- [Manu] Hello, and welcome to this video series
on the Power BI Adoption Framework,
which is about empowering every decision maker.
I'm Manu.
- [Paul] And I'm Paul.
- [Manu] In this video, we'll talk about what to secure.
When thinking about the security of your data,
there are two important factors to consider:
Where is the data,
and who has access to it?
Whether the data is on-premises or in the cloud
can be very important from a regulatory perspective
or if your company has not yet started using the cloud.
This needs to be considered for different datasets,
and the decision can be impacted by the level
of granularity at which you store the data,
such as whether you show row-level
personally identifiable data
or aggregate the data to a higher level.
Once you have chosen where to store the data,
you also need to consider who you are sharing the data with,
which can again come down to the level of granularity.
Do you need row-level security so the users have
only access to subsets of data,
or can you share the report and dashboard publicly?
When using data sources in a self-service model
with Power BI, there are a number of considerations.
You can classify your data to make sure users understand
what they can do with different data sources.
At Microsoft, we use low, medium, and high business impact
to classify our data.
And we have a SharePoint site which details
what can be done with each of these different data types.
You should think about who has access to data sources.
Currently, a user might retrieve an extract of data,
but it might save time and effort to give them access
to the underlying source.
When doing so, you need to consider what level of data
they're getting access to.
Should they have access to row-level data
that might contain personally identifiable information,
or should they have access to aggregated data?
To aggregate or alter the data,
you might need to do some data preparation
and maybe give users access to that model
rather than the underlying source
so that they don't need to understand
the relationship or other complexities.
Common data sources are Excel and CSV files,
which is how most customers start to use Power BI
while they consider the implementation
of governance on corporate data sources.
You might want to provide guidance on how to connect
to data sources, including Excel and CSV files
and how security can be applied
within the Power BI data model.
The fun thing here is, I'm sure you've worked with people
who use to export reports to Excel and now with Power BI,
we are seeing people doing this the other way around.
- [Paul] When connecting to data with Power BI,
the location of the data is important for performance
and for when you come to scheduling a refresh.
If the data is on-premises,
you need to plan for installing a Power BI gateway.
Do you need to use a personal gateway,
or if this is for a wider audience,
do you need to install an on-premises gateway?
Then you need to consider where you're going to install it
and how many of them you require.
For some data sources, you need to think about
how you're going to connect to the data.
If you're connecting to a database or Analysis Services,
do you want to connect using direct or live query
so you can always see the latest version of the data?
Or do you want to connect to various data sources
and combine and mashup this data,
in which case you can use import mode
and then schedule a refresh to occur
to meet your user requirements.
There are two type of models based on the two ways
to connect to the data sources:
A cloud model is where the data
and metadata are stored in Power BI.
This will be data from data sources
like flat files, online sources, or streaming data sources,
but could also be from databases.
When creating or refreshing a model, a cache of the data
at that point in time is taken.
You can then schedule a refresh
to get the latest version of the data.
An external model is where only the metadata is stored
in Power BI and the data exists in an external source.
This would be using DirectQuery to a database
or using live query to connect to an Analysis Services model
either on-premises or in Azure.
When connecting with direct or live query,
no data from the source is imported into Power BI Desktop.
Each time you interact with a visualization,
Power BI queries the data source and calculates
the results you seek,
so you're always looking at the latest data
whilst interacting with the report.
With composite models, you can combine data
from more than one DirectQuery source
or combine DirectQuery with imported data as well.
We've included a link to the slides used
in this presentation in the description below.
Thanks for listening, and if you have
any feedback or questions, please leave a comment.
See you in the next one.