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.

Tables miss the big picture. Graphs unlock deeper insights. When your data is too complex, key insights stay hidden. 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗯𝗿𝗶𝗻𝗴𝘀 𝗰𝗹𝗮𝗿𝗶𝘁𝘆—𝗳𝗮𝘀𝘁. That’s where tools like Neo4j Bloom come in. Visualization platforms transform connected data into an intuitive experience anyone can explore. No complex queries, just patterns and insights at your fingertips. It’s like a search engine for your graph data. Type a name, concept, or relationship and instantly see the connections. If you are using Neo4j and Bloom you can leverage: ✅ 𝗖𝘂𝘀𝘁𝗼𝗺 𝗩𝗶𝗲𝘄𝘀: Adjust node colors, sizes, and labels to match your focus. ✅ 𝗖𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗙𝗼𝗿𝗺𝗮𝘁𝘁𝗶𝗻𝗴: Highlight patterns or anomalies with rule-based colors. ✅ 𝗩𝗲𝗿𝘀𝗮𝘁𝗶𝗹𝗲 𝗟𝗮𝘆𝗼𝘂𝘁𝘀: Switch between org charts, geographic maps, and more. These tools become even more powerful when paired with AI. LLM integration turns natural language questions into Cypher queries. For example, asking "Which customers are most likely to churn?" can return high-risk customers in the visualization. Graph visualization tools like Neo4j Bloom bridge the gap between data complexity and business insight. They transform raw data into relationships that drive decisions. Whether you’re conducting fraud investigations or mapping customer journeys, graph visualization gives you the clarity to act. 💬What is your favorite approach to visualizing connected data? Share it in the comments. 📢 Know someone struggling to understand complex data? Share this post to help them out! 🔔 Follow me, Daniel Bukowski, for practical insights about building with connected data. 

🔥 Microsoft just open-sourced Data Formulator, and it's already at 3.2K stars. Why? It's bridging the gap between no-code simplicity and AI-powered data analysis in a way I haven't seen before. The magic is in how it handles data transformation: While other tools force you to write complex transformations or rely purely on natural language, Data Formulator lets you drag-and-drop visualization properties while AI handles the heavy lifting behind the scenes. What's truly innovative: - Beyond-dataset analysis: Drop a field that doesn't exist yet (like "growth_rate" or "market_share"), and the AI automatically creates it based on context. No SQL, no Python, no data prep needed. - Smart visualization pipeline: Each chart becomes part of a "Data Thread," maintaining context as you explore. Want to see "only top 5" or "as percentage of total"? Just ask - the system understands the full transformation chain. Latest addition that's turning heads: An experimental feature that extracts structured data from images and messy text, instantly ready for visualization. Think about all those PDF reports and screenshots sitting in your backlog... Running locally is dead simple: pip install data_formulator and you're ready to go. Github repo link in the comments. Enterprise teams: How would this fit into your current BI stack? Curious about the balance between automation and control in your visualization workflows. #DataScience #AI #OpenSource #DataViz

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.