For as long as I can remember, Oracle Analytics users have been asking for one simple but powerful capability: a native Gantt chart.
Something to track timelines, visualise dependencies, and monitor progress - all within the same dashboards where KPIs and trends already live.

With the November 2025 update, Oracle Analytics Cloud (OAC) finally delivers.
It’s a long-requested feature that transforms how we visualise projects, portfolios, and operational workflows.

The Wait Is Over

Until now, anyone wanting to visualise schedules inside OAC had to get creative — using bar charts to simulate timelines, or embedding third-party components. It worked, but it was clunky.

The new Gantt Chart visualisation makes this native and intuitive.
It allows analysts and project teams to show project timelines, milestones, and progress bars directly inside their OAC workbooks — fully integrated with data security, filters, and visual interactions.

This isn’t just a pretty new chart. It’s a meaningful step toward operational analytics, where OAC becomes a live window into how work is progressing, not just how metrics are trending.

What’s New in the November 2025 Update
The Gantt Chart visual introduces a new way to represent time-based activities.
Here’s what it currently supports:

• Task timelines: Start and end dates rendered as horizontal bars.
• Progress tracking: Percentage complete shown visually within each bar.
• Milestones: Zero-duration tasks represented as markers.
• Grouping: Organise tasks by project, phase, or resource type.
• Baselines: Display baseline start and end dates alongside actuals for schedule comparison.
• Dependencies: Align related tasks sequentially using shared attributes
• Tooltips: Show contextual details such as owner, status, priority, or duration.

​For many teams - PMOs, delivery leads, or operations managers - this fills a long-standing visualisation gap in OAC. The new Gantt visualisation transforms Oracle Analytics Cloud into a capable project-tracking tool. It bridges the gap between analytics and project management - enabling users to track, analyse, and present progress all in one platform.

Try It Yourself – Sample Data
To test the new Gantt, I’ve created a realistic dataset that you can import directly into OAC.

It contains three concurrent projects:

• Website Revamp – A full lifecycle example with UX, build, and content streams.
• ERP Rollout – Multiple workstreams, dependencies, and a go/no-go milestone.
• Mobile App Launch – Parallel iOS and Android sprints with shared backend integration.

It also includes shared dependencies (e.g. a global change freeze) to demonstrate how Gantt timelines can overlap across projects.

Each row in the dataset represents a task, with columns for start/end dates, duration, status, percentage complete, baseline start/end, and dependencies - all mapped for easy use in the Gantt visual.

How to Build the Gantt in Oracle Analytics Cloud

1. Upload the dataset (oac_gantt_test_data.csv) to your OAC instance.
2. Create a new workbook and choose the Gantt Chart visualisation.​

3. Map the fields as below.

Once configured, you’ll see your projects laid out across a timeline - with bars showing duration, coloured progress, and milestone markers for key events.

The Gantt chart shown above gives you a timeline view of your project tasks. Each horizontal bar represents a task’s duration from start date to end date, with markers indicating baselines, milestones and percent complete. This makes it easy to see overlaps, dependencies and progress at a glance.

Why This Matters

This update pushes OAC further into operational reporting territory.
Instead of switching to tools like Smartsheet, Excel, or Project for schedule reviews, you can keep everything inside OAC — governed, secured, and shared via the same semantic model.

For organisations already integrating delivery data (e.g. from Oracle Fusion PPM, Jira, or Primavera) into OAC, this unlocks a new layer of insight:

• Track schedule health directly in dashboards.
• Correlate project progress with KPIs and costs.
• Identify slippage vs. baseline in real time.
• Present timelines cleanly to executives without exporting to PowerPoint.

Community Demand

This feature has been one of the most upvoted requests on the Oracle Analytics Idea Lab.
Many people in the community have asked for a proper Gantt visual for years, especially those working in delivery or programme management roles.

It’s great to see Oracle Product Management not only listen, but execute — and deliver a native visual that feels integrated, performant, and flexible.

​Final Thoughts

The Gantt Chart visualisation is a small feature with a big impact.
It closes a long-standing gap in Oracle Analytics Cloud and moves the platform closer to a true operational analytics experience.

Whether you’re tracking project sprints, release schedules, or transformation roadmaps, you can now visualise timelines, progress, and dependencies — all in one place, without leaving OAC.

At a recent Oracle Analytics Partner Meeting, one demo stood out to me (the others were great as well!) - the new AI Agent for Oracle Analytics Cloud (OAC). I’ve since spoken further with the product manager and been granted early access ahead of its LA (limited availability) in the November 2025 release, and I can already see the foundations of something significant taking shape.
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At first glance, the OAC AI Agent looks and feels similar to the Fusion AI Agent Studio -and that’s no coincidence. Oracle appears to be unifying its redwood AI agent look and feel across platforms, enabling analytics, applications, and custom experiences to have a unified user experience. In OAC, this translates into an embedded conversational interface that sits directly within your analytics workspace. Ask a question, and the agent doesn’t just return a text summary - it understands your semantic model, data lineage, and context before generating a response.

​From Chatty to Knowledgeable: The Librarian Analogy

To understand what makes this so important, it helps to think of the AI Agent as a librarian.

A large language model (LLM) on its own is like a well-spoken librarian with an excellent memory but no access to your organisation’s archive. Ask them a question, and they’ll respond confidently and eloquently but they’re drawing only on general world knowledge and patterns they’ve learned before. The result often sounds convincing, yet it may lack the precision or evidence that a business decision demands.

The OAC AI Agent, on the other hand, gives that librarian the keys to your private archive. When you ask a question, they don’t just rely on memory and their extensive real-world knowledge; they walk into your own library of governed data, reports, and documents, retrieve the most relevant material, and then craft a response grounded in fact.

That’s the power of Retrieval-Augmented Generation (RAG) - it lets Oracle’s AI Agent combine the fluency of language models with the factual grounding of your enterprise knowledge.

How the OAC AI Agent Works
Creating an AI Agent in Oracle Analytics Cloud

​To begin creating an AI Agent, navigate to the menu and select the Create AI Agent option. 

