Dataplatr has introduced Cortex Plus (+) Framework, a cutting-edge framework built upon the robust foundation of the Google Cortex Framework. Designed to meet the complex needs of enterprises using systems like SAP, Oracle EBS, Workday, and Salesforce, Cortex Plus (+) extends the capabilities of the Google Cortex Framework with advanced features for seamless data integration, modeling, and analytics. By leveraging the power of Google Cortex framework and enhancing it with enterprise-grade tools, Dataplatr is setting a new standard for efficient data management, accelerating time-to-value, and significantly reducing total cost of ownership (TCO). This article explores how the Cortex Plus (+) Framework redefines enterprise data solutions, unlocking new possibilities for organizations to harness their data effectively.

1. Introduction to Google Cloud Cortex Framework

The Google Cloud Cortex Framework serves as a foundational platform for businesses aiming to modernize their data infrastructure and maximize value from enterprise data systems like SAP. Built to streamline operations and accelerate analytics and innovation, the Cortex Framework provides an extensive suite of out-of-the-box features that cater to a broad spectrum of business needs.

1.1 Core Features of the Google Cortex Framework

Operational data marts tailored to SAP data structures allow organizations to access actionable datasets instantly. These data marts are prebuilt and ready for immediate use, reducing setup time.

1. Scenario-Driven Architectures
   • Offers pre-designed workflows and blueprints tailored to common enterprise scenarios such as sales performance tracking, supply chain optimization, and customer insights.
   • Accelerates deployment by aligning business processes with proven, scalable architectures.
2. Predefined Data Models
   • Includes prebuilt BigQuery models optimized for enterprise-scale data, reducing the need for custom development.
   • Models cater to SAP and non-SAP data integration, providing a ready-to-use framework for analytics.
3. Change Data Capture (CDC) Processing
   • Automatically updates data in real time by capturing changes from source systems.
   • Uses Cloud Composer to ensure data consistency while adhering to business rules.
4. Prebuilt Dashboards and Reports
   • Delivers Looker dashboards for key business functions like sales analytics, financial performance, order-to-cash cycles, and inventory management.
   • Enables rapid visualization and decision-making with minimal setup.
5. Plug-and-Play Data Marts
   • Offers over 40 operational data marts specifically tailored for SAP data structures.
   • Provides ready-to-query datasets for immediate insights across various business domains.

1.2 Advanced Analytics and Machine Learning

The Google Cortex Framework supports data enrichment by integrating SAP data with Google signals like Trends, Ads, and Maps. It also provides machine learning templates for predictive analytics, enabling businesses to innovate at scale.

1. Data Enrichment
   • Combines SAP data with Google services (e.g., Google Trends, Ads, and Maps) to provide enriched insights.
   • Enables deeper analysis of customer behavior, market trends, and operational performance.
2. Machine Learning Templates
   • Features preconfigured ML use cases such as demand forecasting, product recommendations, and predictive analytics.
   • Simplifies the adoption of AI by providing ready-to-use models and workflows.
3. Demand Forecasts and Alerts
   • Leverages AI to predict demand, identify potential inventory shortages, and analyze seasonal trends.
   • Provides actionable insights to mitigate supply chain disruptions.

1.3 Integration and Deployment

The google cortex framework simplifies the process of ingesting data from multiple sources, including SAP, Salesforce, and Google Analytics. Using Dataflow-powered pipelines, it ensures scalability and reliability while reducing the time needed to integrate data.

1. Streamlined Data Ingestion
   • Utilizes Dataflow-powered pipelines to ingest data into BigQuery with scalability and reliability.
   • Minimizes setup time with predefined connectors for SAP, Google Analytics, Salesforce, and more.
2. Deployment Accelerators
   • Features one-click deployment mechanisms to fast-track implementation.
   • Simplifies the process of integrating complex systems and reduces time-to-value.
3. Transparent Business Rules
   • Implements business rules for data processing through CDC and Cloud Composer.
   • Enhances transparency and control for analysts by making business logic explicit.

1.4 Value Proposition of the Cortex Framework

The Cortex Framework enables businesses to modernize their data operations while reducing complexity and costs. It ensures a faster time-to-value with prebuilt tools and architectures and enhances decision-making with real-time insights.

1. Speed and Efficiency: Predefined templates and centralized data management accelerate deployment.
2. Improved Decision-Making: Real-time analytics provide actionable insights across business functions.
3. Scalable Innovation: Machine learning capabilities and flexible architectures enable businesses to adapt to changing needs.
4. Cost-Effective Modernization: Prebuilt solutions minimize custom development and lower total cost of ownership.

The Google Cloud Cortex Framework is an all-in-one solution for integrating and analyzing enterprise data, providing businesses with the tools to transform operations, enhance decision-making, and innovate at scale.

