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Release Version 4.2.0

The Release Version 4.2.0 includes new features, improvements, and bug fixes.

Skypoint Dataflow:

  • Ingest only metadata after selection – In the Dataflow > connector configuration page, when the "Purpose" is selected as "Metadata" for API connectors, the application will fetch only meta information and ingest it into the platform.
  • Last refresh type – The new column Last refresh type is added in the Dataflow section. It helps to understand whether the last refresh type for Dataflow connectors was Incremental or Full.
  • Import connectors redesign – Users can now see the connectors already supported by Skypoint for ingestion. In case, the required connector is not found, Databricks partner connectors can be used, or the user can raise a support ticket for the Skypoint team to take care of the request.
  • Dataverse connector - New connector support added for Dataverse - Office 365 credentials in Dataflow > imports.
  • Export metadata along with CSV files in Export – Users can now also export a metadata JSON file containing information about the purpose, frequency, etc. The metadata JSON file will be exported with each exported CSV file. Users can add up to 10 key-value pair for metadata Keys.

Skypoint Lakehouse:

  • Added High/Low CLV column – The Customer Lifetime Value (CLV) represents the total spent with your business by a customer during their lifetime. A new column High/Low CLV is added to Databases. You can select the CLV model in that Database and open the Data tab to view the CLV category. Customers with lower CLV are less profitable than those with higher CLV.
  • Automated storage accounts - Automated storage account is added to the event grid for CDC changes to refresh HiveMetastore and Cosmos. When a new instance is created, it will auto-subscribe to the event grid subscription and add all the existing storage accounts for CDC events to refresh HiveMetastore and Cosmos.
  • Segregating data in Bronze, Silver, and Gold - Categorize tables as Bronze, Silver, and Gold in the Lakehouse. This feature helps users to understand what to transform from the databases. The different layers are as follows:
    • Bronze - The raw data ingested from data sources.
    • Silver - The cleansed and transformed data like Map and Match.
    • Gold - The business-level aggregations like Profile, Audience, Timeline, Metrics, and Predictions.

Skypoint Predict:

  • Churn model linked with Databricks MLflow – The customer churn model is migrated from azure ML to data bricks using MLflow. The user can examine large volumes of historical data using machine learning algorithms to identify which customers are more likely to churn and the reasons for customer churn.
  • Customer lifetime value model on Databricks MLflow - The Customer lifetime value model works on Databricks MLflow to identify your high-value and longtime customers.
  • Product recommendation model on Databricks MLflow - The Product recommendation model works on Databricks MLflow to identify previous purchase behavior and customers with similar purchase patterns.

Skypoint Activate:

  • Suggestive audience on Databricks MLflow – Databricks MLflow helps to manage the complete machine learning lifecycle with enterprise reliability, security, and scale. Suggestive audience functionality is added to work on Databricks MLflow.
  • Export Audience created from Predict models – The user can export the audience from Predict models. Predictive audiences help you to classify the audience into distinct personas based on demographics, browsing habits, shopping history, etc.
  • Net Promoter Score (NPS) calculation in Business Metrics – The NPS calculation is added to the Business Metrics to measure customer experience and loyalty. It shows how many customers are likely to recommend your product or service. Knowing your NPS offers a variety of valuable business benefits, such as creating a strategy that targets Promoters, Passives, and Detractors.

Skypoint Empower:

  • Automated updates of inaccuracies in Privacy center: – Support has been added to update the inaccuracies in data sources as per the Data subject requests from the end users on the privacy portal. With this, the manual step of update has been removed and complete automation is taken care of.


    • Skypoint needs write access for the data sources.
    • Skypoint only supports inaccuracies update for the attributes mapped under Resolve.

Skypoint Automate:

  • Refactor the Skypoint Power BI section - Power BI under automate is now available through SQL access. The new button "Setup" is added to get a power BI connection string file and see all the tables.
  • Skypoint Chatbot - User can now use Skypoint Automate teams connector to login to the teams and then search for any specific profiles using MS teams. A bot has been supported for all the search features.

Settings and Administration:

  • Removal of CDM dependency - The CDM dependency is removed for the Campaign Monitor to improve pipeline performance and stability.