In times of uncertainty, control matters more than ever. And nowhere is that more important than with the true cost of your data platform.

This is where Pentaho stands apart.

Know Your Costs. Trust Your Future.

Pentaho gives you clarity—not just in your data, but in your pricing. Unlike platforms that require custom quotes, license gymnastics, or vague “cloud transition” fees, Pentaho’s pricing is:

• Transparent – No hidden costs. No surprise hikes.
• Predictable – Buy only what you need—modular, scalable, and aligned to your roadmap.
• Efficient – Simplify your stack with one integrated platform instead of multiple point solutions.

Why Pentaho Wins on TCO

When it comes to Total Cost of Ownership, Pentaho doesn’t just compete—it leads.

• Lower Upfront Investment – Pentaho’s intuitive, no-code platform reduces the need for costly implementation resources or extensive training.
• Reduced Maintenance Overhead – With fewer moving parts and better integration across tools, your teams spend less time maintaining and more time moving forward.
• Built-in Flexibility – Hybrid-ready architecture means you deploy on your terms—on-prem, cloud, or both. No forced migrations. No rip-and-replace costs.
• Unified Licensing – Get data integration, analytics, cataloging, optimizing, and quality—all under one umbrella. One platform, one contract, one clear cost.

Designed for Simplicity. Built for Stability.

While other platforms pivot, rebrand, or realign, Pentaho stays focused. We deliver a data platform designed to make complexity manageable and value more accessible.

You don’t have to choose between power and simplicity. You don’t have to overpay for uncertainty.

With Pentaho:

• Get to insights faster
• Streamline operations with fewer tools
• Control your budget—and your future

Data chaos is expensive. Smart simplicity isn’t.

Discover how Pentaho stacks up against Informatica.

If you’re evaluating Informatica—or re-evaluating it—now is the time to compare the real cost of staying versus the value of switching.

Pentaho gives you the confidence to move forward—with clarity, control, and a lower TCO.

Schedule a demo to see what Pentaho can do for your business.

The post Cut Through Uncertainty. Cut Down Your Costs. first appeared on Pentaho.

In March, Stewart Bond of IDC published the first IDC ProductScape: Worldwide Data Intelligence Platform Software, 2025 report. The report is a feature-by-feature comparison of 13 vendors’ platforms for prospective technology buyers navigating the fast-changing data intelligence field.

The report noted the following for key differentiators for Pentaho:

• “Data Quality Profiling and Observability: Pentaho excels in data quality profiling, observability, and analytics, leveraging semantic discovery to evaluate data quality and workflows, including data integration capabilities for quality issue remediation.”
• “Patented Data Fingerprinting: The platform uses patented data fingerprinting capabilities to identify data, including duplicates, within and across repositories, and extends data discovery functionality to unstructured documents, valuable for Generative AI use cases.”
• “Open Source Heritage and Community: Originating from the open source community, Pentaho Data Integration (PDI) has a strong global presence, supported by a dedicated direct sales force and a robust partner and OEM community.”

We appreciate this inclusion, and in my view, it really showcases the evolutions and innovation we’ve been building into the product set over the past two years, and is a clear signal that Pentaho has what it takes to deliver an end-to-end data management experience.

Few insights to call out:

• As IDC notes, there is growing demand for intelligence about structured and unstructured data, being fueled by growth of GenAI applications.
• AI is transforming data strategies and focusing businesses on delivering timely, reliable, and high-quality data for activities such as inference and model training.
• Platforms with capabilities in stewardship, cataloging, quality, and data lineage, along with the capacity to build data marketplaces, offer companies the best chance of success.
• IDC noted that many of the brands included in this evaluation have “helped define the data intelligence software market.”

This is great for Pentaho, and I want to celebrate our product and engineering teams for their incredible work in delivering a platform experience that positions us so well amongst the established industry leaders.

Pentaho’s platform is built with a clear vision: to provide organizations with the power to simplify their data management burdens and become more data-fit, turning all of a company’s data into the right quality and trusted information for core operations and AI.  We’re well on our way, and we’ve got lots more to come this year, including innovations around data lineage, AI models, data products, observability, a unified UX/UI experience and much more.

To experience the latest that Pentaho has to offer and hear more about our roadmap, reach out to our team or request a demo.

