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Welcome to DataPro 144: Designing for Intelligence

The data world is shifting fast, from dashboards and notebooks to agents that reason, write code, and navigate virtual worlds. In this issue, we look at what it means to design not just with AI, but for AI: platforms, workflows, and visualizations that collaborate, adapt, and inform with intelligence.

We explore the tools reshaping how we build, the models pushing open boundaries, and the quiet craft of designing dashboards that speak clearly in a noisy world.

🔍 Key Highlights This Issue:

📓 AI-First Colab Notebooks: Google’s Data Science Agent in Colab Enterprise (BigQuery + Vertex AI) turns prompts into pipelines, coding, debugging, and visualizing in real-time.

🤖 Gemini Code Assist: GitHub PRs meet Gemini 2.5, think code reviews with instant summaries, bug detection, and smart suggestions built-in.

🛡️ MCP Security Survival Guide: Why agentic systems like MCP demand new security thinking. A breakdown of real-world exploits and how to avoid them.

🧠 Anthropic’s Persona Vectors: Mapping and moderating LLM behavior, new research shows how traits like sycophancy or hallucination can be tracked and controlled during training.

🔌 InfiniBand vs. RoCEv2: A practical guide to choosing your AI network stack. Scale performance isn't just about GPUs, it’s how fast they talk to each other.

📊 Tableau Dashboard Design: Not all dashboards are created equal. A deep dive into four design strategies, guided, exploratory, scorecard, narrative, from Learning Tableau 2025.

🧪 Post-Processing Beats Modeling? Lessons from the Mostly AI synthetic data challenge, how smart sampling and refinement outperformed complex models.

🧩 OpenAI’s gpt-oss Models: Open-weight LLMs that compete with proprietary ones. Reasoning, tool use, and safety, all on hardware you can actually run.

🌍 Google DeepMind’s Genie 3: From video generation to real-time simulated worlds, Genie 3 makes AI environments interactive, consistent, and controllable.

🌐 The Agentic Shift at Google Cloud: Not just tool, but agents, APIs, and foundations for a new AI-native enterprise. The data platform is becoming a thinking partner.

As the boundaries between data, design, and intelligence blur, this is the moment to stay curious, stay critical, and explore what thoughtful, agentic systems can truly enable. Let’s build with intelligence, not just for it.

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In the rush to build a new Tableau dashboard, it’s tempting to jump straight into charts and data. But taking a step back to define your dashboard’s purpose and strategy can make the difference between a report that confuses and one that doesn’t. Put simply, effective dashboards are rooted in clear objectives and an understanding of what your audience needs at a glance. (src)

A common professional setting for Tableau users is the executives wanting quick insights without having to wade through noise, the analysts needing interactive exploration, and the broader audiences needing a narrative to make data relatable. A thoughtful dashboard design strategy aligns your Tableau visuals with these needs. (src) It ensures you’re not just throwing data on a page, but actually communicating the ideas. In the long run, a bit of planning on “dashboard strategy” saves time and elevates the impact of your work.

Four approaches to dashboard design

One of the key insights from the upcoming book Learning Tableau 2025 is that there isn’t a one-size-fits-all approach to dashboard design. The book’s authors outline at least four common design approaches, each suited to different scenarios. Lightly adapted from Learning Tableau 2025, here are the four approaches and what they entail:

🔹Guided Analysis – This approach guides the audience through the data to facilitate discovery. In practice, you lead viewers step-by-step so they can understand the data’s implications and arrive at clear actions. A guided dashboard often anticipates a specific analysis path – you’ve done the analysis and now walk the user through those findings in a logical sequence.

🔹Exploratory – An exploratory dashboard is an open sandbox. It provides tools (filters, drill-downs, etc.) for the audience to explore the data on their own. The idea is that the data’s story may evolve over time, so you empower users to investigate trends and relationships themselves. This approach is common in self-service BI scenarios, where different users might have different questions.

🔹Scorecard / Status Snapshot – This is all about at-a-glance information. A scorecard or status snapshot delivers a concise summary of key performance indicators (KPIs) and metrics. It’s the classic executive dashboard: think of a one-page layout with big numbers, up/down arrows, and color-coded indicators. The goal is quick problem identification and monitoring – no heavy narrative, just the vital signs of the business in one view.

🔹Narrative – A narrative dashboard focuses on telling a story with the data. It guides the viewer through a beginning, middle, and end using visuals and text in a cohesive sequence. For example, you might show how a metric changed over time during a specific event (imagine illustrating the spread of a disease or the timeline of a marketing campaign). This approach adds context and commentary to data, making the insights memorable and compelling.

(Extracted and adapted from Learning Tableau 2025 by Milligan et al.)

Putting these approaches into practice

These different approaches matter because of their impact. Matching your dashboard design to your audience’s needs can dramatically improve how your insights land. For instance, if your CEO just wants a daily health check of the business, a scorecard-style dashboard ensures they see all critical KPIs in seconds (and nothing more). If you’re presenting to stakeholders at a quarterly review, a narrative dashboard with a clear storyline might be more effective – it can walk them through performance drivers and outcomes in a logical flow. On the other hand, when you’re building tools for analysts or power users, an exploratory dashboard gives them the flexibility to ask their own questions about the data. And if you’ve conducted deep analysis yourself, a guided dashboard lets you package those insights into an interactive journey, so colleagues can essentially retrace your steps and findings.

Keep in mind that these approaches aren’t mutually exclusive. Often, a well-crafted dashboard will blend elements of each. You might start with a snapshot overview up top (scorecard style), then provide interactive filters for deeper exploration, and perhaps include annotations or highlights to add a mini narrative. The key is to be deliberate: know when you’re trying to simply inform versus when you need to persuade or invite exploration. By aligning the design to the goal, you avoid the common pitfalls of cluttered or directionless dashboards.

