Ahmed Klabi’s Post

Agentic AI is revolutionizing data management by enabling systems to autonomously analyze data, set goals, and execute actions with minimal human intervention. This advancement enhances data analysis, proactive quality management, and query optimization, empowering data teams and streamlining operations. As data volumes grow, embracing agentic AI positions organizations to leverage their data assets more effectively, driving innovation and competitive advantage. Explore more in this insightful blog: https://lnkd.in/dVEpw8zh #agentAI #agenticAI #datawarehouse #businessintelligence #datagovernance

Your data is one of your company’s most valuable assets. 🧼But is it “clean?” If you’re planning on leveraging AI automations for greater efficiency and productivity, then you need “clean,” organized data that is enabled for your chosen platforms and applications. Unfortunately, these common data issues will hinder your progress: ⛔Incorrect formatting ⛔Duplicate data sets ⛔Incomplete data ⛔Inconsistent units of measurement ⛔Wrong field entries ⛔Extraction errors Before you consider any type of AI solutions, invest in clean data. At PCG, we help companies ready their data and launch technological integrations that revolutionize their operations. Want to learn more about data preparation and how to enable your data for a new era of efficiency? Read our complete blog post: https://bit.ly/3Tbbi5W #cleandata #aiautomations #PCG #ProjectConsultingGroup #dataenablement

One of our Board members shared this article with me. It is yet another data point in what will be an ongoing dialogue for the next several years to come pertaining to the relationship of good quality enterprise data (business data - PO's, Sales Orders, Shipments, Receipts, Invoices, Payments, etc.) and AI. If you don't want to read the entire article, there are couple key #DavidLinthicum excerpts highlighted by #JoeMcKendrick at ZDNet: Hitting a "data wall": The main issue enterprises are running up against is "not because the generative AI technology is bad, but because their data's bad," he explained. The challenge is "there's no easy fix for this, you're going to have to stop what you're doing, loop back, and fix your data. For many of these organizations, that particular problem hasn't been addressed for the last 20 or 30 years. [Moreover], It's a significant expense and risk, and someone has to go into the board of directors meeting and tell them we're going to spend $30 million to fix our data before we're able to get into gen AI. Those are tough conversations to have." A lack of strategic direction: "Enterprises need to get better at planning," Linthicum stated. "Not understanding the state of your data until you work on a gen AI project, [that's] not the way to do it. It's looking strategically at how your data needs to align with your utilization of this new technology." More to share ahead... #TeamCentral #ipaas #automation #datamanagement

AI Fabric !! Are you struggling with data complexities? Dealing with hallucinations from generative AI? Challenged by switching between LLMs? AI Fabric might be the solution your team needs. In today’s AI landscape, managing the data that powers AI models is often more complex than building the models themselves. AI Fabric tackles this by creating a unified data layer. How AI Fabric Works: • Unified Data Labeling: Labels data with AI-readable terms like “sales,” “customer engagement,” or “machine status,” allowing models to interpret data more effectively. For instance, sales data might be labeled as “monthly sales” or “transaction volume,” providing contextual information that models can recognize. • Hallucination prevention: Provides structured and validated data to minimize misleading outputs, ensuring AI models deliver more reliable insights. • Flexible LLM Integration: AI Fabric’s modular setup enables seamless switching between LLMs. • Data Governance & Security: Enforces standards, ensuring data integrity and securing sensitive information. • Scalability Across Systems: Scales effortlessly with data volume and new sources. • Workflow Automation & Monitoring: Reduces manual intervention with automated workflows and continuous monitoring. Why It Matters: AI Fabric isn’t about replacing existing systems, it’s about enhancing them. By building a well-structured, governed data foundation, AI Fabric ensures your AI models operate reliably and flexibly, driving meaningful insights faster and smarter. #AIFabric #DataManagement #GenerativeAI #DataGovernance #LLM #MachineLearning #AIIntegration #Automation #Scalability #DataStrategy

As IT leaders embrace generative AI, effective data management becomes crucial. Experts explain the importance of data quality, governance, and compliance for successful implementation. #GenAI #DataManagement #ITLeadership

As companies look to AI innovation, beware of taking short cuts due to pressure for the elusive quick returns. Having a solid data management strategy for structured and unstructured data sets will heavily increase the likelihood of future AI project success.

