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Welcome to BIPro #108 -Where Better Dashboards Begin
This week, we’re going beyond dashboards as decoration and diving into dashboards that deliver. If you've ever opened a report and wondered, "What am I looking at, and why does it matter?", you’re not alone. In our lead story, we explore the value of thoughtful dashboard design in Tableau, unpacking four powerful design strategies, from guided analysis to executive scorecards, that can help you communicate insights with clarity and intent. It’s not just about data on a page; it’s about shaping stories your stakeholders can act on.
Once you're primed on dashboard design, stick around, this issue is packed with technical deep dives and timely updates:
🔹A practical Power BI workaround for start values in waterfall charts
🔹A how-to on building an AI assistant with Amazon Q and S3 clickable URLs
🔹What data literacy really means in 2025 (spoiler: it’s not what you think)
🔹New Google Cloud agents and AI-native foundations for data teams
🔹The debut of Spanner’s columnar engine, finally bridging OLTP and OLAP
🔹And news you need to know: Power BI’s Copilot is going full-screen by default this September
Wherever you sit, BI analyst, data scientist, exec stakeholder, this edition offers tools and thinking to help you work smarter with data, not just harder.
Let’s get into it.
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Growth Lead, Packt
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.
🔵Decoupling Semantic Model for Mirroring Customers: Semantic models are no longer auto coupled with new Mirrored artifacts. This decoupling unlocks custom semantic design, version control, and direct access to raw data, empowering teams to shape business logic independently. Phase 1 is live now; Phase 2 will decouple existing artifacts soon. This shift enhances flexibility, clarity, and scalability across analytics workflows.
🔵On Adding a Start Value to a Waterfall Chart in Power BI: Power BI waterfall charts don’t natively support a custom start value, essential for tracking cumulative metrics like customer growth. This post walks through a practical workaround using a secondary date table and smart DAX measures to inject a year-end starting point. Ideal for clearer visuals when showing progression from a prior baseline into the current reporting period.
🔵Build an AI assistant using Amazon Q Business with Amazon S3 clickable URLs: Create a document-aware AI assistant with Amazon Q Business that serves secure, clickable S3 links for traceability and responsible AI. This guide helps you set up the assistant, ingest enterprise files, and enable authenticated users to access referenced documents, without exposing S3 credentials. A hands-on walkthrough includes sample data, setup steps, and key IAM permissions.
🔵What Is Data Literacy in 2025? It’s Not What You Think. Modern data literacy isn’t just about reading charts, it’s about cutting through noise, distraction, and AI-polished confusion to deliver clear, context-rich insights that drive decisions. This piece redefines what it means to be data-literate in 2025, and offers practical steps for building narratives that land, even with smart, overloaded, and impatient audiences.
🔵Discover insights from Microsoft Exchange with the Microsoft Exchange connector for Amazon Q Business: Access Microsoft Exchange emails, calendars, and attachments through Amazon Q Business using its native connector, designed to streamline search, summarization, and task execution across enterprise communications. This guide walks through setup, indexing, and secure querying so teams can retrieve and act on Exchange data efficiently, with built-in IAM-based controls to ensure compliance and authorized access.
🔵New agents and AI foundations for data teams: Google Cloud introduces a new era of data interaction: the agentic shift. This update showcases AI-native agents built into a unified data platform, empowering data teams with autonomous workflows, multimodal vector search, and real-time reasoning across operational and analytical systems. From BigQuery to Looker, intelligent agents now collaborate and act, transforming how businesses explore, understand, and act on their data.
🔵Spanner's Columnar Engine Unites OLTP & OLAP: Spanner’s new columnar engine brings high-speed analytics directly into your transactional database, eliminating the latency, overhead, and complexity of ETL. With columnar storage and vectorized execution, it accelerates real-time insights on live data while preserving OLTP performance. Integrated with BigQuery via Data Boost, it simplifies architecture and enables federated queries that are faster, fresher, and more scalable than ever.
🔵Standalone Copilot in Power BI will be turned on by default in September: Starting September 5, 2025, Power BI’s standalone Copilot, “chat with your data”, will be enabled by default for all tenants with Copilot already turned on. This full-screen, chat-based AI lets users explore any report, semantic model, or Fabric data agent they have access to using natural language, streamlining insight discovery across the Power BI ecosystem.
See you next time!