AI For Enhancing Data Visualization

I built an AI Data Visualization AI Agent that writes its own code...🤯 And it's completely opensource. Here's what it can do: 1. Natural Language Analysis ↳ Upload any dataset ↳ Ask questions in plain English ↳ Get instant visualizations ↳ Follow up with more questions 2. Smart Viz Selection ↳ Automatically picks the right chart type ↳ Handles complex statistical plots ↳ Customizes formatting for clarity The AI agent: → Understands your question → Writes the visualization code → Creates the perfect chart → Explains what it found Choose the one that fits your needs: → Meta-Llama 3.1 405B for heavy lifting → DeepSeek V3 for deep insights → Qwen 2.5 7B for speed → Meta-Llama 3.3 70B for complex queries No more struggling with visualization libraries. No more debugging data processing code. No more switching between tools. The best part? I've included a step-by-step tutorial with 100% opensource code. Want to try it yourself? Link to the tutorial and GitHub repo in the comments. P.S. I create these tutorials and opensource them for free. Your 👍 like and ♻️ repost helps keep me going. Don't forget to follow me Shubham Saboo for daily tips and tutorials on LLMs, RAG and AI Agents.

Here's how I use AI to bootstrap a Wardley Map with capabilities—or at least get to a solid starting point. The *hard* works starts after this! 1. It starts with a prompt. I frame capabilities using "the ability to [blank]" and use GPT to break them down into sub-capabilities in JSON. (I built a tiny front-end for this, but totally optional.) Example: "Buy lunch for team" → breaks down into planning, sourcing ingredients, managing preferences, etc. 2. I then pull these into Obsidian—my tool of choice—to visualize and view the relationships. 3. Next, I run a second prompt to place each capability on the Y-axis (how close it is to the customer), using roles as a proxy: ops leaders, org designers, engineers, infra teams, etc. This helps with vertical positioning in the value chain. Tip: I always ask the model to explain why it placed something a certain way. Helps with tuning and building trust in the output. 4. Then I add richness: I use another prompt to identify relationships between capabilities—either functional similarity or one enabling another. These are returned in structured JSON. Think: "Analyze data insights" ↔ "Trend analysis" → Similar. This helps expand our graph. 5. To tie it all together: I feed the data into NetworkX (Python) to analyze clusters—kind of like social network graph analysis. The result? Capabilities grouped by both level and cluster. 6. The final output is a canvas in Obsidian—grouped, leveled, and linked. It's a decent kickoff point. From here, I’ll nerd out and go deep on the space I'm exploring. This isn’t a polished map. It’s a starting point for thinking, not a final artifact. If you’re using LLMs for systems thinking or capability modeling, I’d love to hear your process too.

This is when Graph RAG performs much better than naive RAG: When you want your LLM to understand the interconnection between your documents before arriving at its answer, Graph RAG becomes necessary. Graph RAG is not just useful for storing relationships in data. It can traverse multiple hops of connections and retrieve inferred context (e.g. Doc A to Doc B to Doc C) that wasn’t explicitly written in any single document. That’s what makes it powerful for reasoning and synthesis, not just retrieval. RAG returns search results based on semantic similarity. It doesn't consider this: If doc A is selected as highly relevant, the docs closely linked to A might also be essential to form the full context. This is where Graph RAG comes in. Search results from a graph are more likely to give a comprehensive view of the entity being searched and the information connected to it. Information on entities like people, organizations, products, or legal cases is often highly interconnected — and this might be true for your data too. Examples where Graph RAG works better than plain RAG: - Understanding customer support conversations where multiple tickets refer to the same issue or product. - Exploring research papers where concepts and citations form a dependency graph. - Retrieving facts in legal or compliance documents, where clauses refer to previous laws or definitions. - In company knowledge bases, where employee roles, teams, and projects are linked. - For supply chain analysis, where one entity’s data is tied to multiple suppliers or regions. In all these cases, naive RAG may miss key context that sits just one or two hops away, but Graph RAG connects those dots. ♻️ Share it with anyone who works with interconnected or relationship-heavy data :) I share tutorials on how to build + improve AI apps and agents, on my newsletter 𝑨𝑰 𝑬𝒏𝒈𝒊𝒏𝒆𝒆𝒓𝒊𝒏𝒈 𝑾𝒊𝒕𝒉 𝑺𝒂𝒓𝒕𝒉𝒂𝒌: https://lnkd.in/gaJTcZBR #AI #LLMs #RAG #AIAgents

