In today’s data-driven world, the ability to transform unstructured data into actionable insights is critical for organisations. With the November 2024 release of Oracle Analytics Cloud (OAC), the integration of OCI Document Understanding that brings cutting-edge capabilities to businesses looking to unlock the value hidden in their documents has been extended to allow users to register custom models. You can find out more information on how to create a custom model here.

In this blog, we will be looking at document understanding and how it fits into analytics. Here’s how this feature empowers analytics workflows.

From Unstructured to Actionable: The Role of Text Extraction

Many essential business processes rely on unstructured documents such as contracts, invoices, shipping manifests, and feedback forms. These documents often contain vital data, but their formats - PDFs, scanned images, or handwritten forms - make extracting and analysing this data manually a time-consuming and error-prone process.

Key Benefits of Text Extraction for Analytics​

​Text extraction is often viewed as a preliminary step rather than an intrinsic part of analytics, but this perspective underestimates its transformative impact on modern data workflows. In today’s organisations, vast amounts of critical information remain trapped in unstructured formats - documents, emails, contracts, and scanned images. Without the ability to extract and structure this data, analytics initiatives risk missing out on valuable insights hidden in plain sight like having a collection of . By integrating text extraction directly into analytics workflows, businesses not only bridge the gap between unstructured and structured data but also enhance the scope and accuracy of their insights.

While it may seem that text extraction belongs solely to the domain of data preparation, its seamless integration into analytics platforms changes the game. By enabling users to work directly with previously inaccessible information, text extraction ensures that analytics becomes truly comprehensive. This convergence eliminates the need for siloed processes, accelerates decision-making, and empowers users to leverage their data assets fully. As the lines between data preparation and analytics blur, text extraction proves itself not as a separate utility but as an essential enabler of meaningful, end-to-end analytics workflows.

Some of the benefits of integrating text extraction with analytics are:

1. Streamlined Data Preparation

Extracted text is ready for analysis without requiring extensive manual intervention. For example, a retail company can process thousands of supplier invoices, extracting line-item details such as product names, prices, and quantities. This structured data feeds into Oracle Analytics for further preparation and enrichment, such as cleansing inconsistent naming conventions or enriching data with external sources.

2. Improved Decision-Making

By leveraging the extracted text, users can create dashboards that provide actionable insights. A logistics company, for example, might track delivery times and costs across suppliers, identifying inefficiencies and opportunities to renegotiate contracts.

3. Cross-Document Analysis

OCI Document Understanding enables businesses to analyse trends across a corpus of documents. A financial institution can aggregate key metrics from thousands of contracts, such as interest rates or repayment terms, to assess portfolio risk and optimise lending strategies.

4. Advanced Search and Contextual Insights

Once text is extracted, it can be indexed and searched, enabling users to locate specific terms or patterns across document sets. For instance, legal teams can identify clauses that might expose the organisation to risk, while sales teams can quickly review terms in customer contracts to tailor offers.

Registering a Pre-trained Document Key Value Extraction Model in Oracle Analytics Cloud

Oracle Analytics Cloud provides access to some pre trained OCI document understanding models. This process allows you to leverage the AI capabilities of OCI Document Understanding within OAC to automatically extract key data points from your documents. Here are the detailed steps involved:

Access the Model Registration Function: Begin by navigating to the OAC Home Page. In the top right corner, locate the three-dot menu (ellipsis) and select "Register Model/Function." From the options presented, choose "OCI Document Understanding Models"

Establish the OCI Connection: Next, you'll need to select your OCI connection. If you haven't already established a connection between OAC and OCI, you'll be prompted to create one. This connection is crucial as it enables OAC to interact with the OCI Document Understanding service. 

Select the Desired Model Type: Once the OCI connection is established, a "Select a Model" window will appear. Choose "Pretrained Document Key Value Extraction" as the model type. This specific model is designed to identify and extract key data from documents, such as merchant names, addresses, and total prices.

