With Pentaho 10.2 EE, you can also take advantage of key Marketplace Plugins (available here on the Customer Support Portal) to take your data workflows to the next level. Let’s explore the many reasons why upgrading to EE and leveraging our wide universe of fully supported plugins is a game-changer.

Scale & Accelerate Processing of Key Enterprise Data Assets

Databricks Bulk Load – High-Volume Data Transfers, Simplified

• Speed up cloud data lake operations by seamlessly loading large datasets into Databricks tables from anywhere in your data estate. This plugin eliminates complex scripting and ensures efficient data ingestion for advanced analytics.

Salesforce Bulk Operations – Optimize Your CRM Data Flow

• Perform high-speed bulk operations on Salesforce objects to sync, update, and migrate records efficiently. Whether integrating customer data or driving automated workflows, this step significantly boosts Salesforce performance in situations with heavy data workloads.

Enhanced Data Connectivity & Streaming

Kafka Plugins: Enterprise-Grade Streaming

• Leverage advanced Kafka capabilities with improved user experience, security, and scalability. This upgrade enables seamless event-driven architectures for real-time data ingestion across your enterprise.

Elasticsearch REST Bulk Insert (v8 Support)

• The latest enhancement allows direct bulk inserts into Elasticsearch 8, enabling faster indexing and more efficient search operations for real-time analytics.

Enable Advanced Analytics & Data Governance

Google Analytics 4 Integration – Better Reporting, Smarter Decisions

• Directly connect to Google Analytics 4, extract key insights, and populate your data warehouse for deeper analysis and improved decision-making.

Open Lineage – End-to-End Data Lineage for Compliance & Trust

• Gain full visibility into data movement across PDI transformations. This plugin helps ensure governance, auditability, and compliance by tracking lineage metadata. (Enterprise PDC license required)

Next-Gen Data Transformation & Hierarchical Data Handling

Hierarchical Data Types (HDT): Process JSON & Nested Data with Ease

• PDI has various transformation steps to handle hierarchical data like JSON, but these steps do not scale with the complexity of your org’s data. The new HDT plugin vastly simplifies the processing, conversion, and manipulation of hierarchical structures like JSON, letting you accomplish much more with far fewer transformation steps.

The Value of Upgrading to Pentaho Data Integration Enterprise Edition?

Pentaho Data Integration Enterprise Edition isn’t just an upgrade, it’s an investment in efficiency, scalability, and control. With exclusive enterprise-grade plugins, your teams can:

• Move and transform data faster with high-performance bulk operations.
• Enhance analytics with direct integrations for GA4, Elasticsearch, and Databricks.
• Streamline real-time data ingestion with advanced Kafka and Open Lineage tracking.
• Simplify working with modern data structures using Hierarchical Data Types.

More power. More integrations. More insights.

Ready to unlock the full potential of Pentaho Data Integration Enterprise Edition? Let’s talk!

The post Unlock New Possibilities with Pentaho Enterprise Edition: The Power of 10.2 EE Plugins first appeared on Pentaho.

After years of running Pentaho Data Integration Community Edition (CE) on a single machine, a leading organization found itself at a critical juncture. Its financial data operations were straining under the weight of growing regulatory requirements, expanding data sources, and cloud adoption strategies. The move to Pentaho Data Integration Enterprise Edition (EE) on AWS would be more than just an upgrade – it would be a complete re-architecture of their data integration framework.

The Challenge: Securing and Scaling Financial Data Pipelines

The organization had been using CE for financial data extraction, transformation, and reporting, but as workloads increased, several challenges surfaced:

• Lack of governance and security controls over sensitive financial data.
• Inefficient execution of ETL workloads, leading to performance bottlenecks.
• No native cloud scalability, restricting data movement between on-prem systems and AWS.
• Manual encryption and decryption workflows, making vendor file ingestion cumbersome.

In short, the existing architecture had reached its limits, and a once manageable system had become a high-risk, high-maintenance bottleneck.

The Migration: From CE to Enterprise-Grade ETL on AWS

The move from CE to Pentaho Data Integration Enterprise Edition was not just about software – it was about enabling the organization’s cloud-first financial data strategy. The project focused on three key areas: deployment, security, and workload efficiency.

1. Architecting a Secure, Cloud-Native Deployment

The first step was lifting CE off a single machine and deploying it as a scalable, enterprise-ready solution. The new architecture introduced:

• Pentaho Data Integration EE deployed across DEV and PROD environments on AWS EC2, ensuring redundancy and failover protection.
• A centralized repository using AWS RDS (PostgreSQL) to replace the file-based artifact storage of CE.
• SSL encryption enforced across all Pentaho instances, securing financial data at rest and in transit.

This transformation eliminated single points of failure and set the foundation for a scalable, governed ETL framework. 

2. Automating Secure File Ingestion & Data Encryption

A critical aspect of the migration was handling encrypted vendor files – a common requirement in financial data processing. The existing process required manual decryption before loading data, creating compliance risks and operational delays. With Pentaho Data Integration EE, encryption and decryption were fully automated using GPG-based secure key management.

