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.

How to use AI and Python for FP&A Data Visualization? Many #finance and FP&A teams asked me this. So I created a 5 steps framework to help you get started. With an LLM (ChatGPT, Copilot, Gemini, etc)+ Python, you can transform data into powerful visual stories. Here’s the 5-step approach I use: 1. Show ChatGPT your data Paste a few rows of your dataset and ask for visualization suggestions. This step is super important to understand. You do not need to GIVE your data to an "AI Company". You just need to show how your data LOOKS LIKE. Use this prompt: “I'm a FP&A analyst (replace this with your role) working with a dataset and I'd like your help picking the three most effective visualizations for it. Below is a sample of the data (including column names and a few rows). Based on the structure, types of variables, and any potential insights you notice, recommend three visualizations that would best highlight trends, patterns, or relationships in the data. Here is the data: (Paste a few rows of your "dummy" data here, ideally 5–10 rows, including the header. You don't need to add real data but the format of the data is important [e.g. date, number, percentage[)” 2. Get the 3 best examples Let AI recommend the most impactful charts for your dataset. 3. Ask for Python Code Get the code from the LLM and then run it in G. Colab, VS or even Excel. My recommendation: If you want the easiest to start → Google Colab If your company prefers Microsoft Products → Visual Studio If you want to stay in an environment you know → Python in Excel! If you need help with choosing, let me know and I can suggest or send you some courses to start! 4. Execute and visualize Generate dynamic charts that highlight key financial insights. 5. Improve and Customize! 🎨 This is where you take it to the next level: ✅ Refine Styling – Customize colors, fonts, and labels for readability. ✅ Add More Insights – Overlay trend lines, percentage changes, or KPIs. ✅ Make it Interactive – Use Plotly for drill-down capabilities. ✅ Automate Everything – Schedule updates and integrate into workflows. ✅ Leverage AI Further – Use predictive modeling to forecast trends. Hope this is useful and if you want the data and code I used for the belows examples just message me or comment and I can send!

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

Breaking Down Complex GraphRAG Systems: XGraphRAG Makes AI Transparency Achievable Graph-based Retrieval-Augmented Generation (GraphRAG) represents a significant leap forward in AI systems, but analyzing these complex pipelines has been a major challenge for developers. Researchers from Zhejiang University have introduced XGraphRAG, a groundbreaking visual analysis framework that finally makes GraphRAG systems interpretable and debuggable. > The Technical Challenge GraphRAG systems operate through intricate multi-stage pipelines that transform raw documents into knowledge graphs, then leverage these structures for enhanced AI responses. The process involves: Construction Phase: - Split: Documents are segmented into manageable text chunks - Extract: Raw entities and relationships are identified using LLM invocations, creating subgraphs with enriched metadata - Merge: Subgraphs are consolidated into comprehensive graphs, resolving naming conflicts while maintaining traceability to source chunks - Summarize: Graph partitioning based on relationship density creates topic-specific reports at multiple hierarchical levels Retrieval Phase: - Recall: Multi-type retrievals (entity, relationship, and report recalls) based on user queries - Infer: Step-by-step LLM reasoning with explicit linkage to specific recalls > The Innovation Under the Hood XGraphRAG introduces a two-stage visual analysis framework that tackles the core interpretability challenges: Stage 1: Suspicious Retrieval Identification The system constructs parallel inference chains for both actual answers and ground truth, using LLM-assisted comparative analysis to identify "Missing Recalls" (essential information absent from actual inference) and "Unexpected Recalls" (erroneous information included in reasoning). Stage 2: Multi-Facet Relevance Analysis - Global Analysis: Circle packing visualizations reveal topic hierarchies and semantic relationships across the entire knowledge graph - Local Analysis: Node-link diagrams expose direct entity connections, with node size encoding frequency and edge thickness representing topic distances - LLM Behavior Tracing: Three specialized views dissect extraction, merge, and summarization stages, revealing exactly where information processing breaks down > Real-World Impact Key Technical Advantages: - Complete pipeline traceability from final answers back to source documents - Interactive highlighting across multiple coordinated views - Structured LLM invocation analysis with context preservation - Support for both local connectivity and global semantic analysis This research addresses a critical gap in AI system development, providing developers with the tools needed to build more reliable and trustworthy GraphRAG applications. As AI systems become increasingly complex, frameworks like XGraphRAG will be essential for maintaining transparency and enabling continuous improvement.

I've been experimenting with an automation workflow that complements traditional Power BI reporting, particularly useful for generating quick presentation decks and executive summaries. Here's how it works: through a simple interface, users select their preferences (colours, themes, number of cards). An AI agent then connects to Power BI's semantic model via Microsoft APIs, executes DAX queries directly on the dataset, processes the results and generates a professional presentation. All in under 2 minutes. The workflow: 1. User inputs captured through a web interface 2. AI Agent accesses Power BI semantic model via API 3. DAX queries executed in real-time against your data 4. Output formatted and pushed to the presentation payload 5. Professional deck generated automatically with live data This isn't about replacing Power BI Desktop, it's about having another tool in the kit. Sometimes you need a quick executive summary, a client-ready presentation or a snapshot report without spinning up a full dashboard. The real power is in how you structure your knowledge base and build the payload. Once that's dialled in, you can set up custom themes, triggered reports, scheduled deliveries or handle ad-hoc requests. What you're seeing here is a credit risk portfolio analysis pulled straight from the semantic model and transformed into presentation format as an alternative output channel. 🗣️👂🏻Keen to hear: Have you explored automated presentation or summary generation from your Power BI models? What other approaches have you tried for creating quick, polished outputs beyond standard dashboards? #PowerBI #DataAnalytics #BusinessIntelligence #DataVisualization #DataVisualisation #DataAutomation #AIAgent #DataDriven #AnalyticsEngineering #Microsoft #TechAustralia #Australia #AI #Dashboards #Reports

Want to level up your Power BI design skills? Try this: instead of diving straight into visuals, get Generative AI to wireframe your report first. Here’s the exact prompt I used Create a Power BI report wireframe (as SVG) for a one-page Executive Sales Overview dashboard. Audience: sales director and account managers. Purpose: monitor Revenue vs Target, Gross Margin, and Top Performing Regions/Customers. Data: Dynamics 365 CE (Dataverse) Use a dark theme (#121212) with accent #EDBF2C. Layout zones: Header, KPI strip, Main visuals (charts), Detail matrix, and Filter rail. Suggest visuals (cards, bar/line charts, map, matrix) and explain placement briefly. Output: Short explanation of layout (≤100 words). One SVG (1280×720) inside a single code block, using simple shapes labeled with visual names (e.g. “Card: Revenue vs Target”). The result? A clear visual structure before even opening Power BI or Figma. No more staring at a blank page wondering where KPIs should go. It’s a brilliant way to blend creativity and structure, and it saves serious design time. #PowerBI #AI #DataViz #PowerPlatform #DesignThinking

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

🔥 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.