Following up on my previous post which highlights different approaches of accessing Oracle Fusion Cloud Apps Data from Databricks, I present in this post details of Approach D, which leverages the Perficient accelerator solution. And this accelerator applies to all Oracle Fusion Cloud applications: ERP, SCM, HCM and CX.

As demonstrated in the previous post, the Perficient accelerator differs from the other approaches in that it has minimal requirements for additional cloud platform services. The other approaches of extracting data efficiently and in a scalable manner require the deployment of additional cloud services such as data integration/replication services and an intermediary data warehouse. With the Perficient accelerator, however, replication is driven by techniques that are solely reliant on native Oracle Fusion and Databricks. The accelerator consists of a Databricks workflow with configurable tasks to handle the end-to-end process of managing data replication from Oracle Fusion into the silver layer of Databricks tables. When deploying the solution, you get access to all underlying python/SQL notebooks that can be further customized based on your needs.

Why consider deploying the Perficient Accelerator?

There are several benefits to deploying this accelerator as opposed to building data replications from Oracle Fusion from the ground up. Built with automation, the solution is future-proof and enables scalability to accommodate evolving data requirements with ease. The diagram below highlights key considerations.

A Closer Look at How Its Done

In the Oracle Cloud: The Perficient solution leverages Oracle BI Cloud Connector (BICC) which is the preferred method of extracting data in bulk from Oracle Fusion while minimizing the impact to the Fusion application itself. Extracted data and metadata is temporarily made available in the OCI Object Storage buckets for downstream processing. Archival of exported data on the OCI (Oracle Cloud Infrastructure) side is also automatically handled, if required, with purging rules.

In the Databricks hosting cloud:

• Hosted in one of: AWS, Azure or GCP, the accelerator’s workflow job and notebooks are deployed in the Databricks workspace. The Databricks delta tables schema, configuration and log files are all hosted within the Databricks Unity Catalog.
• Notebooks leverage parametrized code to programmatically determine which Fusion view objects get replicated through the silver tables.
• The Databricks workflow triggers the data extraction from Oracle Fusion BICC based on a predefined Fusion BICC job. The BICC job determines which objects get extracted.
• Files are then transferred over from OCI to a landing zone object store in the cloud that hosts Databricks.
• Databricks AutoLoader handles the ingestion of data into bronze Live Tables which store historical insert, update and delete operations relevant to the extracted objects.
• Databricks silver Live Tables are then loaded from bronze via a Databricks managed DLT Pipeline. The silver tables are de-duped and represent the same level of data granularity for each Fusion view object as it exists in Fusion.
• Incremental table refreshes are set up automatically leveraging Oracle Fusion object metadata that enables incremental data merges within Databricks. This includes inferring any data deletion from Oracle Fusion and processing deletions through to the silver tables.

Whether starting small with a few tables or looking to easily scale to hundreds and thousands of tables, the Perficient Databricks accelerator for Oracle Fusion data handles the end-to-end workflow orchestration. As a result, you end up spending less time with data integration and focus efforts on business facing analytical data models.

For assistance with enabling data integration between Oracle Fusion Applications and Databricks, reach out to mazen.manasseh@perficient.com.

Connecting to Oracle Fusion Cloud Applications data from external non-Oracle systems, like Databricks, is not feasible for bulk data operations via a direct connection. However, there are several approaches to making Oracle apps data available for consumption from Databricks. What makes this task less straightforward is the fact that Oracle Fusion Cloud Applications and Databricks exist in separate clouds. Oracle Fusion apps (ERP, SCM, HCM, CX) are hosted on Oracle Cloud while Databricks leverages one of AWS, Azure or Google Cloud. Nevertheless, there are several approaches that I will present in this blog on how to access Oracle Apps data from Databricks.

While there are other means of performing this integration than what I present in this post, I will be focusing on:

1. Methods that don’t require 3^rd party tools: The focus here is on Oracle and Databricks technologies or Cloud services.
2. Methods that scale to large number of objects and high data volumes: While there are additional means of Fusion data extraction such as using REST APIs, OTBI, or BI Publisher, these are not recommended methods for handling large bulk data extracts from Oracle Fusion and are therefore not part of this analysis. One or more of these techniques may still be applied though, when necessary, and may co-exist with the approaches discussed in this blog.

The following diagrams summarize four different approaches on how to replicate Oracle Fusion Apps data in Databricks. Each diagram highlights the data flow, and the technologies applied.

• Approach A: Leverages Oracle Autonomous Data Warehouse and an Oracle GoldenGate Replication Deployment
• Approach B: Leverages Oracle Autonomous Data Warehouse and the standard Delta Sharing protocol
• Approach C: Leverages Oracle Autonomous Data Warehouse and a direct JDBC connection from Databricks.
• Approach D: Leverages a Perficient accelerator solution using Databricks AutoLoader and DLT Pipelines. More information is available on this approach here.

Choosing the right approach for your use case is dependent on the objective of performing this integration and the ecosystem of cloud platforms that are applicable to your organization. For guidance on this, you may reach Perficient by leaving a comment in the form below. Our Oracle and Databricks specialists will connect with you and provide recommendations.

