Challenges

1. Merging data from diverse HR platforms
2. Navigating multiple disparate HR systems, which had created data redundancies and inconsistencies, making it hard to extract a single source of truth
3. Development of data flow mappings
4. Data quality management implementation
5. Data masking for personally identifiable data based on access level
6. ETL design and implementation data integration into the global DWH solution
7. Making sure that the cloud-based HR solution was compatible with other business tools and systems used in different departments, which was crucial for holistic business operations

Goal

Build SaaS for offices all over the world to manage the administrative processes required to track and support employees

Results

1. HR teams, managers, and employees have secure access to necessary data from anywhere and real-time analytics
2. Migration for 39 countries implemented successfully, taking into account all regulations
3. Developed, reinvented, and modernized the HR system for all HR-related tasks and procedures
4. Advanced encryption and security protocols ensured sensitive HR data is protected
5. Collaboration and self-service capabilities make employees more engaged in teamwork. Satisfaction has increased by 50%
6. Various HR tools and applications were easily integrated, breaking data silos

Challenges

1. Data analysis and visualization from different sources through all departments
2. Hypothesis validation, implementation and the deployment of predictive models
3. Setting up Big Data infrastructure in AWS Cloud
4. Implementation of data quality management
5. Row Level Security implementation for secure access
6. Costly hardware and software licensing

Goal

Create a road traffic analysis solution to manage big volumes of data and cut financial losses

Results

1. The company benefits from up-to-the-minute insights into traffic patterns and congestion, enabling it to provide more accurate and timely information to its customers
2. The Client’s management now makes better-informed decisions, leading to optimized operations, and improved service quality
3. The risks of financial losses are eliminated with the help of protection against unauthorized access
4. Data leaks and compliance violations were eliminated

Challenges

1. Seamlessly blending modern solutions with the financial institution’s existing legacy systems
2. Providing systems that can handle exponential data growth without performance hitches
3. Navigating changes in financial regulations across regions
4. Continuously innovating in a rapidly evolving technology landscape
5. Data Ocean Design based on the Data Vault methodology
6. Presentation Layer Design for reporting systems – Mart and Dimensions
7. Row Level Security implementation for secure access by end users to the reporting system

Goals

1. Develop a Cloud-Based Financial Risk Analysis Platform
2. Build Purchase to Pay Golden Model
3. Design a User-Friendly Order-to-Cash Reporting System

Results

1. Optimized efficient data management, operational processes: transaction processing, risk assessment customer service
2. Enabled data scientists and other programmers to leverage innovations such as AI and ML efficiently
3. Created a data model for deep analysis, strategic planning, and forecasting
4. Built Purchase to Pay and Order to Cash Golden Models that allow end users to construct reports themselves in a BI tool