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Business Logical Data Model (LDM)

By Sreenivas Methuku


Data Model is necessary to convert the business specific functionalities to a structured form. It is the backbone of any applications or data repository to hold the data for day-to-day operation, tactical and strategic need of business. A good design in data model would bring scalability and robustness of a solution. A data model is the skeleton of the data warehouse. A robust design in data model brings the scalability and flexibility to the data warehouse. Typically, there are two different modeling techniques heavily used in the data warehouse. Based on Kimball’s bottom-up architecture, the dimensional data model is used to support analytical reporting requirements and based on Inmon’s top-down architecture, the normalized data model is used for the data warehouse to consolidate enterprise data in a single integrated design. While the normalized design is heavily based on the business process, the dimensional data model design is based on Key Performance Indicators (KPIs) requirements.

Many industry standard data models are available such as FSLDM, IFW, OFSA, etc. mainly focusing at the enterprise requirements. These are mostly in normalized data models and are suitable for Inmon’s suggested enterprise data warehouse. These are cost heavy, technology specific, and need heavy customization before implementing for a specific data warehouse solution. For Ex: FSLDM is in Normalized structure and not suitable for providing analytical business insights. Similarly, OFSA is suitable for Oracle specific products. Because of their enterprise focus, these cannot be used for a specific module or business subject area.

In recent time, most of the organizations are implementing Data warehouse in agile approach. Coforge has experienced with such implementation and has followed Module and sub-module wise solution in each sprint. By following the available industry standard data model, it is quite difficult to implement the data warehouse using agile approach. Due to this reason, there is a need of a data model which is easy for customization and is easy for deployment. Coforge suggests a technology agnostic, highly modularized industry standard data model which is a right fit for the warehouse implementation.

Apart from the above drivers, Coforge considers following parameters to bring a quality data model and its associated solution components.

  • Data Dictionary: Availability of the definition with the data model will give good understanding to the actual coverage of the design. The definition would be applicable to the subject area, entity, attribute level while giving full meaning to the data model. The definition is not only covering the business coverage but also gives the full clarity on how the model would be customized for a specific implementation. For example, the banking specific data model would have the definition covering the calculation of some Basel compliance.
  • Integrated Model: Presence of fully integrated model would support enterprise reporting requirements. Some reports are based on across business groups / functional areas. By having an integrated model, it is easy to traverse from one subject area to other subject area through common entities.
  • Subject Area to Entity matrix: A data model can support to modularized design if it covers different subject areas aligning to process / sub-process of the business functionalities. The business group, by and large, handle a set of process areas. The data model would support the business groups by covering the distinct functionalities, KPIs by having respective subject area. The entities for the subject area would be defined by analyzing the KPIs in terms of key business measures and related perspectives.
  • Analytics Use Cases: Analytics use cases would be there on top of the data model to address some business pain areas either by improving the performance of the business process, or by reducing the cost of the business or by increasing profitability. A hidden business insight can be found from the business data model.

Coforge follows certain best practices while designing these models like: Process oriented approach against Data centric or Source centric approach. Below are the salient features of this framework built from vertical specific LDM:

  • Technology agnostic and platform independent
  • Modular in nature
  • Industry best practices in biz KPIs, analytical templates
  • Business process oriented vertical specific data models
  • Highly customizable, flexible and scalable

Coforge’s Value Proposition

While the pre-defined data model brings the quality solution to the data warehouse implementation, it saves time and cost to the implementation to a large extent. It is solely dependent on the coverage of the data model based on the business requirements. There is no ideal solution to address 100 percentage of the requirements as most of the business processes are very much localized to the specific organization and the local compliance rules. The difference of the requirements from the available functionalities need customization / modification in the existing design. Based on the previous implementation, Coforge finds there is 30 to 50% of cost reduction to the implementation by following the business data model with associated components.


LDM is built while analyzing the business / process specific functionalities. At higher level, it is at subject area level connectivity aligning to the process flow or the way different processes are interdependent of each other. By drilling down the higher level details to granular level, the subject specific data model would be entity relationship, then the entity configuration to highlight how the attributes are configured within the entity. The business definition, data dictionaries, business rules are critical while defining the data model. The functional domain experts provide the details to the data modeler to work on the LDM. Later, the LDM is converted to PDM for a specific database by understanding the availability of datatypes, data lengths, indexing techniques, partitioning techniques. This is how a data modeler play a vital role in bringing a data model by understanding both the business and technical aspects.

Following are key considerations for building the logical data model:

  • The data model is adaptable to change easily and can be configured for any new requirements. It supports different reference data architectures
    • Independent data mart architecture
    • Data mart bus architecture
    • Hub and spoke architecture
    • Centralized DW architecture
  • It has light and highly summarized information for better performance. They help in ensuring consistency among various data sources and applications.
  • Apart from business related columns, following information are also captured in different columns
    • Audit information
    • History information
    • Other technical information.
  • Dimensions are clearly utilized based on their associations with KPIs.
  • Based on different kind of conformed dimensions (Highly Conformed, Medium Conformed, Low Conformed), the analysis is conducted across analytical subject area.
  • Standard set of rules and guidelines are followed for normalized and dimensional data models.
  • Different data modeling approaches depend upon how data is being used in query reporting, multi-dimensional analysis, and data mining.
  • Business data model is an important pre-requisite of quality data. Good quality data structure is critical to a long lasting, easy to maintain system. Presence of complete data dictionary. More emphasis on detailed data.
  • Reusable, scalable, flexible, and manageable data mode is in place. Data model follows standard nomenclatures.
  • Data model's naming standards, domains, etc. are adaptable to all standard database.


Organizations for a specific business domain operate more or less in same way and hence, their business functionalities are same. Even though the requirements at business users level could be different, they align to same set of industry standard KPIs. Building the data model for those KPIs would be applicable to those organizations and would address most of the requirements. A business data model brings following benefits:

  • The data model is adaptable to change easily and can be configured for any new requirements.
  • They help consistency among various data sources and applications
  • It helps in bringing a long lasting, easy to maintain system
  • An integrated model helps in bringing single source of truth in the data warehouse solution
  • It helps in business to IT affiliation by correlating business requirements in the data structure
  • Pre-build components and processes in overall generic data model driven solution lead to quick implementation and help in reducing overall BI implementation cost as well as timeline
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