Romulo A. Rios
Siebel Certified Analytics Master Consultant
Projects
- eMeter - Siebel Certified Master Analytics Consultant - 9/2006 - 12/2006 - Case History: The Power Information PlatformTM by eMeter software (PiPe) was developed as a high-function, multi-vendor, multi-technology Meter Data Management (MDM) solution designed to support all aspects of small and large-scale AMI implementations. The PIPe completes a utility's AMI using a Common Information Model specifically designed to capture the complex relationships between assets, premises, customer accounts, users, applications and services that must be managed in any successful AMI program. Incorporating automated business processes and workflows, eMeter uses Siebel eBusiness application for the energy sector and it is now serving over 10 million electric, gas and water meters in the US.
Accomplishments The objectives were to implement Activity, Asset, Agreement and Service Request star schemas from the CRM model of Siebel Analytics application (CME) as well as to built a custom mart for consumption data analysis of register meter and time interval meter reads. The source was a PiPe implementation of Siebel CME 7.8.5 for an Electrical Utility company in Florida with over 4 Million meters installed. It was important to cover all analytics functionalities for this client. But it was equally important to eMeter's team to learn how to implement this application for future engagements. Both objectives were accomplished in nine weeks, one week late due to hardware procurement delays.
Activities Included:
Analytics Business Analysis: Translating high-level business requirements into logical business dimensional model; Defining various logical subject areas and organization of presentation layer metadata; Understanding and defining key metrics, dimensions, hierarchies and attributes.
Analytics Architecture: Translating logical business dimensional model in base physical DB design; Identifying necessary aggregations and defines aggregate requirements and strategy; and Creating analytics server meta data.
- General Motors/IBM Global Business Services - Siebel Certified Master Analytics Consultant - 5/2006 - 6/2006 - Case History: ETL team was giving the task to build report specific table to fulfill requirements for a set of reports in the area of Service Request, Activities and Agent.
Accomplishments: IBM asked to see a prototype on how using Siebel best practices and reports based on time series analysis could be developed in Siebel Analytics 7.8.2, eliminating the need to build report specific tables. Building this kind of tables is not recommended by either Siebel or Industry best practices since it is the result of poor multidimensional database modeling and data warehouse design.
Activities Included:
Analytics Business Analysis: Translating high-level business requirements into logical business dimensional model; Defining various logical subject areas and organization of presentation layer metadata; Understanding and defining key metrics, dimensions, hierarchies and attributes.
Analytics Architecture: Translating logical business dimensional model in base physical DB design; Identifying necessary aggregations and defines aggregate requirements and strategy; and Creating analytics server meta data. - Whirlpool Corporation - Siebel Certified Master Analytics Consultant - 10/2005 - 4/2006 - Case History: Whirlpool had Siebel Analytics Marketing installed for almost three years. In 2 occasions, work was performed unsuccessfully to customize the application to conform to Whirlpool's specification; this was the third and last chance this client was willing to do before given up on this application.
Accomplishments: Scope was divided into 3 manageable phases of about 8 weeks long each. Only one custom dimensional table was needed to delivery the business functionality required. Only SDE mappings were customized for core tables and SDE and SIL custom mappings were built for new dimension and extension tables. More importantly, client was empowered throughout the whole process and its Analytic team was finally confident to work on futures enhancements on its own.
Activities Included:
Analytics Business Analysis: Translating high-level business requirements into logical business dimensional model; Defining various logical subject areas and organization of presentation layer metadata; Understanding and defining key metrics, dimensions, hierarchies and attributes.
Analytics Architecture: Translating logical business dimensional model in base physical DB design; Identifying necessary aggregations and defines aggregate requirements and strategy; and Creating analytics server meta data.
Data Warehouse Architecture/ETL Configuration: Creating ETL routines for detail data and base aggregate fact and dimension tables; Performing QA on ETL programs for detail data and base aggregates; Creating ETL routines for initial install, refresh and on-going update scenarios; and Capturing load and update and optimize ETL routines. - Microsoft - Siebel Certified Master Analytics Consultant - 7/2005 - 8/2005 - Case History: Microsoft uses Siebel to track information about their Sales Process. K2IS was engaged in the initial implementation of Siebel Analytics standalone version to facilitate the analysis of opportunity health pipeline, scorecard comparing actual sales, forecast, quota, etc. Some of the requests in dashboard pages were taking more than half and hour to execute. Columns in the fact tables were used as filters in the dashboards. Also rows of source data were repeated intentionally in order to accommodate case like weighted values, local currency, etc.
Accomplishments: At completion of this project, the size of the database was reduced since duplicate rows were not needed any more and four start schemas were built (Opportunity, Opportunity Product, Quota and Account) Performance was improved dramatically where reports that used to take more than half and hour now run in seconds.
Activities Included:
Analytics Business Analysis: Translating high-level business requirements into logical business dimensional model; Defining various logical subject areas and organization of presentation layer metadata; Understanding and defining key metrics, dimensions, hierarchies and attributes.
Analytics Architecture: Translating logical business dimensional model in base physical DB design; Identifying necessary aggregations and defines aggregate requirements and strategy; and Creating analytics server meta data. - Burger King - Data Warehouse Architect - 5/2002 - 10/2002 - Data Warehouse, Marketing and Operational Data Marts. Environment: Oracle 9I, SQL2000, Informatica Power Center 5.0, Business Objects 5.1, Erwin 3.5.2, 4.0 and 4.1.
Gathered and analyzed business and technical requirements.
Analyzed how the data marts will fit within BK's overall data and IT architectures, strategy and vision.
Worked with project team in translating business requirements into model designs and architecture Key Deliverables.
Developed logical and physical dimensional data models based on business requirements.
Communicated physical database designs to Database Administrators.
Enhanced models to meet new and changing business requirements.
Identified, documented and designed the interfaces that need to exist between the data warehouse, data marts and their source as well as target systems.
Identified, outlined and designed the overall data warehouse / data marts ETL strategies and components, the data movement and data access or presentation strategies and components and tools, and the data exception handling mechanisms required by the ETL processes.
Managed a team of two Informatica developers during the development and testing phases of the ETL.
Outlined the logical and physical hardware and software topology that the data marts will require, and how this topology will fin into the existing IT and Data environments.
Worked with the project manager to outline key data warehouse implementation activities and tasks and help estimate and fine tune the project durations, dependencies, etc.
Helped the Project Manager parallelize some of the activities, and optimize the time of the more skilled resources;
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