Dataplatr Cortex Plus (+) Framework: Enhancing the Google Cortex Foundation

While the Google Cloud Cortex Framework provides a strong foundation for enterprise data management and analytics, Dataplatr has built upon it with the Dataplatr Cortex Plus (+) Framework, addressing the unique challenges faced by modern enterprises. By introducing advanced features and seamless integrations, Dataplatr has created a platform that is more reliable, flexible, and tailored to diverse customer needs. These enhancements empower organizations to achieve faster insights, improve operational efficiency, and unlock greater value from their data ecosystems.

1. Key Enhancements in the Dataplatr Cortex Plus (+) Framework

1.1 Expanded Enterprise Data Integration

Dataplatr Cortex Plus (+) takes data integration to the next level by ensuring seamless compatibility with a broader range of enterprise systems. This unified approach enables organizations to centralize data from diverse platforms, reducing silos and enhancing data usability.

• ERP (Enterprise Resource Planning) – SAP ERP, Oracle ERP Cloud, Microsoft Dynamics 365 Finance & Operations, NetSuite, Infor ERP, Epicor, and Workday (Financials) are robust tools for enterprise resource planning that can seamlessly integrate with Cortex to drive operational efficiency and real-time data synchronization across business processes
• HCM (Human Capital Management) – Workday HCM, SAP SuccessFactors, Oracle HCM Cloud, UKG Pro, ADP Workforce Now, Ceridian Dayforce, and PeopleSoft (by Oracle) provide comprehensive solutions for workforce management and talent optimization, ensuring smooth integration with Cortex for dynamic workforce insights.
• CRM (Customer Relationship Management) – Salesforce, Microsoft Dynamics 365 CRM, SAP CRM, HubSpot CRM, Zoho CRM, SugarCRM, and Pega CRM are powerful customer relationship tools that can connect with Cortex to enhance customer-centric analytics and decision-making capabilities.
• Contact Center – Genesys Cloud, Avaya, Cisco Contact Center, NICE inContact, Five9, RingCentral Contact Center, and Talkdesk offer advanced contact center capabilities, integrating with Cortex to enable seamless customer interaction analytics and service optimization.
• Talent Acquisition – LinkedIn Talent Solutions, Taleo (by Oracle), iCIMS, Greenhouse, Lever, SmartRecruiters, and Jobvite are leading talent acquisition platforms that integrate with Cortex to streamline hiring processes and provide actionable insights into recruitment strategies.

These enterprise data integrations empower organizations to create a unified data ecosystem, regardless of platform complexity.

1.2 Streamlined Ingestion Mechanisms

Dataplatr leverages Google Cloud Composer to replace development-heavy pipelines with a robust, automated ingestion process:

• Centralized Metadata Tables: Simplify object identification and replication with predefined schemas, reducing the need for custom development.
• Automated Change Data Capture (CDC): Logging and audit mechanisms ensure seamless tracking of data changes, including flags for deleted records.
• Historical Data Support: Enables in-depth analysis of archived data for long-term trends.
• Job Monitoring: Automated monitoring enhances operational visibility, ensuring data integrity at every step.

1.3 Advanced Data Modeling with Google Dataform

By integrating with Google Dataform, Dataplatr Cortex Plus (+) simplifies and enhances data modeling, offering unmatched transparency, governance, and adaptability.

• Collaborative and Transparent Process: Analysts gain full visibility into business rules through a centralized SQL-based repository, enabling them to adapt to dynamic business needs without reliance on IT teams.
• Automation and Modularity: Dependency management and modular transformations simplify complex processes, while automated data lineage fosters trust in analytical outcomes.
• Seamless Governance: With Git integration, Dataform ensures robust version control, enabling organizations to maintain governance while accelerating deployment cycles.

1.4 Tailored Reporting with Looker Dashboards

Dataplatr Cortex Plus (+) provides prebuilt Looker dashboards designed for specific business functions, enabling stakeholders to gain actionable insights faster. These dashboards reduce the time spent on report creation while offering metrics tailored to enterprise needs.