Specify the OCI Bucket and Document Type: In the right-side panel of the "Select a Model" window, you'll need to provide two crucial pieces of information:

1. OCI Bucket: Select the OCI bucket where you have stored the document images you want to analyze. Ensure this bucket is located within the same tenancy as your OAC instance2.○
2. Document Type: Choose the specific document type that corresponds to the images in your bucket. For instance, if you are analysing receipts, select "Receipt." This helps the model accurately identify and extract the relevant key values for that particular document type.

Provide a Model Name and Register: Finally, give your model a descriptive name for easy identification within OAC. Click "Register" to complete the process. You can view your registered model under the "Models" tab in the Machine Learning page of OAC. By following these steps, you successfully register a pre-trained document key value extraction model in OAC, setting the stage for streamlined data preparation and enhanced data analysis. You can then create data flows within OAC to apply this registered model to your documents, extract the desired key values, and use this structured data to generate valuable insights. 

Oracle Analytics have just announced the general availability of the AI Assistant. It is a new feature that allows users to interact with their data using natural language. This means you can ask questions in plain English and the assistant will generate visualisations and insights based on your dataset.

Democratisation of Data Analytics: The AI assistant makes data analysis accessible to a wider range of users, not just those with advanced technical skills or even those with a formidable understanding of the data visualisation tool. By allowing users to interact with data using natural language, it eliminates the need for complex SQL queries or programming knowledge. This democratisation empowers more people within an organisation to explore data and derive valuable insights.

This feature is part of Oracle’s broader AI-driven efforts within Oracle Analytics Cloud, which also includes integration with machine learning models and advanced natural language generation for smart data narratives

Below, is a summary of the steps to follow to get the AI Assistant up and running:
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• Enable your dataset: Ensure your dataset is ready for use by checking the Insights panel and enabling it if necessary.
• Train the assistant: Index your dataset to help the assistant understand your data and answer questions accurately.
• Ask questions: Start asking questions using natural language. The assistant will generate visualizations and provide insights based on your data.

This Oracle Analytics blog provides detailed information of these simple steps needed to set this up. It requires some configuration to enable the feature at a dataset level. To improve the user experience, setting up synonyms for the dataset attributes is useful as this gives you the option to provide alternative names for each attribute which will improve the results generated by the AI Assistant. 

Remember to choose the option to index the dataset for the Assistant. There is also an option to index the dataset for the Assistant and the Homepage Ask feature if required. 

Upon initial review, the Oracle Analytics AI Assistant shows significant potential for enhancing productivity and easing the workflow, particularly for those less familiar with advanced analytics tools. While the setup, particularly dataset configuration and indexing, may require some upfront effort, once configured, the tool truly shines. The integration of natural language querying makes it much easier to interact with the data, reducing the need to master the complexities of Oracle Analytics Cloud (OAC).

The feature’s ability to generate instant visual insights from natural language inputs is particularly valuable. This means that once your dataset is indexed and ready, you can quickly derive meaningful insights, spot trends, or explore relationships in the data without having to dive into manual visualisation processes. Auto Insights can also uncover patterns you may not have initially considered, adding further value by highlighting hidden connections.

Subject Areas
This version of the AI Assistant does not currently have the capability to support subject areas. However, this feature is part of the planned future enhancements and is expected to be integrated into the Assistant in due course. Once implemented, it will allow users to engage with the Assistant more effectively, offering deeper insights and tailored assistance based on specific subject areas. 

Update - 19th September 2024

After some conversations with the Product Management team , I have gathered some more information about the AI Assistant pertaining to the rollout process and some details about security and data privacy which you can find below:.

Oracle’s objective is to make the AI Assistant available at no additional cost across all OCPU shapes. However, its release will be managed through a phased, controlled rollout that will span several Oracle Analytics Cloud updates. This approach ensures optimal performance and stability while addressing key concerns around privacy and security.

The Large Language Model (LLM) powering the Assistant is highly resource-intensive and Oracle-managed. Due to its proprietary nature and status as protected intellectual property, specific details regarding the LLM are limited. However, it is important to note that each Oracle Analytics Cloud instance is provisioned with its own private, Oracle-managed LLM.

