Oracle Fusion Analytics Implementation Series: Customization

Published v1 on November 14th, 2023.

Introduction

Oracle Fusion Analytics (Fusion Analytics) is a family of prebuilt, cloud-native analytics services designed to deliver personalized insights for Oracle Fusion Cloud Applications.

overall

Included in Fusion Analytics are:

A library of ready-to-use metrics and dashboards for faster collaboration.
An extensible, Oracle-managed data platform with ready-to-use data extraction pipelines.
An extensible, Oracle-managed semantic data model.
An extensible, Oracle-managed security framework that leverages the security components in Oracle Fusion Cloud Applications.

Fusion Analytics is designed for easy and fast data analytics throughout the entire process, including data pipeline extraction, transformation, loading of Fusion Cloud Applications data to a cross-functional data model, and organization in a semantic model that provides subject areas to business users.

Prebuilt data foundation components are shown below.

Prebuilt Data Platform

Prebuilt and custom components are shown below.

Prebuilt and Custom

This post is an Implementing Oracle Fusion Analytics Series member and guides customizing the prebuilt content in Fusion Analytics. It builds upon and assimilates the planning, preparing, provisioning, and configuring activities presented in preceding posts.

A Fusion Analytics implementation comprises a broad spectrum of activities. Save valuable time and prevent errors by carefully following best practices, using prebuilt components as much as feasible, and carefully planning the customizations rolled out to the business community.

Customization

Customize Fusion Analytics to satisfy business requirements and increase reporting capabilities.

Customization is an implementation phase that follows the rollout of a phase with the same scope containing prebuilt functional areas.

Recommendations and Best Practices

Follow the Fusion Analytics Phased Implementation Approach and begin a customization phase following the rollout of a phase containing the base prebuilt functional areas.
Limit the scope of a customization phase to a single Fusion Analytics application, e.g., ERP.
Limit the scope of a customization phase as much as possible to a related subset of functional areas.
Use a development environment considered the primary development environment. Avoid the use of "Sandbox" development environments.
Subscribe to an Additional Test Environment (ATE) for use in performance testing. Ensure the sizing of the ATE environment, i.e., memory, storage, and the number of OCPUs, is the same as the production environment.
Perform a backup of the production environment and restore the relevant functional areas into the primary development environment.
Restore the production environment into the ATE.

Assumptions

Planning was performed as described in the Planning a Fusion Analytics Implementation post.
Preparation was performed as described in the Preparing a Fusion Analytics Implementation post (under development).
Provisioning was performed as described in the Provisioning a Fusion Analytics Implementation post (under development).
Configuration was performed as described in the Configuring and Validating a Fusion Analytics Implementation post.

Customization Topics

This post's topics cover customization types, considerations, and a checklist.

Data Augmentation
Semantic Model Extensions
Reporting Extensions
Security Extensions
Customization Considerations
Customization Checklist

Data Augmentation

Fusion Analytics service administrators and data engineers perform data augmentation.

Data augmentation enables extending analyses by blending data from multiple sources beyond what resides in Fusion Cloud Applications. Fusion Analytics provides a variety of ways to capture this data, from self-service methods to more governed, curated approaches.

The prebuilt database schema for the Fusion Cloud Applications contains read-only data to ensure all prebuilt visualizations and analyses are never broken. Custom database schemas in the same embedded Oracle Autonomous Data Warehouse (ADW) are used to store data from external data sources efficiently. Fusion Analytics supports any data movement tool for loading data, such as Oracle Data Integration, any third-party tools, or even plain SQL.

Custom data can be used to extend existing dimensions and facts tables or add new ones.

Data Augmentation is accomplished using a variety of methods, including:

Adding descriptive flexfield (DFF) extensions from Fusion Cloud Applications using a Custom Data Configuration.
Using one or more of the data augmentation connectors offered by Fusion Analytics.
- More than 50 native connectors to various sources are supported, such as Oracle Autonomous Database, Oracle Fusion Cloud EPM, Google Big Query, Salesforce, and Snowflake. You can also connect to any Java Database Connectivity (JDBC)-based data source. Get real-time data from Fusion Cloud Applications using the Oracle Cloud Applications connector.