Roeslein & Associates, a global leader in construction and engineering, had complex business processes that could not scale to meet its needs. It wanted to set standard manufacturing processes to fulfill highly customized demand originating from its customers.

Roeslein chose Oracle Fusion Cloud SCM, which included Project-Driven Supply Chain for Inventory, Manufacturing, Order Management, Procurement, and Cost Management, and partnered with Perficient to deliver the implementation.

Ben Mitchler, discussed the migration to Oracle Cloud. Jeff Davis, Director, Oracle ERP at Perficient joined Ben to share this great PDSC story.

Discussion included:

• Challenges with the legacy environment
• On-premises to cloud migration approach
• Benefits realized with the global SCM implementation

If you were unable to catch the live event, the on-demand recording is now available.

An Oracle Partner with 20+ years of experience, we are committed to partnering with our clients to tackle complex business challenges and accelerate transformative growth. We help the world’s largest enterprises and biggest brands succeed. Connect with us at the show to learn more about how we partner with our customers to forge the future.

In today’s fast-paced business environment, efficient procurement processes are essential for maintaining a competitive edge. Organizations must manage a myriad of products and services, ensuring that they are sourced, purchased, and delivered efficiently. Oracle Product Data Hub (PDH) Cloud and Oracle Procurement Cloud are two powerful tools that facilitate this process. A critical component of this integration is item classification, which profoundly impacts procurement activities. In this blog, we will explore the impact of item classification in Oracle PDH Cloud on Oracle Procurement Cloud, along with practical examples.

Understanding Item Classification in Oracle PDH Cloud

Oracle PDH Cloud is a comprehensive product information management solution that serves as a central repository for all product data. One of its key features is item classification, which allows organizations to categorize items based on various attributes such as type, usage, and specific characteristics. This classification system helps in organizing, tracking, and managing products more effectively across the enterprise.

Item classes in Oracle PDH Cloud can be defined using multiple criteria, such as:

• Product Type: Finished goods, raw materials, components, etc.
• Usage: Consumables, repairable items, capital goods, etc.
• Industry-specific Attributes: Chemicals, electronics, pharmaceuticals, etc.

By leveraging these item classes, businesses can ensure consistent and accurate classification of all items, which is critical for effective procurement management.

Impact of Item Classification on Oracle Procurement Cloud

Oracle Procurement Cloud leverages the data provided by Oracle PDH Cloud to enhance procurement processes. The classification of items plays a crucial role in several aspects of procurement:

Streamlined Supplier Management:

• Proper item classification enables procurement teams to categorize suppliers based on the types of products they provide. This facilitates more targeted supplier management and helps in establishing specialized supplier relationships.
• Example: A manufacturing company classifies its items into raw materials, spare parts, and office supplies. Suppliers of critical raw materials are managed with rigorous quality checks and regular performance reviews, while suppliers of office supplies are evaluated based on cost-effectiveness and timely delivery.

Efficient Sourcing and Bidding:

• Item classification helps in the efficient sourcing of products. By categorizing items, procurement teams can develop tailored sourcing strategies for each class of items.
• Example: An electronics company classifies items into components, finished products, and packaging materials. When sourcing microchips (a component), the company invites bids from specialized suppliers with expertise in semiconductor manufacturing, ensuring competitive pricing and high-quality standards.

Optimized Purchase Order Management:

• Accurate item classification ensures that purchase orders are managed more efficiently. Procurement teams can create purchase orders based on specific item classes, improving order accuracy and fulfillment.
• Example: A retail company classifies its inventory into seasonal items, regular stock, and promotional goods. During the holiday season, purchase orders for seasonal items are prioritized to ensure timely stock availability, while regular stock orders are managed on a rolling basis.

Improved Contract Management:

• With clear item classification, procurement teams can develop and manage contracts that are specific to different classes of items. This allows for more detailed contract terms and conditions, tailored to the unique requirements of each item class.
• Example: A pharmaceutical company classifies its items into active pharmaceutical ingredients (APIs), excipients, and packaging materials. Contracts for APIs include strict quality control measures and compliance with regulatory standards, while contracts for packaging materials focus on sustainability and cost-efficiency.

Enhanced Spend Analysis and Reporting:

• Item classification provides a structured way to analyze procurement spend. By categorizing items, procurement teams can gain deeper insights into spending patterns, identifying opportunities for cost savings and strategic sourcing.
• Example: A food and beverage company classifies its items into perishable goods, non-perishable goods, and packaging. Spend analysis reveals that packaging costs have increased significantly, prompting the procurement team to negotiate better terms with suppliers or seek alternative packaging solutions.

Compliance and Risk Management:

• Certain industries require strict compliance with regulatory standards. Item classification helps ensure that procurement processes adhere to these standards.
• Example: A chemical manufacturing company classifies items based on regulatory requirements, such as hazardous chemicals, non-hazardous chemicals, and safety equipment. Procurement processes for hazardous chemicals include adherence to safety regulations, proper documentation, and special handling procedures.