The post Pentaho Included in First IDC ProductScape for Worldwide Data Intelligence Platform Software first appeared on Pentaho.

For example, the EU AI Act represents a significant legal stake in the ground, requiring the use of AI technologies to adhere to principles of justice, transparency, and accountability. This law, along with growing public and government pressure to take on more responsibility in adopting technology, will trigger industry-changing shifts in the world of finance.

It’s important for financial services IT and data leaders to take stock of their ability to effectively manage what is happening in and around the industry, from increasing data breach risks, competition pressures posed by disruptive fintech start-ups, and the demand for personalized and trustworthy AI apps. These are some of the issues we’ll discuss in a two-part series, detailing the challenges and how the Pentaho+ platform is well-positioned to support financial institutions in their efforts to manage complexity, make data-based decisions, and achieve compliance and visibility in their business.

Regulatory Crackdowns on Algorithmic Bias

In 2023, a U.S. bank was fined $25.9 million for applying a credit-scoring AI model that rejected minority applicants. Post-event research indicated a lack of bias-checking in the training data and a lack of transparency in decision-making.

Challenges:

• Bias Prediction: AI models often overestimate the historical bias in data and will produce biased decisions. Also, with data models being designed by humans, there needs to be a way to check data to avoid unconscious bias that might creep in with data selection processes.
• Accountability: Regulators expect to see evidence of bias mitigation and fairness evaluations. This includes safeguards across the board, from how the model was designed to the data that the models are trained on, and the new data sources that the models are being supported by in ongoing analysis.

How Pentaho Helps:

• Automatic Bias Audit: Pentaho Plus uses machine learning algorithms to continually scan datasets for bias and report anomalies in real-time.
• Data Lineage Tracking: All data sources, transformations, and decision points are traced to enable regulators to have comprehensive reporting.
• Easily Explainable AI Dashboards: Clients and auditors can visualize decision pathways for transparency and confidence.

Potential Industry Shift: Let’s say a bank can not only conform but actively promote its AI fairness solutions. Displaying a fair lending model provides a competitive edge and appeals to more socially aware customers.

Banking’s Rising Systemic Risk

The failure of a few mid-sized banks in 2023  exposed real flaws in risk modeling and stress-testing techniques. Supervisors are pushing for more accurate, faster reporting of risk exposure to ward off failures in the system.

Challenges:

• Data Separation: Risk data is often separated into silos, which can be challenging to map holistically. There is also the issue of how to blend structured and unstructured data to gain a clearer and more accurate picture of risk.
• Rapidity and Precision: Real-time reporting and predictive capabilities are crucial to find the weakness. Many banks still rely on manual processes, introducing time lags and errors that can weaken risk analysis and create the conditions for failures.

How Pentaho Helps:

• Unified Risk Data Model: Pentaho integrates disparate data, including structured, semi-structured, and unstructured data, into one holistic understanding of institutional risk.
• Predictive Analytics: With real-time simulations, firms can simulate stress conditions and predict the effects on liquidity and solvency.
• Real-Time Reporting: Automated, real-time regulatory reports ensure teams can meet evolving requirements.

Potential Industry Shift: What if banks leveraged real-time risk dashboards with regulators and stakeholders? This level of transparency could transform the relationship with the banking industry and define the norm.

ESG Reporting and Green Finance

ESG (Environmental, Social, Governance) continues to find support across the globe, and the EU’s Sustainable Finance Disclosure Regulation (SFDR) makes it mandatory for financial institutions to report on portfolio sustainability. In 2024, one major asset manager was criticized for greenwashing and misreporting ESG data.

Challenges:

• Data Integrity: Validating ESG data and being auditable. Even larger organizations have not prioritized clear processes on how to collect and verify ESG data. There is tremendous reliance on manual data collection and Excel spreadsheets in ESG reporting that introduce errors that limit reporting accuracy and reliability.
• Standardization: Reporting is inconsistent across ESG frameworks. The EU leads in this area; however, even if an organization is headquartered in other regions, after a certain financial threshold, they are held to the same reporting standards as EU HQ organizations. Without established policies and data collection protocols, financial institutions will struggle to meet these standards.