• Financial Analytics: Prebuilt modules for Accounts Payable (AP), Accounts Receivable (AR), General Ledger (GL), and Fixed Assets streamline financial performance tracking. Users can monitor cash flow, financial KPIs, and compliance metrics effortlessly.
• People & Talent Analytics: Gain insights into workforce metrics like attrition rates, employee engagement, performance trends, and hiring efficiency, supporting data-driven decisions in HR and talent management.
• Procurement & Spend Analytics: Comprehensive tracking of sourcing efficiency, vendor performance, and spend optimization uncovers cost-saving opportunities while enhancing procurement transparency.
• Sales & Revenue Analytics: Analyze sales pipelines, revenue trends, customer lifetime value, and conversion rates with real-time insights. Prebuilt modules integrate CRM data for targeted performance tracking and forecasting.
• Call Center Analytics: Prebuilt modules analyze call volume trends, first-call resolution rates, average handle time (AHT), hold time, and abandoned call rates. Dashboards offer insights into agent performance, peak traffic periods, and bottlenecks to optimize customer service operations.
• Voice Analytics AI: Advanced AI-powered dashboards deliver actionable insights by analyzing speech patterns, sentiment detection, and keyword frequency in real-time.
• Customer Experience Analytics: Understand customer sentiment, call resolution rates, agent performance, and customer satisfaction (CSAT) scores with prebuilt dashboards tailored to contact center and CRM data.
• Project Performance Analytics: Monitor project timelines, budgets, resource utilization, and milestone completions to ensure successful delivery. Gain insights into project bottlenecks and forecast resource needs.
• Supply Chain & Logistics Analytics: Track inventory levels, order fulfillment rates, warehouse efficiency, and transportation costs. Identify delays and optimize logistics with real-time supply chain visibility.
• Marketing Performance Analytics: Evaluate campaign effectiveness with metrics like ROI, customer acquisition cost (CAC), and engagement rates. Dashboards integrate marketing automation and CRM data for actionable insights.
• IT Operations Analytics: Monitor system uptime, application performance, incident resolution times, and resource allocation. Prebuilt modules ensure streamlined IT operations with proactive issue resolution.
• Compliance & Risk Analytics: Track regulatory compliance metrics, internal audit outcomes, and risk assessments. Dashboards provide visibility into control effectiveness and areas of potential exposure.

These dashboards cater to diverse industries, reducing reporting time and enhancing decision-making capabilities.

1.5 Accelerated Deployment

Dataplatr Cortex Plus (+) transforms deployment with a one-click implementation mechanism, drastically reducing the complexity and time required for go-live.

• Simplified architecture eliminates manual interventions.
• Prebuilt accelerators and logical layers enable rapid deployment, ensuring organizations can begin deriving insights quickly.
• A streamlined deployment process minimizes disruption, allowing enterprises to focus on value creation.

1.6 Faster ROI and Reduced Total Cost of Ownership (TCO)

By minimizing development efforts with preconfigured pipelines, templates, and accelerators, Dataplatr Cortex Plus (+) significantly reduces costs and improves productivity. This translates to a faster return on investment (ROI) and long-term savings for enterprises.

Dataplatr Cortex Framework accelerate time-to-market

• Development Speed Increase: By up to 40%, accelerating time-to-market for analytics solutions.
• Cost Efficiency: Automation and prebuilt templates lower operational costs, delivering faster ROI.

Visual Insight: A time-to-market comparison graph highlights the efficiency gained using Dataplatr accelerators.

2. Key Benefits of Dataplatr Cortex Framework

Dataplatr Cortex Plus (+) Framework builds upon the Google Cortex Framework with enhancements that reduce implementation complexity, accelerate insights, and minimize costs. These benefits make it an ideal solution for enterprises seeking to modernize their data infrastructure while achieving faster time-to-value.

2.1 Reduced Time-to-Value and Total Cost of Ownership (TCO)

Dataplatr Cortex Plus (+) significantly shortens development cycles and reduces operational costs, enabling businesses to derive value more quickly.

• Accelerators Reduce Development Time: By up to 40%, speeding up implementation and delivering business outcomes faster.
• Pre-Built Data Models and ELT Logic: Simplify integration and analytics setup, reducing resource requirements and cost.
• Metadata-Driven Pipelines: Automate data processes to streamline operations and minimize manual effort.

2.2 Pre-Built & Self-Service Analytics

Dataplatr Cortex Plus (+) empowers businesses with prebuilt tools and self-service capabilities, enabling stakeholders to generate insights independently.

• Accelerators for Pipelines: Provide built-in ELT logic for faster, more reliable data pipeline implementation.
• Pre-Built Logical Layers: Deliver comprehensive reports categorized by key subject areas, ready for immediate use.
• Pre-Built Apps Metadata: Simplifies the creation of reports and models, allowing analysts to focus on actionable insights.

Dataplatr Accelerators

2.3 Integrated Tools for Predictive Analytics

Dataplatr Cortex Plus (+) enhances analytics capabilities with integrated tools for machine learning and advanced insights, enabling businesses to stay ahead of the curve.

• Vertex AI and BQML: Offer advanced machine learning and predictive analytics capabilities for high-impact business scenarios.
• Enterprise-Specific Applications:
  • Dataplatr HR Analytics: Provides actionable workforce insights to optimize talent management.
  • Dataplatr Contact Center Analytics: Delivers in-depth customer interaction analysis to improve satisfaction and efficiency.

2.4 Scalable and Secure Infrastructure

Dataplatr Cortex Plus (+) leverages Google Cloud’s secure and scalable infrastructure, ensuring reliable and efficient data processing at any scale.