Integrating Oracle PDH Cloud with Oracle Procurement Cloud

The seamless integration between Oracle PDH Cloud and Oracle Procurement Cloud is essential for maximizing the benefits of item classification. This integration ensures:

• Data Consistency: Item classification data is consistently shared between the two systems, eliminating discrepancies and ensuring a unified view of procurement data.
• Real-time Updates: Any changes in item classification in Oracle PDH Cloud are automatically reflected in Oracle Procurement Cloud, ensuring that procurement processes are always based on the most current data.
• Scalability: As businesses grow and evolve, the integrated system can scale accordingly, accommodating new item classes and changing procurement needs.

Conclusion

Item classification in Oracle PDH Cloud is a powerful tool that significantly enhances the capabilities of Oracle Procurement Cloud. By categorizing items based on various attributes, businesses can achieve greater efficiency, accuracy, and control in their procurement processes. The integration of these two systems ensures that procurement activities are streamlined, compliant, and scalable, ultimately leading to improved operational performance and cost savings.

As organizations navigate the complexities of modern supply chains, leveraging the power of item classification and integrated procurement management systems will be a key differentiator in achieving operational excellence and competitive advantage.

Oracle routinely announces significant future changes for EPM customers in each month’s cloud readiness documentation. If you’re not aware, Oracle EPM cloud updates are published monthly. If you read the details within the ‘Important Actions and Considerations’ section (located at the bottom of each readiness release), you will notice several very interesting announcements that we’ve highlighted below.

Infolets Discontinuing

Starting sometime later in 2024 the Infolets functionality will no longer be supported by Oracle. The ability to create Infolets will cease to exist for Enterprise Profitability and Cost Management, Financial Consolidation and Close, FreeForm, Planning, and Tax Reporting applications. Nothing specifically has been announced about existing Infolets, however any issues with them will not be addressed by Oracle support once support ends.

To many of us here at Perficient, the removal of Infolets is not surprising as we’ve seen many customers prefer to use Dashboards 2.0 instead.

Smart View Native Mode Option Deprecation

Continuing with the theme of eliminating functionality not widely used, the Native Smart View option will no longer be supported later this year. Standard will continue to be the ad hoc mode for development of enhancements by Oracle. The announcement contains two additional aspects customers need to note:

• The expectation for existing Native-mode worksheets is that they will work “as is” when the setting is changed to Standard.

• Smart Forms are not supported in Standard mode and there is no plan to support them in Standard mode.

In order to avoid any negative experience by end users, administrators need to proactively start testing all mission-critical ad-hoc sheets using Standard mode. If the results of the testing results in any issues, customers need to log the issue as an enhancement request on Customer Connect.

Removal of Data Management Job Scheduling

Data Integration now has the ability to schedule jobs. This means that Oracle will soon be removing the job scheduler within Data Management. The change is expected to occur sometime in Q4 this year. This means customers need to start migrating their scheduled jobs to the EPM Job Scheduler console immediately! To help faciliate the migration, Oracle is providing a script (“Migrate Schedules to Platform Jobs Scheduler”) within System Maintenance Tasks in Data Management. This change is applicable to the following solutions: Enterprise Profitability and Cost Management, Financial Consolidation and Close, Planning, and Tax Reporting.

Have a question about the above changes, or want to understand more about Oracle EPM cloud updates, drop us a comment!

In June 2024, Oracle announced fiscal 2024 Q4 and full year financial results revealing the market demand for Oracle Cloud Applications remains strong with Oracle Q4 Cloud Application (SaaS) revenue up 10% and Q4 Fusion Cloud ERP (SaaS) revenue up 14%. As Oracle customers look to reap the benefits of their investment in Oracle, many organizations will seek the expertise of Oracle services providers to guide them on their cloud journey.

Forrester released The Oracle Services Landscape, Q3 2024 report to help organizations understand the value they can expect from Oracle services providers and explore potential partners.

Oracle Services Providers

According to the report, “Technology leaders often use one or more Oracle services providers to: implement Oracle cloud products and solutions, provide continuous change management and ongoing Oracle applications support, and future-proof enterprise operational business processes.”

Perficient’s Recognition and Focus Areas

We believe Perficient has been recognized for our ability to deliver on these promises, helping clients implement Oracle cloud products and solutions, provide ongoing support, improve operational efficiencies and business processes with tangible business results. Perficient self-reported the following extended business scenarios as the top three focus areas:

• Oracle Cloud Enterprise Resource Planning (ERP): We deliver full-cycle projects to solution, roadmap, and implement Oracle Cloud ERP solutions by leveraging deep industry expertise and accelerators to enable businesses to modernize best practices to scale and grow.
• Oracle Cloud Supply Chain Management (SCM): We help modernize and mature our clients’ end to end global supply chain through strategic sourcing and spend control, source-to-pay improvements, cycle time reduction, and inventory and materials management.
• Oracle Cloud Enterprise Performance Management (EPM): We provide quantitative insights on performance metrics and critical details to help drive corporate strategies leveraging Oracle Cloud EPM to improve the speed, flexibility, and accuracy of planning, budgeting, and consolidation cycles.

