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.
​
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
​
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.

Artificial intelligence is no longer a side project. For enterprises, AI has become a strategic priority—transforming how organisations innovate, compete, and operate. Yet most businesses still struggle with fragmented data pipelines, disconnected tools, and governance challenges that slow down progress with the underlying root cause being how disparate data exists in enterprises.

While 78% of organisations planned to use AI in 2024 (Global AI Adoption Statistics: A Review from 2017 to 2025), the reality is that 68% of these organisations have data silos as their top concern (Data Strategy Trends in 2025: From Silos to Unified Enterprise Value - DATAVERSITY), and siloed data can cost companies up to 30% of their annual revenue (What are Data Silos and What Problems Do They Cause?|Definition from TechTarget). The culprit? The average enterprise runs on nearly 900 applications, with only one-third integrated (What Are Data Silos & Why is it a Problem? | Salesforce US), creating the very fragmentation that prevents AI success.

Think of enterprise data like a busy international airport. Passengers arrive from different places, each with different documentation requirements:
​

• Structured data is like UK passengers, travelling with standard passports and predictable checks and these passengers can make use of automated technology to speed through the airport.
• Semi-structured data is like EU passengers, still fairly standardised but with slight differences in documentation and rules.
• Unstructured data is like international passengers from all over the world, carrying varied paperwork that requires more manual checks and scrutiny.

Without a well-designed terminal, air traffic control, and secure customs processes, it would be chaos.
The new Oracle AI Data Platform (AIDP) is that airport terminal for AI—a single hub where all types of data arrive, is organised, governed, and routed to their various destinations so Analytics tools and AI applications can “take flight” safely and efficiently.

Oracle announced the AI Data Platform at Oracle AI World in Las Vegas on 14 October 2025, and it’s now generally available. Customers can access the live product site and documentation today, meaning you can onboard, configure the Master Catalog, and start building governed lakehouse-plus-AI pipelines on OCI straight away. 

Why Oracle AI Data Platform Matters

At its core, AIDP helps enterprises do three things better:
​

• Unify enterprise data for AI: Bring together all your data into a connected platform, removing silos and creating AI-ready pipelines.
• Accelerate AI development: Use integrated tools, notebooks, and GenAI agent frameworks to move faster without the overhead of stitching together separate environments.
• Innovate at scale: Orchestrate AI workloads across Oracle and third-party environments, backed by OCI’s optimised infrastructure for cost-effective performance.

The result? Faster time to value, improved governance, and the ability to scale AI beyond pilots into real enterprise impact.

​A Hypothetical Use Case: From Data Warehouse to AI-Powered Insights

Consider a typical scenario:

• An enterprise has built a robust data warehouse in Autonomous Database (ADW) to consolidate structured data.
• Oracle Analytics Cloud (OAC) provides dashboards and visualisations, helping business teams track KPIs and trends.
• However, AI isn’t being used, and unstructured data—documents, images, logs, call transcripts—sit outside the analytical process.

Here’s how AIDP helps transform this setup:

1. Bring unstructured data into play: AIDP can ingest and catalogue documents, PDFs, and multimedia alongside structured ADW data, enriching the analytical picture.
2. Enable AI-driven insights: Data scientists and analysts can use AIDP’s Spark notebooks to apply machine learning models directly on both structured and unstructured datasets.
3. Governance and trust: With Row-Based Access Control (RBAC), metadata cataloguing, and lineage, all new AI-ready datasets are managed as securely and reliably as the ADW warehouse.
4. Seamless analytics in OAC: OAC continues as the visualisation layer, now enriched with AI-derived features and predictive insights.

​In short, AIDP helps organisations move beyond descriptive dashboards to predictive and prescriptive intelligence, while leveraging the investments already made in ADW and OAC.

How Oracle AI Data Platform Supports the Full Data Workflow

One of AIDP’s key strengths is that it covers the entire lifecycle of enterprise data, much like how an airport manages passengers from arrival to departure.

