hi i'm william rondon cloud advocate
with ibm
and how many times have you called in to
your customer service agent given them
all your information like your name date
of birth account number and then once
you tell them your actual problem they
transfer you to another agent who then
asks you the same exact questions can
you give me your name date of birth
account number like if that first
conversation had never really happened
well this is an example of poor
data governance
and highlights how the inefficient
sharing of data and permissions in a
company can have an impact on you as a
customer
so let's take a look at that customer
service example and put it in the
context of a bank and think about how
that information got into the hands of
the customer service agent in the first
place
and it all starts with a central
repository
for data
and this central repository has all
different types of data flowing into it
from the website or the mobile app where
you as the customer fill out information
so from the central repository we can
have different types of information
being filled out by you as a customer
some of it can be non-confidential like
your name
or your address
while some of it can be confidential
information that you don't want shared
like your
social security number
or your account number
data governance is about protecting this
information
because it doesn't just stay here it
flows to other departments so going back
to that example it can flow to
the customer service department
or
the
marketing department
or the loans department
and so on
so
going back to data governance it's about
allowing the sharing of this information
from that central repository to
different departments without exposing
important information from you as a
customer
and one of the ways that we can do this
in an automated fashion is through a
data governance framework
this framework has three main components
the first being
a policy
the second being rules to implement this
policy
and then the third component being
classification
of these rules
so let's break down each of these pieces
through this customer service example
the first being the policy right if this
was a bank they would either have an
internal data protection policy they're
trying to put in place or be following a
national guideline like gdpr
and this data policy can then be further
broken down into rules specific rules
that make this policy whole and the two
main types of rules are
data protection rules which
you may have heard about
and governance rules
so data protection rules pertain to a
specific type of data asset so going
back to this example here you could set
a data protection rule specifically tied
to social security numbers and censor
how it moves throughout an organization
on the other hand you can use a
governance rule and set it up as a
written description of how to handle
data so i could write up a paragraph
explaining how the customer service
department uses this data or the
marketing department or the loans
so once you have these rules in place
you then want to classify the data
that's flowing into the organization
and two ways to classify this data are
through
business terms
or
data classes
and they can work together but just to
break them apart here let's talk about
business terms
business terms can be thought of as the
language through which data is
interpreted in your organization
so let's say we want to understand
utilization rate for our mobile app for
each of these departments
but utilization rate is being measured
by months in customer service and then
days in marketing and loans
so what we can do is set up a business
term
and call it util for utilization rate
and describe how it's measured so i can
say utilization rate is measured in days
throughout each of these departments and
in that way i standardize how that data
asset is measured between departments
now the other way to measure these data
sources and data assets is through data
classes
and we can understand data classes
through metadata
and metadata
tells you a summary of what's inside of
a data source
so let's say i'm putting in a bunch of
spreadsheets into
my bank and i want to understand what's
in these spreadsheets without actually
opening them well the meta the metadata
of this spreadsheet would tell me how
many rows it has how many columns it has
and when it was made and it can also
specifically tell me what's inside of
that row by title so i can understand if
there's an account number in that row
and center that account number
throughout the movement of that document
throughout the organization
so
with this framework in place with the
right policy rules and classification in
place
i have
the base of my framework
that can be automated through reference
data
which is another word for
code
that i can implement throughout this
architecture and automate the movement
of data throughout
but ultimately we want to tie it back to
the customer service example that i
talked about earlier
so if i had my customer service agent
accessing the same information but i had
a data framework in place i could set a
policy for that customer service agent
to follow that breaks down specific
rules of that data protection movement
or of that data asset movement and
further classify that data asset so the
right customer service agent has the
right data at the right time without
exposing account numbers or social
security numbers throughout
but ultimately this is just one example
of data governance and there's a lot of
different examples that may apply to
your organization
so feel free to leave a comment below
and we can answer how data governance
applies in that example
another way data governance is
implemented is through data fabric so if
you're interested in data fabric and how
it can operationalize this framework
throughout your organization check out
some of our videos
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technology and ibm can help you achieve
your goals today and tomorrow
so thank you for watching and i look
forward to seeing you in the next video