How Pentaho Helps:

• Golden Source ESG Data: Pentaho provides a central database for ESG data that remains consistent across all reports and analyses.
• Automated Data Validation: Algorithms within Pentaho validate ESG data against external benchmarks and standards, detecting discrepancies.
• Configurable ESG Dashboards: These allow portfolio managers to track and optimize sustainability metrics in real time.

Potential Industry Shift: Suppose you could not only be ESG compliant but also launch data-based green finance products. By delivering clear ESG performance metrics, institutions might lure eco-conscious capital and redirect funds to sustainable investments.

In our next blog, we’ll explore the issues around fraud detection and prevention, data sovereignty, cross-border compliance, and safely scaling innovative uses of AI in finance.

Discover how Pentaho supports financial services.

The post Unlocking the Future: Application Case Studies on How the Financial Services Landscape Is Changing – Part I first appeared on Pentaho.

The EU’s evolving AI laws, including the new AI Act, prioritize fairness, transparency, and accountability. These laws will disrupt the way AI is already implemented, requiring banks to redesign the way they manage, access, and use data. Yet, as we’ve seen with other regulations, meeting these acts can provide an opportunity.  As banks evolve to meet these laws, the resulting improvements can increase customer trust and position the banks as market leaders in regulated AI adoption.

Meeting the EU AI Act Moment

There are a few key areas where banks should invest to both adhere to the EU AI Act and reap additional benefits across other regulatory and business requirements.

Redefining Data Governance for the AI Age

Strong data governance sits at the heart of the EU’s AI legislation. Banks must ensure the data driving AI algorithms is open, auditable, and bias-free. Good data governance moves compliance from the status of being a chore to one that is proactively managed, establishing the basis for scalable, ethical AI. They can achieve this through technology that delivers:

Unified Data Integration: The ability to integrate disparate data sources into a centralized, governed environment ensures data consistency and eliminates silos. A comprehensive view of data is essential for regulatory compliance and effective AI development.

Complete Data Lineage and Traceability: Tracking data lineage from origin to final use creates full transparency throughout the data lifecycle. This directly addresses regulatory requirements for AI explainability and accountability.

Proactive Bias Detection: Robust data profiling and quality tools allow banks to identify and mitigate biases in training datasets, ensuring AI models are fair and non-discriminatory.

Building Ethical AI From the Ground Up

Moral AI is becoming both a legal imperative and a business necessity. The EU’s emphasis on ethical AI requires banks to prioritize fairness, inclusivity, and transparency in their algorithms. This demands continuous monitoring, validation, and explainability, all of which can foster stronger customer relationships and differentiate banks as pioneers in responsible AI through:

Real-Time AI Model Monitoring: Integrating with machine learning platforms enables teams to monitor AI models in real-time, flagging anomalies and ensuring adherence to ethical standards.

Explainable AI (XAI): AI explainability is supported by tools that visualize decision-making pathways, enabling stakeholders and regulators to understand and trust AI outcomes.

Collaborative AI Governance: Facilitating collaboration between data scientists, compliance officers, and business leaders ensures that ethical considerations are embedded across the AI development lifecycle.

Streamlined Regulatory Compliance

Regulatory compliance often involves extensive reporting, auditing, and data security measures. Technology that simplifies these processes helps banks navigate the complex EU AI regulatory framework while driving down costs, boosting productivity, and empowering banks to innovate while maintaining adherence to regulations.

Automated Compliance Reporting: Customizable reporting tools generate regulatory-compliant reports quickly and accurately, reducing the burden on compliance teams.

Audit-Ready Data Workflows: A platform with built-in audit trail features documents every step of the data process, providing regulators with clear and actionable insights.

Privacy-Centric Data Management: Support for data anonymization and encryption ensures compliance with GDPR and safeguarding customer information.

Transparency and Accountability: The Hallmarks of Leadership

AI is transforming financial services, but customers’ confidence matters. Banks must be transparent and accountable to generate trust in AI decision-making. When banks treat transparency as a path to redefining relationships, they can transform customer interactions.

Customer-Centric Insights: Intuitive dashboards that allow banks to explain AI-driven decisions to customers, enhancing trust and satisfaction.

Stakeholder Engagement: Interactive visualizations and real-time analytics enable banks to communicate compliance metrics and AI performance to regulators and stakeholders.