​This initiates the process and brings you directly to the AI Agent configuration Immediately upon entering the configuration screen, you are prompted to add a dataset that will serve as the foundation for the AI Agent. It is essential to ensure that this dataset has already been indexed and appropriate synonyms for attributes have been configured. 
 
These preparatory steps are crucial for enabling the AI Agent to effectively leverage the dataset and provide meaningful, context-aware responses.

​You are then taken to the configuration screen

​Configuring and Supplementing the OAC AI Agent
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Step 1: Entering Supplemental Instructions
Begin by providing supplemental information that offers the agent additional context regarding its specific use case. Additional prompt Instructions will help the agent better interpret user questions on a functional domain. This ensures the AI Agent is tailored to the unique requirements and environment it will operate within.

Step 2: Defining the First Message
The First Message serves as an introductory text displayed to users interacting with the agent. It describes the agent’s purpose and sets expectations for what the agent is designed to achieve.

Step 3: Saving the Agent
After all relevant information has been entered, proceed to save the agent. This action records the configuration and prepares the agent for further enhancement.

Step 4: Supplementing with Documents
Once the agent has been saved, you can enhance its capabilities by supplementing the previously entered contextual information with additional documents. Uploading these documents grounds the agent in your organisation’s custom enterprise knowledge, allowing it to provide more accurate and relevant responses.

OAC AI Agent: Technical Foundations

At its core, the OAC AI Agent leverages the vector search capabilities of the Oracle infrastructure which forms the backbone of OAC. This vector search enables the agent’s retrieval augmented generation (RAG) functionality, allowing it to efficiently surface relevant information in response to user queries. The OAC AI Agent achieves this by integrating three essential components, each playing a critical role in transforming natural-language questions into trustworthy, contextual insights.

1. Intent Recognition (LLM Layer)
The large language model (LLM) layer is responsible for interpreting what the user is seeking. It analyses the natural-language query to determine the user’s intent and aligns this intent with relevant datasets, key performance indicators (KPIs), or dashboards available within OAC.

2. Retrieval Layer (RAG Engine)
Once the user’s intent has been established, the agent’s retrieval layer searches for pertinent content across a range of defined governed sources. This process begins with OAC’s own semantic model and expands to include external knowledge repositories. Examples custom knowledge files that have been uploaded to the system or supplemental information defined in the AI agent.

3. Response Rendering (OAC Context)
After retrieving the necessary data and knowledge, the information passes through Oracle’s Analytics Visualisation framework. The agent then generates a natural-language response that is firmly rooted in verified data, ensuring that every response respects OAC’s metadata, data lineage, and security protocols.

​Key Features and Considerations
Dataset Preparation and Management
​

• Indexing and Synonym Setup: Ensure that the dataset is properly indexed and that relevant synonyms have been configured. This facilitates more effective and accurate retrieval of information based on user queries.
• Column Exclusion: Exclude any columns that are not required for analysis or retrieval. This helps streamline data processing and maintains the relevance of the dataset.
• Supported Document Formats: Custom Knowledge documents can be uploaded in both .PDF and .TXT formats, allowing for flexibility in the types of information included.
• Single Dataset Limitation: At present, only one dataset can be used at a time, ensuring focus and coherence in data retrieval and analysis.
• Dataset Filtering: Filters can be applied to the dataset, enabling users to narrow down the scope of information based on specific requirements or criteria.

Context and Accessibility

• Contextual Insights: Context is derived from both supplemental information and any uploaded documents, ensuring responses are grounded in the most relevant and up-to-date knowledge.
• Accessibility: The feature is accessible either from the AI Agent menu option or directly via the AI Assistant in the Insights panel, providing users with flexible entry points for their queries.

 
How the OAC AI Agent Delivers Value

The OAC AI Agent produces responses that are designed to be highly effective for business users. This is achieved through a combination of generative AI capabilities, robust grounding in enterprise knowledge, and adherence to organisational standards.

• Conversational — The responses are naturally conversational, leveraging the power of generative AI to deliver fluid and engaging dialogue that feels intuitive and approachable.
• Grounded — Each response is firmly anchored in knowledge derived from enterprise data and documents, ensuring accuracy and relevance by referencing up-to-date information from within the organisation.
• Governed — All output remains consistent with your organisation’s security protocols and definitions, providing confidence that information is managed and shared in line with established governance frameworks.

​This unique blend of conversational fluency and factual accuracy distinguishes the OAC AI Agent from standalone chat-based AI tools, delivering responses that are both engaging and trustworthy for enterprise use.

​Early Days, Big Potential

​Let’s be clear — this feature is in its infancy. The current build focuses on natural-language exploration incorporating Retrieval-Augmented Generation (RAG) and narrative generation, with a roadmap that will expand its reasoning and automation capabilities over time.

What’s exciting isn’t just the interface, but the architecture that’s emerging beneath it. For the first time, Oracle Analytics is embracing Retrieval-Augmented Generation (RAG). That means the AI Agent won’t rely solely on a large language model to generate responses. Instead, it will retrieve and ground its output in enterprise data and knowledge — both structured and unstructured.

In practical terms, this opens the door for analysts and business users to ask questions that blend internal data with documents, policies, reports, and contextual information stored across the organisation. Whether it’s sales performance data, a product specification PDF, or a customer-service transcript, the AI Agent will eventually be able to bring these sources together to deliver context-aware insights.

Bringing Unstructured Knowledge into the Analytics Conversation

Historically, analytics platforms have struggled to bridge the gap between structured data (tables, metrics, and KPIs) and unstructured information (documents, notes, images, or messages). With RAG, Oracle is moving to close that gap.

This isn’t just about generating summaries — it’s about creating a richer, more informed analytical experience. Imagine asking:

“What were the main factors behind last quarter’s decline in customer satisfaction?”

Today, OAC might point you to a metric or dashboard. With RAG, the AI Agent could augment that response with context drawn from call-centre transcripts, customer feedback reports, or support documentation — all retrieved securely from enterprise knowledge stores.

The result is a shift from data-driven insights to knowledge-driven understanding.

Governed Intelligence, Oracle Style

One of the key advantages here is governance. Unlike standalone chatbots, the OAC AI Agent inherits the same security, metadata, and lineage controls that underpin Oracle Analytics. Responses remain explainable, consistent, and aligned with the organisation’s governed data model — ensuring that insights stay reliable even as AI becomes more conversational.