Connectors

Self-service datasets and dataflows that leverage the capabilities of the underlying Oracle Analytics Cloud platform.

Self Service

Data integration tools. Fusion Analytics supports all data integration tools such as Oracle Data Integrator and Oracle SQL*Developer.

Recommendations

The storage capacity in Oracle Autonomous Data Warehouse supports 50 GB of external data.

Ensure additional capacity is added to the database if required.
Add memory and CPU capacity to support visualizations and queries if required.
Ensure custom data loads do not run simultaneously with incremental pipelines to avoid contention on database resources. View incremental pipeline statuses in the DW_WH_REFRESH_SUMMARY table.
Load custom data outside of business hours to avoid data inconsistencies and performance issues when users access the data.
Use the Low service connection to the ADW. Using the High and Medium services consumes too many resources and causes performance issues for other processes running on the database.
Use custom prefixes, e.g., WC_, when naming custom schemas, tables, and columns.

References

About Augmenting Your Data
Extending Data with Custom Data Configurations
About Data Connectors
Create Datasets Using Data Flows
Configure Custom ETL for Fusion Analytics
Connect to the Fusion Analytics ADW

Semantic Model Extensions

Fusion Analytics service administrators, modelers, and model administrators perform Semantic Model Extensions.

Note: Semantic Model Extensions for security use cases are presented in the Security Extensions section.

The Fusion Analytics semantic model provides simple business subject area views of hundreds of physical tables and views in the database. The mappings, rules, and translations between complex physical data structures are done for you in easily understood and consistent business terms. Conformed dimensions (customer, supplier, product, fiscal calendar, business units, ledgers, etc.) are available for easy cross-subject area analysis.

Subject areas are designed to optimize query execution with fine-grained tuning and data and role-level security. They are the building blocks for determining customization requirements and providing query performance baselines.

The semantic model is extended using a simple, wizard-driven interface supporting multi-user development and publishing. Semantic model changes follow a test-to-production, version-controlled publishing process.

Customizations include:

Creating new subject areas.
Creating new fact and dimension tables.
Adding session variables for use in visualizations and analyses.
Modifying existing subject areas.
Extending existing fact and dimension tables with derived columns.
Extending existing dimension tables with additional attributes and hierarchies.
Extending existing fact tables with additional metrics.

Semantic Model

Recommendations

Use custom prefixes, e.g., WC_, when naming custom semantic model objects.
Create custom dimensions first, followed by custom facts.
Ensure join columns are of compatible data types.
Always specify the primary key and display attribute when defining custom dimensions hierarchies.
Combine multiple extensions for a dimension into one object to minimize the number of joins.
Always set content levels for custom dimensions when joining to custom facts.
Ensure custom fact tables are joined with prebuilt dimensions to use prebuilt and custom columns in the same report.
Minimize the number of semantic layer customization steps by applying current and future modifications to an object in a single customization step.

References

About Semantic Model Customization

Reporting Extensions

Fusion Analytics service administrators, functional administrators, and content authors perform reporting extensions.

Enrich and customize prebuilt analytics with custom visualizations, dashboards, and pixel-perfect reports using prebuilt and custom subject areas and self-service tools.

Catalog folders help organize, find, and manage reporting objects. Create custom shared folders for customized reporting objects.

Visualizations

Use the Data Visualization features of Oracle Analytics Cloud to build compelling, visual stories with automated charts and graphs. Get started quickly with more than forty-five visualization types, such as waterfall bridge reports, performance tiles, natural language, and maps.

VISUALIZE

Dashboards

Use the classic analytical features of Oracle Analytics Cloud to quickly create analyses, prompts, and dashboards using prebuilt and custom subject areas. These subject areas are designed with metrics and attributes that answer specific business questions at summary levels and allow drilling down to the lowest transactional grain.