• Keys were centrally managed, ensuring controlled access and compliance with financial data security policies.
• PDI transformations were designed to decrypt vendor files automatically, removing manual intervention.
• End-to-end encryption was enforced, securing the data from extraction to reporting.

This shift not only streamlined file ingestion but also reduced human error and compliance risks.

3. Optimizing ETL Performance in AWS

 With the deployment stabilized, focus shifted to optimizing financial data processing workloads. Key improvements included:

• Parallelized job execution, eliminating bottlenecks in ETL workflows.
• Direct integration with AWS services, including Redshift and S3, enabling faster data movement and transformation.
• Implementation of Pentaho Operations Mart, allowing real-time ETL performance monitoring and logging.

By optimizing how jobs were distributed and executed, processing times dropped by up to 40%, ensuring faster financial reporting cycles.

The Result: A Cloud-Ready Financial Data Platform

The migration to Pentaho Data Integration Enterprise Edition on AWS delivered tangible improvements across security, efficiency, and scalability.

• Significant reduction in ETL processing time, with parallelized execution and optimized job scheduling.
• Automated file encryption and decryption, removing security gaps in vendor data ingestion.
• Cloud-native architecture, enabling seamless data movement between on-prem and AWS.
• Stronger governance and auditability, ensuring compliance with financial reporting regulations.

Pentaho Data Integration Enterprise Edition for Financial Data

For organizations dealing with sensitive financial data, the transition from Pentaho Data Integration CE to EE is not just an upgrade – it’s an operational necessity. By leveraging AWS for scalability, automating encryption, and optimizing ETL performance, this organization built a future-proof financial data pipeline that ensures governance, security, and speed.

As financial data landscapes continue to evolve, Pentaho Data Integration Enterprise Edition provides the scalability and compliance enterprises need to stay ahead. This robust integration offers both stronger governance and auditability while aligning with financial reporting regulations, making it an invaluable upgrade for any business. If you’re interested in exploring how, contact Pentaho Services to learn more.

The post Scaling Financial Data Operations with Cloud-Ready ETL first appeared on Pentaho.

As data volumes and channels grow, telecommunications firms feel tremendous pressure to deliver tailored experiences to their corporate and consumer audiences. This pressure is only increasing as new service bundles emerge and 5G brings faster speeds, connectivity, and higher delivery expectations, all while price sensitivity and competition expand.  

In this fast-paced world, market leaders like Swisscom, Switzerland’s largest telecommunications provider, recognize the value of truly understanding customer needs. Swisscom has been on a transformative journey to enhance customer service through a comprehensive overview of its operations driven by data.  

Satisfying Multiple Masters  

Swisscom serves a diverse and large clientele of residential consumers and corporate businesses, delivering 59% of the mobile services and 53% of broadband across Switzerland. Each client base has distinct needs, requiring different data types and strategies to effectively meet evolving expectations. 

Residential customers prioritize affordability and broadband speeds. Businesses need dedicated customer service and technical support, often backed by stringent service-level agreements (SLAs). Swisscom operates various business units to meet these various and complex requirements, each using a range of systems from enterprise resource planning (ERP) to customer relationship management (CRM) applications. This created multiple data silos, limiting Swisscom’s ability to achieve a unified view of customer interactions, contracts, service statuses, and billing information.

Swisscom required a centralized hub for real-time operational and customer data visibility, which could help teams streamline service support requests and enhance response times.

Centralizing Customer Intelligence with Pentaho

Swisscom’s Business Customers division searched for a unified platform for data integration and validation to achieve a 360-degree view of its operations. Pentaho Data Integration (PDI) was chosen for its comprehensive feature set, ease of use, and cost-effectiveness.

“Pentaho Data Integration met all our requirements at a very attractive price point,” said Emanuel Zehnder, Head of Information Architecture, Swisscom Business Customers. “We were pleased by the comprehensive feature set and the simplicity of the workflows – particularly the streamlined integration process with Apache Kafka. Pentaho has a centralized integration process, which makes connecting business systems quicker and easier, using Dynamic SQL capabilities.”

Swisscom uses PDI to securely extract valuable information on customers, service operations, products, contracts, assets, and more from disparate systems. With all data stored in a single, easily accessible platform, users are benefiting from a unified view of operations. Over 30 business units now use the central hub to access data managed and processed by Pentaho (over 100 million data records processed daily!), including marketing, sales, quality assurance,e and service operations management. 

“Previously, if a member of staff wanted to check details about customer contracts across products and services, the data would be compiled and harmonized from up to six different inventory systems,” says Zehnder. “This was a time-consuming process that could slow us down in providing status updates and resolving issues.” 

Real-Time Data Drives Real-World Impact

Swisscom can now give stakeholders direct access to consolidated information that provides a clearer, 360-degree view of customer status and needs. “Thanks to the Pentaho solution component, we have been able to create a holistic view of all contracts, service status details, and SLAs in a single, harmonized data model,” says Zehnder. “We also let stakeholders access these details online, so they can check on their accounts and service status at their own convenience, 24 hours a day.” 

