everybody
49:38
wants
49:39
to
49:39
skip
49:39
steps.
49:40
And
49:40
I
49:40
get
49:40
it.
49:41
It
49:41
just,
49:42
that's
49:42
not
49:42
how
49:42
it
49:42
works.
49:43
But
49:45
that's
49:46
how
49:46
it
49:47
feels
49:47
like
49:47
the
49:47
economy
49:48
works,
49:48
is,
49:49
hey,
49:49
I
49:49
gotta
49:49
hit
49:49
my
49:50
5
49:50
to
49:51
8%
49:51
growth
49:52
target
49:53
or
49:53
else.
49:54
Okay,
49:55
that's
49:55
really
49:55
hard.
49:56
What
49:56
happened
49:56
to
49:56
breaking
49:57
even?
49:57
That's
49:57
pretty
49:58
good.
49:58
A
49:58
couple
49:58
of
49:58
things
49:59
go
49:59
into
49:59
that
49:59
and,
49:59
well,
50:00
Salesforce
50:00
in
50:00
particular,
50:01
not
50:01
to
50:01
pick
50:01
on
50:01
them,
50:01
but
50:02
like
50:02
one
50:02
of
50:02
the
50:02
reasons
50:02
they
50:03
expand,
50:03
like,
50:03
like
50:04
it's
50:04
back
50:04
to
50:04
business
50:04
school
50:05
fundamentals.
50:05
If
50:06
you
50:06
want
50:06
to
50:06
grow
50:06
your
50:06
business,
50:06
you
50:07
need
50:07
to
50:07
either
50:08
get
50:08
more
50:08
customers
50:08
or
50:09
make
50:09
your
50:09
existing
50:09
customers
50:10
more
50:10
valuable.
50:10
And
50:10
like
50:11
bloating
50:11
the
50:12
platform
50:12
is
50:14
the
50:14
surest
50:14
possible
50:15
way
50:15
and
50:15
telling
50:15
all
50:16
the
50:16
customers
50:16
that
50:16
they
50:16
have
50:16
to
50:16
use
50:17
this
50:17
and
50:17
it
50:17
integrates
50:17
super
50:18
well
50:18
even
50:18
if
50:19
they're
50:19
not
50:19
experts
50:19
in
50:19
that
50:20
particular
50:20
function.
50:20
It's
50:21
the
50:21
surest
50:21
possible
50:21
route
50:22
into
50:22
that.
50:23
And
50:23
same
50:23
as
50:23
you,
50:24
I've
50:24
come
50:24
across
50:25
a
50:26
number
50:26
of
50:26
different
50:26
companies
50:27
who,
50:29
you
50:29
know,
50:29
it's
50:30
often
50:30
one
50:30
of
50:30
the
50:30
first
50:30
questions
50:31
I
50:31
ask
50:31
when
50:31
I
50:31
speak
50:31
to
50:32
prospective
50:33
clients
50:34
or
50:34
I
50:34
find
50:34
myself
50:35
asking,
50:36
what
50:36
if
50:36
someone
50:37
deletes
50:37
that
50:37
Excel
50:38
spreadsheet?
50:39
I
50:40
just
50:40
let
50:40
that
50:40
question
50:40
kind
50:40
of
50:41
float
50:41
in
50:41
the
50:41
room.
50:41
And
50:43
that
50:44
normally
50:44
sets
50:45
the
50:45
scene
50:45
for
50:46
a
50:46
kind
50:46
of
50:46
different
50:46
mindset
50:47
towards
50:47
data
50:47
transformation.
50:48
Oh
50:48
yeah,
50:49
Excel,
50:49
it's
50:50
A
50:50
good
50:50
tool,
50:50
though.
50:51
I
50:51
think
50:51
we
50:51
keep
50:51
shitting
50:52
on
50:52
it.
50:53
It's
50:53
good,
50:54
it
50:54
works.
50:55
It's
50:55
still
50:55
my
50:55
go
50:55
to
50:56
thing
50:56
to
50:57
mess
50:57
around
50:57
with
50:57
data
50:58
real
50:58
quick.
50:59
And
50:59
that's
50:59
fine.
51:00
It's
51:00
no
51:01
place
51:01
to
51:01
store
51:01
important
51:02
data
51:03
in
51:03
perpetuity.
51:06
It's
51:07
just
51:07
not.
51:09
And
51:09
that's
51:10
the
51:10
difference.
