Chart Types in the Bitrix24 BI Constructor: Visualizing Data That Actually Gets Read

Why Visualization Choice Matters More Than Chart Count

Dashboards in Bitrix24 (Alaio) BI Constructor live or die by how fast a viewer extracts the answer - and the ~5-7 chart types that a typical manager already understands fluently (table, line, bar, card, pie, heatmap, combo) cover the overwhelming majority of real business questions.

Apache Superset, which powers the BI Constructor, offers an impressive library of exotic visuals - word clouds, sunbursts, treemaps, gauge "speedometers," and more. The temptation to use them is real, especially when showcasing a dashboard publicly. The problem: if the viewer needs an explanation to read the chart, the chart has failed. A complex visualization becomes visual noise.

The guiding principle is simple: choose the visualization that answers the question for your audience, not the one that demonstrates technical sophistication.

This mirrors a language analogy: just as fluent communication requires a solid core vocabulary rather than rare words, dashboard fluency rests on mastering a handful of chart types well - then reaching for niche ones only when they genuinely fit the task.

The Core Chart Types: A Reference Table

The table below covers the ~7 chart types that handle roughly 99% of dashboard use cases in Bitrix24 BI Constructor - each mapped to when it works best and a representative metric.

A note on exotic types. Superset also includes treemaps, sunbursts, gauge charts, and word clouds. These exist and occasionally fit niche use cases, but some less-common chart types have been known to compute data incorrectly. If a standard chart type answers the question, use it.

Deep Dive: What Each Core Chart Actually Does

Each of the five foundational chart types has specific strengths and failure modes that determine whether your viewers trust the dashboard or question it.

Table

The table is deceptively powerful. Rows and columns already carry meaning, and conditional cell formatting - coloring cells red when a value drops below a threshold, green when it exceeds a target - adds a second layer of communication on top of the numbers. Use it when precision matters and when you need multiple metrics side by side in one view.

Avoid the habit (familiar to anyone who has spent years in spreadsheets) of defaulting to tables for everything. When the question is "how did sales trend over time?" a line chart communicates the answer in seconds; a table of 52 weekly rows requires effort.

Line and Bar Charts

These two are close relatives. Both show change across categories or time; the difference is continuous curve (line) versus discrete bars. A few bar chart options worth knowing in the BI Constructor:

• Orientation: Switch to horizontal bars when category names are long - they're far more readable than rotated labels.
• Sorting: Sort by descending value to produce an instant top-N ranking.
• Value labels: Numbers printed directly on each bar eliminate the need to cross-reference with the Y-axis.
• Stacking: Stacked bars show both the total and the composition in one view.

Big Number Card

Cards are ideal for KPIs that must be visible at a glance - total revenue, open deals, average deal size. There is an optional sparkline variant that shows a trend line across recent periods. One important limitation: cards do not support clickable links to detail views (unlike tables, where row-level links are straightforward). The workaround is Drill to Detail mode, which surfaces the raw rows behind the aggregated number.

Pie Chart

Pies work when you have a small, fixed set of categories - roughly 5-10 maximum. Beyond that, the slices become too thin to read, and the chart loses its purpose. The failure mode to watch for: if you build a pie on a field with an unknown or unbounded number of categories (like product subcategories in a large catalog), applying a row limit destroys the "share of whole" meaning. Use a pie only when you know exactly how many categories you have.

Heatmap

The heatmap plots a metric at the intersection of two dimensions on X and Y axes. It looks advanced but is widely understood. For co-purchase (basket) analysis it is ideal: product 1 on one axis, product 2 on the other, combined revenue at each intersection. Configure it to show absolute values (not just percentages) and sort by descending measure so the "hot" combinations appear at the top.

The Mixed (Combo) Chart and Dual Y-Axis

The combo chart is the right tool whenever you need to display two metrics that have different units or vastly different scales on the same visualization - each metric gets its own Y-axis so neither is crushed to zero.

A classic example: revenue (large currency numbers) and deal count (small integer) plotted together. On a shared axis, the deal count line would appear nearly flat against the revenue scale, making it unreadable. Dual Y-axes solve this - each metric scales independently.

In the BI Constructor, a combo chart is built from two separate query sets displayed as different mark types (for example, bars for revenue + a line for deal count), each bound to its own vertical axis. The result is a single panel that shows both the magnitude and the frequency in one view.

The BI Constructor assembles chart layers from a dataset and combines them into a dashboard with cross-filtering:

A virtual dataset (a saved SQL query over Bitrix24 data) feeds individual chart types - table, cards, bar chart, heatmap, and combo chart - into a dashboard. Cross-filtering links charts so that clicking an element in one panel re-filters the others, while scoping controls define which panels stay fixed.

flowchart TD
    DS[Virtual Dataset\nSQL query on Bitrix24 data]
    DS --> TBL[Table\nTop-N rows]
    DS --> CARD[Big Number Cards\nKPIs]
    DS --> BAR[Bar Chart\nRanking]
    DS --> HEAT[Heatmap\nMatrix view]
    DS --> COMBO[Combo Chart\nDual Y-axis]
    TBL --> DASH[Dashboard]
    CARD --> DASH
    BAR --> DASH
    HEAT --> DASH
    COMBO --> DASH
    DASH -->|Cross-filter click| DASH

Cross-Filtering: How It Works and How to Control It

By default, every chart on a Bitrix24 BI Constructor dashboard is linked by cross-filters - clicking any element in one chart instantly re-filters all other charts on the same dashboard, using the clicked value as a filter condition.