• BigQuery: A serverless architecture ensures fast and efficient data handling, reducing operational overhead.
• Google Cloud Storage: Provides secure, scalable storage for enterprise data, enabling seamless access and integration.

3. Dataplatr’s End-to-End Framework for Enterprise Data Solutions

Dataplatr Cortex Plus (+) offers a holistic architecture to integrate and streamline enterprise data sources. It provides a unified framework that spans data ingestion, modeling, analytics, and governance, ensuring a seamless flow of information across the organization.

Dataplatr Cortex framework Architecture

The above architecture illustrates the Dataplatr Approach to Transition BW (Business Warehouse) to the Cortex Framework by leveraging Google Cloud technologies and advanced transformation techniques to deliver a comprehensive enterprise data management solution. Below is a detailed breakdown of the workflow presented in the image:

3.1 Data Sources: Enterprise Applications

Dataplatr integrates seamlessly with various enterprise systems, allowing businesses to unify data from multiple sources:

• SAP: A primary source for enterprise resource planning (ERP) and operational data.
• Anaplan: Strategic data for financial and operational planning.
• Salesforce: Customer-centric data for CRM and sales insights.
• Workday: Workforce and financial analytics for HR and payroll data.

This variety of sources ensures flexibility and applicability across diverse enterprise use cases.

3.2 Data Acquisition: Flexible Ingestion

To collect and stage data efficiently, Dataplatr relies on:

• Cloud Composer: Automates workflows for data ingestion, offering a low-code mechanism for scalability and monitoring.
• Datastream: A real-time data replication service that captures changes from data sources and moves them to Google Cloud Storage.

Google Cloud Storage acts as the staging area where raw data is archived and prepared for further processing.

3.3 Data Staging and Transformation: Foundation

Dataplatr uses a multi-layered schema in BigQuery for comprehensive data staging and transformation. Each layer serves a specific purpose:

• SRC (Level 0: Source Schema):
  • Raw, “as-is” data from the source systems.
  • Includes audit trails to track data lineage.
  • Supports historical data storage for trend analysis.
• STO (Level 1: Cleansed Schema):
  • Basic data cleaning, including row-level transformations.
  • Standardizes timestamps (e.g., Date/Curr for date and currency).
  • Prepares data for more complex transformations.
• RPT (Level 2 & 3: Business Logic Schema):
  • Unified schemas applying cross-functional business rules.
  • Data is aligned with enterprise-wide standards for consistency.
  • Includes common dimensions and hierarchies to enable analytics across departments.
• Dept (Level r,w: Department-Specific Schema):
  • Departmental schemas tailored for specific business units.
  • Read-write access ensures flexibility for teams to manage and analyze their data independently.
  • Allows for a hub-and-spoke architecture, granting each department the autonomy to build on shared data layers.

3.4 Advanced Data Modeling with Dataform

Dataform enables streamlined data transformation with advanced features:

• Modular Transformations: Each step of the process is modular, making it easy to manage and adapt as requirements evolve.
• Orchestration Automation: Jobs are orchestrated automatically, reducing manual intervention.
• CI/CD and Version Control: Continuous integration and deployment ensure high-quality data workflows with proper governance.

These features improve transparency, collaboration, and scalability across teams.

3.5 Model Training and Predictions

For advanced analytics and artificial intelligence, Dataplatr integrates machine learning tools:

• BQML (BigQuery Machine Learning): Simplifies machine learning by enabling model creation directly within BigQuery.
• Vertex AI: Powers complex machine learning and AI applications, enhancing prediction accuracy and automation.
• Gemini (Enterprise Chatbot Integration): Supports conversational AI for enterprise-level chatbot solutions, utilizing insights derived from data models.

This combination enables businesses to operationalize AI and predictive insights seamlessly.

3.6 Data Visualization

Data presentation and reporting are streamlined with Looker, offering:

• Prebuilt dashboards with business-ready metrics for quick deployment.
• Plug-and-play visualization templates that require minimal customization.
• User-friendly interfaces that empower business users to generate actionable insights without technical expertise.

Looker ensures stakeholders across the organization can interact with and understand their data effectively.

