Key takeaways
- Interactive charts let readers explore data the way they think, one follow-up question at a time. A static image answers just the questions the designer thought to ask. An interactive chart allows the reader to explore on their own terms: click the outlier bar, see what drives it, drill from region to settlement to customer. That chain of discovery is impossible to pre-bake into a PDF because the designer can’t predict which bar the reader will find interesting.
- Power BI reports’ interactivity features can do more than what’s often realized. Cross-filtering, drilldown, and tooltips let readers explore data by clicking what catches their eye. A bar chart showing revenue by region is ten data points and a ten-option filter at the same time. It answers a question and serves as entry point for follow-ups.
- A wall of slicers is a query builder in a dashboard costume. Each slicer is a question the report designer chose not to answer through the page layout. Chart-driven interactions let the reader start from what they see and dig deeper; slicers ask them to formulate a query before they’ve looked at anything. Slicers can be useful, but also a crutch that hurts UX.
- Design interactions from the reader’s question, not from the data model’s fields. Every interaction on a page should help the reader explore a follow-up question. Designing for interactivity is hard, which is why it’s often skipped for the path of least resistance: the wall of slicers.
The interactive default
Charts on a Power BI report page are interactive by default. Click a bar in a chart and the rest of the page responds; hover over it and a tooltip surfaces context; drill into a hierarchy and the detail appears underneath. No configuration required, this is just how the platform works.
That default is easy to take for granted, but it’s doing something important. A static chart answers the questions the designer chose to show. An interactive chart lets the reader follow up: “That bar looks off–what’s behind it?” They click, and the page responds. The reader doesn’t type a query or pick from a dropdown; they follow their curiosity one click at a time. Interactive data visualization, in the Power BI sense, is exactly this: visuals that respond to clicks, hovers, selections, and drills to reveal layers of data the designer didn’t have to anticipate.
Earlier in this series we argued that good reports start with a question. Interactivity is where you find out whether you did. If the page question is clear, the reader’s clicks follow a natural path through the data. If it isn’t, interactivity gives them more ways to get lost.
A chart that filters the page is pulling double duty. A bar chart with ten regions is simultaneously a comparison of ten data points and a ten-option filter control. It answers a question and opens the door to the next one.
So, if the charts already handle the filtering, what are all the slicers for?
Slicers un-solve interactivity
Open a typical report in most organizations and you’ll find a row of slicers across the top of the page: region, product category, customer segment, date range, and some other usual suspects. They’re important to most aspects of the business so they’ve always been there. Below them are three or four charts squeezed into whatever space is left. The default slicers persistently lay their claim on precious canvas space.
Pages like these are query builders in a dashboard costume. The slicers ask the reader to formulate their question–pick a region, category, time frame, etc.–before they’ve even looked at the data. Some selections produce empty visuals or nonsensical cross-sections; didn’t you know this combination was empty before selecting it?
The pattern repeats, partly because slicers are one of the first interactive elements most people learn in Power BI. You drop a slicer on the canvas, pick a field, and you’re done. Another reason for slicer walls is that slicers are the path of least resistance for everyone involved. The stakeholder asks “Can we filter by X?” and the developer adds a slicer, because it’s quick, the requirement is met, and nobody needs to have the harder conversation about what the page is actually supposed to answer.
Let’s be honest: we’ve been on the delivering side of that dynamic more often than we’d like to admit. If the stakeholder knows exactly which slicers they want, life can be easy: you deliver what’s asked and check another box. It takes more effort to push back, to sketch a page that answers the question through chart interactions instead of controls. The slicer wall persists because committing to a design (one that guides the reader) is harder than deferring to whatever the stakeholder requests.
What interactivity actually looks like
Every slicer you put on the page is a chart you didn’t build. Think about a region slicer next to a bar chart of gross margin by station. The slicer shows eleven labels: Antwerp Relay, Barrow Anchorage, and so on. The bar chart shows the same stations, but also shows which ones are profitable and which ones are bleeding margin. Click a bar and the page filters to that region. Exactly what the slicer would have done. The slicer can’t show you which bar is worth clicking in the first place.
So what does chart-driven interactivity look like in practice? Each of Power BI’s interaction patterns serves a different kind of follow-up question:
Cross-filtering. Clicking a bar in one visual narrows other visuals on the page to that category. The reader goes from “How are we doing overall?” to “How are we doing in this segment?” in a single click. The question path is natural: something in the overview caught your eye, now you want the detail behind it.
Cross-highlighting. Same click, but instead of filtering out everything else, the other visuals show the selection in context of the whole. The selected portion is emphasized and the full distribution stays visible; the unselected portion dims rather than disappearing. This is the right choice when the reader needs to compare the part to the whole (a brand’s share of total revenue, for instance) rather than isolate it.
Drilldown and drillthrough. Drilldown moves from summary to detail within a hierarchy. Year to quarter to month; brand to category to product, and so on. Drillthrough navigates to a separate page filtered to whatever the reader clicked. Both work when the detail answers the obvious follow-up. They fail when the destination is an unrelated view or a granularity the reader didn’t ask for.