To further safeguard data privacy, all interactions between the LLM and each individual Oracle Analytics Cloud instance remain entirely within the customer’s OCI environment. At no point do these interactions leave the customer’s infrastructure, ensuring complete data security and confidentiality.

Generated with AI ∙ 16 July 2024 at 11:02 PM

The Oracle Analytics Cloud July 2024 update is on its way out to the general public as this blog is being written. We will focus our attention on one of the new features that significantly improves the benefits of dataset in the Oracle Analytics data visualisation tool.

You can watch this YouTube playlist below to find out details of some of the main features in the July 2024 update. For more information on the update, click here to get a comprehensive list of the features that are included in the July 2024 update.

In this blog, we'll focus on the data caching feature improvements which are part of the July 2024 update. Caching the data in datasets provides a number of benefits which include the potential for improved query performance. Being able to apply changes made to the source data has been possible previously by doing a full reload of the dataset. This can be a long drawn out process if the underlying data source has a large volume of data.

In the July 2024 Oracle Analytics Cloud update, it is now possible to incrementally update datasets. There is now the capability to only insert new rows and updates to apply any changes to existing data. which will be much better than a full data reload. This improves the data load time as well as the execution of queries against the dataset.

These performance improvements that caching provides come with a downside. If you require real time data, caching is not the option for you. If intraday data changes aren't critical to your business area that you are analysing, then data caching will work for you.

Business Benefits

Incrementally loading data into a dataset in Oracle Analytics Cloud (OAC) offers several business benefits:

Reduced System Load and Improved Performance:
 By only loading new or changed records, the system avoids reprocessing the entire dataset. This reduces the overall load on the system, leading to better performance and quicker data refresh times.

Minimised Downtime:
Incremental loading can be scheduled during off-peak hours or more frequently, ensuring that the system is always up-to-date without significant downtime or disruption to users.

Faster Data Refresh:
Since only new or updated data is processed, the time required to refresh datasets is significantly reduced. This allows for more timely and accurate data availability for business analysis and decision-making.

Scalability:
Businesses can scale their data operations more effectively with incremental loading, handling larger datasets without the need for massive resource increases. This supports business growth and the integration of more data sources.

Enhanced Data Accuracy and Relevance:
 With more frequent and efficient data updates, businesses can ensure that their analytics are based on the most current data. This leads to more accurate insights and better-informed decision-making.

Improved User Experience:
End users experience faster query responses and up-to-date data, which enhances their ability to perform timely and effective analysis. This leads to higher user satisfaction and greater adoption of the analytics platform.

Implementing incremental data loading in Oracle Analytics Cloud supports these benefits, enabling businesses to maintain efficient, scalable, and cost-effective data management practices. Data caching is not available to all data sources. You can get a full list of supported data sources from here.

There are 2 methods that can be used to set the incremental dataset cache refresh; this can be done either in a visualisation or directly within the dataset itself.

Configuration in a Visualisation

​The image below shows you how to set this up in a visualisation.

Change the Data Access from Live to Automatic Caching and then change the Cache Reload Type to Load New and Updated Data. 

Configuration in ​a Dataset

The image below shows how the incremental caching is set up in a dataset.

As described above, you can set up a variety of data sources to refresh incrementally and these can be run manually as above. If there is a requirement for the dataset to be regularly refreshed then a schedule can be set up to enable this. Below, you can see how to set up a schedule that can be used to automate the cache refresh of the dataset. You can also view the details of the schedule and detail of the schedule executions. 

Incremental data loading of datasets in Oracle Analytics Cloud offers significant business benefits by enhancing system performance and efficiency. By only loading new or updated records, it reduces system load, minimizes downtime, and accelerates data refresh times. This approach optimises resource utilisation, providing cost savings and enabling scalability for larger datasets. Additionally, it ensures data accuracy and relevance, improving the user experience with faster query responses and up-to-date information.

​Overall, incremental loading supports efficient, scalable, and cost-effective data management practices, crucial for informed decision-making and business growth.