2.4 Technology Components in the Dataplatr Cortex Framework

2.4.1 BigQuery

• Role in Cortex Framework: BigQuery serves as the central repository for enterprise data within the framework. It provides a serverless, highly scalable, and cost-efficient data warehouse to manage large datasets.
• Optimized for Analytics: By supporting standard SQL, BigQuery enables analysts and data scientists to perform advanced queries without additional learning curves.
• Pay-Per-Use Efficiency: The framework leverages BigQuery’s pay-per-query billing model to allow organizations to scale analytics cost-effectively, analyzing data as needed without upfront infrastructure investment.
• Integration with SAP Data: Cortex integrates SAP operational and transactional data into BigQuery, making it accessible for real-time reporting and machine learning. This integration is key for empowering financial analytics with BigQuery and supports informed decision-making.
•  

2.4.2 Dataform

• Simplifying Data Transformation: Dataform acts as a modeling layer within the Cortex Framework, simplifying SQL-based transformations by automating workflows and managing dependencies across datasets.
• Collaboration and Governance: With Git integration, Dataform facilitates version control, audit trails, and collaborative development, ensuring teams can work on transformations while maintaining consistency.
• Workflow Automation: It reduces manual effort by orchestrating transformations for predefined data models and operational data marts, such as sales analytics or order-to-cash processes, within the framework.

2.4.3 Cloud Composer

• Workflow Orchestration: Cloud Composer is integral to the Cortex Framework as it orchestrates ingestion pipelines and transformation workflows, ensuring seamless data flow from source systems to BigQuery.
• Managing Change Data Capture (CDC): By automating CDC processes, Cloud Composer ensures that data stays up-to-date in real-time, enabling timely analytics and decision-making.
• Scalable Automation: Its scalability ensures that complex workflows, such as integrating SAP data or managing operational marts, run reliably across different data environments.

2.4.4 Datastream

• Real-Time Data Replication: Datastream plays a crucial role in the Cortex Framework by enabling real-time change data capture (CDC). It ensures that any updates to source systems, such as SAP, are immediately reflected in BigQuery.
• Low-Latency Data Flow: Its low-latency capabilities are essential for creating up-to-date dashboards and enabling near real-time analytics for operational decision-making.
• Reliability and Scalability: Datastream is designed to scale with enterprise needs, ensuring consistent data replication even as data volumes grow.

2.4.5 Looker

• Dynamic Data Visualization: Looker provides a rich suite of prebuilt dashboards within the Cortex Framework, enabling business users to derive insights quickly without the need for custom visualizations.
• Plug-and-Play Analytics: With preconfigured Looker blocks for key metrics (e.g., sales performance, inventory levels, and customer insights), Looker delivers actionable intelligence in minutes.
• Collaboration: Looker’s collaborative data exploration tools empower teams to make data-driven decisions by sharing insights across departments.
• Real-Time Reporting: Looker integrates with BigQuery to provide real-time data visualization, ensuring businesses are always operating with the latest information.

2.4.5 Vertex AI

• Machine Learning Enablement: Vertex AI brings advanced machine learning capabilities into the Cortex Framework, allowing organizations to build, train, and deploy ML models seamlessly.
• Prebuilt Use Cases: It powers templates for predictive analytics such as demand forecasting, inventory optimization, and personalized recommendations.
• Integration with Google Signals: Vertex AI enhances models by incorporating data from Google Trends, Ads, and Maps, enabling more contextual and accurate predictions.
• Scalability and Automation: Its ability to handle large-scale data and automated workflows makes it an ideal choice for embedding AI within operational processes.

2.5 Deliverables and Advantages for Customers

1. Blueprints for Data and AI Solutions

• The  Cortex Framework provides comprehensive templates that serve as ready-to-deploy solutions for critical data processes, including:
  • Data Ingestion: Streamlining data extraction from diverse enterprise systems.
  • Data Modeling: Establishing well-structured, reusable models for analytics.
  • Reporting: Delivering insightful dashboards and visualization tools.
• These blueprints are tailored to handle complex business scenarios, enabling customers to jump-start their data projects without the need for extensive custom development.

2.Accelerated Innovation with Deployable Accelerators

• The framework includes reference architectures and deployable accelerators that drastically reduce the time and effort required to implement data pipelines and analytics workflows.
• By simplifying integration and automating key processes, businesses can focus on innovation rather than infrastructure management.

Customizable Analytics Solutions

• Cortex Framework offers predefined metrics and KPIs for key business domains such as:
  • Finance: Budgeting, forecasting, and financial performance analytics.
  • Human Resources: Workforce insights and talent management metrics.
  • Sales and Procurement: Monitoring sales performance and supplier effectiveness.
  • Customer Service: Enhancing customer experience and satisfaction.
• These templates can be customized to align with an organization’s specific business needs, providing flexibility while maintaining a robust foundation.

Cloud-Native Architecture for Scalablity

• Built on Google Cloud’s highly scalable infrastructure, the framework ensures seamless handling of increasing data volumes and computational demands.
• Its cloud-native design minimizes operational bottlenecks, providing a solid platform for future growth and transformation.

1.  