The Swisscom Business Customers unit sees significant platform usage on the horizon as new cloud environments and services create additional data integration requirements. The company already plans to integrate 20 more systems and expects Pentaho Data Integration to handle even more data records.

With a clearer operational view and teams tapping into much more of its data, Pentaho has Swisscom well-positioned to meet the evolving demands of its diverse customer base and achieve higher operational efficiency.  

Learn more about the power of Pentaho here or request a demo.  

The post Swisscom, Switzerland’s Largest Telecom Provider, Achieves 360-Degree Customer View with Pentaho first appeared on Pentaho.

A leading financial services firm faced this very challenge. Their once sufficient Pentaho Data Integration Community Edition (CE) environment had become a bottleneck for advanced analytics and enterprise-wide reporting. Their team was managing hundreds of transformations, many of which had been built in older versions of the solution that no longer aligned with modern best practices. The need for a high-performance, governed, and scalable analytics infrastructure motivated them to migrate to Pentaho Data Integration Enterprise Edition (EE).

Scaling Analytics with an Aging ETL Infrastructure

The company had a well-established ETL framework but was operating multiple versions of Pentaho Data Integration CE, with some developers still using version 6 on local desktops, while others had begun working in version 9 on servers. This fragmentation led to:

• Limited collaboration and version control across teams.
• Performance inefficiencies due to reliance on outdated job execution models.
• Manual promotion of ETL jobs, requiring engineering effort to migrate artifacts between environments.
• Data governance challenges since audit trails and centralized logging were lacking.

The limitations of Pentaho Data Integration CE became even more apparent as the internal team expanded its analytics capabilities, requiring better integration with Snowflake, Oracle, and DB2, as well as a more automated, scalable data pipeline for enterprise-wide reporting.

Building a Future-Ready Analytics Platform with Pentaho Data Integration Enterprise Edition

The transition to Pentaho Data Integration EE included efforts to modernize data integration, enforce governance, and enable scalable analytics. The migration was centered on three key areas: architecture standardization, automation, and performance optimization.

1. Standardizing the Analytics Architecture

One of the first steps was establishing a uniform, scalable architecture that would eliminate the fragmentation between local desktops and server environments. The new framework introduced:

• A dedicated Pentaho EE server, replacing locally installed CE versions for development and execution.
• Centralized job repository on NFS, allowing developers to version, store, and manage ETL artifacts more efficiently.
• CloudBees for artifact promotion, automating the movement of transformations from development to production.
• LDAP-based authentication, ensuring role-based access control across teams.

By transitioning to this standardized environment, the company reduced deployment complexity and improved team collaboration across ETL development efforts.

2. Automating Workflow Execution & Governance

Before the migration, ETL jobs were triggered manually or through scripted batch processes, making workflow automation and monitoring cumbersome. With EE, job orchestration was entirely redefined.

• Autosys Scheduler replaced ad-hoc job execution, ensuring repeatable, reliable job scheduling.
• PDI transformation logging on an external database created an audit trail of job executions for compliance.
• Automated promotion of ETL workflows using a structured CI/CD pipeline eliminated manual intervention in deployment.

This automation-first approach not only increased reliability but also ensured regulatory compliance by providing a clear lineage of ETL processes.

3. Performance Optimization for Large-Scale Analytics

The ability to process high volumes of data efficiently was a key driver for the move to Pentaho Data Integration EE. To optimize performance, the migration team:

• Enabled parallel job execution across a distributed Carte server environment, significantly reducing processing times.
• Optimized integrations with Snowflake and DB2, reducing unnecessary data movement and improving query performance.
• Migrated key workloads to a Linux-based Pentaho EE server, improving job execution stability and reducing hardware dependency.

These enhancements made it possible to scale analytics workloads efficiently, ensuring that EE could support the company’s long-term data strategy.

A Scalable, Governed, and Analytics-Ready ETL Platform

The migration to Pentaho Data Integration Enterprise Edition delivered tangible improvements in analytics, governance, and operational efficiency, including:

• A unified analytics architecture, with standardized ETL development and execution.
• Faster data processing, with parallelized job execution improving transformation speeds.
• Stronger governance, with role-based authentication and centralized logging for auditability.
• Automated deployment pipelines, ensuring faster, error-free promotion of ETL jobs to production.

Achieving Modern Analytics at Scale

Upgrading from Pentaho Data Integration CE to EE enables enterprises to enhance their analytics capabilities and achieve a strategic transformation. With better governance, automation, and scalability, organizations can leverage data more effectively to drive business insights.

If your organization wants to scale analytics while maintaining governance and performance, contact Pentaho Services to learn more.

The post Unlocking Advanced Analytics with Pentaho Data Integration Enterprise Edition’s Data Capabilities first appeared on Pentaho.

Scaling Beyond Pentaho Data Integration Community Edition 

For years, the organization had relied on Pentaho CE 8.3 to orchestrate ETL processes. However, as data volumes surged and operational demands grew, the limitations of the open-source edition became all too apparent.

• Fragmented repository management made version control and artifact promotion difficult.
• Limited orchestration capabilities led to inefficiencies and bottlenecks in data movement.
• A lack of high-availability execution increased the risk of failures in a distributed environment.
• Inefficient hybrid cloud processing required better integration between on-premise servers and cloud storage solutions like Azure Blob Storage.

