►
Description
Maggie Hays (SpotHero) and Dexter Lee (Acryl Data) describe how they implemented Product Analytics in DataHub using the design sprint philosophy.
A
B
B
I
think
it
was
april.
Time
is
weird
right
now.
Who
knows
it
was
within
the
last
month
or
so
I
teamed
up
with
the
guys
over
at
acryl
data
to
run,
what's
called
a
design
sprint.
So
I'll
walk
you
guys
through.
What
is
that?
What
does
that
mean?
What
did
we
do?
What
was
the
point
of
it
and
then
we'll
move
into
a
live
demo?
B
So
can
y'all
see
my
screen?
Look
good
all
right.
So
if
you've
never
heard
of
a
design
sprint,
it's
something
that
was
created.
It
came
out
of
gv
or
google
ventures
and
it's
basically
a
framework
to
rapidly
move
through
discovery,
ideation
solution,
prototyping
and
testing
solving
hard
problems
with
technology
in
five
days
granted
we
did
it
in
three
days.
There
are
a
bunch
of
truncated
ways
that
you
can
do
it,
but
the
the
original
one
was
in
a
five-day,
a
five-day
sprint
and
I'll
walk
you
through
this.
B
If
y'all
are
interested
in
learning
more
about
this,
there's
a
ton
of
information
online,
this
is
the
book.
This
is
kind
of
like
the
main
source
of
record.
I
guess
of
what
a
sprint
framework
looks
like
you
can
find
out
amazon
all
that
also
on
youtube.
There's
a
channel
called
aj
and
smart,
where
they
have.
They
have
videos
that
break
down
every
single
session.
They
call
it
design
sprint
2.0,
so
it
kind
of
gives
you
like
a
refresh
of
it
there
so
ample
ample
context
or
resources
for
you
online.
B
So
on
the
first
day,
we
tackled
understanding
our
problem
like
identifying
and
understanding
our
problem
at
hand,
so
that
we
could
ultimately
build
a
strong
prototype
around
it.
So
we
asserted
that
our
problem
was
that
the
owners
and
admins
of
data
hub
do
not
understand
how
users
are
interacting
with
the
tool.
So
that's
a
big
problem
right.
There
are
a
lot
of
technical
approaches.
B
You
could
take
to
solving
that
and
so
what
what
we
started
doing
was
taking
a
step
back
and
understanding
how
to
contextualize
that
problem
into
the
bigger
picture
of
the
data
hub
strategy.
So
we
talked
about
how
does
this
fit
into
the
long-term
vision
of
data
hub,
and
we
rallied
around
this
this
vision
that
in
12
to
18
months
data
platform
owners
will
want
to
deploy
data
hub
at
the
organization
because
it
gives
them
superpowers
so
right
away.
B
B
And
then
we
talked
about
what
we
identified?
What
question
or
questions
we
would
be
asking
at
the
end
of
this
process
to
understand
if,
if
it
was
a
success
and
we
rallied
around,
are
we
providing
data
platform
owners
with
actionable
insight,
so
user
usage
analytics
is
not
all
like
it
doesn't.
Just
because
you
have
usage
analytics
doesn't
mean
it's
meaningful,
so
we
wanted
to
make
sure
that
we
would
be
able
to
ask
concretely.
B
Do
you
now
have
actionable
insights
so
that
you
can
move
towards
this,
like
future
value
of
data
hub
in
the
long
run?
The
next
thing
we
did
is
we.
We
started
to
break
down
all
of
the
potential
pain
points
within
developing
or
solving
this
problem
within
the
current
stack,
and
we
reframe
this
into.
What's
called
a,
how
might
we
and
really
it's
just
a
way
to
kind
of
like
flip,
a
problem
on
its
head
and
turn
it
into
an
opportunity?
So
we
talked
about
how
might
we
make
the
analytics
infrastructure
easy
to
manage?
B
How
might
we
give
clear
insights
where
there's
poor
data
qual,
sorry,
there's
poor
data
quality
coverage
but
heavily
used
assets
so
that
way
we're
trying
to
solve
this
solution
without
adding
too
much
burden
on
the
the
owners
or
operators
of
the
platform
and
then
also
giving
insight
into?
Where
are
you
seeing
a
lot
of
activity
and
there's
actually
opportunity
to
enrich
that
metadata?
B
To
give
folks
more
more
power,
there
then
another
thing
we
did
was
we
talked
to
our
experts
within
the
data
hub
community
and
wanted
to
make
sure
that
we
had
a
well-rounded
understanding
of
this
problem,
set.
How
folks
even
thought
about
how
products
analytics
would
fit
into
their
management
of
data
hub,
and
so
you
know
sample
questions
in
these
user
interviews
were
what
are
some
like.