Oracle recently made the latest version of their database: Oracle Database 23ai generally available, the next generation of its database incorporates a number of AI capabilities. There are over 300 new features in this new release and we'll be looking at some of these features from an Analytics perspective, empowering you to unlock deeper insights from your data.

Some of the highlights of the Oracle Database 23ai release are discussed in this video below. 

Unified Data Access: By storing and managing diverse data types within a single database system, a converged database eliminates the need for separate data silos and specialised databases for different workloads. This unified data access makes it easier to query, analyse, and generate insights across multiple data types without the overhead of complex data integration or movement between different systems.

Consistent Data Governance: With all corporate data workloads stored in a converged database, it becomes easier to implement consistent data governance policies, security controls, and access management across the entire data landscape. This ensures data integrity, compliance, and proper access controls when generating insights from various workloads.

Cross-Workload Analysis: A converged database allows you to perform cross-workload analysis by combining data from different sources and types. For example, you could analyse spatial data in the context of relational data, or enrich graph data with information from blockchain workloads. This cross-pollination of data can lead to more comprehensive and valuable insights.

Unified Query Processing: By having a single query processing engine that can handle diverse data types, a converged database can optimise query execution and leverage advanced techniques like vectorisation, parallelisation, and indexing across different workloads. This can lead to faster and more efficient insight generation, especially for complex queries spanning multiple data types.

Simplified Data Pipelines: Instead of maintaining separate data pipelines and ETL processes for each workload, a converged database allows you to streamline data ingestion, transformation, and analysis workflows, reducing complexity and potential sources of error.

Improved Collaboration: With all corporate data workloads residing in a single converged database, it becomes easier for different teams and departments within an organisation to collaborate, share data, and generate insights collectively. This can foster better cross-functional analysis and decision-making.

Future-Proofing: As new data types and workloads emerge, a converged database architecture can more easily adapt and incorporate them, future-proofing your data infrastructure and reducing the need for frequent migrations or replacements of specialised databases.

​AI Vector Search is another feature that can improve insights derived from your corporate data as it provides the functionality to access data in all the formats allowing foundation models like OpenAI's ChatGPT, Microsoft's Copilot, Anthropic's Claude to name a few to provide more accurate responses with access to all data in the Oracle Database 23ai.

AI Vector Search
​Another noteworthy feature introduced in Oracle Database 23ai is AI Vector Search. This functionality has the potential to significantly improve how users gain insights from their data. Unlike traditional keyword searches, AI Vector Search goes beyond simply matching terms. Instead, it focuses on understanding the underlying meaning and relationships within your data. This is achieved by converting text, images, and even relational data into mathematical representations that capture their essence. By comparing these representations, the database can identify information that aligns most closely with your query, regardless of its format or specific wording. This more semantic approach to searching unlocks new possibilities for data exploration within Oracle Analytics Cloud. Users can uncover previously hidden connections and patterns within their data, leading to a richer understanding and the ability to make more informed decisions.

AI Vector Search is considered a significant benefit of a converged database when it comes to improving generated insights across a wide range of corporate workloads, including relational, spatial, graph, and blockchain workloads. Here's how AI Vector Search can enhance insight generation in a converged database environment:

Unified Data Representation: By representing diverse data types as vectors, AI Vector Search allows for a consistent and unified representation of data across different workloads. This enables the application of machine learning and AI techniques consistently, regardless of the underlying data structure or format.

Semantic Search: AI Vector Search leverages the power of vector representations to capture the semantic relationships between data elements. This enables more meaningful and context-aware searches, leading to better insights by retrieving relevant information based on conceptual similarity rather than just exact matches.

Cross-Workload Similarity Analysis: With vector representations, AI Vector Search can identify similarities and patterns across different workloads, even if the data types are distinct. For example, it could uncover relationships between spatial data and graph data, or find connections between blockchain transactions and relational data, enabling novel insights and discoveries.

Improved Query Performance: AI Vector Search typically uses specialised indexing techniques optimised for vector data, enabling faster and more efficient querying across large datasets spanning multiple workloads. This can significantly accelerate the process of generating insights, especially for complex analytical queries.