The company initiated an upgrade plan to move from Pentaho Data Integration CE to EE for enhanced scalability, governance, and hybrid-cloud performance.

More Than Just an Upgrade

The migration process was more than a software upgrade – it was a complete architectural transformation that would propel the company forward. The transition focused on three key initiatives: scalable execution, stronger governance, and improved operational visibility.

1. Establishing a Scalable Execution Framework

A major concern was job execution efficiency, especially with large-scale batch processing. The old system lacked dynamic workload balancing, causing resource contention and failures.

The new execution model uses Tray Server as a load balancer, to monitor server availability and assign jobs dynamically to the best Carte server. This improved workload distribution and ensured high availability. Performance was further improved by:

• Implementing a slot-based scheduling system for larger jobs to request more resources.
• Using a hybrid execution strategy to map Azure Blob Storage directly to Carte servers, reducing data movement.
• Streamlining Snowflake integration for better ingestion and data processing efficiency.

2. Strengthening Governance and Security

Governance played a pivotal role in the migration journey. Previously, the company’s file-based repository lacked centralized control, posing challenges in enforcing security policies and maintaining version control standards.

With the new system, governance was enhanced through several key measures:

• LDAP Authentication replaced the old manual user management system, allowing for centralized identity management.
• Role-Based Access Control (RBAC) provided granular permissions tailored for different user roles and job executions, enhancing security and compliance.
• Git-backed CI/CD workflows ensured a structured artifact promotion across development, testing, and production environments, bringing consistency and reliability to deployments.

The new deployment pipeline followed a structured approach, eliminating inconsistencies and facilitating faster issue resolutions in:

• Development: Code was maintained in local Git repositories with file-based storage.
• Testing: Artifacts were pushed to a Pentaho EE repository for thorough validation.
• Production: Deployments were orchestrated using the Pentaho job scheduler, with Tray overseeing execution to ensure smooth operations.

This structured approach streamlined governance and significantly enhanced the reliability and efficiency of job executions.

3. Improving Operational Efficiency and Observability

Before the migration, the company struggled with limited visibility into job performance and failures. The upgrade delivered key improvements, including:

• With the Pentaho Scheduler for centralized job management all job executions were orchestrated, which also means they came equipped with monitoring and retry mechanisms to ensure smooth and consistent operations.
• A dedicated logging database was deployed alongside Pentaho EE, meticulously capturing job execution metrics. From these custom dashboards were created, providing real-time visibility into the status of each job, making it easier to identify bottlenecks and performance issues swiftly.
• An OpsMart framework was introduced for performance monitoring. This framework offered pre-built reports and dashboards that detailed ETL execution performance, providing invaluable insights into the system’s operations.

Achieving A Robust, Scalable Multi-Cloud Data Integration Framework

The transition to Pentaho Data Integration Enterprise Edition yielded multiple measurable improvements in performance and governance.

• Job execution became 30% faster, thanks to parallelized workloads and optimized execution nodes.
• Governance and security improved dramatically, ensuring corporate compliance with role-based access controls.
• Automated workload balancing through Tray and Carte significantly reduced job failures.
• Enhanced monitoring and logging provided real-time insights into system performance and job execution health.

The structured transition to Pentaho Data Integration Enterprise Edition not only enhanced execution efficiency but also fortified the company’s governance framework. For any enterprise facing scalability and governance challenges in a multi-cloud environment, Pentaho Data Integration Enterprise Edition presents a robust solution for achieving greater efficiency, reliability, and security.

For enterprises facing scalability or governance challenges in multi-cloud environments, contact Pentaho Services to learn more about building your own path to greater efficiency, reliability, and security.

The post Managing Multi-Cloud Deployments with Pentaho Data Integration Enterprise Edition first appeared on Pentaho.

One of the world’s largest financial institutions was facing growing complexity in its data integration infrastructure. Their existing ETL framework, while initially effective, was struggling to scale with increasing regulatory demands and evolving cloud architectures.

Their goal: lay the groundwork for a resilient and future-proof data infrastructure with contemporary containerized architectures while upholding rigorous governance standards. The move: Pentaho Data Integration Enterprise Edition (EE) with Kubernetes-based execution.

The Drive for Secure and Scalable Data Processing for Financial Operations

The institution’s existing ETL architecture relied on a mix of traditional processing, backed by a large Pentaho Data Integration Community Edition footprint and manual deployment processes. As data volumes grew and regulatory oversight increased, several key challenges emerged:

• Security and Compliance Gaps: The existing system lacked granular access controls and containerized security measures, which posed significant compliance risks. Additionally, the data logging and observability features were insufficient for effectively tracking job execution history.
• Operational Complexity: Managing multiple environments – including on-premises, hybrid cloud, and Kubernetes clusters, all without a centralized orchestration strategy – increased operational complexity. This led to inconsistent ETL workload balancing, causing inefficiencies during peak processing periods.
• Scalability Limitations: With increasing data volumes, the need for efficient parallel execution became evident. However, the existing framework was not optimized for containerized job execution. An incomplete Kubernetes migration left legacy components dependent on outdated execution models, hindering scalability.