2.6 Benefits of Cortex Plus (+) Framework

1. Accelerated Time-to-Value
   • With prebuilt templates and accelerators, the Cortex Framework allows organizations to achieve faster deployment of data and AI solutions. This reduces the time needed to move from data collection to actionable insights, helping businesses make better decisions sooner and empowering more efficient financial analysis AI capabilities.
2. Reduced Complexity and Cost
   • The framework’s scalable and cloud-native infrastructure significantly lowers operational complexity by automating workflows and simplifying integrations.
   • Customers benefit from a pay-as-you-go model, reducing upfront costs and ensuring that they only pay for the resources they use.
3. Lower Risk in Implementation
   • By leveraging reference architectures and well-documented best practices, organizations can mitigate common risks associated with data integration and AI adoption.
   • The prevalidated templates ensure reliable performance and compliance with industry standards, providing a strong foundation for enterprise-grade solutions.
4. Enhanced Business Insights
   • With predefined analytics tailored to key functions, the Cortex Framework delivers insights that drive decision-making. For example:
     • Sales teams can optimize pipelines and improve forecasting accuracy.
     • Procurement departments can identify cost-saving opportunities in supply chains.
     • Finance teams can automate reporting and gain real-time visibility into cash flow, leveraging financial analysis AI to enhance strategic planning 
5. Adaptability for Future Growth
   • The customizable nature of the Cortex Framework ensures it evolves with the business. Organizations can add new data sources, scale resources, or integrate emerging technologies like machine learning without rebuilding their architecture.

The Dataplatr Cortex Plus (+) Framework redefines enterprise data management by building upon the solid foundation of the Google Cortex Framework and introducing enhanced features tailored to modern business needs. By seamlessly integrating advanced technologies such as BigQuery, Dataform, Looker, Vertex AI, Cloud Composer, and Datastream, Dataplatr empowers organizations to centralize their data ecosystems, streamline analytics workflows, and unlock the full potential of AI-driven insights.

With prebuilt templates, customizable dashboards, and deployable accelerators, enterprises can achieve faster innovation and actionable insights without extensive custom development. Moreover, its scalable, cloud-native infrastructure ensures future-proof adaptability, enabling businesses to grow and evolve with confidence. The Cortex Plus (+) Framework provides not only an efficient, low-risk path to digital transformation but also a platform for operational excellence and strategic decision-making. By reducing implementation complexity, enhancing predictive analytics, and delivering tailored solutions, Dataplatr Cortex Plus (+) positions itself as an indispensable partner for enterprises navigating the ever-evolving landscape of data and AI-driven innovation.

---

3. Integrating Oracle EBS with Dataplatr Google Cortex Framework

Dataplatr has developed accelerators across various modules within the Cortex Plus (+) Framework, each designed to address specific enterprise needs with precision and efficiency. One such success story lies in the Accounts Payable (AP) module, where we implemented a comprehensive solution by integrating Oracle E-Business Suite (EBS) with Google Cloud Platform (GCP). This integration, powered by the Dataplatr Cortex Plus (+) Framework, has streamlined the AP lifecycle, optimized processes, and unlocked actionable insights for our clients.

3.1 Solution architecture

Our solution architecture, powered by the Dataplatr Cortex Framework, ensures a seamless flow of data from Oracle EBS to GCP. The process involves:

3.1.1 Data Ingestion

• Landing Data in GCS:
  • Oracle EBS data, including invoices, payment records, vendor details, and audit logs, is extracted and stored in Google Cloud Storage (GCS) buckets as CSV files.
  • Automated pipelines built within the Cortex Framework manage the ingestion process, ensuring reliability and scalability.
• Incremental Updates in BigQuery:
  • Using Change Data Capture (CDC) via Datastream, incremental changes in Oracle EBS are captured and updated into the Operational Data Store (ODS) stage schema in BigQuery.
  • This ensures that the data in BigQuery remains up-to-date and accurate for downstream processes.

3.1.2 Data Transformation

• Cleansing and Denormalization:
  • Data is processed and cleaned using Cortex-enabled Dataform Scripts to remove inconsistencies, normalize formats (e.g., currencies, timestamps), and prepare it for analytical purposes.
• Business Rule Alignment:
  • Transformation rules are applied to align the data with business requirements, such as calculating outstanding invoices, payment aging, or identifying payment discrepancies.
  • Data is denormalized and merged into the ODS schema for seamless reporting and analysis.

3.1.3 Data Storage and Reporting

• Enterprise Data Warehouse (EDW) Layer:
  • Transformed data is stored in a centralized EDW layer within BigQuery, optimized for reporting and advanced analytics.
  • This layer enables cross-functional reporting and provides a unified view of Accounts Payable operations.
• Historical File Archiving:
  • Original files in the GCS buckets are archived for audit and compliance purposes, maintaining a full history of changes and data lineage.
• Real-Time Reporting via Looker:
  • Looker connects directly to the EDW layer to deliver dynamic, real-time dashboards for AP operations.
  • Dashboards cover areas like invoice trends, payment aging, and vendor performance, offering actionable insights to stakeholders.