Collaborative Transparency: Collaborative features ensure that transparency and accountability are integral to every AI project, from design to deployment.

Leveraging Pentaho for Compliant AI

To fully adopt a strategic approach to AI compliance, banks can capitalize on Pentaho’s capabilities to:

• Develop a Unified Governance Framework
  Use Pentaho to create a centralized data governance model, ensuring alignment with EU standards and global best practices.
• Prioritize Data Lineage and Quality
  Leverage Pentaho’s data cataloging and profiling tools to ensure that all datasets meet compliance requirements and ethical standards.
• Foster Collaboration Across Teams
  Involve compliance officers, data scientists, and business leaders in AI governance, using Pentaho to enable cross-functional workflows.
• Monitor AI Continuously
  Implementing Pentaho’s real-time monitoring and reporting features can proactively address compliance risks and optimize AI performance.
• Communicate Compliance Effectively
  Use Pentaho’s visualization and reporting tools to provide stakeholders with clear and actionable insights into AI processes.

The Path Forward to Robust AI Compliance and Performance

Imagine a world where banks don’t just tackle compliance problems but also use them as strategic growth engines. Pentaho’s full-spectrum data integration, governance, and analytics products empower financial institutions not only to adapt to change but to drive the way in ethical AI practice. This openness helps them not only meet regulatory standards in the present but to set the direction of AI use with due care in the future.

Pentaho is well positioned to help transform finance industry systems into intelligent and compliant AI engines, especially ahead of the new AI regulations coming from the European Union. This is a time of significant change for banks where the right combination of modern technology and enabling regulation can re-energize client trust – an approach Pentaho is looking to lead.

Ready to make compliance your competitive advantage? See how Pentaho powers ethical AI for the financial services industry.

The post What Banks Need to Know About EU AI Act Compliance and Ethical AI Governance first appeared on Pentaho.

What’s driving new privacy and data protection efforts? Several factors.

Global data flows: Trade data increasingly migrates across borders and will demand more international cooperation and coordination with data-protection laws. If I buy a sweater from a vendor in Ireland and live in California, there are two different regulations at work in just that one transaction.

Growing awareness of and expectations of data privacy and demands for greater transparency and accountability will push organizations to improve their data operating practices.

Technological evolution: Developments in computer science, including artificial intelligence, the Internet of Things, and biometrics, have changed attitudes around what needs to be protected. This poses new privacy challenges to the old ways of organizing dataflows, which simply do not work in today’s interconnected world, especially with personally identifiable data sitting in massive global data clouds.

Regulatory evolutions: As new attitudes and technologies like GenAI emerge, governments and regulation authorities will be constantly evolving legislation to address new privacy problems and safeguard individuals. This requires constant monitoring and adjustments by organizations to stay ahead of fines and reputational damage.

Foundational Regulations Every Organization Must Understand

Multiple core legislations already significantly influence the global privacy landscape, including:

GDPR (General Data Protection Regulation) (EU): GDPR is a harmonized data privacy law and an enormous piece of human rights-based change. As of 2018, all data controllers are required to comply when using the personal data of all EU citizens. This pushes organizations to adhere to strict privacy by consent, data minimization, and data deletion requirements.

Data Protection Act 2018 (UK): The UK Data Protection Act implements GDPR and provides further detail on the information rights of individuals and the responsibilities of organizations when handling personal data that must be considered.

California Consumer Privacy Act (US): This California law, effective as of 2020, grants certain rights to consumers for their personal information (e.g., right to know, right to delete, and right to opt-out)​

Here, ‘personal data’ means any information relating to an identified or identifiable natural person (‘data subject’) by references such as a name, an identification number, location data, and online identifiers or factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person.

There are also ‘special categories of personal data’ related to racial or ethnic origin, political opinions, religious or philosophical beliefs, or trade union membership, genetic data, biometric data processed to uniquely identify a natural person, data concerning health or data concerning a natural person’s sex life or sexual orientation.

LGPD (General Data Protection Law): This is the Brazilian law equivalent to GDPR that protects Brazilian citizens’ personal data. It defines the rights and obligations of organizations collecting personal data from citizens, on and offline.

