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 Enterprises today face mounting pressure to manage data growth, meet strict regulatory mandates, and retire legacy systems effectively. Many organizations long relied on IBM’s InfoSphere Optim solutions for application-retirement archiving and structured data lifecycle management. However, with withdrawal of support, connectivity and access risks have escalated. The new datasheet from Solix outlines how they provide a seamless migration path enabling full access, governance and control of archived data.  Replace IBM Infosphere Optim with SOLIXCloud This article examines: The risks posed by continuing with IBM InfoSphere Optim, Why Solix is a compelling alternative, Key benefits and migration considerations for enterprise archiving. The Risk of Staying with IBM InfoSphere Optim As outlined in the datasheet, IBM announced effective December 16, 2022 the withdrawal of marketing, entitlements and support for critical components of InfoSphere Optim—specifically the “Opt...

  Introduction: From Passive Storage to Intelligent Insight For decades, file archiving has been seen as a passive storage function—simply moving old or inactive data into low-cost storage to save space. However, as digital data volumes explode, this traditional approach has become unsustainable. Organizations are now sitting on vast amounts of dark data —unstructured files that contain valuable business insights but remain untapped. How Deep Neural Networks Are Redefining the Future of File Archiving Enter deep neural networks (DNNs) —the foundation of modern artificial intelligence. These powerful models are now redefining the very nature of file archiving by transforming it into a dynamic, intelligent layer that can understand, classify, and extract insights from data at scale.    What Are Deep Neural Networks—and Why They Matter in Archiving Deep neural networks mimic how the human brain processes information. They analyze data through multiple layers, learning ...

 For businesses using Salesforce as their CRM or customer-platform, the Salesforce Informatica acquisition brings both promise and complexity. It promises richer data-capabilities — but also compels customers to rethink their data strategy, especially around data governance, integration, quality and analytics. 1. Opportunities for Salesforce customers With Informatica under the Salesforce umbrella, existing Salesforce customers can expect tighter integration of data-catalog, data-governance and data-quality features. This means: unified customer records, improved data-trust, better analytics, and a smoother AI-journey. For example: “Unified customer data… real-time data integration across diverse sources.”  2. Challenges and risks to manage Despite opportunity, customers must watch for: Integration timelines (the deal is expected to close early FY 2027)  Changes in licensing, support or ecosystem partners Potential vendor-lock-in or diminished vendor-neutral...

 As organizations modernize their IT infrastructure, they often reach a critical decision point — whether to retire or decommission legacy applications. Both strategies aim to reduce operational overhead and improve agility, but they differ fundamentally in how they handle data . The success of either strategy depends on one central factor: how well your enterprise manages and archives data . Without a proper archiving plan, even a well-intentioned retirement or decommissioning project can expose you to compliance risk, data loss, or unnecessary costs. This article explores the best practices for enterprise data archiving when choosing between application retirement and application decommissioning. The Role of Data Archiving in Application Lifecycle Management Every application — whether operational or retired — produces valuable business data over time. As applications reach end-of-life, organizations must decide how to preserve, secure, and access that data in the future. ...

  The Hidden Challenge of Bias in AI Governance Artificial Intelligence (AI) has become the cornerstone of modern enterprises, driving efficiency, automation, and innovation. Yet, amid the excitement of generative AI and machine learning adoption, a critical issue continues to undermine the reliability of enterprise AI systems — BIBO: Bias In, Bias Out . In simple terms, when bias enters an AI system through flawed data, design, or human assumptions, it inevitably influences outcomes. Even with advanced models and cutting-edge infrastructure, bias remains the silent saboteur of AI success. For organizations investing millions in AI transformation, ignoring bias isn’t just a technical risk — it’s a governance failure. This article explores how enterprises can identify, mitigate, and govern against bias to ensure fairness, transparency, and trust in AI. Understanding BIBO: What Bias In, Bias Out Really Means Bias In, Bias Out is a modern extension of the classic computing princ...

 To move from pilot to production, your Enterprise RAG architecture must be secure, scalable and well managed. This article dives into the architectural elements and design-principles you should follow to build a strong foundation for Enterprise RAG. Core architectural layers Data ingestion & indexing Collect internal data: documents, databases, emails, logs. Pre-process: tokenise, embed, generate vector representations. Index in vector database or semantic search platform.  Retrieval engine Accepts query, converts to embedding, retrieves top-k relevant docs. Re-ranking, filtering, metadata controls. Augmentation layer Selected retrieved context is joined with query and possibly prompt template. Policy controls: only authorised data, redaction filters. Generative layer (LLM) Receives augmented prompt and returns output. May include cite/source embedding, traceability. Governance, monitoring & security layer Access c...