This approach also complements Oracle’s broader AI ecosystem. The same underlying framework powers Fusion Applications and APEX AI Agents. As these services evolve, we can expect deeper integration, shared prompt orchestration, and unified management of knowledge sources across the Oracle Cloud stack.

Looking Ahead

The OAC AI Agent represents a starting point, not a destination. It’s a glimpse into where analytics is heading — from dashboards and KPIs towards context-aware conversations grounded in enterprise knowledge.

As I explore this feature further through early access, I’ll be focusing on:

• How RAG is implemented and where knowledge sources can be defined.
• The interplay between OAC’s semantic model and retrieval layers.
• How well the agent can integrate unstructured enterprise knowledge into analytical reasoning.

For now, it’s early days — but the direction is clear. With the AI Agent, Oracle Analytics isn’t just adding generative AI to dashboards; it’s laying the foundation for a new class of governed, knowledge-aware analytics experiences.

Stay tuned — I’ll share a deeper hands-on review once the November 2025 update goes live.

In the previous post, we traced how Fusion Data Intelligence (FDI) evolved from OBIA. In this second instalment of our FDI‑introductory series, you’ll explore the underlying technology and architecture that power FDI’s cloud-native analytics platform.

2. The FDI Architecture Ecosystem (The “Big Picture”)

At its core, Fusion  Data  Intelligence (FDI) is a fully managed, cloud-native analytics platform running on Oracle Cloud Infrastructure (OCI). It stitches together your Fusion Cloud Applications, Oracle-managed data pipelines, Autonomous Data Warehouse (ADW), and Oracle Analytics Cloud (OAC) into a seamless, scalable end-to-end analytics solution - one that Oracle deploys, operates, and continuously evolves for you (there is some configuration that administrators need to carry out).

First, Fusion Cloud SaaS applications - including ERP, HCM, SCM and CX pillars - serve as the transactional data sources. Oracle provides prebuilt ingestion pipelines tailored to each functional Pillar, handling everything from data extraction and change data capture (CDC) to transformation and consistent mapping into analytics-ready format .

These pipelines write data directly into an OCI-hosted Autonomous Data Warehouse, which transform and load the Fusion data into a unified star-schema data model covering multiple functional domains. The schema is:

• Immutable and prebuilt, reducing modelling effort,
• Extensible, allowing for additional external or custom Fusion data,
• Optimised for high-performance querying across SaaS application pillars via Conformed Dimensions like customer, product, ledger and fiscal calendar .

This architecture supports refreshes that can be scheduled incrementally or on demand, with zero downtime - ensuring analytics remains uninterrupted during data pipeline updates. Custom flexfield extensions from Fusion Applications can be included in the pipelines, bringing bespoke business data into the analytics layer.

Once data arrives in the Autonomous Data Warehouse (ADW), Oracle Analytics Cloud takes over for semantic modelling and visualisation. A prebuilt semantic layer wraps the raw star schema into business-friendly subject-area views - covering finance, human resources, supply chain and customer experience - complete with standardised key metrics and dashboards .

Through OAC, FDI delivers not just dashboards but intelligent, action-driven analytics, featuring natural-language querying, ML-based forecasting and anomaly detection to name just a few.

🔗 Summary Flow

1. Fusion SaaS Apps (ERP/HCM/SCM/CX) →
2. Oracle-managed data ingestion pipelines (CDC, ETL, flexfield support) →
3. Star-schema data model in Autonomous Data Warehouse (extensible star schema, conformed dimensions) →
4. Oracle Analytics Cloud (semantic layer, dashboards, AI powered insights and intelligent apps).

This end-to-end ecosystem is fully managed by Oracle - covering provisioning, upgrades, performance tuning, and integration with Fusion App releases - offering a friction-free, scalable approach to enterprise analytics (there is some configuration that needs to be done by administrators).

3. Data Movement & Integration

​​FDI’s data movement layer is built around Oracle-managed, prebuilt pipelines that automate ELT and Change Data Capture (CDC) for Fusion Applications (ERP, HCM, SCM, CX). These pipelines are configured and controlled through the intuitive FDI Console, making it easy for administrators to activate, modify or schedule updates with minimal effort. You don’t need to build complex ETL processes - Oracle handles the heavy lifting, while you focus on business relevance and reporting needs .

By default, data pipelines are incremental with zero downtime, keeping analytics up-to-date without interrupting service. You also have the flexibility to perform on-demand full reloads, useful for data corrections or model updates - all managed with just a few clicks in the Console .

Crucially, the architecture supports extensibility in two key ways:
​

1. Fusion data flexibility – Custom flexfields defined in Fusion Apps are automatically picked up and mapped into the ADW schema without additional development .
2. External data ingestion – You can supplement your analytics with data from outside Oracle, using FDI Console’s data augmentation connectors (e.g. Salesforce, EBS, PeopleSoft, Shopify), self-service file uploads (Excel), direct ETL tools like Oracle Data Integrator, Oracle Integration Cloud or your choice of third-party loaders to a custom schema in the same ADW instance. For bulk external loads, FDI supports bulk data augmentation jobs that can be scheduled and monitored via the Console UI .

All pipelines and augmentations are managed through the FDI Console. As an administrator, you can configure initial parameters - such as extract start dates, currency preferences, and schedule frequency - directly in the console interface. Any subsequent edits to pipelines, functional areas, or augmentations are seamless, with Oracle handling deployment and execution behind the scenes

4. Lakehouse & Warehousing Foundation

At the heart of Fusion Data Intelligence lies a star-schema model deployed on Oracle’s Autonomous Data Warehouse (ADW) - a cloud-native, self-tuning database that underpins fast, enterprise-grade reporting and analytics. Here’s how it’s structured and why it matters:

⚙️ Prebuilt Star Schema in ADW
When FDI is provisioned, Oracle automatically creates a prebuilt star schema in ADW. This schema includes fact tables and a network of conformed dimensions - shared across multiple functional areas - that serve as the glue for cross-pillar analytics.

