What is Insights in Power BI?

Building a dashboard in Power BI is a great first step, but a finished chart that just shows numbers going up or down isn't the finish line. The real value comes from understanding the "why" behind those numbers. That’s where Power BI's "Insights" features come in, moving you from simply reporting on data to actively interpreting it.

This article will show you what Power BI insights are and walk through the tools you can use - from one-click automated analysis to sophisticated AI-powered visuals - to uncover the hidden stories in your data.

What Exactly Are “Insights” in Power BI?

In Power BI, "insights" aren't just a vague concept, they are a specific set of AI-driven features designed to automatically analyze your data and highlight interesting patterns, trends, correlations, and outliers. Think of these tools as having a junior data analyst built directly into your reports, constantly looking for details you might have missed.

A standard report shows you what happened. For example:

• Website traffic dropped by 15% last month.
• Sales for "Product B" increased by 30% in the last quarter.
• Customer churn rate is at an all-time high of 5%.

An insight aims to tell you why it happened:

• Website traffic dropped by 15% because of a significant decline in mobile traffic from your latest Facebook campaign.
• Sales for "Product B" increased by 30% primarily due to a spike in demand from the Midwest region, making up 70% of the new growth.
• The average customer churns after three months if they haven't adopted a key feature.

Power BI provides several ways to generate these insights, each with varying levels of complexity and control. The main features include Quick Insights, the "Analyze" function on individual visuals, the Key Influencers visual, and the Decomposition Tree.

Your Easiest Starting Point: Quick Insights

If you're new to data analysis or just want a fast, high-level overview, Quick Insights is the perfect place to begin. It scans an entire dataset and automatically generates a series of visualizations that highlight noteworthy statistical patterns.

It's best for getting a broad sense of your data or finding unexpected correlations before you start building a report from scratch.

How to Use Quick Insights

Quick Insights is run from the Power BI Service (the web-based version), not the Desktop application.

1. Navigate to a workspace in your Power BI Service account and find the dataset you want to analyze.
2. Hover over the dataset and click the three-dot menu (...).
3. From the dropdown menu, select Get quick insights.

Power BI’s machine learning algorithms will then analyze the data in the background. After a minute or two, you'll get a notification that your insights are ready. Clicking it will take you to a special report page filled with pre-built charts. You might see things like:

• Correlations between two different metrics (e.g., "Advertising Spend has a high correlation with Sales Revenue").
• Outliers that don't fit the general pattern (e.g., "There was an unusually high number of support tickets on October 15th").
• Seasonal trends in your data (e.g., "Website traffic trends upwards on weekends").
• Distribution of data across different categories.

A Practical Tip for Using Quick Insights

Treat this feature like a brainstorming partner, not a definitive analyst. It will generate a dozen or more charts, and not all of them will be useful or even make sense. Some correlations are just coincidences. Your job is to sift through the results, find the ones that spark a question, and pin the most interesting ones to a dashboard to investigate further. It's a fantastic way to quickly get ideas for building a more focused report.

Going Deeper: Using “Analyze” on a Specific Visual

Where Quick Insights analyzes an entire dataset, the "Analyze" feature is a more focused tool that you can use on a single data point within a visual you’ve already created. This is extremely useful when you see a sudden spike or dip in a chart and want to immediately understand the cause.

How to "Analyze" an Increase or Decrease

Imagine you have a bar chart showing total sales by month, and you notice sales plummeted in February. Instead of manually slicing and dicing your data across different categories to find the cause, you can ask Power BI to do it for you.

1. On your Power BI report, find the visual you want to investigate.
2. Right-click on the specific data point that surprised you (e.g., the February bar in your sales chart).
3. In the context menu, select Analyze and then choose either Explain the increase or Explain the decrease.

Power BI pops up a new window with a "waterfall" visual. Its AI algorithms have analyzed the data across all other fields in your dataset to find the most likely contributors to that change. For instance, it might tell you that the decrease in February sales was most influenced by:

• A 40% drop in sales from a specific product category.
• A steep decline in performance from one sales rep.
• A regional dip, with the East Region accounting for 80% of the decrease.