Scalability: AI Vector Search can scale more efficiently than traditional search methods, as vector representations are typically more compact and can leverage techniques like dimensionality reduction. This scalability is crucial when dealing with the ever-increasing volumes of data across diverse corporate workloads.

Integration with AI/ML Models: The vector representations used in AI Vector Search are compatible with many AI and machine learning models, enabling seamless integration of these advanced techniques for enhancing insight generation. For example, natural language processing models could be used to query and analyse text data across different workloads.

I'd strongly recommend spending a bit of time taking this LiveLabs workshop to get an understanding of the vector data type basic principles. 

Here's a breakdown of the steps to configure this setup:
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1. Setting Up SharePoint Permissions:

• Ensure Oracle Database 23ai has read access to the relevant document libraries within your SharePoint site. This might involve configuring crawl permissions for an appropriate service account used by Oracle Database 23ai.

2. Oracle Database 23ai Configuration:

• Within Oracle Database 23ai, define an external data source pointing to your SharePoint site. This typically involves specifying connection details and authentication methods.
• Metadata Definition: Define the metadata fields you want to store within Oracle Database 23ai. This could include properties like document titles, creation dates, authors, or even custom tags associated with the documents in SharePoint.
• Vector Indexing: Create vector indexes for the chosen metadata fields. This allows Oracle Database 23ai to perform semantic searches based on the meaning conveyed through the metadata.​

3. Oracle Analytics Cloud Configuration:

• Establish a secure connection between Oracle Database 23ai and Oracle Analytics Cloud.
• Define the data source within Oracle Analytics Cloud, pointing it to the relevant database instance and tables in Oracle Database 23ai containing the SharePoint metadata.

4. Utilising NLP in Oracle Analytics Cloud:

• Design dashboards and visualisations within Oracle Analytics Cloud that allow users to interact with the SharePoint data through NLP queries.
• Users can then ask questions related to the documents stored in SharePoint based on the available metadata (e.g., "find all documents about marketing campaigns created last quarter").

Benefits:

• Centralised Search: Users can search for relevant documents across your SharePoint site using a single platform (Oracle Analytics Cloud) with natural language queries.
• Improved Discoverability: Leverage AI Vector Search to find documents based on meaning within the metadata, not just filenames or basic keywords.
• Flexibility: You can keep your documents within the familiar SharePoint environment while enabling a more powerful search experience through Oracle Database 23ai.

Important Considerations:

• This is a general guideline, and the specific configuration steps might vary depending on your SharePoint version and Oracle Database 23ai setup. Refer to the official documentation from Microsoft (SharePoint) and Oracle for detailed instructions.
• Security measures are crucial to ensure proper access control to both the SharePoint documents and the metadata stored within Oracle Database 23ai.
• Performance considerations exist when dealing with vast amounts of documents and metadata. Optimising data extraction and indexing processes might be necessary.

By following these steps and considerations, you can leverage Oracle Database 23ai's AI Vector Search capabilities to unlock a more intuitive and semantic search experience for your SharePoint documents within Oracle Analytics Cloud.

By leveraging the AI Vector Search feature, the company can gain deeper and more nuanced insights from their data, leading to better-informed business decisions and a more responsive approach to customer feedback

However, it's important to note that realising the full benefits of a converged database for generating insights across diverse workloads may require careful data modelling, performance optimisations, and leveraging the advanced analytical capabilities provided by the database system, such as machine learning, graph analytics, and spatial analysis functions.​​​​​​​​​​​​​​​​

Conclusion
​In conclusion, Oracle Database 23ai with its converged database architecture and AI Vector Search has the potential to unlock a new level of data exploration within Oracle Analytics Cloud. By storing all your corporate data, structured and unstructured, in a single platform, you can leverage the power of AI to search for insights across different data types using natural language. This not only simplifies data exploration and data integration for users but also empowers them to uncover hidden connections and patterns that might be missed with traditional keyword searches. As a result, Oracle Database 23ai paves the way for a more intuitive and insightful data analysis experience within Oracle Analytics Cloud, ultimately leading to better data-driven decision making.

