Improved Employee Performance
Employees receive actionable, personalized recommendations, allowing them to optimize workflow and enhance productivity.
Technologies used
PostgreSQL
Python
Teradata
Apache Airflow
Motivation
The customer, a leading banking company, struggled to track hundreds of thousands of employees’ performance metrics across multiple departments. The goal was to automate the analysis of incoming financial data and generate personalized recommendations to help employees identify areas for improvement, optimize workflows, and increase overall efficiency while handling large, diverse datasets accurately.
Main challenges
Challenge 01
Data Variability
The banking system included hundreds of thousands of employees and objects with diverse properties, making it difficult to analyze all data accurately. Azati addressed this challenge by designing a robust data collection and processing workflow, ensuring that all user and departmental variations were considered to generate meaningful recommendations.
#1
Challenge 02
Metrics Diversity
With over 1000 financial metrics across departments, it was challenging to provide precise and relevant recommendations for each employee. Azati proposed a tailored scoring and ranking system, combined with Airflow-managed workflows, to accurately calculate and analyze metrics while delivering actionable, personalized recommendations for each department and employee.
#2
Our approach
Understanding the Business
Azati started by thoroughly analyzing the banking client's goals and workflows. This involved discussions with managers, product owners, and department leads to identify key performance indicators, understand existing bottlenecks, and determine how recommendations should be visualized for employees. This step ensured that the system would provide actionable insights aligned with the company's business objectives.
Data Collection and Preprocessing
The team gathered large volumes of financial and operational data from multiple sources, including internal databases and Teradata systems. Data cleaning and normalization processes were applied to remove outdated or irrelevant information and to ensure consistency across departments. Weighting factors were applied to prioritize recent and relevant metrics for more accurate recommendations.
Metrics Analysis and Recommendation Engine
Azati developed algorithms to calculate over 1000 metrics related to employee performance and financial indicators. Metrics were ranked and analyzed to generate personalized recommendations for each employee. The recommendation engine was designed to consider department-specific criteria, ensuring that insights were relevant and actionable for each workflow.
Airflow-based Workflow Management
The system’s data processing and recommendation generation were managed using Apache Airflow. DAGs (Directed Acyclic Graphs) were created to schedule, monitor, and manage all tasks efficiently. This approach allowed for scalable, repeatable workflows, easy error recovery, and timely delivery of recommendations to employees.
Visualization and Delivery
Finally, the recommendations and metrics were presented to employees via a user-friendly interface. Dashboards were designed to clearly highlight areas for improvement, prioritize recommendations, and provide actionable steps. Managers could also monitor overall performance trends across departments, enabling data-driven decisions.
Facing the same challenge?
Bring your complexity. We'll bring the plan. Select a convenient slot to start a conversation with our experts.
Schedule a callSolution
01
Automated Data Collection
The system automatically collects large volumes of financial and operational data from multiple internal and external sources. It ensures that the information used for analysis is clean, consistent, and up-to-date, significantly reducing manual effort and data errors.
Key capabilities:
- Daily automated data extraction
- Data cleaning and normalization
- Prioritization of recent and relevant metrics
- Support for multiple database types including PostgreSQL and Teradata
02
Metrics Calculation and Analysis
The system computes and analyzes over 1000 financial and performance metrics to provide actionable insights. It ranks and weights metrics according to relevance and department-specific priorities, ensuring that recommendations are precise and meaningful.
Key capabilities:
- Computation of diverse financial and operational metrics
- Ranking and weighting of metrics based on relevance
- Department-specific analysis for precise recommendations
- Continuous metric updates for real-time insights
03
Personalized Recommendation Engine
Based on the analyzed metrics, the system generates tailored recommendations for each employee. These recommendations focus on actionable steps to improve performance and optimize workflow efficiency, taking into account individual and departmental goals.
Key capabilities:
- Employee-specific recommendations
- Departmental performance alignment
- Actionable suggestions to improve workflow
- Automatic updates with new data inputs
04
Airflow Workflow Management
Apache Airflow orchestrates the entire recommendation system workflow, scheduling, monitoring, and executing tasks efficiently. It ensures scalability, reliability, and the seamless handling of complex pipelines for large-scale data processing.
Key capabilities:
- Directed Acyclic Graph (DAG) workflow orchestration
- Task scheduling, monitoring, and error handling
- Scalable and repeatable processing pipelines
- Seamless integration with data sources and recommendation engine
05
Dashboard Visualization
Dashboards present metrics and recommendations in an intuitive, visual format. Managers can quickly identify areas of improvement, monitor employee and department performance, and take informed actions to boost productivity and efficiency.
Key capabilities:
- Clear visualization of employee performance metrics
- Prioritized recommendation presentation
- Department-wide performance tracking
- Support for actionable decision-making
Results & business impact
Enhanced Operational Efficiency
Managers can make data-driven decisions, monitor department performance, and identify improvement areas quickly.
Scalable and Reliable System
Airflow-based workflows ensure timely processing of large datasets with minimal errors, improving system reliability and performance by 30–45%.
Data-Driven Insights
Continuous analysis of over 1000 metrics provides actionable insights, supporting performance optimization and strategic planning across departments.
