Skip to main content

Data Lab

Overview​

Data Lab is an interactive analytics app for structured analysis of large datasets. You upload CSV, Excel, or JSON files, define relationships using shared identifiers, and analyze connected data in tables and a Link Chart. Outputs include filtered tables, derived datasets, dashboards, and optional semantic Record creation aligned to the Ontology. AI features can accelerate matching and column creation, but you still review results and validate formulas.

When to Use This Application​

  • You need to correlate multiple datasets using a shared identifier (for example customer ID, phone number, company name).
  • You need to explore relationships as a network using a Link Chart after starting in tables.
  • You need to prepare investigative subsets from raw exports before importing or sharing them.
  • You need dashboard-style analysis to identify outliers, key entities, or patterns quickly.
  • You need to convert tabular data into semantic Records so other Octostar apps can use it.

Before You Begin​

  • Prepare one or more datasets in a supported format:
    • .csv
    • .xls / .xlsx
    • .json
  • Confirm you have access to the Workspace where you plan to upload or save outputs.
  • If you plan to create semantic entities, confirm you know the intended Concept types and key identifiers for each dataset.

Step-by-Step Walkthrough​

Step 1 — Upload your data​

Open Data Lab from the App Launcher or from the item context menu.

Load datasets using one of these methods:

  • Drag and drop one or more files
  • Use the upload function to select files from your device or Workspace

After upload, datasets appear in the Table Chart.

To rename a dataset or change its icon:

  • Right-click the dataset, or
  • Select the three dots on the dataset tab

Step 2 — Define relationships between datasets​

Upload at least two datasets.

Then define a relationship:

  1. Select two datasets.
  2. Select the shared identifier field, or allow AI to suggest one.
  3. Enter a relationship name.
  4. Create the relationship.

After you create a relationship, you can enable Associative Filter to apply relational filtering across datasets. When Associative Filter is enabled:

  • Only records connected across linked datasets remain visible.
  • Unrelated records are temporarily excluded.
  • All tables update together using the same analytical context.

When the relationship is active, an additional column appears in the table view. This column shows how many related records exist in the connected dataset.

Step 3 — Visualize connections in a Link Chart​

Select a relationship and switch to visual exploration mode.

Open the Link Chart to explore network structure:

  • Records appear as nodes.
  • Connections appear as links.
  • You can identify clusters, central nodes, and indirect links.

If you want to keep relationship counts visible while you work in tables, add the relationship column as fixed:

  • Select the column header.
  • Choose Import Content.

Step 4 — Apply filters and explore connected data​

Apply filters to any dataset in table view.

Select values to include. Data Lab updates all related datasets automatically to reflect the selected context.

Use progressive filtering. Start broad and narrow as you confirm what matters.

Step 5 — Run dashboard analysis​

Open the Dashboard view to analyze data using interactive widgets. Data Lab generates initial widgets automatically, and you can customize them. To filter from the dashboard:

  • Select a value in any widget.
  • All connected datasets update to reflect the selected context. To manage widgets and templates, open the dashboard context menu:
  • Move the cursor to the top-right corner to open the context menu.
  • Add widgets.
  • Save widget templates.
  • Load saved templates.

Step 6 — Extract a new table from a key​

Use table extraction when you want a focused dataset centered on an identifier.

  1. Open the column header menu for the primary key.
  2. Select Transform.
  3. Select additional columns to include.

Data Lab creates a new dataset based on the selected key. The original dataset remains unchanged. You can analyze the extracted dataset independently.

Step 7 — Use AI to accelerate analysis​

Data Lab includes AI features for analysis support.

AI-assisted column creation​

In the table view:

  1. Open the top-right context menu.
  2. Create a computed column.
  3. Describe the calculation in natural language. Data Lab generates the formula automatically. Test the formula before you finalize it.

Advanced Data Chat​

Use Advanced Data Chat to ask analytical questions about:

  • The entire dataset session, or
  • A specific table

Enter your query in natural language. You can optionally limit the number of returned records. Select an output format such as tabular results or aggregated summaries.

Step 8 — Export or create semantic entities​

At any stage, export datasets or convert them into semantic entity records. AI can help by suggesting:

  • Entity types (Concepts)
  • Identifiers
  • Relationships based on the Ontology
  • Column-to-attribute mappings Review and adjust mappings before you create records. After creation, semantic entities:
  • Appear in the investigation area
  • Support tagging and annotation
  • Remain available to other Octostar applications

Step 9 — Export the entire session​

Export the full session to preserve your work and continue later. You can export to:

  • Your local computer
  • A Workspace You can re-import the session later to continue analysis.

Understanding the Output​

Data Lab produces outputs that update as you define relationships and apply filters.

  • Filtered tables
  • When you filter one dataset, connected datasets update dynamically. Counts and summary metrics adjust to the current context.
  • Relational count columns
  • When relationships are enabled, Data Lab aligns records using distinct values of the selected key. Rows are not duplicated. The relationship column shows how many related records exist in the connected table. Repeated occurrences consolidate under the same identifier.
  • Link Chart view
  • The Link Chart shows records as nodes and their connections as links. Use it to identify clusters, bridging entities, and unusual network structures.
  • Semantic entity records
  • When you export to semantic entities, records persist beyond the Data Lab session. You can access them in other Octostar applications for investigation and workflows.

Saving and Exporting Results​

Data Lab supports multiple ways to preserve and reuse outputs.

  • Export datasets
  • Export a table or derived dataset for sharing or downstream processing.
  • Supported export formats for tables (CSV, Excel, JSON) and where the export is saved.]
  • Create semantic entities
  • Convert tabular rows into semantic Records aligned to the Ontology.
  • Configure mappings before creation, including identifiers and relationships.
  • Export session
  • Save the entire analysis session so you can re-import it later.
  • Configure whether the session saves locally or to a Workspace.

Tips for Best Results​

  • Select a stable shared identifier when you define relationships (for example normalized IDs instead of free-text names).
  • Enable Associative Filter when you want all datasets to stay synchronized.
  • Start broad and filter progressively to avoid hiding relevant connections early.
  • Review relationship count columns. High counts often indicate key entities.
  • Clean identifier formatting before linking datasets (for example trim whitespace and normalize case).
  • Test AI-generated formulas before you rely on computed columns.

Known Limitations​

  • Identifier formatting differences can prevent relationships from forming (for example COMPANY NAME vs company name). Clean or normalize values before linking.
  • Mixed data types can affect filtering and computed columns (for example numbers stored as text). Convert types where needed.
  • Over-filtering early can hide relevant connections across datasets. Apply filters progressively.
  • AI-assisted features can produce incorrect formulas or mismatched identifiers. Validate outputs before exporting or creating records.