3.1.4 Automation

• Workflow Orchestration with Cloud Composer:
  • Cloud Composer (Airflow) is used to schedule, monitor, and manage incremental data updates between Oracle EBS, GCS, and BigQuery.
  • Automated workflows reduce manual intervention, ensuring consistent and reliable data updates.
  • Notifications and alerts are configured to flag issues like data inconsistencies or pipeline failures.

3.1.5 Visualization

• Looker for Analytics and Reporting:
  • Looker connects to the EDW layer to generate comprehensive, interactive dashboards for AP teams.
  • Prebuilt Looker dashboards cater to specific AP needs, such as:
    • Invoice Aging Reports: Identify overdue payments and prioritize actions.
    • Payment Performance Analysis: Track on-time payment rates and trends.
    • Vendor Analytics: Monitor vendor compliance and performance metrics.

3.2 Data Flow Diagram

3.3 Data Models

• In the AP module we have created Four data models based on the business requirement. The final looker reports generated are based on these Four models. 
• All the dimensions are shared across these models.
• The fact tables include 

• Invoice information at distribution level for Invoice Model
• Payment schedule and actual payment information for Payments
• Invoice aging information group into various buckets by invoice date, invoice creation date and invoice received date.
• Combined aggregate which captures all the activities that happen in AP lifecycle in terms of invoicing and payments.
• The dimension tables will include the accounting information, Ledger information , Period Information and the Supplier information.
• These dimensions are common across all the models and are joined with the fact tables to get the desired information.
• Among all these models, the combined model is unique because it combines invoice, payment, purchase order & purchase requisitions information together in this model.
• The invoice aging model is also of utmost importance as it helps in tracking the invoices that are yet to be paid and for how long they have been open. Model is capable of bucketing these invoices based on its invoicing date or its creation date in oracle or its receiving date. This model can be extremely helpful in analyzing and minimizing the accruals in terms of supplier payments. 

• Invoices are separated into individual data models like distribution and payment schedules in order to facilitate detailed reporting at granular level, centered around invoice distribution.
• All the models are integrated with the hierarchies that are defined in hyperion to get the hierarchies related to account, department, company and other segments.
•  Additionally in all the data model currency conversion mechanism is applied to convert the transaction recorded in local currency to global currency like USD.

Combined Aggregate Model

• The Combined Aggregate presents the summarized view of the invoice lifecycle in terms of its distribution, payments, purchase orders and requisitions.
• Accounting-APInvoiceDistributions and Accounting-APPaymentSchedules are the Primary tables for the combined data model along with other supporting tables.
• For the combined aggregate the granularity of the data is kept at a combined level of vendor, ledger, operating unit, source, payment terms and periods.
• The accounting related details are obtained by joining the combined table with the CodeCombination table.
• Time-series KPIs such as year-to-date, quarter-to-date are calculated based on two important dates in the AP lifecycle. These dates are invoice date and invoice creation date in oracle.
• Pre-calculated KPIs are added in the model to track the number of early, on-time and late payments.

. 

• Pre-calculated KPIs related to AP balance amount, overdue amount, overdue invoice counts are also added to the model.
• The audit columns are added at the denormalized level and the naming standards are followed.
• As an outcome of this model it becomes easy to track the key AP KPIs through one model only. Some of these KPIs include invoice amount, number of invoices on hold, number of canceled invoices, payment amount, overdue amount, total number of early, on time and late payments, number of purchase orders and requisitions associated with invoices.

4. Key Looker Dashboards and Visualizations

4.1 AP Overview Dashboard

The AP overview dashboard offers a holistic view of the end-to-end lifecycle of invoices, providing crucial insights into the efficiency and performance of the Accounts Payable process.

 Key highlights include:

1. Invoice Metrics:
   • Breakdown of invoice amounts, providing a comprehensive overview of financial transactions.
2. Invoice Payment Lifecycle:
   • Breakdown of invoices based on payment status (paid, unpaid).
   • Insights into the distribution of payments made early, on time, and late.
3. Payment Metrics:
   • Overview of paid amounts, showcasing the total amount paid within the specified timeframe.
   • Analysis of balances or unpaid amounts, aiding in understanding outstanding financial obligations.
4. Overdue Payments:
   • Identification and quantification of overdue payments, offering a clear understanding of the financial impact of delays.
5. Invoice Counts:
   • Count of invoices paid early, on time, and those facing delays, providing a comprehensive picture of payment timeliness.
6. Hold and Canceled Invoices:
   • Tracking the count of invoices currently on hold, allowing for proactive management of potential issues.
   • Monitoring the count of canceled invoices, helping identify and address any anomalies.

Key Takeaways:

• Efficiently track and manage the payment lifecycle of invoices.
• Pinpoint areas of improvement for on-time payments.
• Monitor and mitigate the impact of overdue payments on financial operations.
• Proactively address issues related to holds and canceled invoices.