The
top
questions,
you'd
like
to
be
able
to
answer
around
user
activity
and
what
decisions
would
that
inform.
B
B
We
then
mapped
out
our
kind
of
user
experience
within
data
hub
so
that
we
had
a
very
concrete
understanding
of
where
this
solution
fit
into
that
workflow.
So
we
talked
about
how
you
would
install
data
hub
as
a
poc
have
some
step
of
ingesting
metadata
share
it
with
your
users
gather
feedback.
Maybe
do
some
iteration
cycles
here
from
there.
You
then
move
into
feature
development
and
improving
metadata
to
then
move
it
back
into
this
this
flow
and
we
really
targeted
this
idea
of.
We
are
assuming
that
the
poc
exists.
There
is
metadata.
B
There
are
active
users,
we
are
gathering
feedback
and
making
decisions
about
user
activity
to
inform
future
development
areas
to
improve
metadata
and
ways
to
drive
adoption.
So
again,
this
really
just
helps
us
have
a
very
laser
like
a
laser
focus
of
where
this
problem
fits
into
the
vision
of
data
hub
the
user
life
cycle
et,
then
we
move
into
sketching
solutions.
You
can
see
that
these
came
in
a
variety
of
different
ways.
Some
folks
are
writing
a
pencil
paper.
Some
folks
are
whiteboarding
mocking
things
up
with
the
ui.
B
B
B
C
C
Second,
was
to
utilize
existing
components
of
data
hub,
as
maggie
mentioned
before.
We
don't
want
to
make
operators
lives
even
harder
by
adding
even
more
components
to
deploy.
So
we
wanted
to
use
whatever
components:
we've
already
deployed
to
actually
support
a
initial
prototype
of
the
analytics
class
analytics
product.
The
third
was:
while
we
wanted
to
have
this
default
way
of
using
existing
components.
We
wanted
everyone
to
be
able
to
plug
their
own
architecture
for
consuming
these
usage
events.
C
So
usage
events
are
actually
posted
to
a
kafka
stream,
so
anybody
can
just
plug
in
any
consumer
of
choice
for
data
collection
and
analytics
operators
can
also
wire
third-party
analytics
tools,
like
google
analytics
and
fix
panel
to
the
react
app.
So
please
check
out
this
doc
for
more
details
on
how
to
do
that.
Unfortunately,
for
now
you
have
to
fork
the
repo,
but
we
are
going
to
work
on
making
that
through
config
all
right,
so
moving
on.
Let's
go
on
to
the
the
end
to
end
flow.
C
So
you
can
see
each
component
here
are
existing
components
in
our
data
hub
graph,
so
our
reactor,
so
that
we
have
the
user
mark
here,
as
the
user
interacts
with
the
react
app
it
caught.
It
sends
over
the
events
through
the
track
endpoint
in
the
front
end.
So
the
front
end
collects
these
events
and
poses
to
a
kafka
topic.
So
we
created
a
new
topic
called
data
hub
usage
event
v1,
and
that
is
where
all
the
events
go
through.
C
So
we
added
a
consumer
in
the
mae
consumer
which
already
had
a
connection
to
elasticsearch,
which
is
why
we
chose
this
one.
It
will
listen
to
datahub
usage
event,
v1
and
process
these
events
that
come
through
so
note
that
these
events
are
not
hydrated.
So
what
we
do
like,
for
example,
a
user
urn
comes
in.
We
want
to
know
the
details
about
this
user
to
do
that.
C
We
go
back
to
gms,
so
we
call
the
remote
dow
local
dao
to
get
the
details
about
the
entities,
so
we
hydrate
the
entity
features
and
we
package
it
into
a
single
document
which
we
sent
over
to
the
data
hub
usage
event.
Data
stream
on
elasticsearch,
so
elasticsearch
connects
all
these
usage
events
and
front
end.
A
C
Okay,
one
sec,
you
guys
see
the
yeah
the
data
all
right.
So
what
I
did
here
was
I
modified
any
consumer
job
a
little
bit
so
that
it
prints
out
the
ma
the
usage
event
that
is
coming
in,
so
we
are
in
the
usual
data
hub
app.
So
as
we
click
you
can
see
that
the
events
are
coming
in.
So
we
have
a
page
view
event.
You
can
see
browse
events
browsers
all
click
events
as
well
as
so,
let's
try
searching.
C
You
can
see
the
search
event
that
came
in
so
it
says
it
queried
with
a
query
sample
as
well
as
search
view,
events
that
talk
about
how
many
results
was
in
the
search
page
and
as
we
click
on
it.
