Validation

Content validated on 05/08/2023 with

• ADW Version Oracle Database 19c Enterprise Edition Release - Production Version 19.11.0.0.0

• Oracle Cloud Application 23B (11.13.23.04.0)

• Fusion Analytics Warehouse Application Version  23.R2

 Background

Oracle Fusion Analytics provides analytics for Oracle Cloud applications, powered by Autonomous Data Warehouse and Oracle Analytics. More specifically:

• Fusion ERP Analytics provides accounting data sourced from ERP Cloud in a warehouse designed to support broad set of analytics use cases. 
• Fusion HCM Analytics provides comprehensive workforce, turnover data sourced from Fusion HCM Cloud.
• Fusion SCM Analytics give customers the ability to gain visibility into Supply Chain performance, correlate Supply Chain processes with business goals and detect, understand and predict Supply Chain issues.

Making the right business decisions is intrinsinclty tied to the system of records data accuracy of a company. In many specific use cases, Fusion Analytics data can be complemented with additional data from external source such as On Premise, Legacy applications, Non Fusion SaaS application, OCI source, Third party cloud applications, and so on...

To support these data enrichment and consilidation requirements, Fusion Analytics is introducing a new Connectors to leverage the Fusion Analytics Oracle data pipeline infrastructure to extract data (full and incremental) from these external sources following a defined schedule, potentially reducing custom data extraction code, infrastructure and platform costs.

This blog describes how to utilize Fusion Analytics (FAW) Data Augmentation feature to replicate transaction data from US Bureau of Labor Statics (BLS) into ADW to extend the Data Layer of FAW. 

The US Bureau of Labor Statistics publishes the following statistical data sets:

• The Consumer Price Index (CPI): An aggregate of the prices of a relatively fixed basket of goods, which is used as a standard gauge of inflation and the cost of living.
• The Producer Price Index (PPI): A measure of the average prices American producers receive for their goods and services.
• Local Area Unemployment Statistics (LAUS): A range of localized data about labor efficiency and unemployment.
• The National Compensation Survey (NCS): Produces comprehensive aggregates of workers' earnings across a variety of sectors.
• Current Population Survey (CPS): Sponsored jointly with the Census Bureau, this is a monthly survey that seeks to determine the demographic characteristics and employment status of all individuals of a household who are of working age.

These data sets can provide critical insight when merge with Fusion CX data,for instance, to target campaign, potential customers, to generate new leads and in fine to produce new revenue.

Suggested Prerequisite Reading Material:

* Data Augmentation with FAW 

* Data Replication with OAC 

* FAW Semantic Model Customization

Overall prerequisites

* Have access to an Fusion Analytics Instance (OAC, ADW) with Administrative privileges

* Have configured SQL level access to Fusion Analytics ADW, as described in Krithika and Gunaranjan blog post here.

 Architecture Overview

FAW Data Augmentation allows customers to leverage the FAW data pipeline to augment reports with datasets created :

1. By adding a new dimension in the target instance,
2. By adding a new fact in the target instance.

The diagram below details the architecture for the Connectors :

Figure 1: FAW Data Augmentation flow with BLS Connector

The high level configuration flow is as follow:

• US Bureau of Labor Statistics provides a public data API that can be leveraged by third party consumer. With FAW, the API used does not require registration nor authentication.

• FAW Configuration

  • Enable BLS feature

  • Create BLS Connection

  • Test Connection

  • Refresh metadata for BLS connection

  • Set Pipeline Settings

  • Create BLS data augmentations

• FAW Data Pipeline processes the data augmentation pipeline jobs, extract the data from BLS tables and load it into ADW, queryable via synonyms starting with DW_BL_X_

• Augmented data can be then queried directly using OAC Data set or a SQL client like SQL developer, through the database synonym named after the table name give in the first step 

• Semantic model can be extended by using semantic model extension or External Application merge feature allow reporting on augmented data extracted in ADW. We will cover the basics here to be able to build a visualization against the extracted BLS Data. External Application merge will be documented a future blog.

BLS and Fusion Analytics Configuration

Let's watch the video now

Figure 2: BLS and Fusion Analytics Connector configuration

Accessing an OAC Workbook to Gain Business Insight with BLS Data

Now that the Data Augmentation have completed,  we can create Data Sets and Workbooks

Figure 3: BLS visualization

This concludes the activities in the blog.

 Want to Learn More?

Click here to sign up to the RSS feed to receive notifications for when new A-team blogs are published.

 Summary

This blog described how to utilize Fusion Analytics (FAW) Data Augmentation Connector to replicate transactions data from US Bureau of Labor Statistics into Autonomous Data Warehouse (ADW) to extend the Data Layer of FAW. This blog also detailed the steps to allow customers to answer business questions and gaining business insight with BLS and Fusion Analytics Data.

Bookmark this post to stay up-to-date on changes made to this blog as our products evolve.