Common dimensions include:

• Customer / Common Party – used across AR, AP, SCM and CX for a 360° view of customer interactions 
• Supplier – central to procurement and payables
• Product – essential for SCM, finance, and sales analysis
• Fiscal Calendar – shared across financial, HCM, project and supply chain modules
• Business Unit / Ledger – enabling segmentation and consolidated reporting

These shared dimensions enable users to analyse, for example, how procurement spend (SCM) impacts cash flow (finance), or how HR-driven workforce changes correlate with sales performance - a cross-functional insight made possible by a common semantic backbone.

🏗️ Support for External Data & Custom Schemas

​FDI doesn’t just ingest Fusion source data - it enables easy integration of external datasets into the same ADW environment. Whether it’s non-Oracle systems, legacy data, purchased data feeds, or even weather information, FDI supports loading external tables into custom schemas that can extend the star schema and semantic model.

This extensibility is key to bridging out-of-the-box analytics with bespoke business insights - enhancing customer segmentation, supplying additional cost drivers to per-product profitability, or blending external KPIs directly alongside Fusion metrics.

🔍 Benefits of the Lakehouse Foundation

1. Cross-pillar consistency: Reporting across HR, finance, supply chain and CX benefits from shared dimensions and semantic logic.
2. Query performance: ADW’s columnar, parallel engine delivers high performance on aggregated workloads across large datasets.
3. Scalability & elasticity: Cloud-native scaling ensures performance keeps pace with data growth—without manual tuning.
4. Governance in one place: Shared data model and schema simplify security, data lineage and audit tracking.

✅ Summary
Under the hood, FDI’s star-schema in ADW provides a robust, extensible greenfield analytics foundation. Built on conformed dimensions and a scalable data warehouse, it enables seamless mash-ups of Fusion data with external sources, supporting rich, multi-domain analytics that truly span the enterprise.

5. Semantic Layer & Pre‑Built Metrics

FDI abstracts hundreds of physical tables into logical business subject areas - finance (GL profitability, AP ageing, AR revenue, Trial Balance), HCM (talent acquisition, workforce core), procurement (spend, POs), and CX (campaign ROI, opportunity pipeline) - all underpinned by conformed dimensions. It includes a KPI library with over 2,000 standard metrics, accessible via Oracle Analytics Cloud’s intuitive key-metric editor and drag‑and‑drop visualisations. In essence, this semantic layer creates a unified business vocabulary that simplifies reporting and ensures consistency across the enterprise  .

🔐 Complimenting Fusion-Defined Security

FDI leverages Fusion’s built-in role-based security model, so the semantic layer inherits data roles, duty roles, and row/object-level filters defined in Fusion Cloud Applications. Access control is enforced through the Oracle Identity and Access Management (IAM) Service and the FDI Console, ensuring that users only see data they’re authorised to view. This unified approach simplifies administration and compliance by avoiding double entry of security definitions  .

🧩 Hiding Complexity Through Logical Abstraction

Rather than exposing raw tables, FDI offers a logical semantic layer that shields users from underlying complexity. Here’s what it achieves:

• Simplified reporting — Subject-area views like “GL Profitability” or “Spend by Supplier” present meaningful business concepts, not table joins.
• Consistent vocabulary — Terms like “Customer”, “Supplier” or “Fiscal Calendar” remain consistent across pillars and dashboards.
• Reusability — Prebuilt key metrics can be used across workbooks and dashboards without redefinition.
• Governed extensibility — Analysts can add custom dimensions or metrics without touching base tables, via semantic extensions that maintain upgrade compatibility  .

✅ Summary: User Experience & Governance Wins

7. Governance, Security & Lineage

Fusion Data Intelligence isn’t just about delivering insights - it’s built on a robust foundation of security governance and data lineage that brings trust, safety, and compliance to the analytics lifecycle.

🔐 Security Inherited from Fusion & Managed via OCI IAM
FDI inherits its security framework directly from Fusion Cloud Applications. Role-based access, including data roles and duty roles configured in Fusion, are seamlessly enforced within the FDI semantic layer and Autonomous Data Warehouse (ADW). This ensures that users can access only the data they are authorised to see - without duplicating access definitions in multiple systems.

User and group management within FDI is handled through OCI’s Identity and Access Management Service (IAM). You can sync your Fusion App users and roles into OCI IAM or manage them natively via OCI, and then assign access through system and job-specific groups tailored to FDI. This 1:1 mapping ensures governance is inherited and consistent across both transactional and analytics layers.

Oracle also manages infrastructure-level security - covering upgrades, patching, encryption, IAM policy enforcement, key management, and auditing - helping to maintain compliance and relieve the operational burden on your team. 

🧭 Data Lineage & Quality Built-In

​Trusted analytics demand transparency - and FDI delivers that through built-in data lineage and validation mechanisms. The system tracks the flow of data from source tables in Fusion Apps, through ingestion pipelines, into curated star schemas, and finally into Semantic Layer metrics and dashboards.

Fusion SCM Analytics documentation provides end‑to‑end lineage spreadsheets that detail column‑ and table-level mappings, making it easy to trace every KPI back to its source fields. You can also monitor pipeline activity in the FDI Console, which records execution timestamps, row counts, and error logs - providing a clear audit trail of data loads and transformations.

Further, FDI includes validation metrics that reconcile data loaded into ADW against transactional data in Fusion. These can be scheduled or run on‑demand, with reports surfaced directly in OAC - making it easy to identify data drift or discrepancies and swiftly pinpoint areas for correction
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✅ Summary: Trust, Safety, and Compliance

8. Why This Architecture Matters for Organisations 🚀
Fusion Data Intelligence goes far beyond traditional BI. It sits at the heart of Oracle’s broader Data Intelligence Platform, delivering a unified, 360° view across all enterprise data—transactional, analytical, structured, and unstructured  .

🌟 A Unified Data-Intelligence Ecosystem

Unlike legacy stacks - OBIA, ODI, siloed data centres - FDI is built on Oracle’s next-generation Data Intelligence Platform. It blends data lakes, Autonomous Data Warehouse, Oracle Analytics Cloud, OCI AI services, and GoldenGate streaming into a seamless, managed ecosystem  . This means organisations can now handle batch and real-time data, include external sources and apply AI/ML—all within one secure environment. 

