MMH Data Warehousing, Corporate Portal & e-business Applications.

Data Warehousing, Corporate Portal & e-Business Intelligence Applications

  

Mimno, Myers & Holum

Bottom Up Methodology

The bottom-up methodology that we recommend, which is derived from RAD techniques, typically incorporates the following 12 steps.

  1. Analyze Strategic Business Needs - Identify business drivers, sponsorship, risks, and ROI. Conduct survey of high-level business managers to identify specific, painful business problems, e.g. inability to share information across departments; multiple, inconsistent sources of data; lack of ability to generate reconcilable financial reports; inefficient, paper-driven procurement processes; lack of effective utilization of Internet access; and proliferation of non-integrated, stovepipe applications
  2. Survey User Needs - Survey user needs and identify desired functionality. Unlike the top-down approach, the survey of user needs is very short (one day per business unit or less) and leads to the specification of a top-level data model for each business unit that will utilize a data mart. The top-level data models are then synthesized to identify common data sources, facts, dimensions, transformations, etc.
  3. Design Enterprise Architecture- Design long-term, enterprise data warehousing architecture on paper. Following the survey of user needs, a workshop is convened whose function is to define the long-term vision for the data warehousing application, i.e., what will the architecture of the DW look like 2 to 3 years in the future
  4. Define Functional Requirements for Initial Subject Area - Define functional requirements for the initial subject area. A second workshop is convened to bring together the business users and development team for the first data mart to be implemented. Deliverables of the workshop include a preliminary project plan for the development effort, functional requirements for the first data mart, budget, required skill sets, etc.
  5. Research and select DW components and tools- Following research, a short list of potential tools is defined, including ETL tools, data modeling tools, data cleansing tools, and BI tools. Selection of the finalists in each category may require a Proof of Concept test. Following selection and installation of the tools, the 90-day timebox is entered (steps 6-9 below)
  6. Design Target Database for Initial Data Mart- Modeling of the target data base for the initial data mart proceeds through three steps: design of an entity-relationship diagram, then a logical dimensional model, and finally a physical model of the database schema
  7. Build ETL Rules for Initial Data Mart- Build data mapping, extraction, transformation, and data cleansing rules. The data mapping and transformation rules are defined first in natural language, and then implemented using only the transformation objects supplied with the ETL tool. The objective is to avoid coding any extraction, transformation, or load processes
  8. Build Aggregation Functions for Initial Data Mart- Build aggregation, summarization, partition, and distribution functions. The ETL tool is used to compute aggregates in one pass of the source data, using incremental aggregation techniques
  9. Complete Initial Data Mart- Complete development of the initial architected data mart, using an exact subset of the enterprise data warehousing architecture. The deliverable at the end of the 90-day timebox is a fully functional data mart for the initial business unit
  10. Build additional data marts- Additional data marts are built by a primary development team using common templates and components, such as conformed dimensions, common transformation objects, data models, central metadata definitions, etc.
  11. Expand to an Enterprise Architecture- Expand to an enterprise architecture, including a central data warehouse and an optional Operational Data Store. Development of the central data warehouse and ODS are deferred until they are clearly required. A central data warehouse is often required when detailed, atomic data from multiple data marts must be accessed to generate cross-business reports
  12. Enhance and Administer DW Application- A secondary team may be used to enhance and maintain completed data marts. The primary team transfers transformation templates, data models, conformed dimensions, metadata, etc. to the secondary team to simplify the enhancement and administration of completed data marts

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