Perficient’s Expertise and Industry Focus

Forrester presented an overview of vendors in the market, including geographic and industry focuses and top use cases. They segmented vendors based on geographic focus, industry focus, deployment options, and size.

Perficient was listed among small providers in this space with $10 to <$100 million in annual Oracle services revenue. Perficient’s listing as a consultancy in this Landscape report includes our geographic presence (North America and Asia-Pacific) and industry focus areas (healthcare, manufacturing/production of industrial products, and natural resources and mining).

Download the Forrester report, The Oracle Services Landscape, Q3 2024, to learn more (Link to report available to Forrester subscribers and for purchase).

This is a quick and easy recipe to install the EPM Integration Agent on your on-premises environment.

The goal is to showcase how easy the installation can be done.

Steps

1. Download EPM Agent installer from your EPM Cloud Instance. On Data Exchange, go to the Actions drop-down menu and click Download Agent
   
2. Unzip .zip file on your on-premises server. 
3. Create a EPM folders using the CreateAppFolder.bat command. 
4. Encrypt the password using the EncryptPassword.bat command:You enter your password here. It will not display what it is being entered.
   After hitting enter, it will produce an encrypted password that needs to be copied to the configuration file.
5. Edit your .ini file. This configuration file is created by the CreateAppFolder.bat script and is is located inside the config folder.For this example, only the CLOUD_URL, CLOUD_USER_NAME and CLOUD_PASSWORD are needed.
6. Start the EPM Agent.The command to start the agent is epmagent.bat path\configfile.ini
7. Verify Agent in your EPM Cloud instance. On the Actions menu, click on Agent.This action will open the Agent Cluster page. Select the cluster (in this case I used EPMCluster, the default cluster name). Click on the EPMCluster to display the Agents running under this cluster

And with this, you will have an running agent. On a future post, I’ll show a sample usage of this agent to extract data from an On-Premises database.

Also published here.

Thanks for reading.

In the realm of modern business operations, efficient inventory management is paramount. Companies must keep track of a plethora of items, ensuring that they are properly categorized, tracked, and managed. Oracle Product Data Hub (PDH) Cloud and Oracle Inventory Management Cloud are two powerful tools that work in tandem to achieve these goals. A critical component of this system is item classification, which significantly impacts inventory management. In this blog, we will delve into the intricacies of item classification in Oracle PDH Cloud and its profound impact on Oracle Inventory Management Cloud.

Understanding Item Classification in Oracle Cloud PDH Cloud

Oracle PDH Cloud serves as a central repository for all product information. One of its key features is item classification, which allows organizations to categorize items into various classes based on attributes such as type, use, and characteristics. This classification system facilitates better organization, tracking, and management of products across the enterprise.

Item classes in Oracle PDH Cloud can be defined based on multiple criteria, such as:

• Product Type: Finished goods, raw materials, components, etc.
• Usage: Consumables, repairable items, capital goods, etc.
• Industry-specific Attributes: Chemicals, electronics, pharmaceuticals, etc.

By leveraging these item classes, businesses can streamline their product data management processes, ensuring that all items are consistently and accurately classified.

Impact of Item Classification on Oracle Inventory Management Cloud

Oracle Inventory Management Cloud relies heavily on the data provided by Oracle PDH Cloud. The item classification data plays a pivotal role in several aspects of inventory management:

Enhanced Inventory Visibility and Control:

• Proper item classification allows for a more granular view of inventory. This means that inventory managers can easily track and locate items based on their classes, improving overall visibility.
• Enhanced control mechanisms can be established, ensuring that specific classes of items are handled according to predefined protocols. For instance, hazardous materials can be managed with stricter controls compared to regular consumables.

Improved Demand Planning and Forecasting:

• Accurate item classification enables better demand planning and forecasting. By understanding the different classes of items, inventory managers can predict demand more effectively.
• This leads to more accurate inventory levels, reducing both excess stock and stockouts, thereby optimizing the supply chain.

Streamlined Procurement Processes:

• Item classification aids in streamlining procurement processes. By categorizing items, procurement teams can standardize their purchasing strategies based on the specific needs of each item class.
• For example, high-value or critical items can be procured through more rigorous vendor evaluation processes, while routine consumables can follow a more simplified procurement procedure.

Efficient Inventory Replenishment:

• With clear item classes, inventory replenishment strategies can be tailored to each class. Fast-moving items can have automated replenishment triggers, while slow-moving items can be managed through periodic reviews.
• This ensures that the right amount of inventory is maintained at all times, optimizing storage costs and reducing wastage.

Compliance and Regulatory Adherence:

• Certain industries require strict compliance with regulatory standards. Item classification helps ensure that items are managed in accordance with these standards.
• For instance, pharmaceutical companies can classify items based on regulatory requirements, ensuring that storage, handling, and documentation are compliant with industry regulations.