51:10
And
51:10
I
51:10
see
51:11
these
51:11
chains
51:11
of
51:11
data
51:11
provenance
51:12
where
51:12
it's
51:12
like,
51:13
oh,
51:13
well,
51:14
employee
51:15
A
51:15
pulled
51:15
this
51:16
into
51:16
Excel
51:17
off
51:17
of
51:17
the
51:18
data
51:18
warehouse,
51:19
and
51:19
then
51:19
they
51:19
sent
51:20
it
51:20
to
51:20
employee
51:20
B
51:20
and
51:20
then
51:21
they
51:21
made
51:21
their
51:21
own
51:22
version
51:22
of
51:22
it.
51:22
And
51:22
then
51:22
employee
51:23
C
51:23
cut
51:24
this
51:24
out
51:24
and
51:24
made
51:24
it
51:24
for
51:24
their
51:25
own
51:25
department.
51:25
And
51:25
suddenly
51:26
data.
51:26
Data
51:26
has
51:26
gone
51:26
through
51:26
like
51:27
five
51:28
uncontrolled
51:28
transformation
51:29
hops
51:29
and
51:30
then
51:30
it
51:30
gets
51:30
brought
51:31
back
51:31
to
51:31
the
51:31
data
51:31
leader
51:32
of
51:32
like,
51:32
why
51:32
is
51:32
this
51:33
wrong?
51:36
Straight
51:36
so
51:36
far
51:36
from
51:36
the
51:37
path.
51:37
I
51:37
love
51:38
the
51:38
hey,
51:38
I'm
51:38
gonna
51:39
get
51:39
a
51:39
dump
51:39
out
51:39
of
51:40
SAP
51:40
or
51:41
Salesforce.
51:42
I'm
51:42
gonna
51:43
throw
51:43
it
51:43
into
51:43
tableau.
51:45
I'm
51:45
gonna
51:45
do
51:45
a
51:46
bunch
51:46
of
51:46
stuff,
51:47
then
51:47
I'm
51:47
gonna
51:48
extract
51:48
that
51:49
into
51:49
a,
51:50
you
51:50
know,
51:51
CSV
51:51
file
51:52
and
51:52
then
51:52
I'm
51:52
gonna
51:52
re
51:53
upload
51:53
that
51:53
into.
51:54
It's
51:54
like,
51:55
okay,
51:56
like,
51:57
people
51:57
do
51:57
what
51:57
they
51:57
have
51:58
to.
51:58
To
51:58
your
51:59
point,
52:00
you
52:00
know,
52:00
around
52:01
hierarchy
52:02
of
52:02
needs.
52:02
I
52:03
want
52:03
some
52:03
stability
52:04
here.
52:05
I
52:05
want
52:05
a
52:05
job.
52:06
I
52:06
need
52:06
to
52:07
get
52:07
something
52:07
done.
52:08
I
52:08
don't
52:08
have
52:09
time
52:09
to
52:09
mess
52:09
around
52:10
with
52:10
your
52:10
bad
52:11
data.
52:11
It's
52:12
just
52:12
data
52:12
that's
52:13
not
52:13
servicing
52:13
somebody
52:14
the
52:14
way
52:14
it
52:15
needs
52:15
to
52:15
be.
52:15
And
52:15
that
52:15
comes
52:16
down
52:16
to
52:16
what
52:16
do
52:16
you
52:16
need?
52:17
Like,
52:17
what's
52:18
the
52:18
user
52:18
experience?
52:19
So
52:19
I'll
52:19
create
52:20
my
52:20
own
52:20
version
52:20
of
52:20
the
52:20
truth
52:21
because,
52:22
you
52:22
know,
52:22
there's,
52:23
there's
52:23
sufficient
52:23
doubt
52:24
over
52:24
the,
52:24
the
52:25
enterprise
52:25
version
52:25
of
52:26
foosball.
52:26
I'll
52:26
create
52:27
my
52:27
own
52:27
and
52:27
try
52:27
and
52:27
land
52:28
that,
52:29
you
52:29
know,
52:29
full
52:29
circle.
52:30
Back
52:30
we
52:30
go
52:30
again.
52:31
Anyway,
52:31
Nick,
52:32
I
52:32
think
52:32
that's
52:32
like,
52:32
I
52:33
want
52:33
to
52:33
be
52:33
conscious
52:33
of
52:33
your
52:33
time
52:34
and
52:34
I
52:34
think
52:34
there's
52:34
a
52:34
million
52:35
things
52:35
that
52:36
we
52:36
could
52:36
rabbit
52:37
holes
52:37
that
52:37
we
52:37
can
52:37
go
52:37
down.