The organization embraced a Pentaho Data Integration (PDI) EE-based solution that would seamlessly integrate into their containerized, cloud-first strategy while modernizing their data pipeline execution model.

Deploying A Secure, High-Performance Data Pipeline Architecture

The proposed Pentaho architecture was designed to modernize execution workflows, improve governance, and enhance operational efficiency. The approach focused on three core pillars: security, scalability, and observability.

1. Strengthening Security & Governance

To secure financial data pipelines while maintaining regulatory compliance, the new architecture introduced:

• Kubernetes-native security with isolated Pods for ETL job execution, ensuring process-level security and container control. Role-based access controls (RBAC) and LDAP integration were implemented to enforce granular security permissions at both the job and infrastructure levels.
• Advanced observability and auditing through a new Pentaho plugin for real-time tracking, historical logs, and performance analytics. The execution history storage would allow compliance teams to audit job performance and access logs as part of governance requirements.

2. Optimizing Performance with a Composable ETL Framework

The legacy processing model limited parallelization and execution speed. The proposed Kubernetes-aligned framework introduced a more dynamic and efficient approach to workload management, allowing for better resource allocation, improved fault tolerance, and seamless scaling.

• Tray Server & Carte Orchestration: Tray Server dynamically allocates workloads across multiple Kubernetes clusters instead of relying on static worker nodes, ensuring optimal resource utilization and enhanced execution efficiency. The Carte API enhancements allow for real-time execution monitoring and job prioritization that improves overall system responsiveness.
• Containerized Job Execution: ETL jobs executed in independent, process-isolated containers reduces memory contention and allows jobs to scale elastically based on demand. The introduction of a proxy job mechanism ensures efficient job initiation within Kubernetes, optimizing resource allocation and execution speed.
• Push-Down Processing with Spark Integration: The new PDI execution framework leverages Spark for distributed processing, which optimizes large-scale transformations. The architecture supports Pentaho’s continued development of a Spark-based execution model, ensuring a future-proof migration path that enhances performance and scalability.

These innovations collectively ensure a robust, scalable, and high-performance data pipeline, ready to meet the demands of modern data processing.

3. Enabling Observability & Real-Time Execution Monitoring

Real-time execution visibility is crucial to ensuring immediate detection and swift remediation of job failures and performance bottlenecks. Advanced analytics and alerting mechanisms were integrated to enhance system management, reducing downtime and improving reliability for a resilient and responsive data infrastructure.

• Custom Observability Plugin: A new custom observability plugin was developed to provide real-time execution logs, historical tracking, and system-wide performance insights. Execution metrics are stored in a history server, enabling compliance and engineering teams to track job performance over time.
• Kubernetes-Native Job Execution Monitoring: Kubernetes-native job execution monitoring was integrated directly into the Tray and Carte execution APIs, allowing for automated alerting and remediation. The new OpsMart dashboard would provide a single-pane-of-glass view into all ETL executions, facilitating easier oversight and operational efficiency.

With these enhancements, the institution is now poised to leverage improved observability for a more secure, scalable, and efficient data pipeline.

The Power of a Secure, Scalable, and Observability-Driven Data Pipeline

The proposed Pentaho Data Integration Enterprise Edition architecture delivered significant improvements across security, scalability, and operational efficiency.

• Stronger governance and compliance with LDAP-based authentication and detailed execution auditing.
• Scalable, containerized ETL execution ensuring dynamic workload balancing across Kubernetes clusters.
• Enhanced job monitoring and logging, allowing real-time failure detection and historical performance tracking.
• Optimized data movement, with push-down processing reducing bottlenecks in large-scale data transformations.

Delivering Secure Enterprise Data Pipelines at Scale

In today’s current regulatory environment, financial institutions must secure and optimize data pipelines for regulated, high-volume data. The shift to Pentaho Data Integration Enterprise Edition with Kubernetes integration offers the scalability, governance, and security financial services required to stay ahead in a rapidly evolving regulatory landscape. By implementing containerized execution, real-time observability, and enhanced governance controls, this institution is well-positioned to drive their financial data operations into the future.

Is your financial data pipeline equipped to meet the next generation of compliance, performance, and security demands? Discover how you can prepare by contacting Pentaho Services today to learn more.

The post Securing and Optimizing Financial Data Pipelines first appeared on Pentaho.

Grupo EULEN, a leading provider of outsourced business services, has embraced this philosophy. Leveraging the Pentaho+ Platform to transform its data integration and analysis workflows, the company is more effectively tracking key financial and operational metrics, reducing time-to-insight, and enhancing decision-making processes. 

The Need for Increased Data Intelligence

Grupo EULEN serves more than 7,000 clients in over 11 countries across various sectors, including facilities management, security, social and health services, and HR solutions. The organization expanded its footprint through recent acquisitions in the United States, where it faces established multinational company competition.

“We are growing quickly, both in our home market of Spain and overseas. But in each of our service lines, we are competing against long-established multinational companies – so we need to remain as agile as possible to thrive,” said Ricardo Mardomingo, Chief Information Officer at Grupo EULEN.

