REVARTIS’ Post

For the C-suite, focused on integrating strategic themes, it’s vital to make GenAI a reality. Drawing from my experience in digital transformation, I offer actionable insights for CxOs and data leaders to navigate this journey, emphasizing value-driven approaches, resilient data architectures, quality assurance practices, and talent strategies. Prioritize Top 50 Use Cases: Identify and prioritize the top 50 use cases delivering clear value outcomes. Dive into each to uncover necessary data assets, recognizing data’s critical role in realizing generative AI’s potential. Strengthen Foundational Capabilities: Enhance foundational capabilities, especially in vector databases, data processing pipelines, and model integrations. Focus on solutions offering significant advantages, like protocols for managing PII, essential for personalized AI applications. Ensure Data Quality Across Lifecycle: Intervene at critical points in the data lifecycle to maintain high-quality data from source to consumption. Implement automated observability measures to ensure data quality across all assets, establishing thresholds and interventions for unstructured data in GenAI applications. Proactively Address Trust and Compliance: Embed proactive trust, safety, and security measures for enterprise data to stay compliant with regulations. Regulatory compliance is essential for generative AI to deliver business value successfully. Establish Rigorous Tracking and Intervention: Implement rigorous tracking, measurement, and intervention mechanisms for critical data programs to proactively prevent and mitigate risks and challenges. Monitor progress closely to ensure successful enablement of generative AI use cases. Develop Diverse Technology Talent: Build a well-rounded team of technology talent, including cloud and data architects and engineers. Establish the core team responsible for implementing foundational data programs, gradually expanding to include data scientists. Maintain Focus on Traditional Analytics, classic AI: While responding to the demand for generative AI, do not overlook traditional business analytics, AI, and ML initiatives. The traditional BI and analytics pipeline offers a broader range of potential value use cases for the business, and generative AI should complement and augment existing technology assets. Invest in Financial Operations Proficiency: Effective investment, budget management, and redistribution require CIOs/CDOs to cultivate a FinOps-like proficiency to manage the evolving cost framework associated with generative AI. Monitor various expenses, prioritize request routing, or transfer specific data to the cloud to optimize costs. Data governance must not be an afterthought, and a clear understanding of critical business outcomes is essential. Managing data is no longer sufficient; understanding how it shapes market leadership and competitive advantage is imperative. #Data #AI #GenerativeAI

🔍 Unlocking the Power of Metadata: Key Strategies for Effective Data Management Metadata is often the unsung hero in data management, quietly working behind the scenes to make data discoverable, accessible, and useful. Without effective metadata, data can quickly become a maze of silos and inconsistencies, hindering decision-making and operational efficiency. Here’s how metadata management can transform how your organization interacts with its data: 🗂️ Centralize Metadata Storage A single repository creates a “source of truth” for data, reducing redundancy and making data accessible across teams. 📚 Enhance Discoverability with Data Catalogs By tagging data with meaningful attributes, catalogs allow users to search and access what they need, streamlining data usage across projects. ⚖️ Standardize for Consistency Consistent metadata formats make data easier to blend and interpret, which is crucial when integrating diverse data sources. 🤖 Automate with AI Tools Machine learning can automate tagging and categorization, saving time and improving accuracy. 🔒 Implement Governance Policies Clear policies define who can access, update, and manage metadata, ensuring data remains secure, compliant, and reliable. Good metadata management doesn’t just organize data; it amplifies its value. When metadata is well-managed, data transforms from raw information to a strategic asset, empowering data teams to make faster, more accurate decisions. 📊✨ Source https://buff.ly/40lFboz #DataManagement #Metadata #DataStrategy #DataGovernance #AI #Automation

Organizations striving for data readiness in AI implementation face several formidable challenges that can significantly hinder the deployment and effectiveness of AI technologies. Addressing these challenges is crucial for leveraging AI's full potential. Some of the common themes from our clients - *Data Quality and Accessibility - Organizations often struggle with: 1. Inaccurate or incomplete data 2. Inconsistency across sources 3. Outdated or irrelevant information 4. Lack of standardization in data formats *Infrastructure and Resources - 1. Sufficient computational resources for processing large data volumes 2. Modern data platforms and architectures 3. Scalable storage solutions 4. Efficient data pipelines for real-time processing *Data Literacy and Skills Gap - 1. Limited understanding of AI concepts and potential applications 2. Insufficient expertise in data analysis and interpretation 3. Shortage of skilled data scientists and AI specialists *Data Governance and Compliance - 1. Establishing clear data ownership and stewardship 2. Implementing comprehensive data policies and standards 3. Ensuring adherence to regulatory requirements 4. Maintaining data privacy and security *Data Integration Challenges - 1. Harmonize disparate systems 2. Ensure interoperability between different platforms 3. Manage data incompatibility issues 4. Resolve conflicting data Solutions proposed for DRAI - *Comprehensive Data Readiness Assessment - Taxonomy of data readiness for AI (#DRAI) metrics for both structured and unstructured datasets. *Investment in Data Infrastructure - investments in modern, scalable data infrastructure capable of handling the volume, velocity, and variety of data required for AI applications. *Data Literacy Programs - Implement organization-wide data literacy programs to bridge the skills gap and foster a data-driven culture. *Robust Data Governance Framework - Develop and implement a comprehensive data governance framework that addresses data quality, security, privacy, and compliance concerns. *Automated Data Integration Tools - Leverage advanced data integration tools and techniques to streamline the process of harmonizing data from diverse sources. By proactively addressing these challenges, organizations can significantly enhance their data readiness for AI (DRAI), paving the way for successful implementation and unleashing AI's full potential to drive innovation and operational efficiency. #DRAI #AI #DataReadiness #BusinessIntelligence #DigitalTransformation

