In data visualisation, it's often desirable to present both high-level summaries and detailed breakdowns within the same view. This enables users to have a view of the bigger picture while still having access to the underlying granular data points. This seamless integration can be challenging, especially when working with disparate data sources or attempting to convey complex relationships.
Fortunately, Oracle Analytics Cloud offers a powerful charting feature known as the overlay chart, which elegantly solves this problem. By combining multiple chart types into a single visualisation, the overlay chart enables you to layer summary-level metrics on top of more detailed visualisations, providing a comprehensive and cohesive view of your data.

In the data panel, select the visualisations tab and then select the Overlay Chart. 

The overlay chart is a powerful data visualisation tool in Oracle Analytics Cloud that enables seamless integration of high-level metrics and granular details within a single view. By layering multiple chart types, you can construct rich, multifaceted data stories. Whether unveiling executive summaries backed by granular evidence or pinpointing critical operational insights amidst a sea of data, the overlay chart empowers you to communicate with clarity and impact.

AI is rapidly becoming a critical technology that organisations across industries cannot afford to ignore. Those that proactively embrace AI, build the required capabilities, and integrate it into their strategies and operations will be better positioned to navigate the disruptions and capitalise on the opportunities presented by this transformative technology.

Democratising Machine Learning with Oracle Analytics Cloud ‘s AI Integration
At the heart of Oracle Analytics Cloud's capabilities lies the integration of AI and machine learning technologies. AI, which enables computers and digital devices to simulate human-like intelligence by learning, reasoning, and making decisions, has revolutionised various industries, from healthcare to finance. Machine learning, a subset of Artificial Intelligence is a technique that allows systems to automatically learn and improve from experience without being explicitly programmed.

Oracle Analytics Cloud leverages machine learning capabilities to empower users with a user-friendly interface for building, training, and deploying predictive models. This democratisation of machine learning empowers even non-technical users to harness the power of data-driven insights for decision-making.

Integrated Machine Learning Algorithms
​Oracle Analytics offers a set of built-in machine learning (ML) algorithms that you can leverage for various data analysis tasks. These algorithms are designed to be user-friendly and accessible through a drag-and-drop interface, eliminating the need for coding knowledge.  The following algorithms are available:

• Numeric Prediction: Forecast future values for a continuous numerical variable based on historical data.
• Multi-classifier: Classify data points into multiple predefined categories.
• Binary Classifier: Classify data points into two categories.
• Clustering: Group similar data points together based on their characteristics.

A recent update to this capability was the addition of an Auto ML step which analyses your data, calculates the best algorithm to use. This is available for Oracle Autonomous Data Warehouse data sources. The data preparation features can be used to ensure that the data is suitable for the chosen Machine Learning model. 
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You can find out more about the various OCI AI integrations currently available in Oracle Analytics Cloud.

In addition to these user-friendly features, OAC offers access to a wide range of built-in machine learning algorithms, catering to diverse analytical needs and user personas, from casual end-users to data scientists.

Benefits of AI-Powered Analytics with OAC
Integrating AI and machine learning capabilities into the analytics workflow offers numerous benefits for organisations:

1. Actionable insights for better decision-making: By leveraging machine learning algorithms and automated insights, businesses can uncover hidden patterns, trends, and relationships within their data, enabling more informed and data-driven decision-making processes.
2. Automation of repetitive tasks and process optimisation: AI and machine learning can automate tedious and time-consuming tasks, such as data preparation and cleansing, freeing up valuable human resources for higher-level strategic analysis.
3. Improved customer experiences and operational efficiency: By harnessing the power of predictive analytics and machine learning models, organisations can anticipate customer needs, optimise processes, and enhance operational efficiency, resulting in improved customer experiences and cost savings.
4. Driving innovation through data-driven insights: The ability to extract meaningful insights from complex data sources empowers organisations to identify new opportunities, develop innovative products and services, and stay ahead of the competition in an ever-changing business landscape.

The Evolving Role of AI in Analytics
As AI and Machine Learning technologies continue to advance, their role in the analytics domain is evolving. Traditionally, the focus has been on automating repetitive tasks and streamlining data preparation processes. However, the true power of AI lies in its ability to unlock predictive capabilities, enabling organisations to go beyond descriptive and diagnostic analytics.

With AI-powered analytics platforms like OAC, businesses can leverage predictive recommendations and future trend forecasts, gaining a competitive edge by anticipating market shifts, customer preferences, and potential risks or opportunities.

Upcoming Oracle Analytics Generative AI Features
Oracle is at the forefront of integrating cutting-edge AI capabilities into its analytics platform. According to a forward looking statement from James Richardson's blog, here are two highly anticipated features that we might see incorporated into Oracle Analytics:

1. Oracle Analytics AI Assistant: This natural language interface will enable users to query data and generate insights using conversational language, significantly reducing the barrier to entry for non-technical users and democratising data access.
2. Oracle Analytics Story Exchange: This open format will incorporate both data and visualisations, allowing for seamless integration with AI tools. By combining data and visual elements, users can leverage the power of generative AI to create rich, insightful narratives and presentations.

The Future of AI in Oracle Analytics
Oracle's roadmap for AI integration in its analytics platform is ambitious and forward-thinking. While the first AI integrations will be "behind the scenes" to improve productivity, the ultimate goal is to augment human intelligence with AI-powered insights.

Enhancements to features like Auto Insights and the AI Assistant are on the horizon, leveraging the capabilities of generative AI to provide more comprehensive and contextual insights. As AI continues to evolve, its role in analytics will shift from mere automation to true augmentation, empowering users with AI-driven recommendations, forecasts, and actionable insights.

As the volume and complexity of data continue to grow exponentially, embracing AI-enabled analytics platforms like Oracle Analytics Cloud will be crucial for organisations seeking to stay competitive and data-driven. By harnessing the power of AI and machine learning, businesses can uncover actionable insights, automate tasks, and make informed decisions that drive innovation, enhance customer experiences, and ultimately fuel success in the ever-changing digital landscape.