In today’s data-driven environment, dashboards are a staple of communication – and thoughtful design is what separates the mediocre from the truly effective. A bit of upfront strategy about how you present information pays off with dashboards that people actually use and understand. (src) Whether you’re guiding a user through a data story or letting them dive in themselves, choosing the right approach will ensure your Tableau work delivers value, not just charts.

For those who want to dive deeper and see these principles in action, the book Learning Tableau 2025 is packed with practical examples and tips on building impactful dashboards. It’s a resource well worth exploring if you’re looking to sharpen your Tableau skills and design more thoughtful, effective dashboards. By approaching your next project with a clear strategy in mind, you’ll be well on your way to creating dashboards that not only look good, but drive smarter decisions in your organization.

Want to design dashboards that communicate, not just display?
Take the Tableau dashboard design quiz to find your weak point—and see how Learning Tableau 2025 can help you fix it. Take the quiz here!

Then, pre-order your copy of Learning Tableau 2025 to learn how to apply guided analysis, exploratory tools, executive snapshots, and narrative techniques in real projects—so your dashboards deliver insight with impact.

🛒 Pre-order here.

🔶 AI First Colab Notebooks in BigQuery and Vertex AI: Colab Goes Agentic! Google’s new AI-first Colab Enterprise is more than a notebook, it’s your AI teammate. With agentic capabilities via the Data Science Agent, it plans, codes, debugs, visualizes, and iterates, all with human-in-the-loop control. Seamlessly integrated with BigQuery and Vertex AI, this signals Google’s bold move to make AI not just assistive, but collaborative in real data science workflows.

🔶 Gemini Code Assist and GitHub AI code reviews: AI Code Reviews That Just Work. Gemini Code Assist turns pull requests into productivity boosters. Integrated into GitHub, it delivers instant PR summaries, flags bugs, and suggests improvements, all powered by Gemini 2.5. With contextual understanding, interactive feedback, and high trust suggestions, it’s more than automation, it’s collaboration. Teams like Delivery Hero are already seeing faster reviews, better code, and happier devs. Seems like the future of software quality is here, and it’s AI-reviewed.

🔶 The MCP Security Survival Guide: Best Practices, Pitfalls, and Real-World Lessons: MCP Is Powerful. That’s Also Why It’s Dangerous.Agentic systems like MCP are revolutionizing AI workflows, but they’re also exposing critical security flaws. From OAuth mishaps to remote code exploits, real-world breaches show just how risky "plug-and-play" can be. Hailey Quach’s guide is an urgent call: use MCP, but use it wisely. This isn’t just best practice, it’s survival. A must-read for anyone building secure, agentic AI infrastructure.

Source: TowardsDataScience

🔶 Anthropic’s Persona Vectors: Monitoring and controlling character traits in language models. Why Your LLM Might Start Flattering You, or Worse. Anthropic’s new research on persona vectors reveals a breakthrough in tracking and controlling AI “personalities.” By isolating neural patterns tied to traits like sycophancy, hallucination, or even evil, developers can now monitor personality drift, prevent unwanted behavior during training, and flag risky datasets, without degrading performance. If AI character control is the next frontier, persona vectors might be our steering wheel.

🔶 InfiniBand vs RoCEv2: Choosing the Right Network for Large-Scale AI. Choosing the Fast Lane for AI Scale. Training massive AI models isn’t just about powerful GPUs, it’s about how fast they talk. This guide breaks down InfiniBand vs RoCEv2, the two dominant network stacks powering GPU-to-GPU communication. InfiniBand offers unrivaled speed but at a premium. RoCEv2 rides Ethernet’s rails with careful tuning. If you’re building for scale, your network isn’t infrastructure, it’s a performance multiplier. Choose wisely.

🔶 How I Won the “Mostly AI” Synthetic Data Challenge? Post-Processing for Synthetic Data Accuracy. A recent synthetic data competition highlighted the power of post-processing over model complexity. By oversampling, trimming, and iteratively refining generated data, one solution significantly improved distributional accuracy and sequence coherence. Techniques like IPF and group-level swapping outperformed ensemble modeling. The results suggest that aligning generation strategies with evaluation metrics, rather than relying solely on generative models, can be a more effective path to high-quality synthetic datasets.

🔶 Introducing gpt-oss: OpenAI’s Step Toward Transparent AI: Open-Weight Models Are Growing Up. OpenAI’s release of gpt-oss-120b and gpt-oss-20b brings open-weight models closer to proprietary performance on reasoning and tool use tasks. Trained with techniques from internal frontier models, both models offer strong results across benchmarks like MMLU and HealthBench. With full customizability, modest hardware requirements, and a safety evaluation pipeline, gpt-oss models provide a flexible option for developers working on local inference, alignment research, or agentic workflows.

🔶 Google DeepMind’s Genie 3: A new frontier for world models:Simulated Worlds Are Becoming Playable. Genie 3 pushes world models from static simulation to real-time interaction. Unlike earlier video generation models, it enables consistent, navigable environments at 24 FPS, complete with memory, interactivity, and controllable events. This represents a step toward open-ended training environments for agents, but also opens up new questions around scalability, fidelity, and alignment as these systems move from outputting video to becoming the world itself.

🔶 New agents and AI foundations for data teams: Data Platforms Are Becoming Cognitive Partners. Google’s latest update positions the Data Cloud as more than infrastructure, it’s the operating system for agentic AI. With specialized data agents, unified transactional-analytical memory, and built-in reasoning, the traditional data stack is giving way to autonomous, collaborative intelligence. The shift isn’t just technical, it redefines how data work gets done, embedding agency and adaptability directly into the platforms that power decision-making at scale.

See you next time!