Tooltips. Hover a data point to view compact information that helps the reader understand a data point without leaving the current view. Report-page tooltips in Power BI can show richer content (KPIs, small charts, breakdowns) filtered to whatever is hovered. A constraint worth remembering is that a report-page tooltip is a dead end that can’t be interacted with further, unlike regular tooltips that enable drill actions. Since the reader can’t click or drill inside it, the content should inform the next action on the main page–context that helps the reader decide whether to click, drillthrough, or move on.
Beyond these core patterns, two features take interactivity further by changing what the reader sees, not just which slice they see:
Decomposition tree. The reader picks a measure and chooses which dimension to split by at each level. No predefined drill path, just follow the data wherever it leads. Power BI will even find the highest or lowest value in the subsets for you.
Field parameters and calculation groups. Field parameters let the reader change what a chart displays: toggle the y-axis from Revenue to Gross Profit, or switch the breakdown from Region to Brand. This is not just simple interaction but a visual reconfiguration. Calculation groups do something similar for how measures are calculated: the reader picks YTD from a slicer and every measure on the page shifts to year-to-date figures. Both features use slicers as their delivery mechanism, but they control the reader’s perspective, not the filter context.
All of these give agency to the reader’s eyes instead of demanding inputs from their brain (“Which region was mentioned in that mail again?”). If something catches their attention, they are free to click or hover, and the page responds with the next piece of the answer.
The makeover
Let’s make this concrete with the DeepReach dataset, a fictional deep-sea logistics company with shipment data across ocean-floor stations, four product brands, and a bunch of crustacean customers. The page question is: “Where are we profitable, and where are margins under pressure?”
Before: the slicer wall
Here’s what that page looks like when every dimension gets a slicer: system, territory, station, brand, account type, customer, product type & subtype, and calendar year & month. Eight slicers across the top. Below them, a margin percentage card, a gross profit card, a units sold card, a cramped bar chart of margin by territory, and a stuffed table. Most of the page is input controls; the data is an afterthought.
The reader arrives, sees eight dropdowns, and has to decide what to filter before seeing what’s worth filtering to. Most people leave the defaults and scroll past. The report is interactive but lacks direction.
Step 1: Name the page question
“Where are we profitable, and where are margins under pressure?”
Writing this sentence tells you immediately what belongs on the page. Geography matters; margins vary across stations. Brand matters; Siren Optics runs at 52% gross margin while Abyssal Alloys sits at 22%. Time matters; margins shift over time and the reader needs a frame of reference. Account type? Maybe, but it’s a secondary concern. That’s one slicer we can justify removing already. This is the start with the question principle applied to interactions: if you’ve done that work, the right interactions tend to follow.
Step 2: Replace geography slicers with a bar chart
Three slicers–system, territory, station–become one bar chart showing gross margin % by station, with variance to prior period. The chart supports drilldown through the full hierarchy: click a region to see its territories, click a territory to see its stations. The reader spots underperformance and investigates it in the same visual.
A bookmark toggle next to the bar chart switches to a shape map. Three systems, colored by margin performance. The map shows geographic patterns the bar chart can’t and anchors the page in physical reality. The bar chart stays the default because it shows precise values and supports drilldown; the map is an alternative lens for readers who want the spatial view.
Three controls removed, one chart gained (with a map behind a toggle), and the page shows more information with the same filtering power.
Step 3: Replace category slicers with charts
The brand slicer becomes a column chart showing gross profit by brand, with prior year comparison. The reader sees which brands carry the profit and how each is trending. Click a bar to cross-filter the page to that brand.
The product section and customer slicers go. In their place, a scatter chart plots customers by gross margin % against gross profit, sized by units sold. Field parameter dropdowns let the reader swap the axes: toggle from gross margin % to revenue, or from gross profit to units sold–whatever comparison matters in the moment. The scatter doesn’t just replace one slicer; it replaces a different slicer for every combination of measures the reader wants to compare. For customer-level detail beyond what the scatter shows, the matrix in the bottom right provides that path, and the exploration page shows the full breakdown.
Step 4: Audit what’s left
Zero slicers. The date range–which most makeovers would keep as the last justified slicer–is handled by the KPI line chart in the top left. It shows the last thirteen months of gross margin % with a dynamic title and comparison to the prior equivalent period. The time window is baked into the design; the reader doesn’t need to set it.
More data is visible in a cleaner layout, and every interaction is chart-driven.
Beyond the main page
The main page answers “Where are we profitable?” Two supporting elements extend the exploration.
Exploration page. Right-click any data point on the main page–a station, a brand, a customer, a month–and drill through to an exploration page. A decomposition tree on top, a detail table below. The drillthrough lands the reader with context: the station or brand they clicked is already filtered, and a dynamic title confirms where they are. A field parameter dropdown lets the reader switch the measure the tree analyzes without losing the exploration path they’ve built.