🚀 Enterprise Data Readiness: The Foundation for Successful AI Adoption 🚀 In the era of AI-driven innovation, the readiness of your enterprise data is the make-or-break factor for successful AI adoption. As a solutions enthusiast and systemizer specializing in operational excellence, I’ve seen how companies that prioritize data readiness can unlock AI’s full potential, driving transformative business outcomes. AI thrives on data, but not just any data—clean, structured, and well-governed data is the key to powering AI models that deliver accurate insights and actionable intelligence. However, many organizations face challenges in getting their data AI-ready, leading to suboptimal results and missed opportunities. Here’s how to ensure your enterprise data is primed for AI success: 1. Data Quality Management: Establish rigorous processes for cleansing, standardizing, and validating data across the organization. High-quality data is the bedrock of reliable AI models. 2. Data Governance Framework: Implement strong governance to ensure data is managed, protected, and utilized in alignment with regulatory requirements and business objectives. 3. Integration and Accessibility: Break down data silos and create a unified data architecture that ensures seamless integration and easy access for AI applications. 4. Scalable Infrastructure: Invest in scalable data infrastructure that can support the high computational demands of AI, ensuring performance and agility as your AI initiatives grow. As I explore opportunities to bring my expertise to corporate boards, I am passionate about helping organizations achieve data readiness, setting the stage for AI to drive operational excellence and strategic growth. The future belongs to those who prepare their data today. #AI #DataReadiness #OperationalExcellence #Innovation #Leadership #CorporateBoard

the path to succesful data utilization is worth reading. thanks Marco Geuer for sharing. one size does not fit all i am afraid. while this post is interesting, and certanly Marco Geuer makes a clear point in that a real implementation of a data strategy is KEY. it is also important to understand that culture has strategy for breakfast anytime of the day. also, IMHO #datastrategy and #datagovernance and all #datadriven approaches are like teenage sex, everybody is talking about it but very few are doing it. sure, some companies are, some are data driven natives but most are not and those that are not are the ones that need a lot of help. #AI and #knowledgegraphs #llm #nlp #ml etc all that is needed but IMHO the cornerstone to become data driven it is simply to have senior leadership being bussines data natives. in my experience such leadership gets project managers under control and force them into the unknown territories where data driven lives. the cultural change needs a big push from senior leadership. how to get such leadership uptospeed before their companies fail in #digitalization ?

💡 Why do I rarely talk about AI and a lot about data-driven? - Data as a foundation: Why the success of AI relies on a solid data strategy ⚠ While AI is often considered the most exciting technology of our time, it is important to recognize that its success depends largely on the quality and availability of the underlying data. Therefore, the focus of many successful companies is not only on AI itself, but rather on developing a solid data strategy and promoting a data-driven approach. Only by laying these foundations can AI reach its full potential and deliver real, transformative results. ▶ The path to successful data utilization 1️⃣ Develop a sustainable Data Strategy. 2️⃣ Ensure Data Quality. 3️⃣ Develop a consistent Data Infrastructure. 4️⃣ Promote Data Literacy 5️⃣ Establish an agile cross-functional way of working. 6️⃣ Ensure data protection and security THE DATA ECONOMIST What do you think? 🤔 ps: The post was inspired by Winfried Adalbert Etzel post on the relationship between data strategy, data governance and data management. 🤗 #datastrategy #datagovernance #dataquality #datamanagement #datadriven #datainspired