Integrating Oracle PDH Cloud with Oracle Inventory Management Cloud

The seamless integration between Oracle PDH Cloud and Oracle Inventory Management Cloud is crucial for leveraging the benefits of item classification. This integration ensures that:

• Data Consistency: Item classification data is consistently shared between the two systems, eliminating discrepancies and ensuring a unified view of inventory.
• Real-time Updates: Any changes in item classification in Oracle PDH Cloud are automatically reflected in Oracle Inventory Management Cloud, ensuring that inventory management processes are always based on the most current data.
• Scalability: As businesses grow and evolve, the integrated system can scale accordingly, accommodating new item classes and changing business needs.

Conclusion

Item classification in Oracle PDH Cloud is a powerful tool that significantly enhances the capabilities of Oracle Inventory Management Cloud. By categorizing items based on various attributes, businesses can achieve greater inventory visibility, control, and efficiency. The integration of these two systems ensures that inventory management processes are streamlined, accurate, and scalable, ultimately leading to improved operational performance and cost savings.

As organizations continue to navigate the complexities of modern supply chains, leveraging the power of item classification and integrated inventory management systems will be a key differentiator in achieving operational excellence and competitive advantage.

Oracle CloudWorld takes place in Las Vegas September 9-12, 2024 and Perficient will be there, will you? Don’t miss this opportunity to be a part of the global Oracle community and gather with other customers and partners to network and learn. Oracle has more than 1,000 sessions planned and countless hands-on labs. Meet us at CloudWorld Hub in the Venetian to connect with subject matter experts and thought leaders and learn how we’ve leveraged our extensive expertise in Enterprise Resource Planning (ERP), Supply Chain Management, Human Capital Management, Enterprise Performance Management (EPM), Business Analytics, Oracle Cloud Infrastructure, and Oil and Gas to drive digital transformation for our customers.

An Oracle Partner with 20+ years of experience, we are committed to partnering with our clients to tackle complex business challenges and accelerate transformative growth. We help the world’s largest enterprises and biggest brands succeed. Connect with us at the show to learn more about how we partner with our customers to forge the future.

At this year’s conference we celebrate our client, G6 Hospitality, recently named as a 2024 Application Modernization Award Finalist. This honor positions G6 Hospitality among a field of top Oracle customers who have demonstrated excellence in application modernization with Oracle Analytics, Oracle Autonomous Data Warehouse, and Oracle Cloud Infrastructure. This acknowledgment not only reflects our commitment to innovation and excellence but also validates the hard work and dedication of G6 and the Perficient team.

If you haven’t yet made the decision to attend Oracle CloudWorld, here are some compelling reasons to help you Justify Your Trip. CloudWorld is by far the largest gathering of the global Oracle community. Where else can you network and build relationship with your peers? Over the course of three days, you’ll learn from experts at Oracle as well as numerous partners and current customers leaving you inspired about the endless possibilities you can achieve with Oracle Cloud.

Did you know that a full conference pass includes free Oracle Cloud training and certification at no cost — and you can start leaning before the show begins? With access to more than 500 hours of Oracle Cloud Infrastructure training, 3,500 hours of Oracle Cloud Applications training, and three free Oracle Cloud certification exam attempts, what are you waiting for? Registration is open!

We are looking forward to meeting you at Oracle CloudWorld and hope to see you there!

Oracle recently announced the finalists and winners of its 2024 Oracle Excellence Awards. We’re delighted to share that G6 Hospitality was recognized as a finalist for the Application Modernization Award.

This honor positions G6 Hospitality among a field of top Oracle customers who have demonstrated excellence in application modernization with Oracle Analytics, Oracle Autonomous Data Warehouse, and Oracle Cloud Infrastructure. This acknowledgment not only reflects our commitment to innovation and excellence but also validates the hard work and dedication of G6 and the Perficient team.

“We are thrilled by this recognition from Oracle as a 2024 Application Modernization Award finalist,” said Brijesh Ravindran, vice president of IT engineering and architecture, G6 Hospitality. With the infrastructure and foundation in place for a robust analytics platform, G6 Hospitality is now well-positioned to tap into the latest advancements in data intelligence solutions, including accelerated adoption of data democratization, improved data visualization, expanded third-party data, and leveraging AI solutions in conjunction with enterprise performance reporting.

“This is a huge acknowledgment for G6 from Oracle, and we are excited to be one of the finalists,” according to Deepa Unni, Director of IT Development, G6 Hospitality. “As a part of G6’s Application Modernization strategy, this initiative of adopting Oracle Cloud allows us to enable, optimize, and enhance new solutions and services to support our business teams.”

Awards were classified into several categories for infrastructure, including Data Center Migration and Transformation, Application Modernization, Data Platform Innovation, AI Innovation, and Cloud Architect with up to five finalists per category. Honorees in the Application Modernization category have successfully moved Oracle and non-Oracle applications. According to Oracle, “…sometimes these deployments minimize rearchitecting applications, while in other cases they involve modernizing the application to new software versions or cloud services. They deliver higher performance at lower cost than alternatives.”

Recognition from Oracle for Application Modernization Revolutionizing the Hospitality Industry

“Perficient’s dedicated best-in-class team worked hand-in-hand with G6 at every step, making this a remarkable accomplishment,” said Deepa Unni.

The Perficient ERP team also helped G6 upgrade EBS Financials from R12.1.3 to R12.2.12 and consolidate several custom back-office applications.

