Understanding Lightbeam: What Is Entity Resolution & Why It Matters
Lightbeam’s entity resolution uncovers hidden connections, creates unified subject views, and simplifies data governance and compliance.
Understanding Lightbeam: What Is Entity Resolution & Why It Matters
Struggling with fragmented or duplicate data?
In this Understanding Lightbeam episode, see how AI-powered entity resolution helps you:
Uncover hidden connections across disparate data sources
Build a comprehensive view of data subjects for clarity and control
Simplify data governance and compliance with accurate linking
Entity resolution transforms scattered data into unified intelligence—powering smarter decisions, stronger governance, and streamlined compliance.
Transcript
Let's talk about Lightning Entity resolution.
What problem does Lightroom entity resolution solve
for you for the last 20 years?
If you've been using any data scanning tool,
what they mostly solve is they'll look into different
applications that you might have
and then it'll tell you what kind
of sensor data you have within the application.
So for example, a data scanning tool
that's scanning your SQL server might tell you
that there's a credit card and there's an account tied in
that SQL Server database.
Similarly, if you have another tool
that is scanning your SharePoint, that's a hipaa,
the tool might report that you have an address
and a medical record number, uh,
within SharePoint, which is useful.
Uh, it actually tells you what information you have.
But where data privacy
and data security landscape has evolved to now is you need
to understand not only just the what data
You Have, but also whose data you have.
What does whose mean?
If you're a healthcare organization,
you probably care about your patient's data
and patient's privacy a lot more than anything else
because if your patient's data gets exposed,
if you are dealing in student data, uh,
you probably care about student data.
So all of those, that's the who in our system
and that who is what we call as entity.
Um, so an entity could be a student,
it could be an employee, it could be an ex-employee,
it could be uh, patients.
If you're a healthcare organization, it could be a customer
or bank account holder if you're a financial institution,
and so on, so forth.
Now, what Lightbeam does
Is as it scans different
Data repositories like SharePoint,
snowflake equals server and Service.
Now it will report to you if there is any sensitive data.
Case in point here in Snowflake, it is reporting that, uh,
there's a social security number.
An address rate is detected in ServiceNow.
It is reporting that there's a medical record number in
credit card that it has detected.
But using entity resolution, what Light Bean is able
to do is it's able to actually link these data elements
that are detected across different applications, by the way.
So they seem very disparate,
but using our ai, we are able to figure out that actually
All of these Disparate data elements
that we have detected across different applications,
they actually belong to the same person, same student,
same patient, same bank account holder.
And so what Light Boom does is it
Actually creates An entity within the system,
Let's say John Doe, what it actually shows you that
John Doe has it there,
has a send an address within Snowflake.
John doe's address
and medical record number
is actually directed in SharePoint.
Similarly create card and account number
of John Doe belongs in SQL Server
And Within your ServiceNow tickets you got medical
record number and create card of John Doe.
Why is that helpful? Simple case is if John Doe ever comes
to you and tells ask you what data do you have about me?
Maybe they're exercising their data subject request
for access of deletion. You
Don't have to go and Look into different applications
to figure out what data you have about John Doe.
In fact, if you look into something like SQL Server,
you might not even have a name over here for John Doe.
Uh, what Lightbeam does is actually scans these data
repositories and perhaps more than it's connected to.
And it figures out that all of these data elements belong
to John Doe, thereby kind
of making your data subject request, addressing
that in a really trivial, within a couple
of minutes, you can actually address that.
Similarly, if you have, uh, let's say someone reports
that you detected a credit card number in ServiceNow
and you're wondering whose credit card number, um,
was discovered using Live be, we can actually figure out
that that credit card number belonged to John Doe,
who happens to be a, uh, a customer of yours, for example.
So that's what, how Live Beam Entity Resolution brings the
who at the center, the identity at the center
of all data elements that you are detecting.
What problem does Lightroom entity resolution solve
for you for the last 20 years?
If you've been using any data scanning tool,
what they mostly solve is they'll look into different
applications that you might have
and then it'll tell you what kind
of sensor data you have within the application.
So for example, a data scanning tool
that's scanning your SQL server might tell you
that there's a credit card and there's an account tied in
that SQL Server database.
Similarly, if you have another tool
that is scanning your SharePoint, that's a hipaa,
the tool might report that you have an address
and a medical record number, uh,
within SharePoint, which is useful.
Uh, it actually tells you what information you have.
But where data privacy
and data security landscape has evolved to now is you need
to understand not only just the what data
You Have, but also whose data you have.
What does whose mean?
If you're a healthcare organization,
you probably care about your patient's data
and patient's privacy a lot more than anything else
because if your patient's data gets exposed,
if you are dealing in student data, uh,
you probably care about student data.
So all of those, that's the who in our system
and that who is what we call as entity.
Um, so an entity could be a student,
it could be an employee, it could be an ex-employee,
it could be uh, patients.
If you're a healthcare organization, it could be a customer
or bank account holder if you're a financial institution,
and so on, so forth.
Now, what Lightbeam does
Is as it scans different
Data repositories like SharePoint,
snowflake equals server and Service.
Now it will report to you if there is any sensitive data.
Case in point here in Snowflake, it is reporting that, uh,
there's a social security number.
An address rate is detected in ServiceNow.
It is reporting that there's a medical record number in
credit card that it has detected.
But using entity resolution, what Light Bean is able
to do is it's able to actually link these data elements
that are detected across different applications, by the way.
So they seem very disparate,
but using our ai, we are able to figure out that actually
All of these Disparate data elements
that we have detected across different applications,
they actually belong to the same person, same student,
same patient, same bank account holder.
And so what Light Boom does is it
Actually creates An entity within the system,
Let's say John Doe, what it actually shows you that
John Doe has it there,
has a send an address within Snowflake.
John doe's address
and medical record number
is actually directed in SharePoint.
Similarly create card and account number
of John Doe belongs in SQL Server
And Within your ServiceNow tickets you got medical
record number and create card of John Doe.
Why is that helpful? Simple case is if John Doe ever comes
to you and tells ask you what data do you have about me?
Maybe they're exercising their data subject request
for access of deletion. You
Don't have to go and Look into different applications
to figure out what data you have about John Doe.
In fact, if you look into something like SQL Server,
you might not even have a name over here for John Doe.
Uh, what Lightbeam does is actually scans these data
repositories and perhaps more than it's connected to.
And it figures out that all of these data elements belong
to John Doe, thereby kind
of making your data subject request, addressing
that in a really trivial, within a couple
of minutes, you can actually address that.
Similarly, if you have, uh, let's say someone reports
that you detected a credit card number in ServiceNow
and you're wondering whose credit card number, um,
was discovered using Live be, we can actually figure out
that that credit card number belonged to John Doe,
who happens to be a, uh, a customer of yours, for example.
So that's what, how Live Beam Entity Resolution brings the
who at the center, the identity at the center
of all data elements that you are detecting.
