on 06-02-2022 09:00 AM
An AI/ML platform can generate scored data and the result of a prediction in a production system. This data can then be shared within Incorta for creating analytics dashboard and reports.
For this article, we will use the integration between Incorta and TAZI as an example to show how the data can be shared and loaded to Incorta.
Customer Churn: Incorta hosts the customer data including the customer profile, customer interaction with the deploying company, customer order history, customer billing and payment history, and even enriched credit data sourced from outside systems. The data is shared with TAZI and the ML that was built within TAZI. In an AI/ML platform like TAZI, the prediction can be generated after the ML inference job is executed. The result of predicting if a customer may churn with estimated likelihood, can then be used to enriched the original customer data as well as being used in filtering and sorting customers. The corrective action can also be triggered from this additional insight.
Incorta and TAZI are installed together within the same network. The disk level sharing is available.
Select parquet, and give the path:
/incorta/IncortaAnalytics/Tenants/ebs_cloud/data/taziCreditRiskV4/{EPOCH}.parquet
Go to Incorta
Go to Data -> + New -> Add Data Source->Data Lake - Local Files
Enter the same path as the Data Source
Go to Schema -> + New -> Create Schema
Click on +New -> Table -> Data Lake
Select the Data Source you just created. Select File Type: Parquet. Enable Wildcard Union. Give Include: *.parquet
Now you can create dashboards in Incorta.
Go to Content -> + New -> Add Dashboard -> Add Insight
Select the data sets you just created.