A hidden layer of insight is waiting in your data  Here’s what most RAG setups miss  ☑ They fetch facts fast   ☑ They never see how those facts link together  That gap is why plain RAG stalls when you need the why. Enter GraphRAG and its next step, Agentic GraphRAG.  Instead of loose chunks, they stitch a graph:  ☑ Nodes are the entities you care about   ☑ Edges are the relationships between them  Your LLM now walks the graph and reasons over connections. A quick security incident example  It’s 3 AM. An alarm reports a breach.   Fifty CVE IDs pour in.  Which CVE is critical? Which is noise?  Agentic GraphRAG jumps in:  ☑ Pulls the incident data into the graph   ☑ Runs reasoning across known software, versions, past exploits   ☑ Ranks the threats and writes a short human readable note   ☑ Suggests next steps like a senior analyst would  The result feels like an autonomous analyst that understands relationships, reasons dynamically and even self corrects. If you want a RAG that does more than fetch, try building a knowledge graph first.  I’m Shrey Shah & I share daily guides on AI.  If this helped, hit the ♻️ reshare button to help someone else level up their AI.

Ever feel like you need a data science degree just to get a simple answer from your company's BI dashboard? 😩 You're not alone! We've all been there, squinting at a complex chart when all we really needed was a quick number. A recent Google Cloud blog post highlighted a fantastic stat: when paired with Looker, their new Conversational Analytics API can reduce data errors in natural language queries by as much as two-thirds. 🤯 That's a huge step toward making data accessible to everyone, not just the data gurus. The Problem: Data Trapped in Dashboards 🔒 For years, we've relied on business intelligence (BI) tools to make sense of our data. But the reality is, these tools can create a bottleneck. You have a question, but you have to go into a specific dashboard, filter, and drill down just to get an answer. It's a disconnect between how we work (using natural language) and how our data is stored (in complex structures). This inefficiency slows down decision-making and creates a barrier for non-technical team members. 😩 The Solution: A New Way to Talk to Your Data 🗣️ Enter the Conversational Analytics API from Google Cloud. Instead of forcing you to go to the data, this solution brings the data to you. It's an API that lets developers embed natural-language query functionality directly into the tools you use every day—like your internal company chat or a custom app. Think of it as having a personal data analyst in every meeting, ready to answer your questions on the fly! 🤖 Why Your Organization Will Love This ❤️ 👉 Faster, Smarter Decisions: When anyone on your team can get data-driven insights instantly, decisions are made faster and with more confidence. 🚀 👉 Empowering Everyone: This breaks down the data barrier, giving non-technical staff the power to explore data without needing to rely on a data team. It democratizes data access. 🧑🤝🧑 👉 Reduced Development Burden: For developers, this API uses Google's advanced AI and an intelligent architecture, meaning they don't have to build complex data-to-text functionality from scratch. It’s a win-win! 🎉 👉 Enterprise-Grade Security: The API works with your existing Looker semantic layer and access controls, so you can be sure the right people see the right data. 🛡️ The way we interact with information is changing. We don't want to dig for answers; we want them to be available where and when we need them. Tools like Google Cloud's Conversational Analytics API are leading the charge in this new, more intuitive era of data analytics. It’s about making data personal, accessible, and truly part of our daily workflow. Let’s start the conversation! 🗣️ #DataAnalytics #GoogleCloud #ConversationalAI #Looker #BusinessIntelligence #Innovation #FutureOfWork #TechTrends

