Overview of the Data Inventory

Your Data Inventory is the base truth of all data in your company: data silos, the datapoints inside them, and metadata about what each datapoint represents. This is the system-level reality, and it powers other features across the Transcend platform, from keeping your ROPA live-updated to generating Consent rules for that newly-added advertising platform to encoding erasure logic in your data warehouse.

The Data Inventory can be automatically populated via data discovery, using Transcend's suite of discovery products:

  • Silo Discovery finds your databases, SaaS tools, and vendors.
  • Structured Discovery scans your databases and SaaS tools and classifies the data stored inside them, right down to the field or column level.
  • Unstructured Discovery scans your unstructured file stores to find and classify personal data inside documents.

With so many systems, it can be hard to track down exactly how each one is used. It's easier if you know who's responsible for that system. In Transcend, assign data silos to individuals within your organization. All you need to do is hover over the data silo row in Data Inventory and either enter the owner's name for existing Transcend users or enter the email of users who do not yet have a Transcend account—they'll receive an invitation with instructions.

Each data silo listed in Data Inventory has fields that help you understand your complete privacy footprint. We'll go through each to help you understand their purpose.

You can manually add a data silo at any time by clicking on the "Add Data Silo" button.

The name and, where possible, an automatically-loaded logo of the data silo in your inventory. Any data silos added to your Data Inventory will also appear in Integrations and vice versa. Any changes made in Data Inventory will also be reflected in Integrations.

The person who is responsible for managing this data silo in your organization. Setting the Owner for a Data Silo here will be reflected everywhere in Transcend.

The total number of datapoints housed in this data silo. Clicking on this field will show you the filtered list of datapoints associated with this data silo in the Datapoints tab.

Which categories of individuals have personal data in this system. An example of a data subject might be "Employee" or "Customer".

A summary of the purpose of this data silo. This is typically pre-populated by Transcend- but you can overwrite it if need be.

The Datapoints tab gives you a detailed view of every piece of data your systems house across your company's digital footprint.

The system that a datapoint belongs to. Read more about Data Silos.

The name of a given datapoint. This is associated with the literal name of an object inside a system. The datapoint corresponds to objects in SaaS tools, or tables in relational databases.

Properties are the fields on an object or datapoint. For example, in a relational database, the property corresponds to a column name, and in Salesforce, a field of a custom object.

Here you can choose the category that best describes the type of data in a datapoint. This describes what kind of data is contained here (e.g., credit card numbers would have the Financial data category). A default set of data categories will be pre-populated by Transcend. Data categories may also have subcategories (e.g., credit card numbers would have a more specific subcategory: Financial | Credit Card Number). Feel free to override our data categories if your use case needs customization.

Here, you can choose the purpose that best describes why your company uses this data. This describes why you collect this data (e.g., Contact information in Mailchimp is likely used for Marketing purposes). A default set of data categories will be pre-populated by Transcend. Feel free to override our labels if your use case needs customization.

This field is used to help describe what this datapoint is and how it's used. This also comes pre-labeled for the most common tools, and you can edit any datapoint's description.

The Data Categories tab is a great way to see the breakdown of how many datapoints you have in each category. Clicking the datapoint count on a category will link you to a filtered list of all the datapoints belonging to that category.

The Purposes of Processing tab is similar to the Data Categories tab in that all purposes of processing are counted across all your datapoints. Similarly, you can click on the datapoint count for any purpose of processing to see a filtered list of all datapoints that are used for that purpose.

The Audit Trail tab records a log of all activity in your Data Inventory—from datapoints created, to categories added, to integrations updated. This log shows events by users in your org as well as updates automatically made by Transcend.

You can expand events to see the diff'ed changes, which can be useful for debugging or troubleshooting.

You can filter the audit trail by the User who performed the event, the Event Type, whether the event was automated (i.e., by Transcend), and by a particular data silo.

Each tab in the Data Inventory has extensive filtering options, allowing you to filter by any metadata, such as by Data Silo, Data Category, or Purpose of Processing.

Use the filter-search bar to pull the data you need.

Columns in the Data Inventory can be reordered and hidden as desired. Click on the "Columns" button in the top right above a table to reveal these settings. Changes made to on the "Organization" tab will be applied to the whole organization, while the changes made on the "Personal" tab will only apply to your view of the given table.

Deselect columns from the list on the left to hide them from your table. Drag and drop columns on the right to reorder them in your table.

You can export a CSV from any table in Data Inventory. Any filters applied to the table will also apply to your export. You can also use our GraphQL API to access your Data Inventory programmatically.