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Description
Saxo Bank and ThoughtWorks present how they implemented Business Glossary using Data Ops and Data Mesh principles.
A
A
A
We
are
a
financial
industry,
so
we
are
inspired
by
fibo
and
we'll
take
often
an
example
of
the
book,
but
the
glossary
that
we
have
created
is
like
an
aggregator
which
can
expand,
apart
from
fiber
to
other
ontologies
specific
to
the
industry
as
well
as
provides
the
ability
to
create
a
organization
specific
for
us
with
sap
specific
glossary
as
well.
How
does
it
help
us
so
once
it
is
out,
it
helps
us
to
identify
the
relationship
between
different
terms.
A
This
is
an
example
from
fibo.
Eventually
we
want
to
target
graphical
representations,
but
we'll
stick
to
tabular
on
on
data
hub
or
other
workbench
products,
I'm
just
laying
down
the
pain
point
which
led
us
to
come
up
with
this
solution.
Why
we
kind
of
developed
this
a
couple
of
years
back
when
we
started
on
this
journey
in
saxo
bank,
and
I
was
taking
personal
interviews
across
the
organization
to
understand
the
pain
points
around
the
data,
the
common
problem
that
came
from
different
system
owners
and
system.
A
Smes
was
data,
quality
issues
and
inconsistencies
where
they
were
spending
a
lot
of
time:
solving
tickets
because
of
data
flowing
across
systems
a
quick
example
and
a
very
common
example.
I
have
a
data
set,
a
and
system
a
data
set
b
in
system
b
and
data
set
c
in
system
c,
and
if
I
I
got
a
couple
of
a
few
data
elements
where
account
in
in
system
a
data
set,
a
the
account
name
is
named
differently
in
data
set
b,
account
number
they're
same
things,
and
in
system
seats
account
id.
A
So
the
etl
that
flows
from
data
set
a
to
b
is
dependent
on
the
mapping
sheet
that
has
been
created
by
system
a
and
b
and
similarly
etl
from
data
set
c.
The
system
is
dependent
on
mapping
ships
too,
if
the
sme
leaves,
or
some
knowledge
is
going
here
and
there
and
another
version
of
mapping
g2
is
created.
The
etl
process
is
screwed
up,
validation,
scale
and
account
id
and
account
are
no
more
consistent,
and
then
this
leads
to
a
lot
of
issues.
Now,
how
can
we
resolve
it
right?
A
A
If
you
can
point
all
these
account
names
account
id
and
account
number
across
these
systems,
expand
the
schema
then
being
ingrained
in
schema.
The
dependence
on
the
mapping
sheet
goes
away,
the
dependency
and
smes
go
away
and
the
data
flows
across
systems
can
be
consistent
can
be
correct
quickly.
How
do
I
have
enabled
it?
So
what
you
have
done
is
if
this
looks
familiar.
A
A
It
could
be
fibo
or
anything
else,
then.
The
design
principles
that
we
have
stuck
to
is
stuck
to
data
ops
principles
which
is
based
on
communication,
collaboration,
integration,
automation
and
measurement.
We
believe
that
business
philosophy
can
be
evolved,
staying
agile,
iteratively,
taking
care
of
business
needs
in
the
digitization
journey.
This
will
also
show
in
the
end,
if
we
get
time
how
we
wanted
to
make
sure
that
technology
is
involved
right
from
the
start,
when
a
business
function
is
introduced
into
the
organization
enhance
the
metadata.
A
So
apart
from
the
data,
elements
now
will
also
have
industry
standard
ontologies
defined
at
the
metadata
layer
obviously
schema
maturity,
because
the
the
business
terms
are
now
engraved
in
the
schema
schema
versioning.
Any
changes
into
the
metadata
regarding
the
data
elements,
data
types
or
business
terms
will
cause
the
schema
to
be
versioned
and
it
will
enforce
ownership
not
only
of
the
metadata
but
also
of
the
business
terms,
the
appropriateness
and
validity
on
the
producers
quickly.
How
we
have
actually
realized
the
physical
implementation
both
for
data
sets
and
for
business
terms.
A
Our
schema
definition
is
in
protobuf,
so
the
messages
which
are
defining
a
business
terms
use
options
to
define
the
type
of
the
business
source
that
oncology
source
that
we
are
using
and
their
url.
So,
with
this
I'll
quickly,
stop
sharing
and
hand
it
over
to
madhu
for
the
next
set
of
first
slides
and
the
demo.
B
B
So
I
wanted
to
talk
about
how
we
are
trying
to
relate
the
data
sets
with
the
business
terms
that
can
enhance
the
value
of
the
elements
and
may
be
better
meaningful
to
the
data
sets.
I
have
taken
a
simple
example
with
the
purchase
order,
which
has
these
elements,
which
is
id
revision,
number
status,
employee
id
vendor
a
number
of
elements.
We
have
like
order
line
item
this
element.
B
Now,
if
I
talk
about
like
vendor
id
to
map
to
the
supplier,
identifier
may
be
another
table,
we
call
it
the
product
supplier,
id
and
map
to
supplier
identifier.
This
actually
enhances
the
value
to
this
data
set
and
other
by-product
is.
If
you
define
a
certain
business
rules
at
a
supplier,
identifier
level,
you
can
actually
drive
those
business
rules
against
these
data
sets
and
other
than
association
of
the
data
set,
which
is
enriching.