When you create a dataset in Oracle Analytics, the system performs column-level profiling to generate semantic recommendations for data enrichment. Additionally, when creating workbooks, you can enhance visualisations by including knowledge enrichments from the Data Panel. These recommendations are based on automatic detection of specific semantic types during the profiling process. 
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Semantic types include geographic locations identified by city names, recognisable patterns (such as credit card numbers, email addresses, and social security numbers), dates, and recurring patterns.

In the share price dataset below which has a date column selected, the panel on the right hand side provides semantic recommendations related to the date column which can be added to the dataset to provide additional classifications in addition to the original date from the data set like the month, quarter and year. 

This is very helpful in adding new ways to look at data but this is limited to the system built-in semantic recommendations  that come with the Oracle Analytics platform. Most companies have domain specific knowledge which would be useful in this context to provide enrichment possibilities to users. 

In the sample file below which contains geographic information about airport codes, this can be used to give additional geographic context to datasets that only contain the 3-digit airport code. 

To demonstrate this feature, we will be adding some custom knowledge that can be used to enrich 3-digit airport codes with additional information about the airport name, country and continent. 

As an Administrator, click the Reference Knowledge button in the Oracle Analytics administration console. 

Select the file (either an XSLX or CSV format) with the custom knowledge to upload.  I have noticed that the first column in the data to be uploaded to custom knowledge should be the unique key used to identify each record uniquely.  In the case above, the 3-digit airport code is unique and should be the first column in the data. 

​Review the content in the window below and click OK if all looks good. 

Now that the custom knowledge has been uploaded to Oracle Analytics, it is now available to be used in workbooks that have the airport code in their datasets. This workbook has a list of London airports and their approximate passenger numbers form 2014 - 2023.  We can make use of the custom knowledge to enrich this workbook.

Navigate to the Data tab and you should now see recommendations for the Airport column with options as seen in the screenshot below. 

The enrichments can be seen here and will now be available to use within the workbook. 

Alternatively, the enrichments can also be seen directly in the workbook data panel enabling non-administrator users to have access to these enrichments. 

What would improve this feature would be having an option to derive the custom knowledge from a database source for the purposes of data governance. A refresh schedule would be a welcome addition to the feature as well ensuring that the custom knowledge is sourced directly from a database but also routinely refreshed to ensure that the custom knowledge is kept up to date. ​

This functionality serves as a valuable tool that can empower users to achieve a deeper understanding of their data and extracting insights that have greater relevance to the end user. 

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.

REST (REpresentational State Transfer) APIs (Application Programming Interface) are a way for software systems to communicate and share data over the internet using HTTP requests. REST APIs enable different systems to exchange data in a standardised way.  Simply put, an API is a set of rules which enable different software applications to communicate with each other defining how the software applications should interact without having to know about the internal complexities of each application. 

This analogy further explains an API; think of an API as a waiter in a restaurant. When you (the client) want to order food which is analogous to requesting information or services, you do not need to fully understand what the Chef needs to do to get your order prepared.  You, the client made your request to the waiter who acts as an intermediary (in the way that an API is the go-between) ensuring that communication between the client and the kitchen is executed and then returns the food ordered to the client.

Some key aspects of REST APIs:

1. They use HTTP methods like GET, POST, PUT, DELETE to perform operations.
2. Data is structured in either JSON or XML format.
3. APIs are organised around resources which are identified by URIs.
4. APIs are stateless, meaning no client context is stored on the server between requests.

The January 2024 update includes an extension to the available REST API endpoints that now provide catalog management capabilities. In Oracle Analytics Cloud, REST APIs allow external applications to integrate with and extend the capabilities of OAC. Some examples of how REST APIs are used:
​

1. Catalog object deployment and management of object permissions
2. OAC system settings configuration
3. Querying data from OAC datasets and subject areas. The API can return data in JSON format.
4. Automating processes by chaining together sequences of API calls.

Mike Durran from the Oracle Analytics Product Management team has put together a video available on YouTube that walks through these endpoints.