This dashboard empowers stakeholders with valuable insights, enabling informed decision-making and optimizing the overall efficiency of the Accounts Payable process.

4.2 AP Aging Dashboard

The AP Aging Dashboard is designed to provide a comprehensive view of unpaid invoices, categorized into age buckets. This enables quick identification of outstanding payment trends and helps in assessing the financial health of the accounts payable process.

Key Highlights Include:

1. Age Buckets: 
   Invoices are categorized into age buckets such as 0-30 days, 30-60 days, etc., allowing for a clear understanding of the aging of unpaid invoices.
2. Date Criteria:
   Age buckets are created based on different dates, including invoice date, invoice creation date, invoice received date, and due date. This flexibility provides insights into payment delays from various perspectives.
3. Trend Analysis:
   The dashboard facilitates trend analysis by allowing users to observe the month-on-month changes in the number and value of unpaid invoices. This helps in identifying patterns and addressing potential issues promptly.

Benefits:

1. Timely Payment Monitoring:
   Enables real-time tracking of unpaid invoices, allowing for proactive management and timely resolution of payment issues.
2. Customizable Views:
   Users can customize the dashboard to focus on specific date criteria or age buckets, tailoring the analysis to their specific needs.
3. Visual Representation:
   Visualizations, such as graphs and charts, enhance data interpretation, making it easier for stakeholders to grasp trends and patterns quickly.
4. Performance Insights:
   Provides insights into the effectiveness of accounts payable processes and aids in optimizing cash flow management.

Recommendations:

1. Regularly monitor the trends in age buckets to identify any increasing trends in overdue invoices.
2. Investigate and address any significant changes in the aging of invoices promptly.
3. Utilize the flexibility of date criteria to gain insights into specific aspects of the accounts payable process.

4.3 AP Invoices on Hold Dashboard

The AP Invoice Hold Dashboard is designed to provide insights into invoices that are placed on hold. It focuses on showcasing the top reasons for holds and identifies the key suppliers whose invoices are frequently held. The main key performance indicators (KPIs) tracked are the hold amount and the count of invoices on hold.

Key Highlights Include:

1. Hold Reasons Analysis:
   The dashboard highlights the primary reasons why invoices are put on hold, offering a detailed breakdown of the top factors contributing to delayed payments.
2. Top Suppliers on Hold:
   Identifies and ranks the top suppliers whose invoices are most frequently placed on hold. This insight helps in managing relationships and addressing potential issues with specific suppliers.
3. Hold Amount and Count:
   Key KPIs include the total amount and count of invoices currently on hold. These metrics provide an overview of the financial impact and the volume of invoices affected by holds.

Benefits:

1. Issue Identification: 
   Quickly identifies and prioritizes the reasons causing invoices to be on hold, facilitating prompt issue resolution.
2. Supplier Relationship Management:
   Pinpoints suppliers with frequent holds, allowing for proactive communication and collaboration to address recurring problems.
3. Financial Impact Assessment:
   Provides a clear understanding of the financial impact of held invoices, enabling better cash flow management and forecasting.

Recommendations:

1. Regularly review the top reasons for invoice holds and take corrective actions to minimize delays.
2. Engage with high-frequency suppliers on hold to improve communication and address recurring issues.
3. Monitor hold amount and count trends over time to gauge the effectiveness of process improvements

We have implemented the Oracle E-Business Suite (EBS) Accounts Payable (AP) module within the Google Cloud Platform (GCP) ecosystem using the Dataplatr Cortex framework. By leveraging Cortex, we deliver a solution that not only streamlines the end-to-end lifecycle of AP operations but also unlocks powerful insights and operational efficiencies for organizations.

Using Cortex, we seamlessly manage every aspect of the AP process, from invoice management to payment processing. Our Cortex-driven models are meticulously designed to handle invoice data, payment schedules, and hold management, ensuring a smooth transactional flow while maintaining compliance.

By integrating Oracle EBS with GCP through the Dataplatr Cortex framework, organizations can fully harness the power of cloud computing and robust ERP solutions. This approach enables greater efficiency, accuracy, and strategic insight into AP processes, paving the way for sustained growth and success in the digital era.

The Dataplatr Google Cortex Framework transforms enterprise data management by integrating advanced features that address the complexities of modern systems like Oracle EBS. By automating processes, enhancing data modeling, and providing ready-to-use analytics, it offers unparalleled efficiency and scalability.

For enterprises looking to modernize their data ecosystems, Dataplatr’s enhancements to the Google Cortex Framework deliver faster time-to-market, reduced costs, and actionable insights, making it the ultimate choice for enterprise data solutions.

Contact us at Info@dataplatr.com or Book time with me to organize a 100%-free, no-obligation call

Follow us on LinkedIn for more interesting updates!!