Service Background

Case study: Skarbnytsia, a company with over 400 branches, transitioned from a legacy system to a scalable data ecosystem.

Tags:
Business Intelligence (BI)

About the project

Transformed a large volume of historical transactional data into a transparent and manageable decision-making system

Skarbnytsia is the largest pawnshop chain in Ukraine, which has been engaged in private lending since 1992. The brand has over 400 branches in 97 cities. The company operates on the basis of its own CRM system. It offers high standards of service to its customers. In addition, it has a modern mobile application and loyalty programs. Skarbnytsia is constantly modernizing the industry and introducing innovations. The team proves that financial support can be fast, affordable, and secure at the same time.

The client's problem

The customer approached us with a request to develop a data warehouse. The brand was operating on an outdated ERP system. It did not meet modern requirements for analytics and scaling. Large arrays of historical transactions were stored in it without the possibility of export and integration. In addition, they were not synchronized with modern data tools.

Our client was unable to work with key data areas, in particular:

  • launching user segmentation;
  • building personalized communication.

Our main task was to develop a mechanism that would allow us to fully utilize data for business development.

Case information

Data transformation at Skarbnytsia has become a strategic asset for business development

  • Our team created a centralized data warehouse for analytics for Ukraine’s largest pawnshop network.

  • Our approach:

  • We began our work with a detailed analysis of the client’s strategic goals and wishes. After that, we set about describing all existing IT systems, their limitations, interrelationships, and real possibilities for working with data. Next, we formed an architectural vision for the case.

    The next step was to create a set of technological tools that effectively support business goals. After agreeing on the budget and architecture, we moved on to building a centralized data warehouse based on Microsoft solutions. Then we transferred the project to a Time & Material model, which allowed us to easily respond to technical risks and adjust our work in real time.

Work results:

  • Working together with the customer’s IT team yielded excellent results. We developed a scalable data warehouse, laying the foundation for all future analytical initiatives. Key achievements:

  • implemented bronze and silver levels of data storage;

  • set up automatic rules for updating indicators;

  • introduced quality and consistency checks;

  • reduced the dependence of ETL processes on changes in Legacy ERP;

  • increased the flexibility and stability of the entire architecture.

  • As a result, the customer received a reliable and effective platform that allows them to confidently move towards data-driven solutions.

Key facts about the project

12 months

Project duration

Large

Project size:

Project Detail

Project complexity

Completed

Project status:

Our Team: Project Manager Business Analyst Data Engineer Data Analyst Backend Developer DevOps Developer

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