Contextual Insights in Oracle Analytics Cloud: Transforming Decision-Making with AI

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!