The term Bottom-Up-Methodology refers to the architecture of a data warehouse.

A data warehouse is an IT system that offers mutual information from different internal and external sources to support business decision making.

The system contains roughly spoken of an area, where data from heterogeneous sources are loaded, aggregated and summarized and several departmental-individual data marts, where the processed data is loaded again and can be evaluated according to the different departments’ information and decision needs.

There are several approaches to building a data warehouse. The two most prominent ones are:

• top-down: first defining the information needs of the business and determining the structure of the enterprise data warehouse beforehand,
• bottom-up: starting from the available data and first developing single parts (data marts), then incrementally combining them to an integrated solution.¹⁾

Ralph Kimball developed the bottom-up approach. Kimball starts by building the data marts, those being modelled in a Star Schema. Each data mart contains data on an atomic level as well as on a summarized level, representing all of todays and the future’s information needs. The different data marts are connected via a dimensional bus system, thus allowing the user to access all the data in all data marts. Hence, Kimball built a virtual data warehouse, where data marts are directly loaded with data from the operational systems through a staging area, where data is extracted, transformed and loaded (ETL).

• The bottom up methodology promises high flexibility and user-friendliness, because it is based on the individual business departments’ information needs.
• Its light infrastructure provides for a short response time. The integration of new data is easily done in this model.²⁾
• The incremental development by building data mart after data mart enables a quick usage and cost-efficient development of the data warehouse.
• The connection of all data marts makes queries through all data possible for the user. Redundant data storage can be avoided.³⁾

Especially Bill Imnom as the proponent of the opposing architecture, the top down approach, attests the bottom up approach several disadvantages.⁴⁾ He postulates a missing integration and doubts that redundancy can be avoided. In his view, data marts are developed totally autonomously from each other and thus may contain redundant data. Separate data marts containing different data may obstruct a company-unified view. An integrated sight is not possible. This is why each data mart needs to be constructed based on standardized dimensions and fact models. This approach enhances the complexity of constructing an integrated data warehouse and increases the danger of departments building stand-alone solutions. Furthermore, a data warehouse following the bottom up approach does not support batch processes. In order to conduct operational reporting processes, additional structures need to be implemented.

Building a data warehouse using the bottom up methodology provides for quick and cost efficient usage of data. This approach is suitable in less decentralized company structures, as stand-alone solutions and thus data redundancy should be avoided. The bottom up approach requires the strict and disciplined following of standardized dimensions in order to enable an integrated view on company's KPI's. When following these requirements, a user-friendly, flexible and early-to-use data warehouse can be built.

• Data Warehouse
• Federated Approach
• Hybrid Approach
• OLAP
• Top-down Approach

• Design of the data warehouse: Kimball Vs Inmon
• A Definition of Data Warehousing

• Jordan, Claus; Schnider, Dani; Wehner, Joachim; Welker, Peter: Data Warehousing mit Oracle - Business Intelligence in der Praxis. München: Carl Hanser Verlag, 2011

— Michaela Kaiser 2011/05/05 16:35