“Perficient brought in their ‘A’ team from their CoE of DBAs during a critical upgrade challenge and resolved the issue timely and efficiently, completing the project under budget and on time, overcoming numerous challenges along the way,” said Brijesh Ravindran. “Perficient’s unwavering commitment to understanding G6’s unique needs allowed us to create tailor-made solutions that optimized their application modernization initiatives, delivered successful outcomes, and drove growth through enhanced end-user experience.”

In the dynamic landscape of the energy and utility sector, efficient management of product data is paramount for success. From power generation to distribution, organizations in this sector rely on accurate and timely information about their products to optimize operations, ensure compliance, and deliver reliable services to customers. Oracle Product Data Hub (PDH) Cloud emerges as a robust solution tailored to the unique needs of the energy and utility industry, offering powerful tools for centralizing, and managing product data effectively.

Centralized Product Data Management

In the energy and utility sector, where diverse product portfolios span from transformers to smart meters and renewable energy solutions, managing product data across multiple systems and departments can be challenging. Oracle PDH Cloud addresses this complexity by providing a centralized repository for all product-related information.

With Oracle PDH Cloud, energy and utility companies can consolidate data from various sources, including suppliers, engineering systems, and regulatory databases, into a single, unified platform. This centralized approach ensures data consistency, enhances collaboration among teams, and enables better decision-making across the organization.

Streamlined Item Classification

Effective item classification is crucial for organizing the vast array of products and components used in the energy and utility sector. From classifying transformers based on voltage capacity to categorizing renewable energy systems by technology type, Oracle PDH Cloud’s Item Classification capabilities simplify this process.

Energy and utility companies can define custom classification schemes tailored to their specific requirements, incorporating attributes such as product type, energy source, efficiency rating, and environmental impact. By standardizing classification practices, organizations can improve data accuracy, facilitate procurement processes, and enhance asset management strategies.

Examples of Oracle PDH Cloud in the Energy and Utility Sector

Smart Meter Management: A utility company deploying smart metering infrastructure relies on Oracle PDH Cloud to centralize data related to meter models, specifications, and installation guidelines. Through streamlined item classification, the company categorizes smart meters based on communication protocols, metering standards, and geographic deployment zones, enabling efficient inventory management and maintenance planning.

Smart meters are advanced devices that measure and record electricity consumption in real-time, enabling utilities to implement demand response programs and monitor grid performance. Item classification for smart meters may include attributes such as communication protocol (e.g., Zigbee, LoRaWAN), metering standard (e.g., ANSI C12.19, IEC 62056), geographical deployment zone, and meter functionality (e.g., prepaid, net metering). Classifying smart meters facilitates procurement, installation, and interoperability testing, ensuring efficient deployment and operation.

Renewable Energy Integration: A renewable energy developer leverages Oracle PDH Cloud to manage product data for solar panels, wind turbines, and energy storage systems. By classifying renewable energy assets according to technology type, capacity, and performance characteristics, the developer gains insights into project costs, resource availability, and regulatory compliance, facilitating informed decision-making and project optimization.

Renewable energy systems, such as solar panels, wind turbines, and energy storage solutions, play a pivotal role in transitioning to a low-carbon energy future. Item classification for renewable energy systems involves categorizing assets based on technology type (e.g., photovoltaic, wind, battery), capacity (e.g., kilowatts, megawatts), efficiency rating, and environmental certifications (e.g., IEC 61730, UL 1741). Classifying renewable energy systems enables developers and utilities to assess project feasibility, estimate energy output, and comply with regulatory requirements.

Grid Infrastructure Enhancement: Energy companies have a need to modernize the grid quickly and make it as effective as possible due to the increasing demands for EV superchargers and other innovations that require more electricity than current grids can handle. A utility undergoing grid modernization initiatives utilizes Oracle PDH Cloud to standardize product data for transformers, switchgear, and distribution automation devices. Through automated data enrichment and classification, the utility enriches asset records with maintenance schedules, lifecycle information, and compatibility matrices, enabling proactive asset management and reliability-centered maintenance strategies.

Grid infrastructure components encompass a wide range of equipment used in electricity transmission and distribution networks, including switchgear, circuit breakers, and distribution automation devices. Item classification for grid infrastructure involves attributes such as voltage rating, current rating, interrupting capacity, and operating mechanism (e.g., air-insulated, gas-insulated). Classifying grid infrastructure components enables utilities to optimize asset management, plan network upgrades, and ensure grid reliability and resilience.

Transformers: Transformers are essential components in electricity distribution networks, used to step up or step-down voltage levels. In item classification, transformers can be categorized based on parameters such as voltage capacity, insulation type, cooling method (e.g., oil-filled, dry-type), and application (e.g., distribution, substation). Classifying transformers enables utilities to manage inventory, plan maintenance activities, and ensure compatibility with other grid infrastructure components.

In each example, nouns such as transformers, smart meters, renewable energy systems, and grid infrastructure components serve as key entities in item classification within the energy sector. By categorizing these items based on relevant attributes, organizations can streamline procurement, maintenance, and operational decision-making processes, ultimately enhancing efficiency and performance in energy systems.

