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PerformanceRanking

Versions​

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: Must be an instance of Tabular Connection
          • Nested Model: Tabular Connection
            • Required Input
              • Connection: The connection type to use to access the source data.
                • Name: tabular_connection
                • Tooltip:
                  • Validation Constraints:
                    • This input may be subject to other validation constraints at runtime.
                • Type: Must be one of the following
                  • SQL Server Connection
                    • Required Input
                      • Database Resource: The name of the database resource to connect to.
                        • Name: database_resource
                        • Tooltip:
                          • Validation Constraints:
                            • This input may be subject to other validation constraints at runtime.
                        • Type: str
                      • Database Name: The name of the database to connect to.
                        • Name: database_name
                        • Tooltip:
                          • Detail:
                            • Note: If you don’t see the database name that you are looking for in this list, it is recommended that you first move the data to be used within a database that is available within this list.
                          • Validation Constraints:
                            • This input may be subject to other validation constraints at runtime.
                        • Type: str
                      • Table Name: The name of the table to use.
                        • Name: table_name
                        • Tooltip:
                          • Validation Constraints:
                            • This input may be subject to other validation constraints at runtime.
                        • Type: str
                  • MetaFileSystem Connection
                    • Required Input
                      • Connection Key: The MetaFileSystem connection key.
                        • Name: connection_key
                        • Tooltip:
                          • Validation Constraints:
                            • This input may be subject to other validation constraints at runtime.
                        • Type: MetaFileSystemConnectionKey
                      • File Path: The full file path to the file to ingest.
                        • Name: file_path
                        • Tooltip:
                          • Validation Constraints:
                            • This input may be subject to other validation constraints at runtime.
                        • Type: str
                  • Partitioned MetaFileSystem Connection
                    • Required Input
                      • Connection Key: The MetaFileSystem connection key.
                        • Name: connection_key
                        • Tooltip:
                          • Validation Constraints:
                            • This input may be subject to other validation constraints at runtime.
                        • Type: MetaFileSystemConnectionKey
                      • File Type: The type of files to read from the directory.
                        • Name: file_type
                        • Tooltip:
                          • Validation Constraints:
                            • This input may be subject to other validation constraints at runtime.
                        • Type: FileExtensions_
                      • Directory Path: The full directory path containing partitioned tabular files.
                        • Name: directory_path
                        • Tooltip:
                          • Validation Constraints:
                            • This input may be subject to other validation constraints at runtime.
                        • Type: str
        • ID Column: Optional unique ID column in the dataset. This column will be excluded from modeling, 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. Leave blank if you don't have one.
            • 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 NameArtifact Keys
rank_performanceperformance_ranking
Last updated on by Jeremy Meiss

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