Grupo EULEN established a Business Intelligence Competence Center to drive enhanced performance and operational efficiency. This internal team oversees data management and analytics projects aimed at creating efficiencies, reducing costs, and identifying new growth opportunities. Even with this dedicated team and focus, the company was finding it difficult to drive more value from its data due to outdated legacy tools and disparate systems.

Flexibility, Scalability, and Cost with Pentaho

Grupo EULEN partnered with innovAhead to redesign its data integration and reporting workflows by implementing the Pentaho+ Platform.

“We were impressed by the solution roadmap, with new functionality released regularly, and by the data integration capabilities of Pentaho,” said Mardomingo. “Pentaho offers excellent data integration and analytics capabilities without high licensing costs. When we looked at other data management solutions, we found complex pricing models and monthly fees. With Pentaho, we get the ideal combination of great functionality and cost-efficiency.” 

Using Pentaho Data Integration and Pentaho Business Analytics, Grupo EULEN now extracts financial, HR, and customer transaction data from its core business systems with minimal disruption to production systems. The analytics tools within Pentaho allow the creation of OLAP cubes and detailed reports that track key performance indicators (KPIs), which are then distributed to stakeholders. 

“In the past, we relied on limited legacy tools to create query-based reports,” noted Mardomingo, “Similarly, pulling data from the heterogenous systems used by different areas and business models was difficult. We wanted more sophisticated data integration and analytics solutions to deliver more value to the wider business and to reduce time-to-insight.” 

The implementation of Pentaho has significantly improved Grupo EULEN’s ability to make data-driven decisions. Approximately 1,000 employees now access information from the platform, including “Power Users” in the Business Intelligence Competence Center and business executives who rely on regular reports. 

Initially focused on core operations in Spain, the new business intelligence model is set to be rolled out globally. 

Time-to-Insights Creates Bottom Line Value

The ability to quickly assess the performance of its various service lines helps Grupo ELUEN identify issues and take proactive measures to optimize efficiency. For example, the company recently identified that its average time to collect customer payments was becoming excessive. By redesigning its accounts receivable processes, Grupo EULEN successfully reduced payment collection periods and improved cash flow. 

Juan Carlos Garcia, Leader of the Business Intelligence Team at innovAhead, states, “With Pentaho, we have greatly improved the time-to-insight, with users now able to access the data they need to track key business metrics near-instantly. Grupo EULEN is becoming a more agile company, as we can quickly pull information on the performance of EULEN’s different service lines and products, identify issues, and take proactive steps to reduce costs or optimize efficiency, and then measure the results.” 

As Grupo EULEN continues to evolve its business intelligence capabilities, the company plans to collaborate further with innovAhead and Pentaho around additional use cases.  

“We are interested in exploring other reference cases for Pentaho, to see how other clients are using the platform,” said Mardomingo. “In the next 12 months, we will complete another upgrade to access the latest-and-greatest functionality to support our data analysis strategy for 2025 and beyond.” 

Grupo EULEN’s commitment to leveraging data-driven insights through the Pentaho+ Platform enhances its data fitness and operational efficiency, positioning the company to thrive in an increasingly competitive landscape.  

Learn more about the power of the Pentaho+ Platform here or request a demo.  

The post Competing Globally Through Data Agility: Grupo EULEN Becomes Data-Fit with Pentaho first appeared on Pentaho.

A recent TechTarget Enterprise Strategy Group Responsible AI study dug deep into many of the issues and concerns organizations have when looking to deliver responsible AI. The survey included 374 professionals at organizations in North America (US and Canada) involved in the strategy, decision-making, selection, deployment, and management of artificial intelligence initiatives and projects.

The paper clearly outlines that organizations need to combine strategies and investments to ensure they are using AI in ways that are ethical, free from bias, and safely contribute to both employee and organizational success.

Lack of Trust = Real World Impacts

The companies surveyed are keenly aware that underperforming in being responsible in AI is already having negative impacts on brand reputation, increased customer skepticism, and abandonment of future projects – all of which undercut the potential of AI.

The top five impacts listed by those surveyed as being either severe or significant include erosion of public trust (55%), distrust from stakeholders (55%), completely scrapped projects (54%), loss of market share (53%,) and reputational damage (55%).

Of note is how organizations see revenue impact due to responsible AI challenges. Of those surveyed, 15% said the loss of customers was severe, and 43% said it was significant. When combined with the loss of market share, we clearly see that not having the essential elements that support responsible AI is having bottom-line impacts.

Addressing AI Fairness Concerns

One area the organizations surveyed are focusing on is being able to measure AI fairness and ethics across multiple dimensions, crucial in targeting areas for improvement and heading off bias and compliance concerns. Interestingly, the top three areas being used to measure AI fairness and ethics – continuous monitoring and evaluation (46%), specific metrics such as demographic parity, equalized odds, individual fairness, etc. (45%) and maintaining compliance with legal and regulatory standards (44%) – all rely heavily on data and can be positively impacted by solutions that automate data classification, policy and governance application and alerts.