1. Ingestion from multiple sources (Arrivals Hall)
   • Data enters from many places: SaaS apps, IoT devices, on-prem systems, and third-party feeds.
   • Like flights arriving from different countries, each data source brings its own rules and timing:
     • Structured data (UK passengers) with standard passports and predictable checks.
     • Semi-structured data (EU passengers) with slightly different but still fairly standardised documents.
     • Unstructured data (Other international passengers) carrying diverse documents that require more careful checks.
2. Data storage (Baggage Claim & Holding Areas)
   • Object Storage manages unstructured data (the oversized luggage, odd-shaped items that don’t fit neatly).
   • Autonomous Database (ADW) holds structured data (the regular suitcases, perfectly tagged and easy to track).
   • Open table formats like Delta Lake, Iceberg, and Hudi ensure every type of “baggage” is stored consistently, like a baggage system designed to handle every airline’s rules.
3. Transformation and enrichment (Customs & Security)
   • Just as passengers go through passport control and security checks, data must be cleaned, validated, and enriched.
   • Spark-powered compute and workflow orchestration make this process smooth, while ensuring compliance and efficiency.
4. Governance and security (Immigration & Border Control)
   • The Master Catalog is the record of who entered, when, and what they carried.
   • RBAC and lineage enforce strict policies—only the right people can access the right data, just as border officers verify visas and permissions.
5. AI and advanced analytics (Departure Gates)
   • Once cleared, passengers board their flights to final destinations.
   • In AIDP, this is where data powers machine learning, GenAI agents, and predictive analytics—transforming raw arrivals into actionable journeys.
6. Consumption and collaboration (Connections & Departures)
   • Finally, passengers (data) connect to their flights—whether that’s Oracle Analytics Cloud dashboards, third-party BI tools, or Delta Sharing with partners.
   • Smooth transfers ensure data doesn’t get delayed, lost, or misdirected.

By covering every stage of the workflow, AIDP ensures that UK (structured), EU (semi-structured), and international (unstructured) passengers all move smoothly through the airport, reaching their destinations as trusted, AI-driven insights.

​What is the Medallion Architecture?

The Medallion Architecture is a layered data design pattern used to organise data in a data lake or lakehouse for clarity, quality, and reusability. It’s structured into three main layers: Bronze, where raw data is ingested “as is” from source systems; Silver, where data is cleaned, validated, and enriched for consistency and reliability; and Gold, where curated, business-ready data is optimised for analytics, reporting, and machine learning. This layered approach improves data quality at each stage while maintaining traceability from raw to refined insights.
In AIDP, this spans Object Storage, open table formats (Delta/Iceberg/Hudi), and Autonomous Data Warehouse (ADW), all governed by the Master Catalog and RBAC.

Bronze — Land (raw, “as is”)
Purpose: Capture the truth of what arrived, without fixing it yet.

• Structured (databases/SaaS): Land extracts or CDC snapshots into Object Storage and/or register ADW tables via an External Catalogue for zero-copy access. Keep source fidelity (datatypes, nulls, odd codes).
• Semi-structured (JSON/events): Land JSON, Avro, CSV, Parquet as-is in Object Storage; record schema hints only.
• Unstructured (files/media): Land documents, images, audio, logs in Object Storage as Volumes.
• Operations/Governance: Minimal transforms. Stamp ingest metadata (source, load time, checksum); start lineage; coarse RBAC.

Airport analogy: arrivals hall — busy, mixed, unfiltered.

Silver — Refine (cleaned, standardised, enriched)
Purpose: Make data structurally sound, consistent and joinable.

• Structured track:
  
  
  • Standardise datatypes, units, currencies and codes; de-duplicate; enforce keys and constraints.
  • Build conformed dimensions, SCD staging, and validated facts.
  • Write out as Delta/Iceberg/Hudi or materialise into ADW staging if warehousing downstream.
• Semi-structured track:
  
  
  • Parse/flatten JSON, infer/lock schemas, normalise arrays/maps to relational sets.
• Unstructured track:
  
  
  • Use Spark + OCR/NLP/speech to extract entities/tables/text.
  • Normalise into rows/columns; de-dup; add confidence scores.
• Convergence:
  
  
  • Join structured with extracted signals (e.g., customer_id, invoice_no, email/phone hash for entity resolution).
  • Apply quality tests (row counts, referential integrity, domain checks).
  • Everything catalogued with lineage back to Bronze (files/tables).

Airport analogy: organised lounge — fewer people, rules applied, order emerging.

Gold — Serve (curated, business-ready)
Purpose: Publish trusted datasets for BI, ML and sharing.

• Warehouse-centric pattern: Load Gold into ADW for fast SQL, governance, and your existing semantic layer; OAC/Power BI/Tableau connect via SQL/JDBC. Ideal when most reporting already lives in ADW.
• Lakehouse-centric pattern: Keep Gold as Delta/Iceberg/Hudi on Object Storage; expose via JDBC/Delta Sharing; OAC blends lakehouse Gold with ADW facts if needed. Ideal when you want minimal data movement and time-travel/ACID on the lake.
• Outputs: Conformed facts/dims, KPI marts, and feature tables for ML/GenAI.

​Airport analogy: premium lounge — calm, curated, ready to board.

AIDP makes implementing this pattern simpler, with built-in orchestration and governance.

​Ready to transform your data chaos into AI-powered insights? Explore Oracle AI Data Platform and see how it can serve as your enterprise's AI airport terminal.