House Cleanup Mirrors Data Governance
Key Points
- The speaker uses a house‑clean‑out analogy to illustrate data governance, emphasizing its foundational role for leveraging data in AI.
- “Discovery” in data governance means identifying all data assets across cloud, on‑premise, and SaaS environments, including the hidden or unknown ones.
- “Classification” involves assigning each data element to categories such as customer, product, or financial data, much like sorting household items into heirlooms, photos, or toys.
- Policies (e.g., how to handle personally identifiable information) are enforced by rules that automatically apply actions like masking, mirroring the decisions to keep, donate, or discard items during the house clean‑up.
Full Transcript
# House Cleanup Mirrors Data Governance **Source:** [https://www.youtube.com/watch?v=uPsUjKLHLAg](https://www.youtube.com/watch?v=uPsUjKLHLAg) **Duration:** 00:05:21 ## Summary - The speaker uses a house‑clean‑out analogy to illustrate data governance, emphasizing its foundational role for leveraging data in AI. - “Discovery” in data governance means identifying all data assets across cloud, on‑premise, and SaaS environments, including the hidden or unknown ones. - “Classification” involves assigning each data element to categories such as customer, product, or financial data, much like sorting household items into heirlooms, photos, or toys. - Policies (e.g., how to handle personally identifiable information) are enforced by rules that automatically apply actions like masking, mirroring the decisions to keep, donate, or discard items during the house clean‑up. ## Sections - [00:00:00](https://www.youtube.com/watch?v=uPsUjKLHLAg&t=0s) **Data Governance Explained via House Cleanup** - The speaker likens data discovery and classification to sorting items while cleaning an inherited home, illustrating key data governance concepts. ## Full Transcript
hi today we're going to talk about data
governance now i know some of you are
thinking this is really either boring or
super complex
but we're going to use an analogy today
to help drive home some of the key
concepts around data governance after
all data governance is foundational and
critical to helping you take advantage
of your data in the ai world
now recently i had the task of cleaning
out a house as i prepared for a major
modernization and renovation
this just wasn't any ordinary house
though it's a house i bought from my
parents who lived there for over 30
years and let's just say
my mom wasn't real good about throwing
away things
so the first thing i had to do was
understand what i had so i started in
the basement and had to go through
and discover
all of the different items clothing bins
photographs family heirlooms
everything that was down there
that process in the data world is called
discovery
discovery is a process of understanding
all of the different data assets you
have across your repositories which may
be in the cloud on-prem or even from
some sas applications
now the easy part is discovering the
data that you know about the hard part
is discovering the data that you don't
know about
for me
that was discovering all the different
things
in my
attic
now once i had the opportunity to go
through each
different part of my house i had to
start classifying the items
i had to understand if it was something
that was
a family heirloom whether it was a
picture or a photograph set of
photographs
or even toys financial records all kinds
of different stuff
in the data world the process of
classification is assigning data to
different categories
whether it's customer data product data
financial data
and providing that that label or that
classification to it
now after that this is where it gets
really fun in my world i had to go
through and decide what to keep
for me i had my wife set forth some
policies about what we were keeping what
we were donating
and what we were just throwing in the
garbage
let's take toys for example my mom loved
to keep a lot of toys from when i was
growing up
in certain toys there were missing parts
the policy was
if they were missing parts we would
donate them hoping that somebody may be
able to use it if it was broken we would
throw it away
now
we started applying these rules
rules are ways to help you enforce your
data policies and again policies are
about setting guidelines and standards
about what to do with your data in the
data world one policy that is very
common yet critical is around personally
identifiable data
personally identifiable data such as a
social security number must be masked
the rules help you enforce that if it is
a social security number you must mask
it
and that helps you enforce the policy
now for me
as i went through the process
of
enforcing my policies i decided what i
loved
what i was throwing away
and
what i was donating
to charity
in the data world
you must go through the same process
and as i did so in my world the things
that i was keeping
i repackaged
in bins and i labeled the different bins
describing what was in each of them
now this is a little bit like metadata
metadata in the data world helps
describe what that data asset is
simply put it's like a card catalog at a
library that describes a book it
provides the author the subject the
copyright
in the data world it does much of the
same thing it tells you where the data
came from what it is
so that when you want to use it it's
easier to find and easier to use when
the time comes
now what was cool in my world was i
found a few things
that helped me
generate some money
there were a couple of family heirlooms
that i didn't necessarily need anymore
but i was able to sell
wouldn't that be cool like if you were
able to monetize your data
by understanding what it is and helping
your organization
that's the importance of data governance
that's the value that it brings to your
organization
for me
the one thing i wish i could do
was automate that process
the great news for you
in the data world you can automate it
thanks for watching if you have any
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