What is Data Analytics System?

Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.


  • Collect data from any sources system and merge it into one data store

  • Provide Full automated reporting system for any data

  • Analyze data and figure out the data dependencies

  • Build data models based on the most famous prediction algorithms

  • Supports both classification and regression algorithms

  • Facilitate the data processing aligned with IFRS9 standard to build an Expected Loss Model 

  • Exhaustive techniques and models for expected credit loss reconciliation/attribution analysis and amortization of deferred balances

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1-   Data Collection and Cleansing

Oracle Data Integrator (ODI): is an Extract, load and transform (ELT) (in contrast with the ETL common approach) tool produced by Oracle that offers a graphical environment to build, manage and maintain data integration processes in business intelligence systems.


  • High-performance bulk data movement and data transformation

  • ELT architecture for improved performance and lower TCO

  • Heterogeneous platform support for enterprise data integration

  • Knowledge modules for optimized developer productivity and extensibility

  • Service-oriented data integration and management for SOA environments

 2-   Classification & Measurement

ALTANMYA DAS will do the following:

  • Analyses the data source tables and find out the columns correlation to the selected target column.

  • Create snapshots of the original data and keep it for tracking and historical reporting purposes.

  • Apply calculations, formulas and cleansing rules on the snapshots.

  • Create data models using Classification / Regression algorithms.

  • Train the system on the snapshot historical data.

  • Get the best accurate data model and select it as a primary model for this prediction.

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3-   Calculate Impairment & Provisioning

  • After Creating Training Flow for each prediction (PD, LGD, EAD, …), Now it’s Time to apply the prediction on the current active customers. So we need to:

  • Create Apply Flow process to apply the selected training models on the target data

  • Calculate the prediction

  • Merge all results with the source data

  • Apply any dynamic formula on the data

4-  Reporting Console

  • Create Any Report from all available source and result data stores

  • Create Dynamic reports and filters

  • Create Charts and pivots

  • Export Data to Excel

5-  Implementations Phases

  • Analysis Phase: Data structures collection and review data sources

  • Design: Design the data mapping processes ETLs


  • Install and configure DB – Technical

  • Install and configure applications and ODI - Technical

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  • Implement the ETL processes in the ODI – Technical

  • Build the training models and apply flows - Business

  • Build reports – Business

  • Estimated project time is 4 months.

The time and cost is subject to the data sources and data types.


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