In the era of rapid digital technology advancements, data is widely recognized as a valuable asset for organizations. Efficient handling of vast data volumes is imperative for businesses at large, especially within the financial sector, to maintain a competitive edge. Consequently, forming partnerships with a reliable technology partner becomes essential.
EVOTEK and the partner serve as a notable illustration of a robust alliance committed to developing efficient data processing solutions. The success of two collaborative projects, focusing on optimization, integration, analysis, and data management, attests to EVOTEK’s substantial capacity and wealth of experience.
1. Incentive Program Data Analysis Project
This project was undertaken to analyze data from corporate customers participating in incentive programs. The primary emphasis was on establishing a data warehouse to consolidate information from diverse sources, followed by in-depth analysis aimed at facilitating informed decision-making for customers.
Category: Data Analytics Data Warehouse
Expertise: IBM DataStage IBM Netezza
- Challenges in collecting and integrating data arise from the dispersion and diversity of data sources across various information systems within the organization.
- The quality of input data poses a potential impact on the performance of analytical models, potentially resulting in outcomes that do not align with expectations.
- The complexity of operations and frequent changes in incentive programs contribute to a high volume of updates and adjustments required in the data processing workflow.
- Collection and Cleansing of Terabytes of Heterogeneous Data: The task involves gathering and cleaning vast amounts of diverse data from multiple sources.
- Time- and Resource-Intensive Data Cleaning and Standardization: The process of cleaning and standardizing data demands a significant investment of time and effort.
- Frequent and Complex Business Changes: The dynamic nature of the business requires continuous updates to ETL processes and analytical models.
- High Security Standards for Customer Data: Given the sensitivity of customer data in the financial industry, Technology solutions must ensure absolute information security.
- Establish an ETL (Extract – Transform – Load) process designed to extract, transform, and load data from various sources into a centralized data warehouse. The process is meticulously designed to ensure data consistency and quality.
- Develop data analysis models and algorithms to support decision-making and personalize services for customers. These models and algorithms are constructed based on a profound understanding of the financial industry and incentive operations.
- Integrate and operate data mining and analysis solutions on modern technology platforms, including IBM DataStage and IBM Netezza
- Successfully constructed a data warehouse that seamlessly integrates corporate customer information participating in incentive programs from various data sources.
- Efficiently collected and processed terabytes of data.
- Achieved a 100% clean, standardized, and successful integration of data into the data warehouse system for analysis.
- Successfully developed analytical and machine learning models with high accuracy, enabling forecasts and recommendations for incentives.
- Accomplished the seamless connection and integration of analytical data into the customer management system to support decision-making.
- Enhanced sales efficiency for incentivised products, resulting in significant economic benefits for the institution.
2. Data Lake Project: Customer Data Consolidation
With a sizable number of subordinate distributors, the management of customer data becomes a complex undertaking. EVOTEK is entrusted with the responsibility of consolidating customer data across the entire scope of the parent company. Simultaneously, EVOTEK conducts business data analysis operations on this unified dataset.
Category: Data Analytics Data Warehouse
Expertise: HDFS, Spark, Kafka Oracle, Postgre Tableau, Airflow Netezza
Each distributor is managing customer data individually, leading to data fragmentation and difficulty in implementing cross-marketing campaigns between distributors.
- Complexity of IT Systems: Consolidating distributed and inconsistent IT systems among distributors presents a substantial challenge, necessitating extensive system integration efforts.
- Differences in Data Models: Variations in the storage and modeling of customer data among distributors create difficulties in synchronizing and synthesizing data.
- Resistance from Sales Departments: Some distributors express apprehension about sharing sensitive customer data with the corporation.
- Human Resources with Data Skills: Additional staff with expertise in data technology is required to operate the systems after the project’s completion.
- Evotek addresses these challenges by consolidating all customer data from the Group’s distributors into a unified Data Lake. The Data Lake is constructed using HDFS technology and is integrated with big data processing technologies such as Spark and Kafka.
- Centralizing customer data enables the use of analytical applications like Tableau to analyze and visualize data, thereby supporting decision-making. An ETL system based on Airflow is implemented to periodically synchronize data from original sources (Oracle, Postgres, Netezza) into the Data Lake.
The project successfully establishes a unified customer dataset for the entire system, enabling personalized experiences and tailored services for each customer. This achievement significantly enhances customer satisfaction and brand loyalty.