The table cross-filters to display the detail behind whatever node is selected. The reader can also navigate to this page directly from the tab bar and start from scratch, without any drillthrough filter. One page, two entry points: guided when the reader has a specific follow-up, open-ended when they arrive with their own question.
Report-page tooltip. Hover over any data point on the main page and a tooltip surfaces compact context: the key numbers and a cost-profit composition bar, scoped to whatever the reader is hovering. Enough to judge performance at a glance and decide the next action: click to cross-filter, right-click to drillthrough, or move on. Since a tooltip is a dead end (the reader can’t click or drill inside it), the content stays focused on informing that next click.
When slicers earn their place
A dropdown advertises itself but cross-filtering doesn’t. The designer who removes slicers is betting that the page layout will make the interactions obvious, and that bet only pays off when the page question is clear enough that clicking feels natural. That’s a real cost, especially when your users are accustomed to slicer-driven reports.
Some cases genuinely need a slicer:
- A known parameter. A date range selector, a scenario toggle (actuals vs. budget, current year vs. prior year), a currency selector in a multi-currency model. The common thread: the reader arrives with a parameter to set, not a question to explore. Most reports benefit from an explicit date control, even if a rolling KPI can sometimes design it away.
- Too many values to fit in a visual. A dimension with hundreds of entries doesn’t work as a bar chart. When there’s no meaningful way to display the values visually, a slicer or search box is the honest choice.
- Lookup fields. Serial numbers, quotation IDs, customer codes, etc. Values the reader has on a sticky note or in an email. They need to type or paste, not browse. No chart interaction can replace that.
- Users who aren’t there yet. Weaning a team off slicer walls is a change management effort, not only a design strategy. If your users are just getting comfortable with Power BI, replacing familiar controls with chart-driven interactions can make a report feel broken rather than improved. Meet them where they are.
We’re not here to tell you slicers are bad and you should stop using them. We do want to point out that they can stand in the way of good report UX when overused: when every stakeholder request becomes another dropdown and no one asks what the page is supposed to answer.
What doesn’t earn its place are filters the reader can’t see or undo. Visual-level filters, page-level filters, and leftover slicer selections that leave the reader unsure what they’re looking at. Interactivity should be transparent; the reader should always know what’s filtered. A “Clear all filters” button is a band-aid for a design problem.
Bookmark sprawl falls in the same category. Three or four bookmarks toggling chart types or KPI views can work. Twenty bookmarks pretending to be website navigation create a maze where the reader loses track of where they are and how to get back, not to mention the poor developer tasked with maintaining the bookmark maze.
The model must keep up
When chart interactions replace slicer walls, the semantic model’s job changes. Cross-filtering through a well-structured star schema plays to VertiPaq’s strengths: filter propagation along existing relationships is fast and predictable. But the conditional formatting DAX that makes interactive design work (dynamic colors and titles, KPI indicators, visuals that respond meaningfully to selections) carries its own filter context complexity, sometimes harder to reason about than the slicer combinations it replaced.
Consistent measure definitions hold up under chart-driven exploration because the filter context changes in predictable ways. That said, some of the interactivity features that benefit the reader add their own complexity to the model.
NOTE
Field parameters add DAX-defined tables to the model; lightweight, but they need to be managed for self-service consumers and documented for future developers. Calculation groups introduce precedence rules: when multiple groups interact, the evaluation order matters and can produce results nobody anticipated. Both features are worth using when the reader’s question demands them, but weigh the interactivity gain against the modeling overhead before adding them reflexively.
Tabular Editor 3 helps manage this complexity practically. Calculation groups that consolidate dozens of time intelligence variants into a single reusable object are easy to build and maintain in Tabular Editor. Best Practice Analyzer rules can flag measures with performance risks, like excessive FILTER usage or non-optimized iterator patterns. Bulk measure editing makes governance of large models feasible; when you have 200 measures, you better have a way to edit multiple at once. And the ability to test measures against specific filter contexts during development means you catch surprises before the report consumer does.
Further recommended reading
- Data visualization best practices for Power BI reports–the foundational principles this series builds on, including the “question first” approach to report design.
- Better tables in Power BI reports–deliberate formatting applied to a specific visual type; the same “choices over defaults” thinking applied to tables.
- Better KPIs in Power BI reports–progressive makeover from bare numbers to contextualized KPI cards; context over chrome.
- IBCS notation standards–practical notation conventions for business charts that reduce cognitive load and improve comparability across reports.
In conclusion
Power BI’s interaction model goes deeper than most reports use. Cross-filtering, drillthrough, field parameters, decomposition trees, report-page tooltips–the reader can follow their curiosity through the data without typing a query or navigating a menu. Most reports tap a fraction of this because slicers crowd out the rest.
A practical exercise: open one of your report pages and count the slicers. For each one, ask whether a chart on the page already shows that dimension. If it does, the slicer is redundant; remove it and let the chart do both jobs. You’ll free up space for visuals that show data and filter and make room for the deeper interaction features that a cluttered page can’t support.