Dashboard

Pixel-Perfect Reports

Use the Oracle Analytics Publisher (formerly BI Publisher) reporting solution to author, manage, and deliver reports and documents easier and faster than traditional reporting tools. Use a web browser or familiar desktop tools to create everything from pixel-perfect customer-facing documents to interactive management reports against practically any data source. View reports online or on a schedule that can deliver thousands of documents per hour with minimal impact on transactional systems.

BIP

Recommendations

Use custom prefixes, e.g., WC_, when naming custom catalog folders and reporting content.
Organize catalog folders to ensure that correct business users have access to relevant custom reporting objects.
Identify groups of users with read access to shared custom catalog folders.
Identify groups of users with write access to shared custom catalog folders.

References

Visualize and Analyze Data
Create Analyses and Dashboards
Configure Pixel-Perfect Reports

Security Extensions

Fusion Analytics service administrators, security administrators, and model administrators perform Security Extensions.

Custom data, semantic model, and reporting objects are secured using a variety of methods that comprise Security Extensions.

The following security objects are used to secure most customization objects.

Custom User Groups
- Custom Job Roles in Fusion Cloud Applications
- Custom User Groups in Fusion Analytics
Custom Application Roles
- Custom Data Roles
- Custom Duty Roles
Custom Data Security
Custom Object Security
Custom User Security Assignments

Recommendations

Leverage prebuilt security constructs as much as possible before developing custom security.
Secure catalog folders to ensure that only specified business users can access relevant custom reporting objects.
Secure groups of users with read access to shared custom catalog folders.
Secure groups of users with write access to shared custom catalog folders.
Ensure custom data is secured at the row level and is visible only to relevant user groups with appropriate data roles.
Ensure custom objects are secured and are visible only to relevant user groups with appropriate duty roles.

References

Typical Workflow to Manage Users, Groups, Application Roles, and Data Access
Set Up Custom Security
Custom Security in Fusion Analytics
Configuring Custom Data Security in Fusion Analytics Warehouse in Nine Steps
Configure Data Security
Configure Object Security
Configure Object Permissions
Security Extensions

Customization Considerations

Consider the following when developing a customization phase.

User Acceptance
Performance
Capacity and Growth
Release and Patching
Backup and Rollback
Security
Support

Recommendations and References are under development.

Recommendations

References

Customization Checklist

The checklist provides an opportunity to document and ensure that customizations meet requirements and have followed best practices.