AI-Powered Data Governance: Transforming Enterprise Data Management Effective governance of distributed data stores is crucial for the success of large organizations. Leveraging AI in data governance offers significant advantages, enhancing data management and driving enterprise-wide benefits. The Role of AI in Data Governance The surge in AI adoption is fueled by the abundance of accessible data. Collecting, cleaning, organizing, and securing this data has become critical. By automating data governance, AI significantly improves data management, benefiting machine learning operations, data analytics, regulatory compliance, and data quality. Key Areas Where AI Enhances Data Governance Data Cataloging AI assists in the automated discovery and classification of data, reducing manual labor and enhancing accuracy. AI-powered cataloging tools enable natural language search and reveal data lineages, illustrating data origins and transformations. Metadata Management AI-infused tools automate the identification and categorization of metadata, making it easier to reconcile differences and monitor dynamic metadata. This ensures that data storage, usage, and flow are continuously optimized. Data Quality AI addresses data quality by improving accuracy, completeness, consistency, uniqueness, timeliness, and validity. AI/ML tools automatically infer missing values, normalize formats, and flag anomalies, saving time and enhancing reliability. Data Modeling AI streamlines data modeling by automating feature engineering and model selection processes. This supports AI/ML applications and predictive analytics. AI-powered tools can also generate synthetic data to train models when real data is insufficient. Data Policy and Life Cycle Management AI helps define and enforce data policies based on regulatory requirements and internal rules. Generative AI drafts and updates policy documentation, while AI analytics establish data retention policies, identify obsolete data, and automate archiving or deletion. Data Availability AI-powered disaster recovery systems predict potential failures and establish preventive measures, ensuring data integrity and minimizing downtime. AI-driven storage management systems replicate and distribute data to ensure high availability and low latency. The Human Element in AI-Driven Governance While AI handles many repetitive tasks in data governance with greater accuracy, human oversight remains essential. Contextual decisions and creating enterprise data architectures require human intelligence and experience. AI complements human efforts, eliminating tedious tasks but not replacing human judgment. AI is revolutionizing data governance by automating critical tasks, enhancing data quality, and ensuring compliance, making it an indispensable tool for modern enterprises. #AI #DataGovernance #MachineLearning #DataQuality #AIinBusiness #EnterpriseData #DataManagement #TechInnovation #DigitalTransformation

🚀 The Secret to AI Success: Why a Smart Data Strategy is Your Competitive Edge 🚀 In AI and Gen AI, data is your most powerful currency—but only if it’s strategically harnessed and continuously refined. Data is the lifeblood of AI: no matter how advanced the model, it’s only as impactful as the quality and strategy of the data fueling it. For today’s leaders, a robust data pipeline isn’t just “nice-to-have.” It’s the difference between scalable innovation and being left behind. Here’s why a smart data strategy is your ultimate advantage: 📊 Data Quality = Trust & Differentiation Your AI’s reliability is only as strong as the data behind it. Clean, precise, bias-aware data builds trust and unlocks actionable insights, setting you apart. Premium data means premium results—organizations with strong data governance see up to 20% higher model accuracy. 🛠️ Data Pipelines = Agility & Scalability A resilient data pipeline isn’t just for today; it’s for tomorrow. Agile pipelines keep your models adaptable, ready to evolve with every AI advancement. Companies with resilient pipelines respond 30% faster to market shifts, setting the pace for innovation. 📈 Data Strategy = Winning the Long Game Gen AI is a compounding investment. A future-ready data approach ensures long-term relevance in an evolving market. This isn’t just deploying AI today—it’s staying competitive tomorrow. The High-Impact Data Lifecycle: 1️⃣ Collection: Relevant, diverse, bias-aware data ensures model accuracy and ethics. 2️⃣ Preparation: Clean, label, and structure data meticulously. 3️⃣ Pipeline Management: Scalable, resilient pipelines support agility. 4️⃣ Deployment: Enable continuous learning from real-time data. 5️⃣ Monitoring & Refinement: Keep models sharp and relevant. 💡 The Gen AI revolution is here. Don’t risk being sidelined by a weak data foundation. Investing in a smart data strategy today ensures you’re ready to scale and compete tomorrow. 🔍 Which area in your data strategy could use a boost? Let’s discuss how to future-proof your data pipeline for long-term success. 👇 Save this 📌 or share ↪️ if data strategy is on your roadmap. #DataStrategy #GenAI #MachineLearning #AILeadership #DataDrivenInnovation #DigitalTransformation #FutureProofAI #AITransformation #DataInnovation

As we continue the acceleration into this AI future that is unfolding, “data ubiquity” will empower everyone with the right data at the right time. But how will it be harnessed to perform useful business tasks? According to this report from McKinsey & Company: “Value is increasingly coming from how well companies combine and integrate data and technologies.” Okay, fair enough. But what does that mean in technical terms? A critical choice will be whether or not to centralize data architecture. While a federated “data mesh” approach is feasible, it is likely to simply extend the existing problem of data silos. One of the problems we’ve been solving for since the beginning here at Tray is how to create enterprise capabilities that transcend silos and build business unit independence without compromising on governance and data integrity. Now that AI is on the scene, the need for “composability” within a defined framework is more critical. The problem comes into even greater focus when we begin thinking about gen AI agents communicating across the enterprise to do work in the background. The companies that stay ahead of the curve in this dynamic environment will learn to integrate AI into the tech stack from top to bottom while also building crucial guardrails into their systems so that data is clean, protected, and available across the enterprise. Good integration that coordinates data, technology, and especially AI models across the enterprise will be essential for staying competitive in the years to come. https://lnkd.in/dUspHmip #AI #CTO #DataUbiquity #data #automation

Many CIOs are skipping the crucial data management step before rushing forward with AI deployments. #AI #Artificialintelligence #Data #datamanagement #Datagovernance