This is Oracle's vision as Data Intelligence Platform has been announced but is not yet generally available. 

🔄 Consistent Insights Across Pillars

FDI’s architecture supports conformed dimensions and shared semantic models spanning finance, HR, SCM, and CX. This allows for unified KPIs and analytics, enabling stakeholders to ask and answer cross-domain questions like:

• “How is workforce turnover impacting revenue?”
• “What supply chain inefficiencies are driving higher costs?”

The result is enterprise-wide analytics based on a single source of truth  .

💡 Full Extensibility with Governed Access

As part of Oracle’s Data Intelligence Platform, FDI offers extensive extensibility. Users can bring in external datasets, extend semantic models, build custom analytics, and consume OCI AI services - all within Oracle’s security framework. Governed self-service means broad analytical freedom without compromising data integrity  .

🛠 Evergreen Platform, Zero Infrastructure Burden

The platform is fully managed and evergreen. Oracle handles everything - from provisioning, patching, tuning, and upgrades to integrating the latest AI services. Teams can focus on driving value rather than
wrestling with infrastructure  .

As you’ve seen, Fusion Data Intelligence delivers a fully managed, cloud-native analytics ecosystem - bringing together Fusion SaaS, Oracle’s Autonomous Data Warehouse, and Analytics Cloud under one secure, AI-enhanced platform. It unifies data across domains, embeds intelligent insights and governance, and eliminates legacy complexity - truly delivering on Oracle’s vision of a Data Intelligence Platform. Now it’s your turn: take a moment to reflect on how FDI could accelerate insight‑driven transformation in your organisation. 

The rise of Agentic AI is transforming the analytics landscape, but it comes with an often-overlooked challenge: database strain. Traditionally, operational databases are ringfenced to prevent unstructured, inefficient queries from affecting critical business functions. However, in a world where AI agents dynamically generate and execute SQL queries to retrieve real-time data, production databases are facing unprecedented pressure.
​
Additionally, Retrieval-Augmented Generation (RAG), a rapidly emerging AI technique that enhances responses with real-time data, is further intensifying this issue by demanding continuous access to up-to-date information. RAG works by supplementing AI-generated responses with live or external knowledge sources, requiring frequent, real-time queries to ensure accuracy. This puts even more strain on traditional database infrastructures. In a previous blog post, I looked at how Agentic AI will improve the experience for users of the Oracle Analytics ecosystem.

The Problem: AI Agents, RAG & Uncontrolled Query Load

A well-managed production database is typically shielded from unpredictable query loads. Database administrators ensure that only structured, optimised workloads access production systems to avoid performance degradation. But with Agentic AI and RAG, that fundamental principle is breaking down.
Instead of a few human analysts running queries, organisations may now have dozens or even hundreds of AI agents autonomously executing SQL queries in real time. These queries are often:

• Ad hoc and unpredictable, making performance tuning difficult
• Highly frequent, since AI agents are designed to work autonomously
• Complex and computationally expensive, often scanning large datasets

This creates significant challenges for traditional RDBMS architectures, which were not designed to handle the scale and unpredictability of AI-driven workloads. With Retrieval-Augmented Generation (RAG) in particular, AI models require frequent access to real-time data to enhance their outputs, placing additional stress on transactional databases. Since these databases were optimised for structured queries and controlled access, the introduction of AI-driven workloads risks causing slowdowns, performance degradation, and even system failures.

For users of Oracle Analytics, this shift presents serious performance implications. If production databases are overwhelmed by AI-driven queries, query response times increase, dashboards lag, and real-time insights become unreliable. Additionally, Oracle Analytics’ AI Assistant, Contextual Insights, and Auto Insights features, which rely on efficient access to data sources, could suffer from delays or inaccuracies due to excessive load on transactional systems.

To mitigate this, organisations must rethink their database strategies, ensuring that AI workloads are governed, optimised, and properly distributed across more scalable architectures.

The Traditional Approach: Cloning Production Data

One way that organisations have attempted to address this issue is by cloning production databases on a daily or weekly basis to offload AI-driven queries. However, this approach presents several major drawbacks:

• Clones are not real-time – Since they are refreshed periodically, they lack the latest data required for up-to-the-minute AI-driven insights.
• Cloud clones are expensive – Storage and compute costs for cloud-based clones can be prohibitively high, making large-scale adoption unsustainable.
• Complexity & Latency – Maintaining and synchronising clones across multiple environments increases operational overhead and slows down analytics workflows.
• Doesn’t scale with RAG demands – Since RAG requires real-time retrieval of data to generate accurate responses, stale clones fail to meet its needs.

For AI-driven analytics, relying on periodic database clones leads to significant inefficiencies. Since AI agents require access to up-to-date, contextual information, working with outdated data introduces data integrity risks and lowers the effectiveness of AI-generated insights. Additionally, managing and maintaining multiple clones across different environments adds a significant administrative burden, requiring additional governance, access control, and monitoring.

For Oracle Analytics users, these challenges could lead to outdated insights, reduced trust in AI-generated recommendations, and a poor user experience due to lagging or inconsistent data. Given these drawbacks, it’s clear that cloning is not a viable long-term solution for handling the database demands of Agentic AI.

A Shift in Data Architecture: Data Lakes & Lakehouses

Instead of relying on traditional RDBMS architectures, organisations are increasingly adopting data lakes and lakehouses to support AI-driven analytics. These architectures offer several key advantages:

• Decoupled storage and compute – Unlike traditional databases, lakehouses allow AI agents to access structured and unstructured data without directly querying transactional systems.
• Scalability – Cloud-based lakehouses scale seamlessly with AI workloads, reducing the risk of database bottlenecks.
• Cost-effectiveness – By storing data in low-cost cloud object storage, lakehouses significantly reduce the expenses associated with cloning full databases.

However, while lakehouses present an effective alternative, the migration from a traditional RDBMS to a lakehouse architecture is not without its challenges. The shift requires significant investment in re-architecting data pipelines, ensuring data governance, and retraining teams to work with new query engines and tools. Moreover, performance tuning for structured transactional data in a lakehouse can be complex compared to optimised RDBMS queries, which may lead to initial adoption friction.
​
For users of Oracle Analytics, this shift could mean that existing reports and dashboards need to be refactored to work efficiently with a lakehouse structure, adding additional effort and complexity.

