How to establish lineage transparency for your machine learning initiatives

Machine
learning
(ML)
has
become
a
critical
component
of
many
organizations’
digital
transformation
strategy.
From
predicting
customer
behavior
to
optimizing
business
processes,
ML
algorithms
are
increasingly
being
used
to
make
decisions
that
impact
business
outcomes.

Have
you
ever
wondered
how
these
algorithms
arrive
at
their
conclusions?
The
answer
lies
in
the
data
used
to
train
these
models

and
how
that
data
is
derived.
In
this
blog
post,
we
will
explore
the
importance
of
lineage
transparency
for
machine
learning
data
sets
and
how
it
can
help
establish
and
ensure,
trust
and
reliability
in
ML
conclusions.

Trust
in
data
is
a
critical
factor
for
the
success
of
any
machine
learning
initiative.
Executives
evaluating
decisions
made
by
ML
algorithms
need
to
have
faith
in
the
conclusions
they
produce.
After
all,
these
decisions
can
have
a
significant
impact
on
business
operations,
customer
satisfaction
and
revenue.
But
trust
isn’t
important
only
for
executives;
before
executive
trust
can
be
established,
data
scientists
and
citizen
data
scientists
who
create
and
work
with
ML
models
must
have
faith
in
the
data
they’re
using.
Understanding
the
meaning,
quality
and
origins
of
data
are
the
key
factors
in
establishing
trust.
In
this
discussion
we
are
focused
on
data
origins
and
lineage.
 

Lineage
describes
the
ability
to
track
the
origin,
history,
movement
and
transformation
of
data
throughout
its
lifecycle.
In
the
context
of
ML,
lineage
transparency
means
tracing
the
source
of
the
data
used
to
train
any
model
understanding
how
that
data
is
being
transformed
and
identifying
any
potential
biases
or
errors
that
may
have
been
introduced
along
the
way. 

The
benefits
of
lineage
transparency

There
are
several
benefits
to
implementing
lineage
transparency
in
ML
data
sets.
Here
are
a
few:

• 
  Improved
  model
  performance:
  By
  understanding
  the
  origin
  and
  history
  of
  the
  data
  used
  to
  train
  ML
  models,
  data
  scientists
  can
  identify
  potential
  biases
  or
  errors
  that
  may
  impact
  model
  performance.
  This
  can
  lead
  to
  more
  accurate
  predictions
  and
  better
  decision-making.
• 
  Increased
  trust:
  Lineage
  transparency
  can
  help
  establish
  trust
  in
  ML
  conclusions
  by
  providing
  a
  clear
  understanding
  of
  how
  the
  data
  was
  sourced,
  transformed
  and
  used
  to
  train
  models.
  This
  can
  be
  particularly
  important
  in
  industries
  where
  data
  privacy
  and
  security
  are
  paramount,
  such
  as
  healthcare
  and
  finance.
  Lineage
  details
  are
  also
  required
  for
  meeting
  regulatory
  guidelines.
• 
  Faster
  troubleshooting:
  When
  issues
  arise
  with
  ML
  models,
  lineage
  transparency
  can
  help
  data
  scientists
  quickly
  identify
  the
  source
  of
  the
  problem.
  This
  can
  save
  time
  and
  resources
  by
  reducing
  the
  need
  for
  extensive
  testing
  and
  debugging.
• 
  Improved
  collaboration:
  Lineage
  transparency
  facilitates
  collaboration
  and
  cooperation
  between
  data
  scientists
  and
  other
  stakeholders
  by
  providing
  a
  clear
  understanding
  of
  how
  data
  is
  being
  utilized.
  This
  leads
  to
  better
  communication,
  improved
  model
  performance
  and
  increased
  trust
  in
  the
  overall
  ML
  process. 

So
how
can
organizations
implement
lineage
transparency
for
their
ML
data
sets?
Let’s
look
at
several
strategies:

• 
  Take
  advantage
  of
  data
  catalogs:
  Data
  catalogs
  are
  centralized
  repositories
  that
  provide
  a
  list
  of
  available
  data
  assets
  and
  their
  associated
  metadata.
  This
  can
  help
  data
  scientists
  understand
  the
  origin,
  format
  and
  structure
  of
  the
  data
  used
  to
  train
  ML
  models.
  Equally
  important
  is
  the
  fact
  that
  catalogs
  are
  also
  designed
  to
  identify
  data
  stewards—subject
  matter
  experts
  on
  particular
  data
  items—and
  also
  enable
  enterprises
  to
  define
  data
  in
  ways
  that
  everyone
  in
  the
  business
  can
  understand.
• 
  Employ
  solid
  code
  management
  strategies:
  Version
  control
  systems
  like
  Git
  can
  help
  track
  changes
  to
  data
  and
  code
  over
  time.
  This
  code
  is
  often
  the
  true
  source
  of
  record
  for
  how
  data
  has
  been
  transformed
  as
  it
  weaves
  its
  way
  into
  ML
  training
  data
  sets.
• 
  Make
  it
  a
  required
  practice
  to
  document
  all
  data
  sources:
  Documenting
  data
  sources
  and
  providing
  clear
  descriptions
  of
  how
  data
  has
  been
  transformed
  can
  help
  establish
  trust
  in
  ML
  conclusions.
  This
  can
  also
  make
  it
  easier
  for
  data
  scientists
  to
  understand
  how
  data
  is
  being
  used
  and
  identify
  potential
  biases
  or
  errors.
  This
  is
  critical
  for
  source
  data
  that
  is
  provided
  ad
  hoc
  or
  is
  managed
  by
  nonstandard
  or
  customized
  systems.
• 
  Implement
  data
  lineage
  tooling
  and
  methodologies:
  Tools
  are
  available
  that
  help
  organizations
  track
  the
  lineage
  of
  their
  data
  sets
  from
  ultimate
  source
  to
  target
  by
  parsing
  code,
  ETL
  (extract,
  transform,
  load)
  solutions
  and
  more.
  These
  tools
  provide
  a
  visual
  representation
  of
  how
  data
  has
  been
  transformed
  and
  used
  to
  train
  models
  and
  also
  facilitate
  deep
  inspection
  of
  data
  pipelines.

In
conclusion,
lineage
transparency
is
a
critical
component
of
successful
machine
learning
initiatives.
By
providing
a
clear
understanding
of
how
data
is
sourced,
transformed
and
used
to
train
models,
organizations
can
establish
trust
in
their
ML
results
and
ensure
the
performance
of
their
models.
Implementing
lineage
transparency
can
seem
daunting,
but
there
are
several
strategies
and
tools
available
to
help
organizations
achieve
this
goal.
By
leveraging
code
management,
data
catalogs,
data
documentation
and
lineage
tools,
organizations
can
create
a
transparent
and
trustworthy
data
environment
that
supports
their
ML
initiatives.
With
lineage
transparency
in
place,
data
scientists
can
collaborate
more
effectively,
troubleshoot
issues
more
efficiently
and
improve
model
performance. 

Ultimately,
lineage
transparency
is
not
just
a
nice-to-have,
it’s
a
must-have
for
organizations
that
want
to
realize
the
full
potential
of
their
ML
initiatives.
If
you
are
looking
to
take
your
ML
initiatives
to
the
next
level,
start
by
implementing
data
lineage
for
all
your
data
pipelines.
Your
data
scientists,
executives
and
customers
will
thank
you!

Explore
IBM
Manta
Data
Lineage
today