PaperTrail’s Post

A biotech firm faced fragmented data across siloed systems, delaying critical reporting and analytics. SingleStone’s AI-driven solution built a unified enterprise domain model and modernized their data warehouse, slashing reporting delays and enabling real-time insights. This transformation boosted decision-making speed, operational efficiency, and innovation, positioning the firm for growth. By integrating AI, SingleStone turned data chaos into a strategic asset. The result? Faster, smarter analytics that drive competitive advantage. How could unified data transform your organization’s efficiency? https://loom.ly/r9X7_h4

This evolution underscores a critical moment for AI strategy: designing systems that are not only powerful but also transparent, flexible, and scalable. As enterprises increasingly integrate these advanced architectures, the collaboration between AI and open-source tools is reshaping the future of data platforms. #AI #OpenSource #EnterpriseAI #DataPlatforms #DigitalTransformation

Plot twist: Sometimes the best AI solution is better data consulting first 📊 Before our franchise client added any AI, we: ✅ Consolidated 8 different reporting systems ✅ Eliminated 40+ hours of manual work monthly ✅ Created real-time dashboards across all locations THEN we added intelligent automation 🤖 The foundation matters more than the flashy stuff ⚡ We make the case for a smart foundation in our latest blog: read here- https://lnkd.in/dexB6-K3 #DataConsulting #FranchiseData #SmartFoundations

C-Suites and Boards are exerting increased pressure on IT and the business to do something meaningful and competitive with AI. The challenge of what to do often lands on the head of the CIO. While many companies are starting to embrace use cases based on the publicly available LLM tools (ChatGPT, etc.), these tools will provide competitive parity at best over the next few years. They are unlikely to produce a sustained competitive advantage due to their low barrier of entry and increasing availability to the enterprise. The real sustained competitive advantage, if it is to be found, is in leveraging AI in its various forms on a company's own data and processes. The secret that most CIOs know (I speak from experience) is that their enterprise data is not in good enough shape and consolidated in an Enterprise Data platform that can feed AI in a way to create a specific competitive edge for the company. Sagar Paul in the article points out, "Traditional data pipelines create strong barriers to AI's success that cannot be solved through incremental improvements. The challenges of semantic ambiguity, quality degradation, temporal misalignment, and format inconsistency require architectural transformation." While the path to data quality and enterprise data platforms are well-known and well-supported by tools and technologies, it is an expensive and time consuming process. It is not as "sexy" as AI, but is an absolute pre-requisite to success in AI and other data concerns like reliable analytics and reporting. One of the largest challenges is that getting enterprise data ready requires commitment (time, focus and money) from the business resources who know the data and what it means. Not just for the data project but on an ongoing and sustained operational efforts basis. This means that enterprise data projects are not IT projects - they are business projects that require sustained commitment and funding at the highest levels. IT leaders must take the long view on AI and its future evolutions by finding a way to convince their organizations that the data groundwork must be invested in and focused on in parallel to other more immediate AI use cases.

In the typical modern organization, the unstructured #data that makes up everything from emails and images to video, audio, sensor data, and a myriad of other examples accounts for up to 90% of all the information it owns and uses. Unfortunately, what these same organizations also have in common is a shared inability to extract something meaningful from this potentially valuable asset. Adding to the problem are unsustainable legacy #datamanagement processes, whereby many administrators must still manually identify, classify, and relocate data across systems. As a result, the gap between data growth and an organization’s ability to exploit it efficiently is widening, undermining the efforts of businesses that claim to be ‘data-led’. The only way to address these issues at scale is to shift from reactive, manual processes to a proactive, policy-based model. This requires not just visibility but the ability to act on that insight consistently, across all storage environments. https://lnkd.in/dwP-Jjhm #worlddatasummit #dataliteracy #datagovernance #ml #dataanalytics #ai

Really great piece by Jonathan Reichental, Ph.D. on how relatively simple AI techniques can unlock real value from data governance before complexity and cost overwhelm the effort. Here's a few takeaways from what HEMOdata see happening in this space: - The increased emphasis on quick wins. Many companies postpone tasks like automating metadata creation, classification, lineage and so on because they feel too tedious but the truth is they provide IMMEDIATE value. - How building frameworks now (even if they're not perfect) pays dividend by creating visibility, reducing risk and more steadily enabling AI & analytics. - The idea that better governance isn’t just about compliance or risk-mitigation but about enabling innovation. When your data is organized, you move faster with more confidence. Where HEMOdata make a difference: - We help organizations leverage smart metadata management so data assets become discoverable with richer context & without manual overhead. - Our focus on data lineage & classification alongside leveraging our partner solutions make it easier to show where data came from, how it’s used and who owns or is accountable for it. Immediate visibility here often gives leadership the confidence to invest further. - Once the basics are in place the scalability of governance is realized and adding newer AI models, data sources or regulatory pressures becomes a lot less painful. Some common blockers we see: - IT / data teams may see governance differently than business units. It’s often necessary to make a clear business case (not just risk) to get buy-in from stakeholders. - Over-engineering can stall momentum so you want frameworks that evolve by keeping governance light but effective. - Ensuring tools & processes support continuous monitoring. Because governance isn’t a “one and done” thing. Trends, regulations & data volumes keep shifting. In short, if your organization is trying to unlock value from data, start with the simple AI-enabled governance moves. They offer low risk, fast benefits and lay the foundation for more advanced analytics and innovation. At HEMOdata, we’re here to help companies move from “messy, manual data” toward “trusted, usable data.” Excited to see how this space continues to evolve. https://lnkd.in/eZ4eKBvf #HEMOdata #datagovernance #AI #data