What if you could analyse complex data just by asking questions in plain English? That’s what Structify is trying to solve. While doing some research on AI tools in the data space, I stumbled upon Structify, and what they’re building felt different. In most ops-heavy companies, data is everywhere, but insights are nowhere. Marketing wants performance data. Ops wants supply chain alerts. Strategy wants macro trends. And all of them end up chasing the one overworked data engineer in the team. The problem isn’t data scarcity. It’s that most of it is messy, siloed, or simply too technical to work with. Structify is solving that. They’ve built an AI powered no code workspace where anyone can connect internal databases or external sources, clean the data, and get insights just by using natural language. No Python, no SQL, no dashboards. Just clear answers. And this isn’t just an early prototype. They’ve already helped companies like AWS, PayPal, and the Sacramento Kings save over 40 hours per week on data tasks and reduce millions in spend. Structify just launched their self-serve product, and I’m genuinely excited to see how more non-technical teams start using it. The dream of talking to your data is not that far anymore. Ronak

Imagine creating business dashboards by simply describing what you want to see - no more clicking through complex interfaces or writing SQL queries. This is the promise of Generative Business Intelligence (#GenBI), and I've just published an in-depth article (https://lnkd.in/e5kKDdfY) exploring this exciting new domain. Together with Michael Driscoll, we dive deep into how GenBI transforms how we interact with data, combining the power of generative AI with the robustness of BI-as-Code. To help navigate the article, I've structured it into four essential parts that take you from understanding the fundamentals to seeing GenBI in action: > 1️⃣ Understanding GenBI. What is it? How does it compare to GenAI and the evolution from traditional BI to GenBI. > 2️⃣ Analysing BI-as-Code and why it's needed for doing anything GenAI-related. The benefits of code-first analytics, a potential workflow. > 3️⃣ The core components of GenBI: Integrating the BI tools and metrics layer with a natural language interface to allow human interfaces to proceed by AI; enriched with common knowledge from LLMs or RAGs. > 4️⃣ GenBI in Action: From GenBI Prompts to generate dashboards, metrics or improve visualization - to practical GenBI implementation, to use OpenAI integration within your BI Tool to get content knowledge about the domain based on existing BI artifacts (data source, metrics, measures). One compelling aspect I found while writing the article and highlighting it is that the so-called "self-service BI" could get another boost. The unreasonably effective #humaninterface that GenBI allows could enable business users and domain experts across the organization to create new dashboards and metrics or contribute to the data stack despite their lack of deep data engineering or business intelligence knowledge. --- With the declarative foundation, these BI-as-Code tools bring to the table, we can harness the best of both worlds, making it more approachable for businesses and users with the natural interface—bridging the gap. In the article, I tried to demonstrate how we can move from manual dashboard creation and mouse-clicking (taking hours/days) to near-instantaneous generation through natural language while maintaining the benefits of version control and automation. I hope you enjoy it. I'm curious about your feedback and ideas around Generative Business Intelligence.

This technical guide demonstrates a breakthrough two-phase methodology for constructing knowledge graphs using agentic AI systems. The approach seamlessly integrates structured data with unstructured text through specialized agents including Schema Proposal, Entity Extraction, and GraphRAG components. We detail advanced domain, lexical, and subject graph implementation with intelligent entity resolution using fuzzy string matching and similarity algorithms. The revolutionary multi-agent system automates complex schema generation, text chunking, and relationship mapping. #KnowledgeGraphs #AgenticAI #GraphRAG #BusinessIntelligence #DataIntegration #ConnectedData #AIAgents #DataAutomation #IntelligentSystems #DataDrivenDecisions

