PerformanceRanking

Versions

• 1.0.0

v1.0.0

Basic Information

Class Name: PerformanceRanking

Title: Performance Ranking

Version: 1.0.0

Author: Joe Jenkins

Organization: OneStream

Creation Date: 2025-10-20

Default Routine Memory Capacity: 2.0 GB

Tags

Statistics, Data Analysis, Data Transformation, Evaluation Metrics

Description

Short Description

Ranks performance across multiple metrics using weighted percentile averaging.

Long Description

The Performance Ranking Routine evaluates and ranks targets based on a customizable blend of performance metrics. It applies a weighted percentile averaging algorithm to generate a performance score which can compare targets globally or within specified scopes (e.g., geography, product vertical, or business group). Users can define metric weights, select direction preferences (higher or lower), and control the ranking granularity.

This routine is ideal for use cases such as employee evaluation, product benchmarking, and store performance ranking. By generating standardized percentile scores and aggregate rankings, it provides actionable insights that help organizations identify top performers, diagnose underperformance, and make informed, data-driven decisions.

Use Cases

1. Store Performance Ranking

Analyze how each store, branch, or facility performs across financial and operational dimensions, including sales productivity, utilization efficiency, cost structure, and customer experience. Rankings can be generated and compared within specific scopes—such as by region, division, or company-wide—to highlight localized strengths and uncover broader performance trends. These insights enable leadership to identify top-performing sites, address operational bottlenecks, and strategically prioritize investments or process improvements that drive long-term growth and efficiency.

2. Employee Performance Evaluation

Measure and compare individual or team contributions using quantifiable performance indicators such as productivity, quality, efficiency, and collaboration. The resulting scores reveal where excellence is concentrated and where targeted development, coaching, or training could deliver the greatest improvement. By standardizing these results across roles, departments, or business units, leaders gain the ability to align recognition, compensation, resource allocation, and career advancement decisions with consistent, objective data.

3. Product Ranking

Measure and compare individual and team contributions through clearly defined, quantifiable performance indicators such as productivity, quality, efficiency, innovation, and collaboration. The resulting scores pinpoint areas of exceptional performance and reveal where additional development, coaching, or targeted training can deliver the greatest improvement. By standardizing outcomes across roles, projects, or departments, this approach enables leaders to make consistent, data-driven decisions regarding recognition, resource allocation, and career advancement.

Routine Methods

1. Rank Performance (Method)

• Method: rank_performance

  • Type: Method

  • Memory Capacity: 2.0 GB

  • Allow In-Memory Execution: No

  • Read Only: Yes

  • Method Limits: N/A

  • Outputs Dynamic Artifacts: No

  • Short Description:

    • This method performs the core functionality of the Performance Ranking Routine.

  • Detailed Description:

    • It processes the input parameters, applies the ranking algorithm to evaluate entities based on the specified performance metrics, and generates a ranking artifact.

  • Inputs:

    • Required Input
      • Source Data Connection: The source data definition.
        • Name: source_data_connection
        • Tooltip:
          • Detail:
            • Click on the drop down to specify your dataset source.
          • Validation Constraints:
            • This input may be subject to other validation constraints at runtime.
        • Type: TabularConnection
      • ID Column: Unique ID column in the dataset. This column will be excluded from ranking, validated (non-null, int-castable, unique), and merged back into outputs to preserve record identity.
        • Name: id_column
        • Tooltip:
          • Detail:
            • Pick a single column that uniquely identifies each row.
          • Validation Constraints:
            • This input may be subject to other validation constraints at runtime.
        • Type: str
      • Column(s) to Ignore in the Analysis: The column(s) to ignore in the dataset. Defaults to None.
        • Name: ignore_columns
        • Tooltip:
          • Validation Constraints:
            • This input may be subject to other validation constraints at runtime.
        • Type: list[str]
      • Scope Column(s): Optional columns for subdividing rankings. If set, rankings are calculated within each group (e.g., stores by state, employees by department). Defaults to None.
        • Name: scope_columns
        • Tooltip:
          • Validation Constraints:
            • This input may be subject to other validation constraints at runtime.
        • Type: list[str]
      • Metric Selection: Select the metrics to use for performance ranking. These will be the columns used to calculate rankings.
        • Name: metric_columns
        • Tooltip:
          • Validation Constraints:
            • The input must have a minimum length of 1.
            • This input may be subject to other validation constraints at runtime.
        • Type: list[str]
      • Metric Weight: Weight values for each selected metric (0.1 to 100.0). Enter comma-separated values in the same order as selected metrics. All weights must sum to 100.0.
        • Name: metric_weights
        • Tooltip:
          • Detail:
            • Enter weight values separated by commas. Example: 30.0, 75.0, 2.0
            • Order must match your selected metrics.
          • Validation Constraints:
            • This input may be subject to other validation constraints at runtime.
        • Type: str
      • Metric Direction: Direction preferences for each selected metric; is a higher number better or is a lower number better? Enter comma-separated values: higher or lower.
        • Name: metric_directions
        • Tooltip:
          • Detail:
            • Enter directions separated by commas. Example: higher, lower, higher
            • Options: 'higher' or 'lower'. Order must match your selected metrics.
          • Validation Constraints:
            • This input may be subject to other validation constraints at runtime.
        • Type: str

  • Artifacts:

    • Performance Ranking Results.: The performance ranking transformed dataframe. After this routine is run, the dataframe will contain the rankings based on the specified scope and feature columns. Additionally, the routine creates a global ranking based on all data entries.
      • Qualified Key Annotation: performance_ranking
      • Aggregate Artifact: False
      • In-Memory Json Accessible: False
      • File Annotations:
        • artifacts_/@performance_ranking/data_/data_<int>.parquet
          • A partitioned set of parquet files where each file will have no more than 1000000 rows.

Interface Definitions

No interface definitions found for this routine

Developer Docs

Routine Typename: PerformanceRanking

Method Name	Artifact Keys
rank_performance	performance_ranking