52:38
I'll
52:38
definitely
52:38
send
52:38
you
52:38
the
52:38
link
52:38
to
52:39
that
52:39
golf
52:39
book
52:39
and
52:39
you
52:39
can
52:39
let
52:40
me
52:40
know
52:40
when
52:40
you
52:40
get
52:40
down
52:40
to
52:40
seven.
52:41
Yeah.
52:41
Although
52:42
we'll
52:42
see.
52:43
The
52:44
year
52:44
has
52:44
not
52:44
started
52:45
well,
52:45
my
52:45
friend.
52:46
Me
52:47
neither.
52:47
Like
52:48
again,
52:48
looking
52:49
at
52:49
the
52:49
data
52:49
last
52:49
three
52:49
years,
52:50
my
52:50
chart
52:50
always
52:51
does
52:51
the
52:51
same
52:51
thing.
52:51
We
52:51
go
52:52
up
52:52
and
52:52
then
52:52
we
52:52
go
52:52
down.
52:53
We
52:53
go
52:53
up
52:53
and
52:53
then
52:53
we
52:53
go
52:53
down.
52:54
And
52:54
it's
52:54
just,
52:54
it's,
52:55
it's
52:55
shortening
52:56
the
52:56
up
52:56
and
52:56
lengthening
52:57
the
52:57
down.
52:58
But
52:58
I've
52:58
found
52:59
as
52:59
the
52:59
handicap
52:59
come
53:00
down,
53:00
it
53:00
comes
53:00
down,
53:01
it
53:01
gets,
53:01
the
53:02
harder
53:02
and
53:02
harder
53:02
it
53:03
gets.
53:03
I,
53:03
I
53:03
think
53:04
like,
53:05
you
53:06
know,
53:06
comparing
53:07
18
53:07
to
53:08
12
53:08
to
53:09
12
53:09
to
53:09
9
53:10
like
53:11
that.
53:11
Those,
53:11
those
53:12
last
53:12
three
53:12
have
53:12
been
53:13
so
53:13
much
53:13
harder
53:13
than
53:13
18
53:13
to
53:14
12
53:14
was.
53:14
Right.
53:15
Like
53:15
18
53:16
to
53:16
12
53:16
is
53:16
basically
53:16
keep
53:16
it
53:17
in
53:17
bounds
53:17
and,
53:17
and
53:18
stop
53:18
three
53:18
putting
53:19
so
53:20
much.
53:21
12
53:21
to
53:21
9
53:22
feels
53:22
like
53:23
a
53:23
whole
53:23
different
53:23
kettle
53:23
of
53:24
fish.
53:24
Anyway,
53:24
we
53:24
can
53:24
talk
53:25
about
53:25
that
53:25
a
53:25
different
53:25
time.
53:25
Maybe
53:26
we
53:26
can
53:26
get
53:26
a
53:26
game
53:26
sometime
53:27
if
53:27
we're
53:27
ever
53:27
in
53:27
the
53:27
same
53:28
geography.
53:28
That
53:29
would
53:29
be
53:29
cool.
53:29
But
53:30
thanks
53:30
so
53:31
much
53:31
for
53:31
coming
53:31
on
53:31
and
53:32
sharing
53:32
so
53:32
much
53:33
today.
53:35
I'd
53:36
love
53:36
to
53:36
give
53:36
you
53:36
a
53:36
chance
53:36
to
53:37
talk
53:37
about
53:37
what
53:38
you're
53:38
going
53:38
to
53:38
do
53:38
next
53:38
and
53:39
where
53:39
people
53:39
can
53:39
find
53:39
you.
53:40
Oh,
53:41
sure.
53:41
So
53:42
what
53:42
I'm
53:42
doing
53:43
next
53:43
is
53:43
I'm
53:44
going
53:44
fully
53:44
back
53:45
to
53:45
school
53:45
full
53:46
time,
53:46
accelerated
53:47
to
53:47
become
53:48
a
53:49
psychotherapist
53:50
essentially.
53:51
It's
53:51
something
53:52
that
53:52
I've
53:52
always
53:53
been
53:53
interested
53:54
in.
53:54
I've
53:54
definitely
53:55
participated
53:55
in.
53:56
I
53:56
think
53:57
it's
53:57
really
53:58
what
53:58
I'm
53:59
finding
53:59
as
53:59
I'm
53:59
doing
54:00
the
54:00
studies
54:00
is
54:00
a
54:00
lot
54:01
of
54:01
my
54:01
consulting
54:02
and
54:02
data
54:02
driven,
54:04
like
54:04
shticks
54:05
I
54:05
would
54:05
call
54:06
they're
54:06
going
54:06
to
54:06
be
54:06
pretty
54:07
useful.