I spent this weekend breaking down Google DeepMind's Gemini 3 pro. Here are my big takeaways on multi modal AI, especially on how it relates to building AI for healthcare. Most of the information that drives real decisions today does not sit in paragraphs. It lives in charts, tables, diagrams, forms, dashboards, and mixed media that combine numbers, shapes, colors, and text. These complex visuals are how people in finance, healthcare, manufacturing, logistics, and research make sense of the world. For AI systems to be truly useful, they must do more than look at a picture of a document. They must understand it. They must track relationships inside a table, follow the logic implied by arrows in a diagram, compare two lines on a chart, and reconcile the visual story with the written one. This is the difference between extraction and reasoning, and it is becoming one of the most important challenges in multimodal AI. Rohan Doshi points out that complex visuals contain subtle cues humans notice automatically a tiny sub segment in a pie chart, a nested row in a table, a faint trend line in a plot, a color code that changes meaning across pages. These details matter because they change how a decision maker interprets the information. A table means nothing without the labels. A chart means nothing without the legend. A diagram means nothing without understanding how the arrows relate. The intelligence is in the structure. Especially in healthcare. People often assume that medical AI is about detecting objects. In reality, the hard part is reasoning across complex visuals. Radiologists never rely on a single slice or a single feature. They look across dozens of images, compare patterns, integrate anatomy with patient history, and draw conclusions from the relationships between visual elements. The meaning emerges from context. At Turmerik, we have worked with the documents that power clinical research and patient care. Protocols filled with diagrams. Lab panels packed with nested tables. Imaging reports that mix visuals and prose. These documents slow down clinicians because they are long AND because their visuals require deep interpretation. Most work today focuses on whether a model can answer a question correctly. That is important, but it is only the foundation. The real potential lies in models that can check whether a chart contradicts the text flag surprising outliers explain why two visuals tell different stories or guide a user toward the most important pattern even if they did not know to ask. #MultimodalAI #VisualReasoning #MedicalAI #AIinHealthcare #DocumentAI #FrontierAI

After burning through $40 worth of Gemini coding tokens, I finally got it working. I’ve been trying to get AI to not just answer a user’s enterprise data question, but to also pick the right visualization to explain it. AND for it to then justify that choice in plain English. Here's a breakdown of how it works: The Core Idea: An AI Data Visualization Expert Think of the system's AI as a data visualization expert. It's been trained not just on language, but on the principles of good data visualization. This is achieved through two core strategies: giving the AI specialized knowledge and forcing it to explain its reasoning. --- 1. How It Chooses the Right Chart The AI's smart selection comes from a combination of context and a specialized "rulebook" it must follow. a.  The Rulebook: The AI is given an internal guide on data visualization. This guide details every chart the system can create, explaining the ideal use case for each one. For instance, it instructs the AI that line charts are best for showing trends over time, while bar charts are ideal for comparing distinct categories. b.  The Context: When a user asks a question, the system bundles up the user's goal, a sample of the relevant data, and this "rulebook." This package gives the AI everything it needs to make an informed decision. c.  The Decision: Armed with this context, the AI matches the user's goal and the data's structure against its rulebook to select the most effective chart type. It then generates the precise configuration needed to display that chart. --- 2. How It Explains Its Thought Process Making the AI's thinking visible is key to building user trust. The system does this in two ways: by showing the final rationale and by revealing the live thought process. a.  The Rationale: The AI is required to include a simple, human-readable `rationale` with every chart it creates. This is a direct explanation of its choice, such as, "A bar chart was chosen to clearly compare values across different categories." This rationale is displayed to the user, turning a black box into a transparent partner. b.  Live Thinking Stream: The system can also ask the AI to "think out loud" as it works. As the AI analyzes the request, it sends a real-time stream of its internal monologue—like "Okay, I see time-series data, so a line chart is appropriate." The application can display this live feed, giving the user a behind-the-scenes look at the AI's reasoning as it happens. By combining this expert knowledge with a requirement for self-explanation, the system transforms a simple request into an insightful and trustworthy data visualization.