Each
of
these
actions
that
you
take
inside
the
entity
page
inside
the
search
page
inside
the
browse
page
will
translate
to
a
certain
usage
events
that
comes
in
so
now.
These
usage
events
are
all
sent
over.
You
can
see
that
the
elastic
search
connector
is
sending
the
bulk
request
to
our
data
stream
there.
C
So
once
we
go
to
the
analytics
beta,
what
we
do
is
each
of
these
components
are
configurable
inside
the
code.
In
the
data
hub
front
end,
we
have
highlight
cards,
we
have
time
series
charts
and
then
we
have
tables
as
well
as
stacked
bar
charts.
So
we
created
these
main
four
different
visual
cards
that
we
want
to
support,
and
then
we
implemented
all
of
them.
So
you
can
see
here.
This
is
searches
last
week
and
then
top
search
queries
that
come
in
you
can
see
sample.
C
There
was
top
search,
five
searches
as
well
as
section
views
across
different
entity
pages,
so
we
have
lineage,
we
have
ownership,
we
have
schema
and
so
on
also
actions
by
entity
type.
You
can
see.
We
have
update
tags
here.
I
updated
a
few
yesterday
to
just
show
you
guys,
and
then
we
have
top
view
data
sets.
Of
course
we
will
be
continuing
to
add
more
charts
here,
so
it
would
be
great
if
we
could
get
feedback
here.
So
I
wanted
to
go
over
the
charts
that
we
see
for
our
own
demo.data
hub
page.
C
So
you
can
see
we
have
amazing.
We
have
421
weekly,
active
users,
crazy
thanks
for
using
the
demo.
You
can
see
the
searches
that
are
happening
as
well
as
the
various
search
queries.
So
we
can.
We
can
gather
a
lot
of
signals,
but
what
users
are
doing
on
this
platform
by
just
looking
at
these
few
charts?
B
Yeah
one
thing
I'll
add
here:
dexter:
could
you
actually
just
display
open
your
or
hide
the
terminal
in
there,
so
you
can
see
like
a
full
s,
a
full
view
of
the
dashboard
perfect.
Thank
you.
So
what
we're
trying
to
do
is
like
find
find
ways
to
contextualize
not
only
activity,
but
also
where
is
their
opportunity
to
really
like
leverage
the
power
of
of
data
hub
right.
B
So,
if
we're
thinking
about
the
the
number
of
data
sets,
so
we
have
92
data
sets
and
half
of
them
have
owners
assigned
that's
great.
So
what
that
means
is
that
we're
halfway
towards
having
fully
documented
data
sets
within
data
hub
right?
So
it's
it's
not
even
just
the
what
are
people
looking
at,
but
what
are
people
looking
at
that's
specific
to
the
value
that
data
hub
is
driving
the
other
part,
and
so
speaking,
from
my
perspective
as
a
product
manager
managing
this
type
of
tool?
B
I
want
to
understand
how
do
I
decide
where
to
invest?
My
team's
energy
are
people
only
looking
at
data
sets.
Are
they
looking
at
pipelines
now,
and
maybe
our
pipelines
aren't
well
documented
or
in
the
actions
that
they're
taking?
Are
they?
Are
they
adding
tags?
Are
they
manage
changing
owners?
Are
they
looking
at
ownership,
detail,
lineage,
etc?
B
That
way,
I
can
start
to
narrow
down.
You
know
where
to
have
my
team
and
my
stakeholders
start
to
and
invest
in
having
more
robust
and
and
more
meaningful
metadata
within
there.
The
other
thing
that
we're
thinking
about
is
you
know.
Looking
at
the
terp
terp
is
not
a
word
excusing.
The
top
search
queries
to
understand
like
what
are
people
even
looking
for
in
here?
B
So
I
think
we
can
also
start
to
leverage
this
idea
of
finding
the
gap
of
what
people
are
searching
for
and
trying
to
do,
but
we're
not
actually
meeting
that
demand
and,
like
like
dexter,
said,
if,
if
you
have
ideas
or
questions
about
how
to
make
this
more
impactful
or
meaningful,
I
will
route
you
all
over
to
the
actual
team.
But
we're
definitely
excited
to
see
where
this,
where
this
heads.
A
Cool
thanks
a
lot
maggie
and
dexter
yeah.
It
was
a
great
experience
and
I
was
talking
to
young-
and
you
know,
nick
and
ben
over
on
the
linkedin
side
as
well,
and
they've
actually
built
a
very
complex
and
very
expensive
analytics
capability
on
the
product
stream
as
well,
so
at
a
future
date.
We
can
get
into
that
as
well.
That
includes
sessions
and
a
lot
of
deeper
analytics.
So
it's
pretty
cool
what
people
are
doing
with
it.