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Component Data Model
Description
Data Model standardises the relationship between the data elements and defines rules for their pro-
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2 Data Warehouse
Data Warehouse is used for reporting and data analysis. It consists of relational database & analytics platform(s)
3 BI Reporting Solution
BI Reporting Solution uses data from multiple sources to prepare it for analysis and present it in a single report
4 ETL
ETL Solution extracts data from multiple sources, transforms the same to a standard format and loads
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Decision Options Industry Standard Data Model
Data Model pre-built by Service Provider Customer Built Data Model
Appliance Standard DB on Hardware Stand-alone applications Analytics embedded within DWH
Tools from Data Warehouse Vendors Specialised Tools for ETL
Data model Industry standard data model Standard data models have significant in-built wisdom around the reporting needs of various user groups and thus can accelerate the implementation of the data warehouse. Industry standard data models are preferable if the reporting needs of businesses are standard, and the applica- tion landscape is homogenous.
Data model pre-built by service provider If the reporting needs of businesses are specific, an industry standard data model will need to be adjusted significantly to meet their needs. This will undermine the case for a standard data model. Such busi- nesses are better off deploying a custom data model or a pre-built data model from a DWH implementation partner.
Custom built data model If the level of standardisation of require- ments is really low, businesses are better off investing in a custom data model. The choice of custom data model is also driven by the need for unique insights that are not available through standard data models.
Data warehouse Standard DB on hardware The conventional approach is to imple- ment data warehouses in standard data- base and hardware. If real-time analytics are not required by the business and the transaction volumes and querying require- ments are relatively low, this approach will help optimise the investment.
Appliance In some cases, for instance when real-time
analytics and Big Data are involved, perfor- mance of DWH queries are limited by the constraints of underlying hardware. The “appliance” model of integrated database and hardware is much more suitable for these requirements. With the increasing adoption of mobile channels, social media and other real-time pricing engines, the appliance model is the preferred option for large-scale DWH implementations.
Analytics applications Stand-alone applications For complex and specialised reporting requirements, dedicated analytics engines will be necessary. These engines could either be part of the DWH platform, or be separate stand-alone applications. The case for stand-alone applications is stronger when the nature of reporting requirements vary frequently due to factors such as regulations.
Analytics embedded within DWH For less complex requirements, the procedures can be embedded within the data warehouse platform. However, this choice is suitable only if the analytics requirements are likely to remain constant over a period of time. Changes in analytical requirements could require re-design of the data warehouse, risking the integrity of the platform.
Implementation partner Finally, the role of the implementation partner is critical for the success of the programme. The implementation partner takes end-to-end responsibilities for the following activities:
• Extraction of data from source © IBS Intelligence 2016
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Data quality assessment and cleansing
History data migration from ODS Data warehouse Installation Data model customisation
Database and query performance tuning
Report development
New core banking system source integration
Installation of environment Post go-live support
Project and change management
Given the wide range of responsibilities, the implementation partner should be chosen by careful consideration of track record, solution approach, implementation capabilities and specific team resources who will be deployed on the programme. A data warehouse strategy initiative can help businesses make these decisions and set the right foundation for their DWH programme.
About the author
Sudeep Nair is a Senior Director at Cedar Management Consulting Inter- national, a US-based management consulting firm. Sudeep has over 15 years of international consulting expe- rience (USA, UK, Continental Europe & India) and is a recognised expert in delivering significant improvements for businesses across organisation, process and technology. He can be reached at:
Sudeep.Nair@cedar-con-
sulting.com
data warehousing
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