Optimising Performance with Modern Storage Solutions

Beyond adopting new architectural patterns, organisations can leverage modern storage solutions like Silk to mitigate the strain on production databases. Silk provides a virtualised, high-performance data layer that optimises storage performance and scalability without requiring a complete architectural overhaul.
By using Silk or similar intelligent storage virtualisation and caching technologies, organisations can:

• Provide instant, efficient data replication – Unlike traditional cloud-based clones, modern solutions can create real-time, low-overhead replicas, ensuring fresh data access without excessive costs.
• Optimise query performance – Intelligent caching and virtualisation technologies help ensure AI workloads do not compromise the performance of mission-critical systems.
• Reduce the complexity of migrating from an RDBMS – By providing compatibility with existing RDBMS environments, these solutions offer a more gradual and manageable transition to modern data architectures.

For organisations using Oracle Analytics, integrating such solutions could help sustain real-time data access while alleviating the performance burden on production databases. However, despite these advantages, storage virtualisation and caching solutions are not a panacea. Organisations must still ensure that their AI workloads are properly governed to prevent excessive resource consumption, and they need to assess whether virtualised storage aligns with their broader data architecture and security policies.

Conclusion: Preparing for the Future of AI-Driven Analytics

Agentic AI and RAG are here to stay, and with them comes a fundamental shift in how data is accessed and managed. However, blindly allowing AI-driven queries to run against production databases is not a sustainable solution. To support the evolving demands of AI, organisations must modernise their data strategies by:

• Shifting AI workloads to scalable architectures like data lakehouses
• Implementing AI query governance to optimise performance
• Leveraging modern storage technologies like Silk to mitigate traditional RDBMS bottlenecks

For Oracle Analytics users, this shift will require rethinking how data is stored, accessed, and processed to ensure that the platform continues to deliver timely insights without compromising performance. The key takeaway? Traditional database architectures were not designed for AI-driven workloads. To fully embrace the potential of Agentic AI and RAG, organisations must rethink their data foundations - or risk being left behind.

How is your organisation adapting to the challenges of AI-driven analytics? Let’s continue the conversation in the comments!

As organisations strive to make faster, smarter decisions, analytics tools must evolve to offer more than static dashboards and manual data exploration. Enter Contextual Insights, a game-changing feature set to debut in Oracle Analytics Cloud (OAC) as part of the January 2025 update.

Thanks to the Oracle Analytics Product Management team, I was given early access to this feature and have had the opportunity to explore it hands-on. In this blog, I’ll share my insights, experience, and feedback to help you understand the transformative potential of Contextual Insights.

What Are Contextual Insights

Contextual Insights are dynamically generated insights that appear based on the context of the data being analysed. Powered by Oracle’s advanced AI algorithms, these insights surface anomalies, trends, and patterns without requiring users to manually search for them.

For instance, imagine you’re analysing sales data for a retail chain. Contextual Insights could highlight a sudden drop in sales for a specific region or a spike in returns for a particular product category—all without you needing to ask.

Key Features of Contextual Insights

    1.    AI-Powered Recommendations
Contextual Insights leverage AI to suggest deeper analysis opportunities, such as exploring correlations between variables or detecting unusual behaviour in your data.
    2.    Dynamic Visualisations
The insights are not just textual; they include visual representations such as trend lines, scatter plots, or bar charts, making the findings easier to understand at a glance.
    3.    User-Centric Design
These insights are tailored to the user’s role and the context of their query, ensuring that the most relevant information is surfaced.
    4.    Seamless Integration
Contextual Insights work seamlessly with other OAC features like Auto Insights, Narratives, and the Natural Language Query (NLQ) interface.

How Contextual Insights Fit Into OAC​

Oracle has designed Contextual Insights to seamlessly integrate into the Oracle Analytics Cloud (OAC) experience, enhancing usability while preserving the intuitive workflows users rely on. This integration ensures that insights appear naturally during the analytical process, enriching user interactions without introducing complexity.

Key Examples of Integration:
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    •    Enhanced Visualisation Analysis:
When users view a visualisation, Contextual Insights proactively surfaces anomalies, outliers, or unexpected trends specific to the data currently in focus. For instance, a sales trend chart might highlight a sudden dip in revenue for a specific region or product line, prompting immediate investigation.
    •    Ad-Hoc Analysis Empowerment:
In exploratory scenarios, where users are conducting ad-hoc analysis, Contextual Insights helps uncover patterns or correlations that might not have been apparent. For example, when analysing marketing campaign data, it might reveal that a spike in customer engagement correlates with specific demographic segments or external factors.

A Unified Workflow for Enhanced Decision-Making:

This tight integration allows Contextual Insights to complement the natural flow of analysis, ensuring that users discover actionable insights without needing to disrupt their existing processes or switch contexts. By blending seamlessly with tools like dashboards, visualisations, and exploration interfaces, Contextual Insights empowers users to focus on making data-driven decisions rather than spending time searching for information.

Bridging Technical Gaps:

For non-technical users, this integration is particularly valuable, as it eliminates the need for advanced data expertise to identify meaningful insights. Meanwhile, for advanced analysts, it acts as a catalyst, streamlining deeper exploration and enabling quicker hypothesis testing.

By embedding Contextual Insights deeply within OAC, Oracle delivers an analytics experience that is not only smarter but also more intuitive and inclusive, ensuring users at all levels can uncover hidden opportunities with ease.

Why Contextual Insights Matter

In a world where data-driven decisions are becoming the norm, the ability to uncover relevant, actionable insights at the right moment can be the difference between staying ahead of the curve and falling behind.
1. Democratisation of Analytics
Contextual Insights empower users who may not have technical expertise in data analytics, widening the reach of OAC across organisations.

2. Enhanced Productivity

By surfacing insights automatically, analysts save time previously spent on manual data exploration.

3. Proactive Decision-Making
Contextual Insights shift the focus from reactive reporting to proactive planning by identifying trends and anomalies in real-time.