54:07
So
54:08
I'm
54:08
doubling
54:09
down
54:09
into
54:09
that.
54:10
But
54:10
I'm
54:10
also
54:11
kind
54:11
of
54:12
looking
54:12
for,
54:13
you
54:13
know,
54:13
consulting
54:14
gigs.
54:15
So
54:16
if
54:16
anybody
54:16
needs
54:17
help,
54:17
I'm
54:18
around
54:18
for
54:18
about
54:18
10
54:19
hours
54:19
a
54:19
week.
54:20
Happy
54:21
to
54:21
help
54:21
with
54:21
things
54:22
around
54:22
data,
54:22
strategy,
54:23
analytics.
54:24
I
54:24
know
54:24
enough
54:25
about
54:25
AI.
54:26
I
54:26
kind
54:26
of
54:27
call
54:27
myself
54:27
like
54:28
a
54:28
functional
54:29
architect.
54:30
Like,
54:30
I'll
54:31
get
54:31
you
54:31
close,
54:32
you're
54:32
going
54:32
to
54:32
want
54:33
to
54:33
double
54:33
check
54:33
it
54:33
and
54:34
that's,
54:34
you
54:34
know,
54:34
I
54:35
hope
54:35
it's
54:35
right,
54:36
right,
54:36
Graham.
54:36
But
54:37
it's
54:38
not
54:38
a
54:38
strategy,
54:38
Nick.
54:39
It
54:40
gets
54:40
me
54:40
close
54:40
enough.
54:42
So
54:42
yeah,
54:42
I'm
54:43
just,
54:43
you
54:43
know,
54:43
I'm
54:43
excited
54:44
to
54:45
define
54:45
the
54:46
next
54:46
half
54:46
of
54:46
my
54:47
life
54:47
and
54:47
still
54:48
carry
54:48
on
54:50
my
54:50
data
54:50
driven
54:51
mindset
54:51
as
54:52
I
54:52
help
54:52
people
54:53
one
54:53
on
54:53
one,
54:53
I
54:53
hope.
54:55
Cool.
54:56
Well,
54:56
kudos
54:56
and
54:56
congratulations
54:57
for
54:57
taking
54:57
control
54:58
of
54:58
your
54:58
own
54:58
path.
54:58
I
54:58
think
54:59
that's
54:59
the
54:59
ultimate
55:00
sign
55:00
of
55:00
career
55:00
maturity.
55:02
Wish
55:02
you
55:03
nothing
55:03
but
55:03
the
55:03
best
55:03
for
55:03
it.
55:04
And
55:04
you
55:04
know,
55:04
for
55:04
anybody
55:04
watching
55:05
who
55:05
wants
55:05
to
55:05
be
55:05
connected
55:06
to
55:06
Nick,
55:06
you
55:06
can
55:06
find
55:06
him
55:06
on
55:07
LinkedIn
55:07
or
55:07
I'd
55:07
be
55:07
more
55:08
than
55:08
happy
55:08
to
55:08
connect,
55:10
connect
55:10
you
55:10
to
55:10
him
55:11
myself
55:11
if
55:11
you
55:11
want
55:12
to
55:12
reach
55:12
out
55:12
to
55:12
me.
55:12
So
55:13
just
55:13
let
55:13
me
55:13
know.
55:13
And
55:14
thanks
55:14
so
55:14
much
55:14
for
55:15
another
55:15
great
55:16
chat.
55:16
Yeah,
55:16
thanks
55:17
for
55:17
having
55:17
me.
55:18
Thanks
55:18
for
55:18
tuning
55:19
in
55:19
to
55:19
scale
55:19
your
55:19
business
55:20
with
55:20
data.
55:21
If
55:21
today's
55:21
conversation
55:22
gave
55:22
you
55:22
even
55:22
one
55:23
insight,
55:23
one
55:23
aha
55:24
moment,
55:24
or
55:24
one
55:25
new
55:25
idea
55:25
to
55:25
take
55:26
action
55:26
on,
55:26
do
55:27
me
55:27
a
55:27
favor,
55:28
subscribe,
55:29
leave
55:29
a
55:29
review
55:30
and
55:30
share
55:30
this
55:30
with
55:30
someone
55:31
who's
55:31
ready
55:31
to
55:31
level
55:31
up.
55:32
Until
55:32
next
55:33
time,
55:33
keep
55:33
asking
55:34
the
55:34
right
55:34
questions,
55:35
keep
55:35
challenging
55:35
the
55:36
status
55:36
quo
55:36
and
55:37
keep
55:37
scaling
55:37
with
55:38
data.