Tackling Speed, Compliance and Cost with Automation

As data estates grow and become more complex, it’s essential to make sure technology is supporting AI efforts across multiple vectors, including privacy, reliability, governance, resiliency and eliminating bias. The survey shows that escalating costs (37%), increased regulatory scrutiny (28%) and slower time to market (26%) were all factors that are creating pressure for businesses with responsible AI.

All these areas can be addressed through strong policies and automation. Across the entire data lifecycle (data access and transformation, classification, analysis, and data tiering and re-tiering based on usage and value) well defined policies create the right guardrails, and automation makes it possible to cost effectively and consistently apply and enforce those policies, ultimately increasing the availability of trusted and governed data for AI.

Data Management’s Role in Enabling Responsible AI

Modern data management serves a key role in providing the data foundation through which AI can deliver on its transformational promise while operating responsibly.

Creating a full understanding of the data used by AI requires a strong and scalable approach to data classification, lineage, quality and governance, leveraging metadata to truly understand what data is available and how it should be used. All these elements must work in concert to avoid bias and ensure completeness when creating the data products that will feed AI systems. This is a key driver in how we’ve designed the Pentaho+ platform to help organizations become data-fit and AI ready.

How does this play out in real life? One example is data classification. Say you have strong data quality processes in place. If it is misclassified, even high-quality data can be misused or misinterpreted based on assumptions. Another area is governance. Missing or incomplete data governance policies can allow PII or sensitive information to be unwittingly fed to open models, exposing the organization to potential fines or reputational damage.

Becoming More Responsible with AI Through Data Fitness

As the survey highlights, achieving responsible AI is a complex challenge that touches every aspect of an organization. As companies have looked to manage data at scale to safely and securely deliver what AI demands, they’ve exposed fundamental gaps in their data foundations.

The Pentaho+ Platform delivers battle-tested solutions to create foundational strength for meeting the challenges of an AI-driven world head-on. Our customers see measurable and tangible outcomes, including 3X improved data trust, 7X impactful business results and a 70% increase in productivity. To learn more about how Pentaho+ can help you become more responsible with AI, request a demo.

The post Are Your Data Foundations Ready to Support Responsible AI? first appeared on Pentaho.

• A complex and highly modularized data structure that is difficult for beginners to understand. For example, SAP data is available in specific formats (e.g. cluster tables) that cannot be easily extracted and understood.
• Data from underlying processes is only generated at runtime, requiring ETL process to readjust for complex calculations.
• Proprietary interfaces that require expertise from expensive SAP specialists that aren’t always readily available.

This is where the Pentaho SAP Connector comes into play. The connector can be integrated into any Pentaho Data Integration (PDI) installation as a plugin for simple and direct access to SAP systems and data sources, bringing this valuable data into new workflows and processes all via a standardized user interface within Pentaho with full Pentaho functionality.

The Pentaho SAP Connector in Action

The connector uses SAP standard connections to ensure that communication between the systems is secure, efficient, and performant. There is no need for complex configuration in the SAP system while also complying with SAP guidelines.

The connector enables design and handling of a range of data extractions and transformations, including:

• Flat Table Extract: Extraction of raw data from SAP tables
• RFC Execution: Execution of business applications in SAP and direct connection with the existing business processes
• Report & Query Execution: Execute and extract existing SAP reports without having to replicate business logic in ETL
• Business Warehouse Loads: Utilization of business warehouse objects (e.g. cubes, DSO, queries)

The Pentaho SAP Connector’s seamless integration delivers access to SAP without the need for extensive programming knowledge. Thanks to PDI’s graphical user interface, developers avoid dealing with manual ABAP coding or complicated SAP APIs.

This is an example of the visual, self-documenting interface that makes your SAP ETL project easy to understand and maintain.

Companies can retrieve data from a variety of SAP modules (e.g. FI, CO, MM, SD) and integrate it into almost any target platform – be it a cloud database, a Hadoop cluster or a traditional BI tool. This set of capabilities delivers a powerful, secure and cost-effective alternative to expensive specialized ETL tools or middleware solutions as well as SAP’s own resources.

Opening a Whole Universe of New SAP Data Use Cases

Organizations across every industry that rely on SAP can benefit from leveraging Pentaho Data Integration and the SAP Connector to access and leverage more of their valuable data.  Bringing data out of SAP and into new analysis and workflows can positively impact Financial reports (SAP FI/CO for dashboards and reports), Supply chain management (SAP MM or SD to identify bottlenecks), Customer Engagement (deeper customer behavior analysis), Data warehousing (creating a uniform view of company data) and more.

For example, with the Pentaho SAP connector, the team of Bell Canada has saved an estimated 30 man-hours per month, and staff no longer needs to work at weekends to ensure data is highly available. “The solutions free the IT team from manual tasks and allow them to focus on more strategic projects,” said Jude Vanniasinghe, Senior Manager of Business Intelligence, Bell Business Markets Shared Services, Bell Canada.

Leveraging the Power of SAP Data

The Pentaho SAP Connector is an indispensable tool for companies that want to efficiently integrate SAP data into their BI and analysis processes. It offers a powerful and user-friendly way of handling complex data structures from SAP and gaining valuable insights.