AI is only as trustworthy as the data it learns from. We’re all racing to unlock value from AI—whether that’s through automation, faster insights, or mission acceleration. But here’s the reality: if you can’t trust your data, you can’t trust your AI. The new White House AI Action Plan makes it clear: to build responsible, effective, and trusted AI, we must invest in data quality, transparency, and governance. And that starts long before a model is trained. We’ve seen this mirrored in the DOD Data Strategy and its VAULTIS goals—prioritizing data that is Visible, Accessible, Understandable, Linked, Trusted, Interoperable, and Secure as a foundation for enabling secure and scalable AI across the mission. The latest release of erwin Data Intelligence 15 is built for this moment. With features like certified data models, automated discovery, and deep lineage visualization, it empowers organizations to: 🔍 Understand what data exists—and where it came from ✅ Validate the quality and ownership of critical datasets 🔒 Align with Zero Trust, CMMC, and Responsible AI principles 🤖 Enable AI that is explainable, repeatable, and grounded in trusted inputs Whether you're supporting mission planning, supply chain visibility, or digital health—trusted AI begins with trusted data. If your data isn't trustworthy, your AI won't be either. You can read more here from the erwin team: 🔗 https://lnkd.in/ePUeWKA2 #AI #ResponsibleAI #DataStrategy #TrustedData #erwin #DataIntelligence #VAULTIS #DoDDataStrategy #WhiteHouseAI #AIActionPlan #CMMC #ZeroTrust #DataGovernance #DataQuality #AIGovernance

Beyond Data Lakes: How Data Mesh Delivers AI and GenAI at Enterprise Scale?   Data Mesh has become a leading blueprint for scaling AI and analytics. Unlike centralized data lakes, it distributes ownership to business domains, fostering richer context, faster delivery, and greater resilience. Enterprises such as JPMorgan, Zalando, and Netflix illustrate Mesh‑aligned practices by empowering domain teams to manage their own data products.   By 2025, most organizations run Mesh as a layer over centralized data services rather than replacing data lakes entirely, a pragmatic hybrid model.   What’s new in 2025: Executive conversations now revolve around Generative AI (GenAI) readiness. Data Mesh is emerging as a foundation for this shift: by delivering domain‑specific, contextualized data products, Mesh reduces hallucination risk in enterprise LLMs, increases explainability, and accelerates trusted adoption of GenAI at scale.   Key Advantages Observed in Practice 1. Operational Efficiency ·     Reduced Redundancy:
Financial institutions using domain‑aligned data teams consolidated pipelines, cutting duplication that plagued centralized platforms. ·     Resilience Gains:
Industry pilots show faster recovery from domain‑specific data failures, preventing cascading outages across systems.   2. Business Agility ·     Accelerated AI/ML Deployment:
Netflix’s model teams illustrate Mesh‑aligned practices by building domain‑specific data products (e.g., personalization pipelines). This mirrors Mesh principles and enables faster iteration. ·     Faster Team Onboarding:
Intuit demonstrates the value of self‑serve domain APIs, new data teams can ramp up in weeks rather than months. ·     Adaptive Compliance:
Tier‑1 banks piloting federated governance report shorter response times to regulatory updates such as GDPR.   GenAI Impact: The same principles extend naturally to LLMs. Fine‑tuning and Retrieval‑Augmented Generation (RAG) thrive on richer, contextual domain datasets - improving trust, reducing hallucinations, and increasing explainability in sensitive industries.   3. Cost Optimization ·     Cloud Efficiency:
Zalando’s decentralization experiments reduced duplicated compute/storage and enabled fit‑for‑purpose tooling across domains. ·     Utilization Gains:
Manufacturing organizations report that domain teams improve utilization by rightsizing for their workloads instead of relying on one centralized cluster.     Continue in 1st and 2nd comments. Transform Partner – Your Strategic Champion for Digital Transformation   Image Source: Research Gate

Looking to launch a data and AI governance project that drives ROI? I recommend this checklist for CDOs to help you cut through complexity and get results.  #DataGovernance #AI

Looking to launch a data and AI governance project that drives ROI? I recommend this checklist for CDOs to help you cut through complexity and get results.  #DataGovernance #AI