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Data often has natural levels — accounts belong to account groups, securities belong to sectors, cities belong to states. Hierarchy configuration lets you define these drill-down paths on a Virtual Data Mart so you can explore your data from the top level down to the details.

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When to use hierarchies

Use hierarchy configuration when your data has a parent-child or multi-level structure and you want to:
  • Click on a summary value and see the breakdown underneath
  • Navigate from broad categories down to individual records
  • Build dashboards that let users explore data at their own pace

Chart Interactions

Link separate charts so clicking a value on one filters the next. Ideal for step-by-step exploration across dashboard panels.

Hierarchy Charts

Show all levels in a single visualization like a TreeMap or Sunburst. Ideal for seeing the full picture at a glance.

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Common examples

Data domainDrill-down path
AccountsAccount Group → Account → Position
GICS classificationSector → Industry Group → Industry → Sub-Industry
IssuersIssuer → Security
GeographyCountry → State → City
OrganizationDivision → Department → Team

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Define a hierarchy on a VDM

1

Open your VDM

Go to DataBridge → Virtual Data Marts and open the VDM you want to configure.
2

Go to the Hierarchy section

In the VDM editor, navigate to the Hierarchy Configuration section.
3

Create a new hierarchy

Click Add Hierarchy and give it a name (e.g., “Account Hierarchy”) that describes the drill path.
4

Add levels in order

Add levels from the broadest category to the most granular detail. For each level:
  • Name — a label for this level (e.g., “Account Group”)
  • Column — pick from your VDM columns (e.g., ACCT_GROUP_CD)
  • Label column (optional) — if the column is a code, pick a human-readable column (e.g., ACCT_GROUP_NAME)
The order matters. Level 0 is the top of the hierarchy and each subsequent level drills deeper.
5

Save and publish

Save the VDM. The hierarchy is now available when creating experiments from this VDM.

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Example: Account hierarchy

LevelNameColumnLabel column
0Account GroupACCT_GROUP_CDACCT_GROUP_NAME
1AccountACCT_CDACCT_NAME
2PositionSEC_NAME—
A user exploring this hierarchy would start by seeing account groups with aggregated totals, click into a group to see individual accounts, then click an account to see its positions.

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Import hierarchies from a BI model

If you are importing a BI semantic model into a VDM, hierarchies defined in the model are carried over automatically during import. You do not need to recreate them manually.
  1. Go to DataBridge → Virtual Data Marts → Import from BI
  2. Select the BI model and client
  3. The import translates the model’s hierarchy definitions into VDM hierarchy configuration
  4. Review the imported hierarchies in the Hierarchy Configuration section
  5. Save and publish
When you sync an existing VDM with its BI model, new hierarchies from the model are added without removing or modifying any hierarchies you have already defined.

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Use hierarchies in dashboards

Once a hierarchy is defined on a VDM, you can use it when building experiments and dashboards. There are two approaches depending on how you want users to interact with the data.

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Approach 1: Linked charts (progressive drill-down)

Create separate charts for each level and link them using chart interactions. When a user clicks a value on one chart, the next chart filters to show the details.
1

Create charts for each level

In your experiment, create one chart per hierarchy level:
  • Chart 1: Bar chart grouped by ACCT_GROUP_CD, showing SUM of MKT_VAL
  • Chart 2: Bar chart grouped by ACCT_CD, showing SUM of MKT_VAL
  • Chart 3: Table grouped by SEC_NAME, showing MKT_VAL and QTY
2

Link the charts

On Chart 2, open the chart menu and set Interaction to Chart 1. On Chart 3, set Interaction to Chart 2.
3

Test the drill-down

Click a bar on Chart 1 (e.g., “Fixed Income Group”). Chart 2 updates to show only accounts in that group. Click an account on Chart 2 — Chart 3 shows the positions in that account.
This approach works well for dashboards where you want to show multiple levels side-by-side and let users drill at their own pace.

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Approach 2: Hierarchy chart (all levels in one visualization)

Use a chart type that natively renders hierarchical data — the hierarchy levels are all visible at once inside a single chart.
1

Create a hierarchy chart

Add a chart and choose a hierarchy-capable type such as TreeMap, Sunburst, or Pack.
2

Set multiple dimensions

Add your hierarchy columns as dimensions in order. For example:
  • Dimension 1: ACCT_GROUP_CD
  • Dimension 2: ACCT_CD
Add your measure (e.g., SUM of MKT_VAL).
3

View the result

The chart renders the parent level as outer sections and the child level nested inside. A TreeMap shows groups as large tiles with account tiles inside; a Sunburst shows groups as inner rings with accounts in outer rings.

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Which approach to choose

ConsiderationLinked chartsHierarchy chart
Number of levelsAny (2, 3, 4+)Best for 2 levels
User interactionClick to progressively revealAll levels visible at once
Dashboard layoutMultiple panelsSingle panel
Best forDetailed explorationOverview and proportions
You can combine both approaches in the same dashboard — use a Sunburst for an overview panel and linked bar charts for detailed exploration.

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Supported hierarchy chart types

These chart types display multi-level data within a single visualization:
Chart typeBest for
TreeMapProportional area comparison across levels
SunburstRadial breakdown showing part-to-whole relationships
PackBubble nesting to show containment
Voronoi TreeMapSpace-efficient alternative to TreeMap
TreeOrganizational or structural relationships
Partition (horizontal/vertical)Level-by-level breakdown in a linear layout
Grouped ColumnSide-by-side comparison across 2-3 levels
For drill paths with more than 2 levels, use the linked charts approach.

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Import via SDK

When creating dashboards through the SDK, use hierarchy chart types and order your dimensions array from broadest to most granular: 
{
  "name": "Account Position Dashboard",
  "dataSource": {
    "dbId": "D2026A00002",
    "vdmId": "VDM2026A00001",
    "connType": "VDM"
  },
  "charts": [
    {
      "title": "Market Value by Account Group",
      "type": "treemap",
      "measures": [{ "field": "MKT_VAL", "aggregation": "sum" }],
      "dimensions": ["ACCT_GROUP_CD", "ACCT_CD"]
    }
  ]
}
The first dimension becomes the parent level and subsequent dimensions become child levels in the visualization. For linked-chart dashboards, create separate charts in sections and set up interactions after import through the experiment editor.