You can even switch between different charts in this pop-up window to see how different factors contributed. Once you find a particularly useful insight, you can click the "+" icon to add that new visual directly to your report page, saving you the time of building it yourself.

Using AI-Powered Visuals for Advanced Insights

For more control and deeper exploration, Power BI offers dedicated "AI visuals" that you can add to your report just like a regular bar chart or table. The two most powerful are the Key Influencers and Decomposition Tree visuals.

1. The Key Influencers Visual

This visual helps you answer the question: "What are the main factors that drive a specific outcome?" It analyzes your data and ranks the factors that have the biggest influence on a metric you choose. This is incredibly powerful for moving beyond correlation to identifying potential causes.

For example, you could use the Key Influencers visual to understand:

• What influences a customer to churn? (Is it their subscription tier, location, or usage patterns?)
• What factors contribute to a sales deal being marked as "Won"? (Is it the lead source, company size, or salesperson?)
• Why is the review score for a product being rated as low? (Is it correlated with shipping carrier, warehouse, or the product material?)

To use it, you add the visual to your report and drag the metric you want to analyze (e.g., "Deal Status") into the "Analyze" field. Then, you add the potential factors you want to test (e.g., "Lead Source," "Company Size," "Product Interest") into the "Explain by" field. The visual automatically produces a chart showing which factors are most likely to influence the outcome.

2. The Decomposition Tree Visual

The decomposition tree is one of the coolest tools for interactive root cause analysis. It lets you visually break down, or "decompose," a metric across multiple dimensions in any order you want. It's essentially an interactive, free-form version of the "Analyze" feature.

Let's say your main KPI is "Total Website Visitors." You could start with that single value in the decomposition tree. Then, you can click a "+" sign to break it down by a dimension, like "Traffic Source." You'll see branches for Google, Facebook, etc.

From there, you could click on the "Google" branch and break it down further by "Device Type," revealing the mobile and desktop traffic within just organic search. You can continue drilling down in any order you like, exploring different paths to understand the composition of your main KPI. It lets your own curiosity guide the analysis, making it an excellent tool for deep, exploratory work.

Bringing it Together: Turning Insights into Actionable Decisions

Finding insights is great, but they're only valuable if they lead to better business decisions. Let's walk through a brief, practical scenario of how these tools work in practice.

The Question:

A marketing manager notices that while overall user sign-ups are steady, the rate of users who upgrade to a paid account has dropped this quarter.

The Process:

1. She opens her Marketing Analytics report in Power BI, which includes a line chart of the conversion rate over time.
2. She right-clicks on the data point for the most recent quarter and selects Analyze > Explain the decrease.
3. Power BI suggests that the decrease is highly correlated with one specific traffic source: "Paid Social." A waterfall chart shows that while other channels are stable, conversions from Paid Social have fallen off a cliff.
4. To dig deeper, she adds a Key Influencers visual to her report. She sets the outcome to "Conversion Status = TRUE" and adds "Campaign Name," "Landing Page," and "Ad Creative" as factors to "Explain by."
5. The insight is immediate: The visual shows that one particular campaign, "Q3 Free Trial Offer," is 2.5x less likely to result in a paid conversion compared to all other campaigns.

The Decision:

Instead of guessing, the manager now knows the exact campaign that's hurting her conversion numbers. She can pause that campaign, re-allocate the budget to better-performing ones, and work with her team to diagnose why that specific offer isn't attracting valuable long-term customers. She's gone from a generic problem to a specific, actionable decision in under 10 minutes.

Final Thoughts

Moving beyond basic reporting is what separates a good data culture from a great one. Power BI insights provide a powerful set of AI-driven tools that automate the initial stages of analysis, helping you quickly identify the "why" hidden in your dashboards and charts, so you can focus on making informed decisions.

While Power BI's built-in AI tools are powerful, the setup and learning curve can often get in the way of getting fast answers. We built Graphed because we believe getting insights shouldn't require you to become an expert in a specific BI tool. Instead of clicking menus and setting up complex visuals, you can simply ask questions in plain English - like "Compare revenue from Facebook Ads and Google Ads by campaign for last month" - and get a live, interactive dashboard instantly. It automates away the reporting busywork, letting you and your team get straight to an answer.