Unlocking the Future: Application Case Studies on How the Financial Services Landscape Is Changing – Part I

As financial institutions worldwide face multiple challenges – from tight regulatory compliance to emerging AI opportunities and challenges, the need for operational visibility around data with precision, speed and expertise are key.

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What Banks Need to Know About EU AI Act Compliance and Ethical AI Governance

The EU AI Act is reshaping banking. See how Pentaho simplifies AI compliance and governance to help banks lead with trust and ethical innovation.

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The Key Legislations That Define the “New” Global Privacy Landscape

Global privacy issues are becoming more complex by the day. Organizations can’t afford to be in the dark regarding the unique, multidimensional, and nuanced characteristics of existing and emerging regulations.

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DORA Compliance Strategies for Mid-Tier Banks by Asset Category

Mid-sized banks face a unique challenge in how to improve their Information and Communication Technology (ICT) risk management programs to meet the Digital Operational Resilience Act (DORA) requirements for resiliency against evolving digital threats.

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Swisscom, Switzerland’s Largest Telecom Provider, Achieves 360-Degree Customer View 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. 

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Impressions from Gartner DA Summit Orlando 2025

Yes, AI Was the Theme. But Underneath, It’s Clear We’re in A New Era of Data Management.

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Data Quality in the Age of AI and Machine Learning (Data Quality Series Part 3)

Data quality is a crucial aspect of any organization’s operations, and its impact is growing as artificial intelligence (AI) and machine learning (ML) continue to evolve. However, determining what qualifies as "good enough" data can be a challenge.

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What to Consider When Building a Data Quality Strategy (Data Quality Series Part 2)

Data Quality Series Part 2: Ensuring data quality is about finding the right balance—over-cleaning can remove valuable insights, while evolving data demands flexibility. This blog post explores how businesses can define quality thresholds, manage costs, and leverage AI-driven automation to maintain consistency and usability.

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The Importance and Value of Strong Data Quality Fundamentals (Data Quality Series Part 1)

Data Quality Series Part 1: Discover how strong data quality fundamentals drive AI and GenAI success by ensuring accuracy, completeness, and consistency through end-to-end data management.

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