Whether you want to increase the efficiency of your data integration, save costs or make more informed decisions, the Pentaho SAP Connector can be a crucial building block in your data strategy.

If you’re interested in learning more about the Pentaho SAP Plugin Suite, reach out to Pentaho’s Professional Servies team or engage with a trusted partner like it-novum to discuss how to get started.

The post Unlocking the Power of SAP Data with Pentaho Data Integration first appeared on Pentaho.

Across a wide spectrum of the financial industry – from credit unions to fintech companies and data brokers – now have new data access, privacy, consent, lineage, auditability, and reporting requirements. Compliance with these new CFPB requirements will be a massive operational and technical issue for most companies.

Below is a breakdown of the unique issues that arise with the new CFPB guidelines and how impacted organizations need to rethink their data lineage, privacy controls, automation, and auditing strategies.

The Personal Financial Data Rights Rule (Open Banking) 

The Personal Financial Data Rights Rule from the CFPB seeks to enable consumers to manage, access, and share financial information with third-party providers. Financial institutions have to offer data access, portability, and privacy protection with total control over who has seen the data and when.

Key Challenges and Strategies: Data Access and Portability

Banks and financial institutions must allow consumers to migrate their financial information to third parties. Institutions will need to demonstrate when, how, and why consumer data was passed. They must also protect consumer information and only share the consented data. 

Automated ETL (Extract, Transform and Load) can help institutions collect consumer financial information across diverse sources (CRMs, payment systems, loan management systems) and turn it into common formats for easier management and tracing. This will also support lineage, crucial to providing regulators a full audit trail. Integration with Open Banking APIs and being able to integrate data with third parties directly will be essential.

Role based access is an important control to ensure only authorized users and systems are accessing defined data, and being able to mask or encrypt PII helps when making consumer data anonymous when it is provided to third parties.

The New Data Broker Rules 

The CFPB’s revised data broker rules expand the scope of the Fair Credit Reporting Act (FCRA) and includes Credit Rating Agencies. Data brokers who purchase, sell, or process consumer data now have to respect consumer privacy, consent, and deletion rights.

Key Challenges and Strategies: Data Deletion Requests 

Under this new rule, brokers will need to comply with consumer data deletion requests.  Data brokers must guarantee only explicit consent to share consumer data. Regulators are now demanding an audit trail of who and with whom consumer data was shared. 

Automating data deletion workflows helps organizations automatically detect and delete every reference to a consumer’s data in databases, data warehouses, and third-party data lakes. Being able to purge workflows on request ensures that databases are automatically cleansed, duplicates removed, and consumer records deleted when CFPB requests data deletions. 

Marking and categorizing consumer data and grouping it according to privacy policies and access levels enables data to be more easily managed and deleted when needed. Also, data masking blocks access to non-PII data from third parties to support access and anonymization requirements.  

Being able to track data as it is processed across databases and APIs provides the ability to demonstrate with certainty to regulators how, where and when data was used. All of these capabilities support the regular reporting that can be submitted directly to the CFPB.

Supporting Data Privacy, Consent, and Portability

Both CFPB regulations are focused on consumer consent, privacy management, and data portability. Businesses must now allow consumers to have control over their data and know where it is being shared.

Key Challenges and Strategies: Consent Tracking 

Consumers must be able to cancel their consent to sharing data. They need to have access to and the ability to export their personal data in common formats. This means multiple data silos Data must be synchronized with new consumer consent.  

Visualizing consumer consent data and monitoring change requests over time will be crucial for compliance and reporting.  Organizations will need to have clean data change logs supported by data lineage metadata to provide a full audit trail.

Having data management tools that integrate with REST APIs will make it easier to export consumer data to other banks or fintech providers as needed. The ability to export data in multiple formats, such as CSV, JSON, or XML, allows integration with third-party programs. It will also be important to sync consent updates between multiple data warehouses so that consumer data is removed from the system when consent is revoked. 

Assuring Perpetual Compliance with CFPB Audit & Reporting Requirements. 

In the long term, CFPB compliance will require businesses to consistently be transparent, demonstrate compliance, and issue regulators demand reports. This means organizations must adopt audit-friendly data lineage, be able to produce reports on-demand that capture a wide variety of variables, and be able to spot errors early to triage mishandling, validate missing or incorrect data, and proactively address the issues before auditors discover them.  

Meeting The Consumer Data Privacy New World Order Head On 

The new CFPB data privacy, consumer consent, and broker practices are significant hurdles for financial institutions. Compliance requires data governance, real-time audits, and data sharing. Pentaho’s entire product portfolio — from Pentaho Data Integration (PDI), Pentaho Data Catalog (PDC), and Pentaho Data Quality (PDQ) — meets these issues through data privacy, portability, and auditability.

With Pentaho’s data integration, lineage management, and consent management functionality, financial companies can meet the CFPB’s regulations and reduce the risk of non-compliance fines. Contact our team to learn more! 

The post New CFPB Data Compliance Requirements Will Test the Limits of Financial Data Management Strategies first appeared on Pentaho.