Personal Data Protection Act 2010 (India): This law, while perhaps a much less developed version of GDPR, does provide a regulatory framework on which a better-articulated regime can be built.

The Balancing Act Around Critical Use Cases

Data is everywhere and informs so much of our lives. This has put a larger burden on organizations at every level of society to understand their potential exposure to compliance risks and consistently apply policies and technology to safeguard data

Medicine: Patients’ health data (e.g., medical records, genetic data) must be kept private to ensure physical well-being and avoid misuse related to areas like insurance, employment and receiving benefits.

Finance: The number of rules and regulations in this industry match the level of collection and management of customer data that takes place every second of every day. Fraud protection, anti-money laundering and ethical practices are all regulated and support the consumer trust and confidence that is the lifeblood of financial institutions.

E-commerce: A retailer necessarily collects great amounts of personal data to match buyers and sellers, and even facilitate transactions without friction.

Marketing and Advertising: Ideally, advertisers will gain the ability to target messages very sharply. Striking a balance between the ability to curate experiences and the protection of consumers’ privacy is crucial, especially when crossing international borders into the EU and needing to consider where data is stored and how it is used.

Social Media: Social media companies collect and process immense volumes of data related to user behaviors. Unethical use of data is a high risk in these platforms given their ubiquity and how many users cross different age groups and geographies.

Looking Ahead  

For each part of the global privacy matrix – flagship legislation, use-case categories, and local, regional, and global differences – attention to the whole is required. Only then can organizations deploy strategies that stake out a defensible position where privacy interests are balanced against service and commerce goals while also building and sustaining stakeholder trust.

To explore how Pentaho can help enable your organization to become data-fit and manage regulatory compliance data challenges, request a demo.

The post The Key Legislations That Define the “New” Global Privacy Landscape first appeared on Pentaho.

These banks will need to make huge investments. Those will come in the human resources and IT infrastructure required to implement DORA and detailed technical plans to identify, measure, and mitigate ICT risks. These will involve everything related to cybersecurity, using robust incident response plans and 24/7 monitoring.

Traditionally, mid-sized banks have struggled to adapt to changes across a range of asset sizes. While larger banks have more resources, mid-sized banks have smaller budgets and teams that prevent them from fully complying with many regulations.

The technicalities of these standards add an additional layer of complexity. In many cases, confusion can arise as the regulations are unclear and difficult to read and implement for many banks.

In this blog, we’ll dive into unique issues across asset classes, providing an outline of how mid-market banks can tactically optimize their ICT risk management programs to meet regulatory requirements and create resilience to attack in a ever-changing digital age.

Asset Class: $10–$50 billion

Regulatory Adherence Requirements:

• ICT Risk Management: Create a governance process with clearly defined ICT risk oversight roles and functions.
• Exceedance: DORA issues general guidelines, but not precise recommendations to smaller institutions for the exact risk levels and criteria that must be applied for ICT risk.
• Banks’ Incident Reporting: Banks must notify the authorities of large ICT incidents in specific time periods (e.g., 72 hours in EU regulations).

Key Limitations:

• Resources Shortages: Smaller banks lack ICT resilience teams which causes them to take longer to respond and rectify. They also usually lack powerful monitoring and are unable to deliver incident detection and notification times.
• Uncertainty About Testing Requirements: DORA calls for resilience testing but hasn’t articulated what the minimum acceptable conditions should be for mid-sized banks, leaving room for interpretation that could result in audit collapses.
• Regulatory Ambiguities: DORA’s small institution ICT governance guide does not define the right amount of manual versus automated processes, which causes inconsistencies in the compliance methodologies. It is also not fully explored on a technical level for incident reporting best practices (form, content, detail) making it difficult for regulatory tests.

Asset Class: $50–$150 billion

Regulatory Adherence Requirements:

• Third-Party Risk Control: Banks need to control risks from important third-party providers. DORA emphasizes third-party risk monitoring, but it provides no common evaluation methods for vendors.
• Operational Resilience testing: DORA requires annual resilience testing of ICT infrastructures to prevent disruption. Hybrid ICT environments (legacy + cloud) make testing more difficult since DORA doesn’t provide any guidance on how to connect legacy systems to the new frameworks.