REST APIs provide a standardised way for Oracle Analytics Cloud to exchange data and interoperate with other systems. The APIs allow OAC capabilities to be leveraged from external apps and workflows. REST APIs can help enable clean handoffs between developers and production support teams for Oracle Analytics Cloud deployments, without the production support teams requiring extensive OAC expertise from the operational perspective of Oracle Analytics.

Some of the new REST API endpoints released in the January 2024 update that now allow for catalog management. Here is a list of the new Oracle Analytics Cloud REST API endpoints:
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1. Copy a Catalog item
2. Create a folder
3. Delete a folder
4. Get Catalog item ACL
5. Get Catalog item details
6. Get Catalog item by type
7. Move a Catalog item
8. Update a Catalog item ACL

​The curl command below is an example of how one of these endpoints looks like.

The {{OAC_URL}} portion of the URL (above) to use in the REST API endpoints can be retrieved from your Analytics Instance detail page as below:

​Developers can build automated deployment pipelines and operations runbooks that call OAC REST APIs to perform releases. For example, the pipeline can use APIs to validate the system health, check for availability of required roles/capacity, import metadata like dashboards or reports, and switch traffic to the new version. 

The operations team does not need deep application knowledge, just the ability to invoke APIs and monitor for errors. The actual deployment logic is encapsulated in code and triggered through API calls. This allows a clear separation of development teams from production environments - developers own building the logic and automation leveraging their OAC expertise, while operations teams focus on running and monitoring the operational aspects.

The APIs serve as the interface layer between the two. This prevents fragile handoffs where operations need to learn and understand OAC internals, rely on manual processes or even rely upon the development teams to carry out these activities in production environments. Access to production can be minimised once APIs are implemented.

Overall, OAC REST APIs enable easier collaboration between developers and production teams. Releases can be made reliable and repeatable through automation using APIs, reducing risks and errors caused by manual processes. The APIs abstract away the complexity and provide self-service capabilities to operations teams.

The second part of this blog will dive into more technical detail on how these REST APIs are set up  using an API client tool to interact with the API endpoints. We'll also look at how these REST APIs can be used  by operations teams.

Parameter binding in Oracle Analytics Cloud refers to the ability to bind prompt values to filter components (for example) in data visualisation workbooks. The November 2023 update includes an extension to the parameter functionality which allows you to be able to bind a parameter to a range filter in a workbook.
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Data Visualisation parameters in Oracle Analytics Cloud allow you to vary aspects of a visualisation at runtime using prompt selections. Some key points:

1. Parameters can be set on visualisations like charts, graphs, maps etc. 
2. You can vary measures, dimensions, filters, etc by binding prompt values. For example, a parameter could allow the X axis measure or category dimension of a bar chart to change based on a prompt.
3. Instead of having static hard-coded values, parameters let selections made at runtime drive the visualisations.
4. Data Visualisation Parameters make it possible to create more flexible, interactive visualisations within a single workbook.

Data Visualisation parameters enhance flexibility, interactivity, and reuse by linking runtime user selections to the visualisations in an Oracle Analytics Cloud workbook.

In the workbook above, a parameter has been bound to the Cost Date attribute. The filter on the visualisation at the top of this workbook was set up as a range filter on the Cost Date.

In the visualisation that you would like to apply a range filter, follow the steps below:

1. Right click on the filter and select Modify Filter
2. In this case, select Date Range
3. Choose Range
4. Clink on the Bind to Parameter icon for the low range option and select Create Parameter
5. Repeat the same for the high range option​

These steps create 2 parameters and binds the filter range to these parameters which you can see in the parameters tab in the Data panel. You can modify the automatically generated parameters if required by right clicking on the parameter. 

Useful Use Case

​You may have a situation where you want to show the full history on a canvas and also to show a subset of this in another visualisation. You can achieve this by creating the 2 visualisations. In the visualisation that should show the subset of data, you will need to create a range filter and bind to a parameter as we have done above.

​The next step would be to drag the parameters from the data panel to the filters panel

This will enable you to have the visualisation with a subset of data as well as another visualisation with the full history on the same canvas.