Category	Question	Response
Approach	If using private endpoints, was OCI network logging enabled?
Approach	Is the phased approach used for prebuilt and customization phases?
Approach	Is the scope of the customization phase limited as much as possible to a subset of functional areas?
Approach	Is the scope of the customization phase limited to a single Fusion Analytics application, e.g., ERP?
Approach	Was a previous phase, containing only prebuilt content with the same scope, rolled out successfully into the production environment?
Approach	Was an Additional Test Environment (ATE) procured before beginning the customization phase?
Approach	Were best practices and recommendations followed when customizing workbooks, extending the semantic model, and augmentating data?
Backup and Rollback	Was a customization rollback test performed successfully?
Backup and Rollback	Is there a backup and rollback plan for customizations?
Backup and Rollback	Was the ATE refreshed and restored from the production environment?
Backup and Rollback	Were backup procedures developed for custom schemas in the ADW?
Backup and Rollback	Were copies of semantic model objects created and modified using custom prefixes?
Backup and Rollback	Were custom folders with custom prefixes created for custom reporting content?
Backup and Rollback	Were custom tables in the ADW prefixed for easy identification, e.g., WC_?
Backup and Rollback	Were the development, ATE, and production environments backed up before beginning the customization phase?
Backup and Rollback	Were the relevant functional areas in the development environment restored from the production environment?
Capacity	Are the number of projected users within the service licensing limits?
Capacity	Does the service capacity allow for projected data and user growth?
Capacity	Have customizations impacted ADW storage, memory, or the number of CPUs?
Capacity	If not, has the number of licensed users increased?
Capacity	Have the services been scaled up in the additional test and production environments?
Data Augmentation	Do fact tables use all relevant custom dimension tables?
Data Augmentation	Are all custom primary key column values populated? Primary key columns cannot contain null values.
Data Augmentation	Are Custom Data Configurations used?
Data Augmentation	Are custom prefixes used for custom schema, table, and column names?
Data Augmentation	Are Dimension Aliases used?
Data Augmentation	Are Fusion Analytics connectors leveraged as much as possible before developing custom extraction and loading processes?
Data Augmentation	Have you reviewed the options for selecting the best solution for each augmentation?
Data Augmentation	Are custom data loads synchronized with Fusion Analytics pipelines if not using Fusion Analytics connectors?
Data Augmentation	If not using Fusion Analytics connectors, what are the data sources?
Data Augmentation	If not using Fusion Analytics connectors, what tools are used for extraction, transformation, and loading?
Data Augmentation	Which data connectors are used?
OCI Environment	Were customizations made to Identity Domain or Identity Clouds Service (IDCS) settings?
OCI Environment	Were customizations made to policies and policy rules?
OCI Environment	Were customizations made to service limits and quotas?
OCI Environment	Were customizations made to the compartment structure?
Performance	Are custom queries finishing within the resource time and size limits?
Performance	Do the full and incremental data loads finish in the required timeframes?
Performance	Does custom query performance and response times observed in concurrency testing meet functional requirements?
Performance	Have single-user tests been performed for both authors and consumers using custom security?
Performance	If using private endpoints, was OCI network latency measured, and were the results within the required ranges?
Performance	Is enough time scheduled for a full data load using the prebuilt and custom data pipelines?
Performance	Was concurrency testing performed with the anticipated number of users?
Performance	Was performance testing performed in an ATE sized like the production environment?
Performance	Were full and incremental data loads performed using the prebuilt and custom data pipelines?
Performance	Were the concurrency, performance, and network test results shared with Oracle?
Performance	What is the anticipated number of users in the next 90 days?
Release and Patching	Are the development, ATE, and production environments on the same release and patch level?
Release and Patching	Has a new release or patch been applied to the customized environment?
Security	Are custom security assignments protecting access to custom subject areas, reporting content, and data?
Security	Are custom user groups granted appropriate permissions?
Security	Are network access control and security lists in place for access to customizations?
Security	Are users assigned to custom security assignments that secure custom data, semantic model, and reporting objects?
Security	Did you leverage the ready-to-use security configurations as much as possible?
Security	Has network threat testing been performed, and are all services protected from unauthorized access?
Security	If using private endpoints, were OCI network security lists and network security groups configured at appropriate levels of detail?
Security	Is prebuilt security leveraged as much as possible before developing custom security? (Especially relating to HCM).
Security	Were customizations made to network access control lists?
Semantic Model Extensions	Are custom prefixes used for custom presentation folders and column names?
Semantic Model Extensions	Are custom subject areas and their elements secured using Fusion Analytics Object Permissions?
Semantic Model Extensions	Do custom semantic model objects conform to the prebuilt design principles?
Support	Are audit and diagnostic logs enabled to assist internal and Oracle support activities?
Support	Are the outstanding customization issues within allowable severity limits?
Support	Are there open severity-one issues related to customizations?
User Acceptance	Are the custom subject areas available to appropriate user groups?
User Acceptance	Are the custom visualizations available to appropriate user groups?
User Acceptance	Has unaffected prebuilt content been re-accepted?
User Acceptance	Have all custom metrics been validated?
User Acceptance	Have query response times been accepted?
User Acceptance	Have users accepted all custom content?
User Acceptance	Have you explained how to use the folder and the shared folder structure to authors?
User Acceptance	Is the custom data available to appropriate user groups?