Key Limitations:

• Oversight of Vendor Risk: Mid-sized banks are dependent on 3rd party service providers, but DORA lacks explicit responsibility requirements for failures in such relationships.
• Resources Availability: Mid-tier banks don’t have the economies of scale to shop for specific services from vendors in compliance with DORA.
• Regulatory Ambiguities: DORA’s requirements for ICT resilience scenario testing are general and do not contain detailed scenarios for mid-sized banks’ operational risk, so their testing frameworks are not aligned. The act does not explicitly define what constitutes “critical” third-party services, so under-preparing for compliance reviews might be an issue.

Asset Class: $150–$250 billion

Regulatory Adherence Requirements:

• Data sharing: Financial organizations will need to participate in shared resilience measures such as sharing of information about cyber threats and events. Small banks are left out of mature information-sharing systems run by large banks, which is a zero-sum game.
• Disaster Recovery: DORA establishes pre-established disaster recovery objectives (RPO/RTO). Legacy systems are difficult to align with today’s RPO/RTO due to technical debt and inflexible regulatory benchmarks on banks.

Key Limitations:

• Higher scrutiny: Banks of this asset type are subject to more regulatory scrutiny than large banks, but not the same resources.
• Complex ICT Infrastructure: There is a big challenge with resilience in multi-cloud and hybrid environments because DORA doesn’t specify integration frameworks.
• Regulatory Ambiguities: DORA’s definition of “significant operational impact” is fuzzy, leading to reports of incidents being under- or over-reported during regulatory exams. Minimum compliance requirements for cybersecurity resilience standards (e.g., sophisticated threat management, machine learning analytics) are too general to apply consistently.

Regulatory Uncertainty and Cross-Asset Challenges / Regulatory Inaccuracy:

1. Incident Reporting:

• Ambiguity: DORA sets dates but not details about the level of detail that an incident report should contain. Banks can fail the compliance tests if the incident reports are not complete.

2. ICT Risk Assessment:

• Ambiguity: The act in principle establishes a risk management process but leaves blanks for the minimum acceptable risk levels. Banks can build systems that don’t meet regulatory standards in examinations.

3. Testing Frameworks:

• Ambiguity: Annual resilience testing is required but no one clearly specifies what tests are allowed (i.e., penetration vs. red team exercise). Banks run the risk of missing the compliance exams due to unintended testing requirements.

4. Third-Party Management:

• Ambiguity: DORA sets no standard of what is considered “critical” vendors. Banks may focus on the wrong vendors and miss real risks.

5. Cybersecurity Standards:

• Ambiguity: DORA will require strong security, but doesn’t meet certain international (e.g., ISO 27001) requirements for smaller banks. This can lead to gaps in cybersecurity controls that are implemented adequately.

Recommendations for Addressing Limitations

1. Collaborate with Regulators:

• Get involved with regulators, proactively, and clear confusion about compliance metrics, testing requirements, and reporting.

2. Leverage Industry Standards:

• Implement ICT infrastructures based on existing, widely understood frameworks like NIST CSF, ISO 27001 and COBIT to plug the holes in DORA’s recommendations.

3. Invest in Automation:

• AI-powered incident detection, third party risk management and reporting to optimize compliance and reduce resource consumption.

4. Strengthen Vendor Relationships:

• Add explicit resilience criteria into vendor SLAs and audit regularly to ensure you are compliant with DORA requirements.

5. Scenario-Based Testing:

• Design and run specific test scenarios based on bank size, process and systemic impact.

Final Thoughts

The Digital Operational Resilience Act (DORA) offers mid-tier banks more business stability and provides a way to mitigate cyber risk and disruption. But mistakes and vagueness in the act can be compliance headaches.

One of the best ways for mid-tier banks to overcome these challenges is to be proactive with regulators. That means finding regulators, knowing what they expect, and executing accordingly. Standards and best practices will be a legal requirement and drive efficiency.

Operational risk is better managed with preparation. Modern technology investments like cybersecurity and data backups aren’t just a suggestion, it’s necessary. Smart integration will automate processes, mitigate impact, and enable compliance, giving your bank an operational rock-solid foundation.

By engaging with regulators, executing on international best practices, and taking the lead in technology, mid-size banks will not only have better chances of DORA compliance but also set themselves apart from their competitors in a rapidly changing financial landscape. It’s the future-forward thinking that can make your bank strong and competitive.