This behavior is powerful and, without configuration, occasionally disruptive. A click on a bar in a ranking chart will also re-filter an overview heatmap that is meant to stay static as a reference view. The solution is scoping.

How to configure cross-filter scope

1. Open the dashboard in edit mode.
2. On the chart that should not react to clicks from other charts, open Menu (≡) → Cross-filtering scoping.
3. Uncheck the links to charts that should remain unchanged, then save.

A practical example: in a basket analysis dashboard, the "top-5 combinations" table and the overview heatmap should stay fixed regardless of what a user clicks. The detail cards below them - showing breakdowns for the selected combination - should update. By scoping accordingly, clicking a bar in the ranking chart updates only the cards, leaving the reference panels untouched.

Cross-filtering works via matching field names across charts. When a user clicks a dimension value in one chart, Superset applies that value as a filter to other charts - but only where a field with the same meaning exists. This is why it matters to design your virtual dataset with shared dimensions (product, period, responsible user, pipeline) from the start. If the field isn't in the dataset, it can't be filtered or cross-linked.

Before publishing any dashboard, walk through each chart and verify how its cross-filter behavior affects neighbors. Default "link to everything" settings frequently produce unexpected results.

One historical note: earlier versions of Superset had a bug where cross-filtering scoping settings could reset on dashboard import. This was addressed in ~Superset v4 and the behavior is significantly more stable in current versions of the BI Constructor.

For deeper dataset design considerations, see Datasets and SQL in the Bitrix24 BI Constructor: A Technical Guide and the step-by-step How to Create a BI Report in Bitrix24 with BI Constructor.

Checklist: Choosing the Right Visualization

Run through this five-point checklist before adding any chart to a dashboard - it will eliminate the most common visualization mistakes before they reach users.

• 1. State the question in plain language. "How did revenue trend?" → line chart. "What share do the top-5 products hold?" → pie. "What sells together?" → heatmap. "How many deals closed this month?" → big number card. The question determines the chart; the chart does not determine the question.
• 2. Know your audience's chart literacy. If the manager who will use this dashboard would need an explanation to interpret the chart type, choose a different one. Stick to the 5-7 widely understood types for operational dashboards.
• 3. Count your categories before using a pie. More than ~10 categories in a pie or donut chart makes it unreadable. Use a horizontal bar chart (which handles many categories gracefully with sorting) or a table instead.
• 4. Don't duplicate without reason. If a table already answers the question precisely, adding a chart "for aesthetics" adds cognitive load without value. Every panel on a dashboard should answer a distinct question.
• 5. Default to the standard set. Some exotic Superset chart types have known accuracy issues with certain data shapes. The standard 5-7 types are predictable. Reach for a niche chart only when the standard set genuinely cannot represent the data.

Practical Workflow: Building a Family of Charts from One Table

The fastest way to build a multi-chart dashboard in the BI Constructor is to create a single "anchor" table with all required metrics first, then clone it into specialized visualizations - this takes minutes per chart rather than rebuilding each from scratch.

The workflow:

1. Build the anchor table with every metric you need (totals, counts, percentages, breakdowns).
2. Open the table chart, adjust the chart type and relevant settings, then click Save as New (not Save) to create a named copy. The original table remains intact.
3. Repeat for each visualization: heatmap, bar chart, pie, cards, combo chart.

The "Save as New" step is critical. Clicking plain "Save" overwrites the source chart, losing the original. Using "Save as New" means you can derive an entire dashboard's worth of charts from a single well-structured dataset in a single session.

Additional workflow tips from practice:

• Cards for static KPIs. Isolated numbers (total discount %, product share) that would be buried in a table become scannable at a glance when displayed as Big Number cards.
• Horizontal bars for long labels. Product combination names, manager full names, and multi-word category labels are far more readable in a horizontal bar chart than squeezed under vertical bars.
• Drill to Detail for ad-hoc investigation. When a card shows an unexpected number, use Drill to Detail in view mode to see the underlying raw rows - broken down by day or by individual record - without building a separate detail report.
• Clean up unused assets. Cloning produces orphan charts and datasets over time. The Analyst Workspace → Unused items section lists them for review and deletion.

This approach connects naturally to how Bitrix24 CRM Analytics and Sales Dashboards works at the operational level - the BI Constructor provides the custom layer on top of the built-in CRM reports.

Formatting Numbers and Display Defaults

Superset's default number format abbreviates large values (displaying, for example, "37.3M" instead of the full number) - always review display format settings before publishing a dashboard to an audience that expects exact figures.

Key formatting settings to check on every chart:

• Full number with thousands separators - change the number format from the default abbreviated style to display complete figures. This matters for financial dashboards where "37.3M" and "37,312,500" carry different levels of trust.
• Percentage format - for ratio metrics (discount rate, conversion rate, share of revenue), apply percentage formatting directly in the metric/measure settings rather than computing a multiplied field in SQL.
• Axis scale - on combo charts with dual Y-axes, verify that each axis starts at zero or at a meaningful baseline, otherwise relative changes can be visually exaggerated.

BI Constructor Constraints Worth Knowing

The chart type list in Bitrix24 BI Constructor is fixed and shared across all portals - you cannot install a custom or third-party visualization type, and predictive analytics modules are not available in the current configuration.

A few boundaries to be aware of when planning a dashboard build:

For teams managing sensitive data on self-hosted infrastructure, these constraints play differently - see Self-Hosted Bitrix24: Customizing Beyond Cloud Limits for what becomes configurable at the server level.

If you are evaluating Bitrix24's analytics capabilities against competing platforms, the Bitrix24 vs HubSpot comparison covers reporting depth alongside pricing.