Skip to main content

Functional Specification: Data Workbench UI

Version: 1.0 Date: October 17, 2025 Status: Final Draft

1.0 Executive Summary​

The Data Workbench is ChainAlign's central hub for data quality management. It is the user-facing interface for the insights and tasks generated by the Data Entry Intelligence Service. This UI transforms data quality review from a tedious manual process into an intelligent, guided workflow that amplifies human judgment with AI-powered suggestions.

Purpose: To provide a task-oriented environment where users can efficiently review incoming data, resolve quality issues, confirm schema mappings, and approve data for use in core planning services.

2.0 Design Principles​

  1. Task-Oriented: Users work through a prioritized queue of discrete tasks, not raw data dumps.
  2. AI-Assisted: The UI provides smart suggestions and resolution options, not just problems.
  3. Context-Rich: Every task shows why something needs review, not just what needs review.
  4. Progressive Disclosure: The UI shows a summary first, with details available on demand to avoid overwhelming the user.
  5. Learning System: The UI captures user feedback and decisions to continuously improve the backend intelligence services.

3.0 Information Architecture​

The Data Workbench will be a new top-level section in the ChainAlign application, organized as follows:

  • Dashboard: A high-level overview of data quality health and pending tasks.
  • Task Queue: A prioritized, filterable list of all data quality tasks requiring user attention.
  • Task Detail Views: Dedicated pages for resolving specific task types (Schema Mapping, Quality Review, Anomaly Resolution).
  • Data Entries: A historical log of all data import jobs and their status.

4.0 Page-by-Page Functional Specification​

4.1 Page 1: Data Workbench Dashboard​

  • FR-1.1 (Key Metrics): The dashboard must display summary KPIs: Total Pending Tasks (with a count of critical tasks), Average Data Quality Score (with a 7-day trend), and Recent Data Entries.
  • FR-1.2 (Critical Task List): It must feature a list of the top 3-5 most critical tasks that require immediate attention.
  • FR-1.3 (Analytics): It must include visualizations for Quality Score Trends over time and a breakdown of the most common data quality issues.

4.2 Page 2: Task Queue​

  • FR-2.1 (Filtering & Sorting): The queue must be filterable by task type (Schema Mapping, Quality, Anomaly) and priority (Critical, High, Medium, Low). It must be sortable by priority and creation date.
  • FR-2.2 (Task Cards): Each task must be represented as a card containing a priority indicator, task type, a clear title, summary context (source, record count, quality score), a summary of the key issues or AI suggestions, and a primary action button (e.g., "Review Task").

4.3 Page 3: Schema Mapping Task Review​

  • FR-3.1 (AI Suggestions): The UI must present AI-suggested mappings, clearly indicating the confidence level for each.
  • FR-3.2 (Batch Actions): The UI must allow users to batch-approve all high-confidence (>90%) suggestions with a single click.
  • FR-3.3 (Manual Review Interface): For each field needing manual review, the UI must show the source field name, sample values, the AI's suggestion with its reasoning, and a clear set of user actions: Accept Suggestion, Map to Different Field (with a searchable dropdown), Create New Field, or Skip.
  • FR-3.4 (Transformation Support): The interface must provide an optional input for defining transformation formulas (e.g., converting units, calculating a new value).

4.4 Page 4: Data Quality Review​

  • FR-4.1 (Issue Grouping): The UI must group similar quality issues together for efficient review (e.g., all "Missing revenue_actual" issues).
  • FR-4.2 (Context & Samples): For each issue group, the UI must display the severity, business impact, and a sample of affected records.
  • FR-4.3 (AI Recommendations): The UI must present an AI-generated recommendation for how to resolve the issue (e.g., "Negative demand likely represents returns. Recommend moving to a separate returns_actual field.").
  • FR-4.4 (Resolution Options): The UI must provide a clear set of resolution options for the user, allowing them to apply a decision to all records in the group (e.g., Convert to absolute value, Set to NULL, Reject these records).

4.5 Page 5: Anomaly Resolution​

  • FR-5.1 (Anomaly Grouping): Similar to the quality review, the UI must group similar anomalies (e.g., all statistical outliers for on_hand_quantity).
  • FR-5.2 (Detection Transparency): For each anomaly group, the UI must explain how the anomaly was detected (e.g., "on_hand_quantity > (mean + 3*stddev)") and provide a visualization (e.g., a histogram) showing the outliers.
  • FR-5.3 (AI Insight): The UI must provide an AI-generated insight explaining possible business reasons for the anomaly (e.g., "This could be a seasonal build-up or a data quality issue.").
  • FR-5.4 (Resolution Options): The UI must allow the user to classify the anomaly (e.g., Expected behavior, Data quality issue, Business issue to investigate) and apply that decision to the entire group.

5.0 API Dependencies​

This UI is entirely dependent on the backend API endpoints provided by the Data Entry Intelligence Service.

  • GET /api/workbench/dashboard: Fetches the summary data for the main dashboard.
  • GET /api/workbench/tasks: Fetches the list of tasks for the queue, with support for filtering and sorting.
  • GET /api/workbench/tasks/:taskId: Fetches the detailed information for a specific task, including AI suggestions and sample data.
  • POST /api/workbench/tasks/:taskId/resolve: Submits the user's resolution for a task, which triggers the backend learning loop.
  • GET /api/workbench/entries: Fetches the historical log of all data import jobs.

6.0 Success Criteria​

  1. Efficiency: The average time to resolve a "Needs Review" data entry is reduced by 70% compared to manual spreadsheet-based cleaning.
  2. AI Accuracy: The acceptance rate for AI-suggested schema mappings and issue resolutions is above 80%.
  3. User Adoption: The Data Workbench becomes the standard, required workflow for all new data imports.
  4. Data Quality Improvement: The average tenant-wide Data Quality Score increases by at least 15 points within 3 months of adoption.