Learn more about Pentaho for Financial Service.

The post DORA Compliance Strategies for Mid-Tier Banks by Asset Category 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.

However, on the expo floor it was very interesting to see the “villages” layout hubs for various disciplines (e.g., analytics, AI, data management). I fully understand why the organizers went with this format. From an audience perspective, historically attendees are mostly experienced professionals who know what they need and are looking for discrete solutions.  

However, my impression after walking around the event and talking with a wide range and volume of booth visitors is one of worlds colliding. 

• In conversation after conversation at our booth we heard about teams struggling to get a handle on their data. They need help across the board – better managing data for analytics, preparing data for AI, getting compliance and governance in place. Scaling data access safely and securely. Their needs touch the entire landscape and stack. 

• Discrete solutions aren’t what many buyers really want. They don’t have the time, budget, or energy for stack building or heavy integration. There is increased buyer pressure to find cost-effective solutions that work well together and can scale. 

• AI has raised the bar on data management. All the gaps that have existed for years in policy management, governance, basic data fundamentals are even more important now that the AI horse is out of the barn, with agents coming fast and furious. 

• Quality, quality, quality. How to baseline and understand it, how to ensure it, and to maintain it in a world soon to be dominated by AI. If you can’t help or provide an answer to quality, you’re behind.  

• With quality top of mind, data lineage is becoming even more critical as a foundation for data and model observability. (We have some great things coming soon on this front, stay tuned!) 

• And even in a shifting regulatory environment, data governance is crucial to avoiding abuse and misuse of data in AI models and agents ahead of full production. Most organizations we talked to were concerned about the potential impact of under-governed data and are looking to shore up their stance.  

I also had the pleasure of connecting with Gartner analysts Cuneyd Kaya and Roxane Edjlali. It was really interesting to get their perspectives on a wide range of topics, including why a hybrid solution for model development in an increasingly open source model world makes sense, the value of helping organizations simplify their management of data complexity, and how data will be the key differentiator for any AI success as most algorithms become available off the shelf.  

We see the same needs and trends happening within our customer base. Becoming more data-fit for AI, reinforcing a data foundation for both core operations and AI with strong governance, and cost effectively simplifying data chaos are what we are helping organizations achieve through the Pentaho+ Platform. 

The post Impressions from Gartner DA Summit Orlando 2025 first appeared on Pentaho.

Tailored vs. Generic Data Cleaning Approaches

When it comes to cleaning data for AI or machine learning projects, the approach is typically use-specific or project-specific. Data scientists go straight to the source, shape, cleanse, and augment the data in the sandbox for their project, modifying the data in a way that aligns with the specific needs of the model. This sits in contrast to traditional data cleaning efforts within a data warehouse, where there are multiple levels of approvals and checks in place.

The key question here is whether you can rely on the data warehouse as a source for your AI model. The rise of AI and generative AI (GenAI) has led to a diminished reliance on data warehouses, as models often need data in its raw, unprocessed form to make accurate predictions and discoveries.

Who Pays for Data Cleaning?

One of the most significant challenges in data management is understanding who bears the cost of data cleaning. It’s not always the same team that uses the data that pays for it. In traditional use cases, a line of business (LOB) would determine whether data quality is sufficient for their needs. However, in an AI-driven world, there’s a new intermediary—data scientists or developers—who often sit at the center of the decision-making process when it comes to data quality.

For instance, in a marketing email campaign, the LOB is directly involved in evaluating the data’s quality. However, for a sales territory analysis, the CRO or data scientists are more likely to decide what constitutes acceptable data quality. Data scientists may not always grasp the full impact of quality issues on the data’s usability, for purposes other than data science purposes or ML/AI, as they often don’t experience the consequences of incomplete or inaccurate data directly.

AI and the Element of Discovery

AI’s role in automating data processes has already proven invaluable. However, it also introduces the potential for discovery. AI might uncover correlations that were previously overlooked, but these insights can only emerge if the data hasn’t been excessively cleaned beforehand. For example, small shifts in data, like divorce statistics or the transition from landlines to cell phones, might go unnoticed until systems are updated to account for these changes. AI and ML can help spot these trends and offer valuable insights—but only if the data is allowed to evolve and not prematurely scrubbed of its nuances.

