PI Banking DWH Model

Unlock insights and make smarter business decisions
About the solution

The PI Banking DWH Model is a central repository of data integrated from a wide range of sources. It allows you to unlock insights and make smarter business decisions, giving you a clearer picture of your data.

Description

The PI Banking Data Warehouse Model is business oriented, and designed to support different business needs from regulatory and daily / weekly / decade / monthly operational and management reporting to very complex ad hoc analysis and simulations. Possibility to integrate data between bank and leasing company or bank and insurance company in one common data warehouse model enabling 360 customer view. It is based on industry standards and implementation best practices.

Key business benefits

Enables business users to generate quickly actionable insights and easily customize or extend DWH capabilities.

Implementation can be done incrementally delivering fully functional phases that will result in integrated management information system.

Minimizes development costs.

Reduces the risk of failure by facilitating an incremental approach to delivering integrated ODS and data warehouse solution.

Fosters collaboration and approval between business and IT, as necessary, to turn business requirements into actionable solutions.

Provides a solid basis for regulatory reporting as well as decision support and executive KPIs.

Enables organizations to achieve modern DWH architecture, logical model with clean relationships between entities and future proof DWH to deal with upcoming requirements.

Use cases

The PI Banking DWH Model is relevant to any businesses in the banking industry. Gain insight into customer trends and historical data – optimize discounting, improve retention and segment your customer base to effectively acquire new customers. The Data Warehouse provides the basis for quality analysis of available data by deriving accurate information.

 

The PI Banking DWH Model is developed since 2010, on the basis of the experience in implementation in different financial institutions: Addiko Bank, Raiffeisen Bank Bulgaria, Bank of Zitouna, Nova Hrvatska Banka (ex. Sberbank Hrvatska).

Key implementation steps

Step 1

Business Analysis

Step 2

Solution Blueprint Creation

Step 3

Design

Step 4

Development

Step 5

Implementation & Training

Step 6

Product & Support

Technical specifications

Consists of more than 500 Entities (Tables) grouped in 31 Subject areas, with more than 5.800 attributes, and more than 1.500 keys. Work on the model is a continuous endeavor, in respect to banking regulations, IFRS standards, New Analytical requirements, New markets, Data modelling standards.

About the company

Leading company in the field of analytical systems implementation and strategic ICT consulting in Southeast Europe. The company is registered in Zagreb and operates from offices in London, Stockholm, Vienna, Ljubljana, Zagreb, Belgrade, Podgorica and Sarajevo with more than 170 experienced consultants. We are specialized in implementation of Intelligent Decision Support Systems and provide implementation services for Data Warehouse, Big Data, Data Integration, Data Engineering, Business Intelligence, Data Science, Data Mining, Machine Learning, Planning and Budgeting, Financial Consolidation, Data Governance, Data Quality, Master Data Management and Data Privacy solutions.

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