The
value
of
the
data
sets
business
concepts
or
terms
itself
is
interrelated
or
like
and
hierarchical.
B
So
somehow
you
can
compose
the
business
and
create
a
new
term
altogether.
Let's
say
we
have
a
purchase
order,
date,
value
and
ship
date.
These
are
part
of
like
composed
and
created
the
purchase
order.
That
is
a
kind
of
relationship,
will
help
you
to
discover
your
data
sets
of
interest
and
get
to
the
right
data
set.
B
B
B
The
glossary
node
is
introduced
to
define
the
hierarchy
of
the
ontology
okay,
so
we
could
achieve
very
much
similar
to
the
free
book
kind
of
hierarchy.
If
I
want
to
relate
this
analogy
right,
okay,
glossary
node
is
kind
of
a
package.
You
can
have
a
number
of
hierarchical
levels
and
glossary
term
is
a
class
definition
which
talk
about
the
business
term.
Okay,
if
you
can
say
glossary
term
no
info.
This
talks
about
the
definition
of
the
glossary
term
and
source
of
the
term
which
can
borrow
from
it
can
be
internal
organization.
B
It
can
be
borrowed
from
the
external
organization
and
you
can
even
have
a
link
to
the
external
organization
so
that
people
can
navigate.
So
this
is
the
first
thing
we
onboarded
these
entities.
Then
we
had
expanded
the
data
set
by
adding
a
new
aspect
to
the
data
set
so
that
glossary
term
can
be
related
to
the
data
set
and
data
set
has
a
schema
metadata,
which
is
an
array
of
schema
field.
We
enhance
the
schema
field
to
associate
the
business
term
at
the
attribute
level.
B
With
this,
you
are
able
to
attach
the
term
to
the
data
set
and
the
schema
field
that
help
the
business
user
to
navigate
to
the
data
set
from
the
business
concepts
itself.
The
one
other
thing
which
currently
we
are
working
on
the
design
is
the
terms
itself
are
related,
which
we
have
seen
in
the
previous
example
as
well
as,
given
that
we
are
achieving
to
the
another
relation
which
is
established
within
the
terms.
B
B
B
If
you
see
like
there
is
a
type
name,
and
there
are
attributes,
like
account
number
and
the
balance,
if
you
look
at
the
balance,
itself,
is
another
type,
which
is
a
balance
amount,
and
I
would
also
see
you
could
also
see
the
savings
account
is
kind
of
linked
to
a
customer
account
here,
we're
trying
to
define
that.
Oh
you
see
savings
account.
B
This
is
of
a
term
or
type
customer
account
so
that
you
are
able
to
relate
things
so
that
you
can
okay,
even
though,
let's
say
example
like
in
organization,
your
independently
terms
can
evolve
over
them
and
realize
that
these
are
common.
You
can
relate
it
back
and
proto,
given
a
very
flexibility
so
that
you
can
actually
expand
the
definition
or
metadata
of
a
schema.
We
are
using
an
options
to
do
that,
and
there
are
other
cases
like
okay.
B
B
So
the
response
for,
like
it,
come
up
with
the
self-service
capabilities
where
users
are
can
be
declaratively
defined.
The
data
set,
and
we
have
a
github
process
which
takes
this
thing
and
create
the
topics
in
the
kafka
and
register,
schema
and
extract
the
this
metadata
templates
parse
the
files
and
pushing
into
the
linkedin
data
by
converting
into
a
snapshot
which
is
required
by
the
mc
schemas
with
that
I'll
quickly
move
to
the
demo
in
the
interest
of
time.
B
So
you
see
the
home
page
of
the
data
workbench.
Let's
say
let
me
look
take
it
to
the
business
glossary
and
these
are
like.
We
have
a
domain
hierarchy,
party
domain,
market
domain
and
trading
and
common
these
things,
and
let
me
take
a
simple
example
of
example:
here:
okay,
we
could
see
a
customer
account
earlier
we've
seen
the
example
I
could
navigate
either
I
can
search
through
or
I
can
directly
go
here.
B
If
I
go
to
the
business
term,
I
could
see
the
definition
of
the
term
and
what
is
the
source
and
I
can
navigate
to
the
source
of
the
external
different
it
points
to
the
fibo
here
it
can
be
other
things
and
you
could
see
the
related
data
sets
and
additional
properties
right
with
the
related
data
set.
You
have
two
data
sets.
Maybe
you
can
navigate
to
one
of
data
sets
and
see.
This
is
a
very
much
familiar
to
everybody
so
which
is
the
data
set
home
page
where
you
have
information
now
you
could
see.
B
C
C
On
top,
we
did
the
same
thing
even
at
linkedin,
but
it's
really
nice
to
see
a
lot
of
companies
are
doing
something
similar
and
it's
great
to
see
some
of
these
recipes
emerging
for
how
to
connect
schemas
and
git,
along
with
metadata
in
gate,
along
with
this
kind
of
push-based
architecture,
to
get
metadata
out
and
integrate
it
into
a
common
base.
So
I
highly
encourage
reaching
out
to
them.
C
We
probably
are
going
to
have
similar
support
for
even
in
the
open
source
code
base,
for
you
know
having
protobuf,
schemas
and
applying
annotations
on
them,
and
so
to
talk
to
them
about
how
they've
done
it
and
try
to
implement
similar
practices
at
your
organization.
I
think
it's
definitely
a
game.
Changer.