The Governance Dilemma

The evolution of data governance becomes increasingly complex as organizations adopt AI and machine learning. Technologies like Hadoop highlighted some of the risks associated with direct pipelines, such as losing data lineage or creating copies that introduce potential privacy concerns. These risks are magnified with large language models (LLMs), where there is no human gatekeeper overseeing the quality of data. Poor quality data can lead to misleading outputs, with no clear way to detect or correct these issues.

Striking the Right Balance

Data quality is now clearly recognized as a key component in providing AI with data that can be confidently used to create insights that drive decisions, either by a human or by a downstream application or system.  Getting the data quality balance right – where the data models use can be accurate and trustworthy while still leaving room for exploration and deeper insights – will only become more important as companies rush to adopt Agentic AI. The Air Canada customer experience snafu is a clear public example of where having strong data quality parameters in place is vital to democratizing AI and having both organizations and their customers trust and adopt AI experiences as authentic and valuable.

The post Data Quality in the Age of AI and Machine Learning (Data Quality Series Part 3) first appeared on Pentaho.

• How do you define “quality”?
• Can data be “too clean”?
• How can we consistently apply data quality rules when data changes every day?
• How can we ensure data quality within budget?

In this blog, we’ll review each of these topics with guidance on where data leaders and their teams need to focus to build a strong and lasting data quality strategy.

Current Quality vs. Ideal Quality: Striking the Right Balance

The struggle between current quality and ideal quality often comes down to setting a threshold of desired quality. In traditional data systems, quality was often assessed in a silo, but today, businesses need to think about data quality in the context of its broader usage in achieving business outcomes. What’s the quality score threshold required to meet business needs?

Ultimately, it is necessary that the quality of data is adequate to support the correctness of decisions in the context of business goals.  While pushing to achieve higher quality is important, it’s critical to balance quality with business goals, as perfection is not always necessary if the data serves its purpose.

The Risks of Over-Cleaning Data

While cleaning data is necessary, there’s a risk of over-cleaning, especially when the cleaning process removes important details. A great example of this is middle initials in names. If you clean this data too aggressively, you might lose valuable information, potentially leading to bias in the data. Furthermore, customer data might be incorrectly excluded if there’s a mismatch in the golden record, causing critical information, like address changes, to be missed.

In some cases, too much cleaning could unintentionally eliminate valid records that would have been useful. It’s important to remember that data quality should not just be about removing “bad” data but also about understanding which data is valuable to retain.

The Changing Nature of Data

Over the past decade, the landscape of data has drastically changed. The concept of a golden record—a single source of truth—has become more complex. With the rise of social data and real-time interactions, organizations now need to be more flexible in how they collect and use data.

When organizations look back at their data from 10 years ago, they must acknowledge that it may no longer be as relevant. The world has changed, and so has the data we use to make decisions. The need for more dynamic and up-to-date data has become more critical.

Data as an Asset and Its Cost

Data is often referred to as the new oil, but it comes with significant challenges. Organizations must grapple with the balance between how much data they collect, the regulatory limitations surrounding it, the cost of storing and cleaning it, and whether it will ultimately be useful. Moreover, when models are trained using data from one region, they may not translate effectively to another. For instance, a model trained on US data may not perform well with EMEA data due to cultural and regulatory differences.

Creating the Conditions for Consistent Data Quality

These challenges – how to define quality, thresholds for cleaning, data’s changing nature and the cost of cleaning data for different purposes – are only going to increase in complexity as we go forward.

No organization can meet a 100% quality threshold – doing so is overly cost prohibitive and would grind operations to a halt.  Data leaders need to create a consistent policy approach and have clear guidelines on what quality means based on use case and role.

Data leaders also need to consider how to leverage AI and machine learning to automate many of the processes that inform data quality – classification, scoring, and sensitivity. Solutions that enable the automation of data quality processes can do the heavy lifting while containing costs, enabling the organization to scale its ability to deploy a consistent data quality framework across the business.

In our next blog on data quality, we’ll explore what data quality means in the age of GenAI and Agentic AI.

The post What to Consider When Building a Data Quality Strategy (Data Quality Series Part 2) first appeared on Pentaho.