How Contextual Insights Complements Auto Insights
While Auto Insights in Oracle Analytics Cloud focuses on providing users with automatically generated, high-level summaries and narratives about their data, 

Contextual Insights takes this a step further by tailoring those insights to the user’s specific context. Auto Insights excels at offering a broad overview, such as key performance indicators or summarised trends, whereas Contextual Insights dynamically surface patterns, anomalies, and trends based on the user’s immediate data interactions. Together, these features create a seamless experience where users can move from a high-level understanding to in-depth exploration, uncovering actionable insights with minimal effort. This synergy ensures that users at all skill levels can maximise the value of their data, moving from descriptive to diagnostic analytics effortlessly

Real-World Use Cases

Contextual Insights unlock powerful opportunities across a variety of business sectors by surfacing patterns, trends, and anomalies that have the potential to drive more informed and proactive decision-making.

1. Retail: Identify unexpected dips in regional sales and explore correlations with weather patterns or competitor activity.
   
2. Finance: Detect anomalies in expense reports that could signal fraud or misallocation.
   
3. Supply Chain: Forecast seasonal demand fluctuations to optimise inventory levels.
   
4. Healthcare: Spot anomalies in patient outcomes across different departments or treatments, enabling targeted improvements in care delivery.
   
5. Manufacturing: Detect production delays or quality issues in real-time, with insights into root causes such as supply chain disruptions or equipment malfunctions.
   
6. Public Sector: Monitor trends in service delivery, such as identifying underserved communities or tracking anomalies in public funding allocations.

How to Get Started

Once the January 2025 update is live, enabling and using Contextual Insights will be straightforward. 

    1.    It is configured at a visualisation level.
    2.    Ensure that Contextual Insights is enabled for your visualisations. .
    3.    Select the data item to analyse and select the "Explain Selected" option from the context menu. 
​
For more detailed steps, refer to Oracle’s YouTube videos below explaining the feature and also detailing the configuration steps to set up Contextual Insights. 

Final Thoughts

​Contextual Insights represent a major leap forward in empowering organisations to make faster, smarter, and more informed decisions. By integrating advanced AI-driven capabilities directly into Oracle Analytics Cloud, this feature enables users of all skill levels to uncover hidden opportunities and respond proactively to emerging trends.

As analytics tools evolve, features like Contextual Insights showcase Oracle’s commitment to democratising analytics and fostering innovation. Whether you’re a data novice or a seasoned data scientist, Contextual Insights can transform how you explore and act on your data.

Embrace this feature to unlock the full potential of your analytics workflows and drive meaningful outcomes in your organisation. If you’re ready to explore its possibilities or need support with OAC, reach out or leave a comment below!

The Oracle Analytics REST API Connector is now available in the September 2022 Oracle Analytics update in preview mode.

The connector now allows access to data directly from REST API endpoints.

​To access the REST API Connector, you will need to enable the feature in the Console System Settings preview section by switching on the toggle.

You will need to log off and back on again to apply the change to your instance. You should now see the REST API connector in the create > connections menu on the Oracle Analytics homepage.

In the next screen, you enter details of your REST API that you want to access. The basic information required for connecting are:
​

1. Base URL of the REST API
2. REST API endpoints
3. Authentication type
4. Authentication credentials

​
You can either enter the information in here directly or you can use a JSON template to enter all of the REST API information. A sample JSON template file can be found at the bottom of this post.

If you decide to enter information of the REST API for your connector using a JSON file, you will need the contents to match the syntax below. If your API has several endpoints that you will be using then enter each endpoint URL in the endpoints section.

​Ensure that the correct authentication type is entered. Once all completed, the connection can be used as a standard connection to create a dataset. .

During the testing of this new functionality, the REST API used BasicAuth authentication and the API key was supposed to be entered in the username field. The issue here is that the password is a mandatory field and can't be left blank. The way round this was to re-enter the API key in the password field as well.

Once the connection has been created, you can create a dataset using the REST API connection in the normal fashion.

In the connection panel of the New Dataset window expand the REST API connection to the AUTOREST schema to access the result sets of the endpoints. The RESULTS tables contain the data from the endpoints and the others contain metadata of the endpoints.

The new REST API connector is a great addition to the Oracle Analytics capability. It now gives users another means of bringing additional data in to uncover even more insights from your enterprise data assets.

Photo by Isaac Smith on Unsplash

The Oracle Analytics team have been busy working on the latest version of Oracle Analytics Cloud - 5.9 which is packed with a number of interesting features and enhancements that we'll cover in some detail in this blog post. 

There is now a permanent Search field and smaller Data, Visualisations and Analytics icons which have been relocated to the top of the side bar. This results in the reduction of wasted space on the left of the side bar.    

Progress Bars

The animated progress bars that are displayed when visualisations and data are loaded have been updated.

The thin blue horizontal progress bars that were visible in previous Oracle Analytics versions across the screen when a. visualisation or canvas was refreshed have been changed as you can see in the images above. 

TRIM Function
​
There is now one click access to TRIM whitespace from a text column in the Data Preparation stage of Data Visualisation. This removes whitespace from both sides of the selected column  

US ZIP Codes

​This new feature is available in a column that contains ZIP code data. Oracle Analytics suggests a recommendation step to repair any of the ZIP codes that have missing leading zeros.

Filter Enhancements

You can now move a Visualisation level filter to the Canvas level by simply dragging it from the side bar to the Canvas level filter section. You can also do the opposite; moving a filter from the Canvas level to a specific Visualisation.

Sorting Enhancements

​There is now the capability in OAC 5.9 to sort by multiple dimensional attributes. In the screenshot below, you can see that the state_name and county_name have sorts defined.

​Columns added to the Size and Tooltip of a visualisation can also be included in the visualisation sorting. 

You can also sort by a measure but note that this will overwrite any dimensional attribute sorting that has been previously defined for the visualisation. 

Export Limits

For those who still export data dumps to Excel ?, row limits have been increased. Formatted exports that include XLSX, PPTX, HTML and PDF files have been increased to 400,000 rows with instances that have 16-52 OCPUs. Instances with 2-12 OCPUs will have their limit increased to 200,000 rows. 