Oracle Analytics Cloud (OAC) and Microsoft Power BI are two of the most popular platforms organisations use for data visualisation, reporting, and business analytics.
It is common for companies today to have a "hybrid analytics ecosystem" meaning they utilise both OAC and Power BI, rather than standardising on just one. There are several reasons a hybrid approach may emerge:
Oracle Analytics and Microsoft Power BI are 2 popular choices for businesses around the world for their Analytics solutions. In many cases, there is what I'll refer to as a "hybrid analytics ecosystem".

Hybrid Analytics Ecosystem

Many organisations today have a "hybrid analytics ecosystem" - meaning they use both Power BI and Oracle Analytics Cloud (OAC) or any other combination of analytics platforms for different analytics and reporting needs. There are several reasons this hybrid approach may emerge:
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• Through mergers and acquisitions, a company may end up with both OAC and Power BI if different units or mergers used different analytics platforms historically.
• Organisations may choose different tools for different use cases. For example, OAC for governed enterprise reporting and Power BI for self-service data exploration.
• Companies may adopt both but be in transition from one to the other. Using both allows a gradual migration approach.
• Global companies may use one platform in some regions and the other platform in other regions.
• In some cases, companies get into this situation because there is no centralised reporting/analytics strategy.

Whatever the reason, it's common for organisations to have a footprint in both Power BI and OAC (or any other combination of analytics platforms). This used to be seen as a challenge - "which one do we standardise on?" But modern integration and alignment features allow both platforms to coexist and complement each other rather than compete. This provides flexibility to leverage the strengths of both tools.
The goal of this post is to show how Power BI and OAC can work together effectively in a hybrid analytics model to deliver insights across the business.

Benefits of each platform

An important benefit of Power BI is its tight integration with Microsoft 365 and components like Excel. Users are already familiar with Excel for ad hoc analysis, and Power BI makes it easy to share those same models and data to the entire organisation via dashboards and apps. The Microsoft stack alignment enables a seamless flow between data, analysis, and organisational sharing. For organisations invested heavily in Microsoft technology, Power BI is a natural fit to extend that analytics foundation.

A key strength of Oracle Analytics Cloud is its semantic model that abstracts the complexity of the underlying data and technology stack from end users. Business analysts can access and analyse data without needing to understand the physical tables and relationships. OAC also enables strong data governance with centralised security rules, data logic definitions, and stewardship processes applied consistently across the semantic models. This top-down approach ensures quality and trust in the data while still empowering decentralised analytics.

How They Can Work Together

There is Power BI supported connectivity to Oracle Analytics that gives Power BI users access to Oracle Analytics content.

Oracle Analytics provides an easily configured connector that enables Power BI Desktop access to the Oracle Analytics semantic model. More details on how to set it up can be found here. 

​It should be noted that there are a number of limitations:

1. The connectivity to Oracle Analytics only works from Power BI desktop.and nor the Power BI service. 
2. Power BI can only access cached data in Oracle Analytics that has been previously loaded into OAC. It does not allow direct queries or live connections to data sources. This means data may be stale if the cache has not been refreshed.

Conclusion

Power BI and Oracle Analytics Cloud both offer compelling capabilities for data visualisation, reporting and analytics. Rather than viewing them as competing tools, organisations should consider how they can work together in a hybrid analytics environment.
The key is to play to the strengths of each platform:

• Use OAC as the governed source of truth for enterprise data models, metrics and KPIs. Take advantage of its advanced modelling, calculation and data preparation features.
• Leverage Power BI's ease of use and flexibility for self-service data exploration and quickly sharing insights through interactive reports and dashboards as well at its tight integration with Microsoft 365 products like Excel and PowerPoint.

With strong data governance, user training, and integration between the platforms, Power BI and OAC can complement each other to deliver insights to users across the organisation. There doesn't need to be an "either/or" choice.
By taking a hybrid approach, both business users and data experts can win with the analytics capabilities they need. Rather than analytics chaos, a "best of both worlds" environment can emerge. Organisations can optimise their analytics investments and empower people with the right insights at the right time.
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