Technical Writing – Enterprise Data Model

Posted: December 13, 2013 in Technical writing

Enterprise Data Model Diagram

Information Model

Examples of qualifying evidence may include an enterprise data model for an organization, an enterprise master data model, a metadata data model, a data warehouse or data mart model, or a model for a content management data store. The models can be conceptual, logical, or physical.

Any logical model should be of sufficient detail to support user requirement analysis and component modeling. Be prepared in the interview to:

* Describe the modeling techniques and methods you used.

* If the model was implemented, describe what performance and availability requirements you had to meet

Maxis BI Transformation

Maxis started a Transformation project for Business Intelligence (BI) solution to understand market drivers and changes as well as customers’ needs. This would enable Maxis to respond quickly to changes – to thwart threats and proactively take advantage of opportunities. This opportunity included to build end-to-end integration of existing system with IBM Telecom Data warehouse (TDW) using DataStage and reports are build using Cognos.

I was involved in Maxis BI as Information Architect to integrate the Interconnect, Dealer Management, RIMS and MIMS with Maxis EDW. Maxis EDW build using IBM Telecom Data Warehouse (TDW) industry model. I had followed industry model development pattern for building the SOR (System Of Record), SOR Contains the atomic level transactional and reference data required for the EDW. I had analyzed the source data for all the applications as well as reporting requirements to build the Logical Data Model on TDW SOR.

For presentation layer I followed the Dimensional modeling pattern using summarized data on top of SOR, because producing report directly from SOR is performance incentive, It is also TDW best practice to built dimensional model on top of SOR for analytical reporting. I had followed some industry best practice principal to build the Dimensional Data store (DDS) model. Given below:

Time periods are either uniform or can be rolled up into uniform periods.

Several Summary entities may need to be used to get a complete picture.

Populated from the System of Record.

Records time-variant numerical data about entities.

Use to run report queries and Ad-hoc queries, but as some of the aggregation is in “Subscriber” level, it has to be judiciously chosen.

The Lookup tables will store the “Descriptive” values and corresponding unique Identifier

Revenue Assurance SMB

The Intent of the Revenue Assurance SMB project was to provide an effective Revenue Management solution for Bell Canada Small and Medium Business domain and as this was the first project under revenue assurance 2 years roadmap and another objective was to built a method and Architecture framework for entire RA Roadmap.

I developed enterprise revenue assurance framework for Bell Canada program. I developed the Dimensional Data Models for SMB project, because objective of the RA application was mainly for reporting purpose. Dimensional modeling has some advantages over normalization method for BI application. Dimensional model optimized for data querying, while normalized models seek to eliminate data redundancies and are optimized for transaction loading and updating. The predictable framework of a dimensional model allows the database to make strong assumptions about the data that aid in decision making. I also prepared guidelines for future data model development for RA program. Also analyzed the Reporting requirements and designed the CUBE (s) for those.

I produced the following Models:

Conceptual Data Model

Data Mart

Process Model

I designed the Data Integration required for the Bell Canada Revenue assurance Project. I used Architectural thinking approach to select the RA product, ETL and BI tool required to Support the RA 2.0 application.

Please refer the Bell Canada Revenue Assurance SMB Project Profile for More Details.

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