Most organizations have huge amounts of data stored in many forms in various locations.
Finding relevant data quickly and connecting disparate data sources can be challenging
and time-consuming. Watson Knowledge Catalog unites all information assets into a single
metadata-rich catalog, based on Watson’s understanding of relationships between assets
and how they’re being used and socialized among users in existing projects. Let’s
have a look at the overview of different tool categories that we’ve previously discussed.
Watson Knowledge Catalog corresponds to the Data Asset Management, Code Asset Management,
Data Management, and Data Integration and Transformation. Watson Knowledge Catalog is
a data catalog that is integrated with an enterprise data governance platform. It also
merges the analytics capabilities of Watson Studio. The data catalog assists data scientists
to easily find, prepare, understand, and use the data as needed. Watson Knowledge Catalog
protects data from misuse and enables the sharing of assets with automated, dynamic
masking of sensitive data elements. Data-profile visualizations, built-in charts and statistics
help users to understand data assets. Seamless integration with Watson Studio helps data
citizens to drive production of their data in a suite of powerful data science, AI, machine-learning
and deep-learning tools. Joining with Watson Studio directs the building, training, and
deploying of models. Users can interactively discover, cleanse, and prepare data with a
built-in data refinery. Possible connections to more than 30 IBM and third-party data sources
help to catalog and use your data in the original locations. IBM Watson Knowledge Catalog has
various deployment choices on IBM Cloud™ and can be run anywhere with IBM Cloud Pak™
for Data. The latter is a fully-integrated data and AI platform built on Red Hat® OpenShift®
Container base. It can be deployed easily into any public or private cloud or other
enterprise platforms. A catalog contains metadata about the contents
of assets and how to access them. And a set of collaborators who need to use the assets
for data analysis. The metadata is stored in an encrypted IBM Cloud object storage instance.
Any data that you want to store in the Cloud, you can upload to the cloud object storage
of your choice, and then specify that object storage when you create the catalog. This
split between where the data's metadata is stored and the actual location of the data
is important. It means that you can keep your data where ever it is. You don't need to move
it into the catalog because the catalog only contains metadata. You can have the data in
unpremises data repositories in other IBM cloud services like Cloudant or Db2 on Cloud
and in non-IBM cloud services like Amazon or Azure, in streaming data services or even
dark data sources like PDFs. Included in the metadata is how to access the data asset.
In other words, the location and credentials. That means that anyone who is a member of
the catalog and has sufficient permissions can get to the data without knowing the credentials
or having to create their own connection to the data. Since the new catalog is empty,
let's take a look at an existing catalog. On the Browse Assets tab you can see "recommendations",
"highly rated assets", and "recently created assets", as well as a list of all the assets.
You can type a search term to find assets, and you can filter by asset type, such as
Data Asset or Notebook. Or filter by tags that were assigned to the asset when it was
added to the catalog. When you view an asset, you get a preview of the data and other information
like a description, ratings, tags, where the source is located, and any classifications.
On the Access tab, those with permission can add members to view this particular asset.
And the Review tab shows reviews and lets you contribute a review. When assets are added
to a catalog with Data Policies enabled, Watson Knowledge Catalog automatically profiles and
classifies the content of the asset based on the values in those columns. The Profile
tab contains more detailed information about the inferred classifications. You can see
the other possibilities for classifying each column and the confidence scores for those
other possibilities. On the Lineage tab, you'll see the various events that Watson Knowledge
Catalog has captured that occurred in the lifecycle of this data asset, allowing you
to trace what's happened to the asset since it was created. On the Access Control tab,
you can see the current list of catalog members. you can also add members which is pretty similar
to adding collaborators in a project. Most catalog members will likely have the editor
role. The viewer role is intentionally restricted and only a select few users will have the
admin role. Watson Knowledge Catalog includes capabilities
to automatically mask sensitive data according to your organization's governance policies.
For example, you can see in the diagram that the first name, last name, and gender data
in the data set have been masked. You’ve learned how IBM Watson Knowledge Catalog can
help organizations deal with their numerous data and other assets. In the next video we’ll
look at Data Refinery, a powerful tool for analyzing and preparing data.