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

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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
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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.