Conclusion

In conclusion, Oracle PDH Cloud and Item Classification process are invaluable assets for energy and utility companies seeking to optimize product data management and drive efficiency in their operations. By centralizing product data, streamlining item classification, and leveraging industry-specific examples, Oracle PDH Cloud empowers organizations in the energy and utility sector to overcome challenges, capitalize on opportunities, and deliver sustainable value to stakeholders in today’s rapidly evolving marketplace.

Natural language AI has proliferated into many of today’s applications and platforms. One of the high in demand use cases is the ability to find quick answers to questions about what’s hidden within organizational data, such as operational, financial, or other enterprise type data. Therefore leveraging the latest advancements in the GenAI space together with enterprise data warehouses has valuable benefits. The SelectAI feature of the Oracle Autonomous Database (ADB) achieves this outcome. It eliminates the complexity of leveraging various Large Language AI Models (LLMs) from within the database itself. From an end user perspective, SelectAI is as easy as asking the question, without having to worry about GenAI prompt generation, data modeling, or LLM fine tuning.

In this post, I will summarize my findings on implementing ADB SelectAI and share some tips on what worked best and what to look out for when planning your implementation.

Several GenAI Models: Which One to Use?

What I like about SelectAI is that switching the underlying GenAI model is simple. This is important over time to stay up to date and take advantage of the latest and greatest of what LLMs have to offer and at the most suitable cost. We can also set up SelectAI with multiple LLMs simultaneously, for example, to cater to different user groups, at varying levels of service. In the future, there will always be a better LLM model to use, but at this time these findings are based on trials of the Oracle Cloud Infrastructure (OCI) shared Cohere Command model, the OpenAI GPT-3.5-Turbo model and the OpenAI GPT-4 model. Here is a summary of how each worked out:

Cohere Command:

While this model worked well for simple questions that are well phrased with nouns that relate to the metadata, it didn’t work well when the question got more complex. It didn’t give a wrong answer, as much as it returned a message as follows apologizing for the inability to generate an answer: “Sorry, unfortunately a valid SELECT statement could not be generated…”. At the time of this writing, the Command R+ model had just been introduced and became generally available, but it wasn’t attempted as part of this exercise. It remains to be found out how effective the newer R+ model is in comparison to the other ones.

OpenAI GPT-4:

This LLM worked a lot better than Cohere Command in that it answered all the questions that Command couldn’t. However, it comes at a higher cost.

OpenAI GPT-3.5-Turbo:

This one is my favorite so far as it also answered all the questions that Command couldn’t and is roughly 50 times less expensive than GPT-4. It is also a lot faster to respond compared to the OCI shared Cohere Command. There were some differences though at times in how the answers are presented. Below is an example of what I mean:

Sample Question: Compare sales for package size P between the Direct and Indirect Channels

Responses Generated by Each Model:

• Cohere command: Sorry, unfortunately, a valid SELECT statement could not be generated
• OpenAI gpt-3.5-turbo: This was able to generate a good result set based on the following query, but the results weren’t automatically grouped in a concise manner.

SELECT s.PROD_ID, s.AMOUNT_SOLD, s.QUANTITY_SOLD, s.CHANNEL_ID, p.PROD_PACK_SIZE, c.CHANNEL_CLASS
FROM ADW_USER.SALES_V s
JOIN ADW_USER.CHANNELS_V c ON s.CHANNEL_ID = c.CHANNEL_ID
JOIN ADW_USER.PRODUCTS_V p ON s.PROD_ID = p.PROD_ID
WHERE p.PROD_PACK_SIZE = 'P' AND c.CHANNEL_CLASS IN ('Direct', 'Indirect');

• OpenAI gpt-4: This provided the best answer, and the results were most suitable with the question as it grouped by Channel Class to easily compare sales.

SELECT c.CHANNEL_CLASS AS Channel_Class, SUM(s.AMOUNT_SOLD) AS Total_Sales 
FROM ADW_USER.SALES_V s  
JOIN ADW_USER.PRODUCTS_V p ON s.PROD_ID = p.PROD_ID 
JOIN ADW_USER.CHANNELS_V c ON s.CHANNEL_ID = c.CHANNEL_ID 
WHERE  p.PROD_PACK_SIZE = 'P'AND c.CHANNEL_CLASS IN ('Direct', 'Indirect') 
GROUP BY c.CHANNEL_CLASS;

Despite this difference, most of the answers were similar between GPT-4 and GPT-3.5-Turbo and that’s why I recommend to start with the 3.5-Turbo and experiment with your schemas at minimal cost.

Another great aspect of the OpenAI GPT models is that they support conversational type questions to follow up in a thread-like manner. So, after I ask for total sales by region, I can do a follow up question in the same conversation and say for example, “keep only Americas”. The query gets updated to restrict previous results to my new request.

Tips on Preparing the Schema for GenAI Questions

No matter how highly intelligent you pick of an LLM model, the experience of using GenAI won’t be pleasant unless the database schemas are well-prepared for natural language. Thanks to the Autonomous Database SelectAI, we don’t have to worry about the metadata every time we ask a question. It is an upfront setup that is done and applies to all questions. Here are some schema prep tips that make a big difference in the overall data Q&A experience.

