Understanding Lightbeam: How Entity Resolution Connects Your Data
Lightbeam’s entity resolution uses AI to link patterns and relationships, creating unified views for better governance and decision-making.
Understanding Lightbeam: How Entity Resolution Connects Your Data
How do you bring fragmented data together into one clear view?
In this Understanding Lightbeam episode, explore how entity resolution delivers clarity:
Lightbeam’s AI detects patterns and relationships hidden across data sources
Combines local and global resolution to build a unified entity profile
Empowers security, privacy, and compliance teams to make informed decisions
With Lightbeam, entity resolution transforms scattered data into connected intelligence for better governance and protection.
Transcript
So we talked about lightweight solution in previous video.
Now let's see how Lightbeam solution works.
To recap, what Lightam entity solution does is it actually
tells you the identity of the data.
Most systems for the last 20 years,
most data scanning tools, all the care are is
what data do you have in a given data repository.
What Lightbeam does is,
is actually figures out all this data elements
that's present across the different data repositories,
and it actually tells you whose data do you know
that's the entity part of it.
How does it work? As Lightbeam is scanning your
different data repositories.
Let's say it's scanning a data warehouse like Snowflake,
a document repository like SharePoint.
Uh, when it is scanning slow, it might actually come across
a social security number
and an address in a way that our AI hints
that these are related and this SSN
and this address is actually related.
Now light makes a note of that.
Then when it's actually scanning SharePoint,
it actually might detect the same address,
which here is actually related to medical record number.
And, and there's a lot of technique
that goes into figuring out that this address
that is present in SharePoint is actually related
to the medical record number that's also present in
that same document or other documents in that SharePoint.
Uh, but we'll read that topic for addition, uh, review.
Uh, here what you can see is
that Lightroom is actually figuring out
that there on an address and there's an address
and medical record number, you can see
that it starts creating a link between these two records
that it has, uh, detected.
Similarly, that medical record number was directed in
ServiceNow in conjunction to credit card.
So it, it has now started creating a link to say, okay,
this address is related here.
This medical record number is related here,
and this credit card is actually related here in a
completely different database, uh, that we have over here.
What Lighten is doing is actually linking these disparate
elements together, and it's actually figuring out that all
of these disparate elements that actually linked together,
and they all belong to the same identity called genre.
So the technique that we are using here is called reference
resolution, where we are doing local
resolution within a data source, which is Snowflake
or SharePoint, our ServiceNow SQL server.
And then we are doing a global entity solution
linking disparate pieces
of data elements across different data repositories
that you might have in your, uh, environment.
So that's, our library is doing global entity solution.
And again, essentially it helps you understand
what data do you have about an identity across your entire
data infrastructure.
Now let's see how Lightbeam solution works.
To recap, what Lightam entity solution does is it actually
tells you the identity of the data.
Most systems for the last 20 years,
most data scanning tools, all the care are is
what data do you have in a given data repository.
What Lightbeam does is,
is actually figures out all this data elements
that's present across the different data repositories,
and it actually tells you whose data do you know
that's the entity part of it.
How does it work? As Lightbeam is scanning your
different data repositories.
Let's say it's scanning a data warehouse like Snowflake,
a document repository like SharePoint.
Uh, when it is scanning slow, it might actually come across
a social security number
and an address in a way that our AI hints
that these are related and this SSN
and this address is actually related.
Now light makes a note of that.
Then when it's actually scanning SharePoint,
it actually might detect the same address,
which here is actually related to medical record number.
And, and there's a lot of technique
that goes into figuring out that this address
that is present in SharePoint is actually related
to the medical record number that's also present in
that same document or other documents in that SharePoint.
Uh, but we'll read that topic for addition, uh, review.
Uh, here what you can see is
that Lightroom is actually figuring out
that there on an address and there's an address
and medical record number, you can see
that it starts creating a link between these two records
that it has, uh, detected.
Similarly, that medical record number was directed in
ServiceNow in conjunction to credit card.
So it, it has now started creating a link to say, okay,
this address is related here.
This medical record number is related here,
and this credit card is actually related here in a
completely different database, uh, that we have over here.
What Lighten is doing is actually linking these disparate
elements together, and it's actually figuring out that all
of these disparate elements that actually linked together,
and they all belong to the same identity called genre.
So the technique that we are using here is called reference
resolution, where we are doing local
resolution within a data source, which is Snowflake
or SharePoint, our ServiceNow SQL server.
And then we are doing a global entity solution
linking disparate pieces
of data elements across different data repositories
that you might have in your, uh, environment.
So that's, our library is doing global entity solution.
And again, essentially it helps you understand
what data do you have about an identity across your entire
data infrastructure.
