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From YouTube: Coalesce Pitch: Data Team Maturity
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A
B
A
Today,
we're
gonna
talk
a
little
bit
about
data
team
maturity,
so
amateur
data
organization
is
first
and
foremost
amateur
analytics
organization.
Well,
this
seems
obvious
to
organizations
that
work
in
data
a
day
in
and
day
out,
when
most
people
hear
data,
they
think
about
machine
learning
and
recommendation
algorithms,
not
reporting.
A
So
when
you
think
about
the
maturity
of
a
data,
organist
did
an
analytics
organization.
I
recently
wrote
a
blog
post
up,
presented
these
three
tiers,
recording
insights
and
predictions
reporting
being
what
your
organization
understands
as
facts,
insights
being
how
your
data
team
actually
adds
value
and
predictions
being
a
nod
to
the
future.
A
What
I've
come
to
realize
since
publishing
that
blog
post
is
that
it's
actually
a
flywheel
where
your
data
team
produces
your
reporting
that
generates
insights,
which
leads
to
predictions,
but
then
those
predictions
call
for
more
reporting
to
see
how
you
performed
against
those
report
names
against
that
reporting.
It's
not
enough
to
just
know
where
your
team
is
on
this
maturity
scale,
though,.
B
So
what
we're
proposing
in
this
talk
is
a
discussion
about
how
to
level
up
your
team
to
really
move
up
that
pyramid
or
increase
the
rate
that
that
flywheel
is
turning
we're
gonna
hit
three
main
areas.
First,
discussion
about
investing
in
your
team
aim
both
from
a
headcount
perspective,
but
also
in
a
skills
perspective
skills
of
your
individual
contributors
on
the
team,
but
also
everybody
in
the
organization.
B
B
How
can
you
generate
value
quicker
with
the
work
that
you're
doing
we're
going
to
advocate
strongly
for
leveraging
open
source
analytics
ie
using
things
like
DBT
packages,
but
also
leveraging
the
the
one
of
our
core
values
of
iteration
and
how
that
can
be
really
impactful
for
teams?
We
think
we
believe
you
know
data.
Is
it
as
an
incredible
tool,
but
this
road
of
maturity
can
be
very
bumpy,
and
so
we're
advocating
that
with
a
strong
team,
you
can
create
a
very
data-driven
organization
and
quickly
find
yourself
seeing
the
team's
value.
B
So
we
care
about
this
a
lot
really
because
we've
been
there.
We
believe
that
data
teams
have
a
ton
of
unreached
potential
and
we're
hoping
that
this
this
framework
can
help
organizations
kind
of
better
understand
where
they
are
and
in
terms
of
maturity
and
help
them
become
better
and
it's
it's
something
that
we've
applied
in
our
own
work
experience.
B
B
We
think
it'll
be
valuable
and
important
for
the
community,
because
this
is
something
that
will
impact
all
layers
of
an
organization
and
the
data
function
in
particular
individual
contributors,
managers,
directors,
everybody
in
this
C
suite
this
is-
is
well
we'll
kind
of
touch
their
lives
in
a
good
way
and
hopefully
add
a
ton
of
value
to
the
business.
We
think
this
reframes
the
conversation
around
data
teams
a
bit
more
to
something
that
is,
is
more
powerful
and
empowering
it
and
hopefully
makes
sense.
B
You
know
down
from
it,
I
see
up
to
a
C
suite
executive,
and
this
isn't
really
something
that
we've
we've
heard.
Articulated
Emily
had
put
the
idea
in
a
blog
post,
we've
gotten
some
good
feedback,
but
kind
of
this.
This
flywheel
mentality,
the
specific
steps
that
we're
advocating
for
we'd
love
to
hear
more
feedback
on
and
or
you
to
present
it
and
then.
Finally,
we
want
to
just
advocate
for
like
why
why?
Why
are
we
best
positioned
to
talk
about
this?