The limits of unformatted report exports (XML, Tab Delimited and CSV) have been increased to 2,000,000 rows for instances with 2-12 OCPUs. Instances with 16-52 OCPUs will see their limits rise to 4,000,000 rows. ​

There were previously 2 Area Visualisations in versions of Oracle Analytics prior to version 5.9. There are now 3 types; Area, Stacked Area and 100% Area. 

I personally think that the new Stacked Area visualisation is more "visual" and users can gain a quicker insight visually. 

These new map backgrounds give users more flexibility with their map visualisations. 

It can be useful to gain some insights into the Machine Learning models that Oracle Analytics uses. There have been some enhancements added to the Oracle Machine Learning (OML) inspect tab which exposes much more metadata about the Machine Learning model. 

There has been some renaming and reorganising of the tabs available in the Machine Learning Inspect window. The Details tab contains basic information about the Oracle Machine Learning model including input and output columns. 

The Related tab now shows in-depth information of the Oracle Machine Learning model which is stored in database metadata views. 

You can even access these views and build out visualisations to gain further insights and understanding of the Machine Learning models that you are using in your Analytics.

​The Oracle Analytics team have put together a YouTube video about it which you can view below. 

In previous versions of Oracle Analytics, Text Tokenization was indirectly available. OML ADW capabilities could be used. Francesco Tisiot recently blogged about this with a good example demonstrating a typical use case for textual analytics. 

In Oracle Analytics Cloud 5.9, Text Tokenization is natively available as a data flow step thereby taking away the need to access the ADW instance in order to create a context index on the text column to be analysed and then to link the tokens to the text field in your data that is being analysed. This is now done for you automatically.  

The Text Tokenization functionality only works with Oracle sourced datasets - ATP, ADW and Oracle On-Premise Databases. It makes use of the Oracle Text database functionality and the Database Analytics data flow step is only available for these Oracle sourced datasets. 

​In order to avoid this error:

Ensure that your user account that you use for the connection to the database has the correct privileges:

As you will have gathered, this 5.9 release of Oracle Analytics Cloud comes packed with a wide range of features and enhancements. The release is typically staggered whilst the upgrade is applied to the Oracle data centres in the various regions so watch this space! If you want to see further details of all of these new features, check out the Oracle's "What's New" document here.

If you’re intrigued with the features that Oracle plan to incorporate into the Oracle Analytics platform here is some information on an Oracle Analytics resource worth mentioning; the public roadmap which contains information on Oracle Analytics features that may be included in the platform with an idea of when these may become generally available.

The Oracle Analytics Product Management team descended on London ahead of the Oracle OpenWorld Europe event for the Oracle Analytics Summit London event held at The Soho Hotel in London on the 11th of February earlier this week.

The day kicked off with a Partner Advisory Council session from the Product Management team which included a look ahead at the roadmap for Oracle Analytics as well as a Q&A session which was very informative.

There were several other sessions throughout the day providing participants with a wealth of knowledge on all things Oracle Analytics.

The biggest announcement of the day was undoubtedly Oracle being named a visionary in Gartner's Magic Quadrant for Analytics and BI Platforms. The news was hot off the press and received mixed reactions.

Oracle has had a torrid time in the eyes of the Market Researchers in recent times with the likes of Gartner completely excluding Oracle from the Analytics Magic Quadrant not too long ago. Gartner's justification was regarding the fact that Oracle's Analytics offerings provided little or no self service features which is an opportunity that Oracle missed with the likes of Qlik, Tableau & Power Bi to name but a few that filled the gap in the Self Service Analytics space.

The Oracle Analytics team has filled the Self Service void with a variety of features and products including products and features like Oracle Analytics Desktop, Data Visualisation, Natural Language Processing and all the Augmented capabilities that enable end users to get to their insights with little or no IT intervention. This focus and attention led to Gartner bringing Oracle back into the Magic Quadrant as a niche player.

Oracle has made huge strides in the self service analytics space at the expense of the governed analytics capabilities that hasn't seen much development and enhancements in recent times.

The governed analytics part of the Oracle Analytics product is very mature and is its unique selling point. You hear of many stories where end users have acquired a self service analytics tool and plug it into OBIEE's semantic model. Most of the "new age" analytics tools are geared around self service and there still appears to be a huge demand for governed analytics which Oracle Analytics provides alongside its self service capabilities.

Some have said that Oracle may have taken their eye off the ball in order to focus all attention on getting back into Gartner's good books. As mentioned previously, a lot of attention has been focused on self service capabilities possibly to the detriment of governed analytics capabilities.

There was a mix of the Oracle Analytics Product Management team, Partners and Customers in attendance at the Oracle Analytics Summit and it was great to see and hear first hand from the Product Management team.

The purpose of this blog post is to highlight the results of a number of Research Analysts reports that have been recently published on Analytics platforms and how Oracle Analytics Cloud measured up with other vendors.

451 Research
In the findings of 451 Research, it is claimed that “the sleeping giant has awakened “.451 Research notes that Oracle Analytics Cloud is the second largest part of Oracle’s PaaS business which is a bold statement if true.

Machine Learning and Artificial Inteligence features that have been incorporated into the Data Preparation functionality of the Data Visualisation tool got a mention.

Gartner
In Gartner’s 2019 Magic Quadrant for Analytics and Business Intelligence platforms, Oracle has made a re-entry after a brief absence in 2016 for being too IT centric and being late to the bimodal Analytics party.

Oracle made it back in as a Niche player by addressing this issue by expanding the functionality of the Data Visualisation tool giving end users the ability to mashup data from the IT managed semantic model with spreadsheets for instance. Gartner has recently mentioned a term, “Augmented Analytics” several times and it appears that Oracle has made huge strides in this direction by implementing such things as Data Preparation, Machine Learning and Natural Language generation which are all in line with Gartner’s Augmented Analytics.

Conclusion

You may have noticed the varied opinions of these Research results and I must admit that a lot of great work goes in to creating these reports but there is also a huge amount of subjectivity that is involved in the process.

These reports are useful but shouldn't be used in isolation when you may be on the market for Analytics tools. It should form part of the process.