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Skypoint Platform Glossary

Audiences​

It is a group of profiles characterized by a defined set of attributes-based filters. You can schedule audiences to be auto-updated every day or update them manually for one-time use.

Custom Models​

The feature allows the user to bring their own machine learning model and utilize the data from Skypoint Studio to get the quality score.

Dataflow​

Data import, export, connections, types (semantic labels) management, data transformations, and data quality management.

Data Lake​

A Data Lake is a repository of data that stores a large amount of structured, semi-structured, and unstructured data.

Data Maps​

Mapping different entities in a meaningful manner to formulate better insights with the data. It also provides an easier and powerful way to process, manage and secure the data with minimal complexity.

Data Subject Requests​

This allows the customer to create a new Data subject request and shows the DSRs that are currently in review.

Data Warehouse​

A data warehouse is a centralized repository for integrated data from numerous source data systems (Finance, Sales, Operations, etc.) for reporting and data analysis.

Empower​

An all-in-one automation solution that integrates consent and preference management with data residency and privacy protection to ensure compliance with GDPR, CCPA, and other state laws.

Entities​

Each data source ingested from the dataflows is added as an entity in the entity section. Entities are further added from Stitch Process which is the Profile entity, from Audiences , from Enrichment and from Metrics.

Export​

It exports the entities to several destinations using export connectors provided by the platform.

Instances​

You can consider them as subsidiaries of a single client. As soon as you create an account on the platform, a default instance is created for you namely Sandbox.

Lakehouse​

Lakehouse brings together the benefits of a data lake and data warehouse to create a new open data management architecture based on Delta Lake.

Map​

It defines the profile data in your entities by choosing the profile attributes, primary key, and types (datatypes defined by Skypoint).

Match​

It identifies the unique profiles in your entities by matching records based on certain rules.

Merge​

It creates an entity of profile records by combining duplicate attributes and removing attributes you don’t need.

Metrics​

Helps in tracking the performance of an organization by providing insights such as business metrics, profile metrics, and profile attributes.

Modern Data Stack Platform​

A Modern Data Stack Platform is a data ingestion hub where you manage, clean, and analyze your data with automated pipelines and a myriad of integrations.

Platform​

It consists of notifications, schedule, instance, product, activity stream.

  • Tenant : It displays all the basic details of the tenant with all the services assigned to it.
  • Instance : It displays the various details of the instance such as Tenant Name, Tenant website URL, Tenant identifier, Instance name, Instance identifier.
  • Notifications : It shows the notifications about dataflows and background processes. This sub-section helps to cover the area where we need to check the status of an iteration.
  • Schedule : It allows you to set a schedule to refresh all dataflows and autorun platform processes for the selected instance.
  • Activity Stream : It displays all the activity which was performed in the application.

Predict​

Anticipate customer needs with real-time data and deliver meaningful experiences during every customer touchpoint using custom models and built-in AI.

Profiles​

Profiles depict unified customers. Multiple entities ingested are stitched together with Identity resolution and ML model to generate a 360 view of unique customers.

Relationships​

It is used to create associations/relationships between entities which can be further used when creating Audiences and Metrics.

Resolve​

Establish a single source of truth with our proprietary ML-based identity resolution algorithm that produces rich and precise 360-degree customer profiles.

Stitch​

Data Processing is performed in this section. It consists of three sub-sections: Map, Match & Merge.

Timelines​

It depicts the entire customer's journey consisting timeline details of specific entities and attributes in the customer profiles

Tenants​

Each customer is called a Tenant.