Selective Schema Objects:

Limit SelectAI to operate on the most relevant set of tables/views in your ADB. For example exclude any intermediate, temporary, or irrelevant tables and enable SelectAI on only the reporting-ready set of objects. This is important as SelectAI automatically generates the prompt with the schema information to send over to the LLM together with the question. Sending a metadata that excludes any unnecessary database objects, narrows down the focus for the LLM as it generates an answer.

Table/View Joins:

To result in correct joins between tables, name the join columns with the same name. For example, SALES.CHANNEL_ID = CHANNELS.CHANNEL_ID. Foreign key constraints and primary keys constraints don’t affect how tables are joined, at least at the time of writing this post. So we will need to rely on consistently naming join columns in the databases objects.

Create Database Views:

Creating database views are very useful for SelectAI in several ways.

1. Views allow us to reference tables in other schemas so we can setup SelectAI on one schema that references objects in several other schemas.
2. We can easily rename columns with a view to make them more meaningful for natural language processing.
3. When creating a view, we can exclude unnecessary columns that don’t add value to SelectAI and limit the size of the LLM prompt at the same time.
4. Rename columns in views so the joins are on identical column names.

Comments:

Adding comments makes a huge difference in how much more effective SelectAI is. Here are some tips on what to do with comments:

• Comment on table/view level: Describe what type of information a table or view contains: For example, a view called “Demographics” may have a comment as follows: “Contains demographic information about customer education, household size, occupation, and years of residency”
• Comment on column level: For security purposes SelectAI (in a non-Narrate mode) doesn’t send data over to the GenAI model. Only metadata is sent over. That means if a user asks a question about a specific data value, the LLM doesn’t have visibility where that exists in the database. To enhance the user experience where sending some data values to the LLM is not a security concern, include the important data values in the comment. This enables the LLM to know where that data is. For example, following is a comment on a column called COUNTRY_REGION: “region. some values are Asia, Africa, Oceania, Middle East, Europe, Americas”. Or for a channel column, a comment like the following can be useful by including channel values: “channel description. For example, tele sales, internet, catalog, partners”

Explain certain data values: Sometimes data values are coded and require translation. Following is an example of when this can be helpful: comment on column Products.VALID_FLAG: “indicates if a product is active. the value is A for active”

Is There a Better Way of Asking a Question?

While the aforementioned guidance is tailored for the upfront administrative setup of SelectAI, here are some tips for the SelectAI end user.

• Use double quotations for data values consisting of multiple words: This is useful for example when we want to filter data on particular values such as a customer or product name. The quotation marks also help pass the right case sensitivity of a word. For example: what are the total sales for “Tele Sales” in “New York City”.
• Add the phrase “case insensitive” at the end of your question to help find an answer. For example: “calculate sales for the partners channel case insensitive”. The SQL query condition generated in this case is: WHERE UPPER(c.CHANNEL_CLASS) = ‘PARTNERS’, which simply means ignore case sensitivity when looking for information about partners.
• If the results are filtered, add a statement like the following at the end of the question to avoid unnecessary filters: “Don’t apply any filter condition”. This was more applicable with the cohere command model than the OpenAI models.
• Starting the question with “query” instead of “what is”, for instance, worked better with the cohere command model.
• Be field specific when possible: Instead of just asking for information by customer or by product, be more field specific such as “customer name” or “product category”.
• Add additional instructions to your question: You can follow the main question with specific requests for example to filter or return the information. Here is an example of how this can be done:

“what is the average total sales by customer name in northern america grouped by customer. Only consider Direct sales and customers with over 3 years of residency and in farming. case insensitive.”

Results are returned based on the following automatically generates SQL query:

SELECT c.CUST_FIRST_NAME || ' ' || c.CUST_LAST_NAME AS CUSTOMER_NAME, AVG(s.AMOUNT_SOLD)
FROM ADW_USER.SALES_V s JOIN ADW_USER.CUSTOMERS_V c ON s.CUST_ID = c.CUST_ID
JOIN ADW_USER.COUNTRIES_V co ON c.COUNTRY_ID = co.COUNTRY_ID
JOIN ADW_USER.CHANNELS_V ch ON s.CHANNEL_ID = ch.CHANNEL_ID
JOIN ADW_USER.CUSTOMER_DEMOGRAPHICS_V cd ON c.CUST_ID = cd.CUST_ID
WHERE UPPER(co.COUNTRY_SUBREGION) = 'NORTHERN AMERICA'
AND UPPER(ch.CHANNEL_CLASS) = 'DIRECT'
AND cd.YRS_RESIDENCE > 3
AND UPPER(cd.OCCUPATION) = 'FARMING'
GROUP BY c.CUST_FIRST_NAME, c.CUST_LAST_NAME;

It’s impressive to see how GenAI can take the burden off the business in finding quick and timely answers to questions that may come up throughout the day, all without data security risks. Contact us if you’re looking to unlock the power of GenAI for your enterprise data.