AI Revolutionizing Data Governance In the era of big data, organizations grapple with managing vast amounts of information. Enter artificial intelligence (AI), a game-changer that transforms data governance, quality, and management. In this article, we explore how AI is reshaping these critical aspects. 1. Data Cataloging Challenge: The data landscape is expanding exponentially, making it challenging to keep track of data stores. AI Solution: AI-powered data cataloging tools automate discovery, categorization, and organization of data assets. Benefits: Enhanced visibility into data lineage, origin, and changes, leading to better governance. 2. Metadata Management Importance: Metadata—information about data—is crucial for effective governance. AI’s Role: AI cataloging tools identify metadata, ensuring accurate categorization of data assets. Result: Improved data estate health and alignment with governance policies. 3. Data Quality Enhancement AI Impact: Algorithms automate data cleansing, standardization, and validation. Benefits: Detecting and rectifying inconsistencies, errors, and duplicates in datasets. Outcome: High-quality, accurate data fit for AI/ML applications and general data management. 4. Regulatory Compliance and Analytics Positive Influence: AI connects insights across users, policies, risks, and systems. Features: Advanced automation, real-time adaptation, and scalable analysis of massive datasets. Outcome: Elevated data governance practices. As organizations embrace AI, they unlock their data’s full potential while maintaining robust governance practices. #DataGovernance #AI #DataQuality

🚀 AI-Enhanced Data Observability: Revolutionizing Data Ecosystems In today’s data-driven world, AI-enhanced data observability is transforming how organizations monitor, analyze, and optimize their data. By merging AI with traditional observability, companies can gain deeper insights, automate processes, and proactively manage data—essential for thriving in complex digital landscapes. Key Benefits of AI-Enhanced Data Observability: 🔍 Real-Time Monitoring & Anomaly Detection: AI algorithms monitor data continuously, spotting anomalies instantly. This early detection helps prevent downtime and enhances data accuracy. 🔧 Automated Root Cause Analysis: AI-powered tools identify root issues without human intervention, reducing the time it takes to detect and resolve data quality problems. 📈 Predictive Analytics: Machine learning models analyze historical data, enabling proactive measures to avoid future bottlenecks and optimize workflows. 📊 Enhanced Data Quality: Automated data checks ensure high-quality, consistent information flows through pipelines, fostering better collaboration and decision-making. Impact: By implementing AI-driven data observability, organizations improve efficiency, optimize costs, make informed decisions, ensure scalability, and maintain compliance and security. As AI continues to evolve, expect more accurate predictions, self-healing systems, and advanced data integration. Organizations that leverage AI in data observability today will lead the way in innovation and competitiveness tomorrow. #DataEngineering #AI #Automation #DataObservability #PredictiveAnalytics #DataQuality

Unlocking the Power of AI Starts with Data Readiness! 💻 In today's fast-paced digital landscape, artificial intelligence isn't just an advantage – it's a necessity. But here's the catch: the success of your AI initiatives hinges on the readiness of your data. That's where we come in. At DevObsessed, we know that clean, well-organized data is the foundation of effective AI implementation. Here's why we put data readiness front and center: 1️⃣ Accuracy and Efficiency: Clean data means AI systems operate with precision, boosting efficiency and slashing costly errors. 2️⃣ Scalability: As your business grows, so does the complexity of your data. A solid data infrastructure ensures seamless scalability for your AI solutions. 3️⃣ Innovation Readiness: Future-proof your business by laying the groundwork for emerging AI technologies with the right data infrastructure. So, what's the first step with DevObsessed? 1️⃣ Readiness Assessment: We dive deep into your data and platform architecture, pinpointing areas for improvement and optimization. 2️⃣ Custom Strategy Development: No cookie-cutter solutions here. Your business gets a tailored plan that tackles your unique challenges head-on. 3️⃣ Execution and Support: From data cleansing to AI system integration, we're with you every step of the way. The AI revolution isn't looming on the horizon – it's already here. But is your data ready to seize its full potential? With DevObsessed by your side, you don't have to navigate this journey alone. Let's get your data house in order and unlock the future together. Reach out today and let's make AI work for you! #AI #DataReadiness #DigitalTransformation #DevObsessed #ITconsulting #omaha