Self Service Data Access (SSDA)

What is SSDA? 

Powered by the Knock Data Cloud (KDC), SSDA creates easy access to business critical data, allowing PMCs to create their own advanced analytics solution or connect their preferred Business Intelligence tools (BI), to drive more effective and informed decision making around their portfolio.

• Easily access critical data- Customers can easily and directly connect to the KDC to explore all business critical data from Knock® in near real time. 

• Connect preferred BI tools and data warehouse tools- Customers with a preferred BI platform or data warehouse can use their existing tools, including creating custom dashboards and reports as well as easily ingesting data into their own warehouse.

• Make better informed decisions- Customers can leverage Knock® data along with other data sources they rely on to get a full picture and make more informed business decisions.

Getting Started

To get started, your Sales representative will work with you to identify your unique needs and make sure that this is the right solution for you. Once they are able to understand what you are looking to get out of SSDA, they’ll collect the following information to help get you access.  Here’s a breakdown:

• Are you interested in accessing data from multiple clients?  Examples include: SQL client like Dbeaver / Python or any other means of programmatic access / Tableau or any other BI tools.

• Do you need more than one snowflake user account?  Knock can provision multiple individual accounts or a generic account that can be shared. 

• Can you commit to an IP range from where you or your team will be connecting to the data?  For security purposes, we need the specific IP address or address range that will be used to access the data. 

• How are you planning to use the data in snowflake?  Are you going to run real-time analytics queries directly or extract the data and process the data into other platforms?

Once the team receives all the above information, we’ll work on setting up your environment and provide the credentials for you and your team to access the data. 

Data Schema and Structure

SSDA is organized into a Star Schema data model. A Star Schema is a widely adopted data modeling approach for relational data warehouses. It involves classifying tables as either a fact table or a dimension table.

Star Schema:

Fact Tables:

These tables store observations and events that can be quantitatively measured (i.e., the data to be analyzed). Information in these tables are those fields which can be counted, aggregated, or otherwise summarized using numerical data.  

For Knock SSDA, this includes data related to calls, emails, visits, applications, etc. that you would measure for BI purposes.

Dimension Tables:

These tables describe business entities that you are modeling and describing in your BI reporting (i.e., the ways in which the data in the fact table can be analyzed). 

For Knock SSDA, this includes prospects, agents, threads, properties, etc.

Linking Fact and Dimension Tables

In a Star Schema, the fact table sits at the center with each referenced dimension table representing the points of the star. Each fact table will hold primary keys of the referenced dimension tables.

Star Schema

As an example, consider a use-case where you would like to explore the marketing source of a prospect event. The star schema is illustrated below in an abbreviated format. 

In each of the dimension tables listed above you would find additional rows that would further define the physical entities. For example, you might analyze the number of prospects originating from different marketing sources that a specific leasing team would be responsible for following up.

Additional Information

As you get onboarded to SSDA the queries that support our Analytics Dashboard can be provided to you by request. This would include all reports currently on the dashboard.