Bottom-Up Approach the advantages

Bottom-Up Approach the advantages of this approach are:

•     Faster and easier implementation-of a cable pieces
•     Favorable return on investment and proof of concept
•     Less risk of failure
•     Inherently incremental; can schedule important dal, marts first
•     Allows project team to learn and grow

The disadvantages are:

•     Each data 'mart has Us own narrow view of data
•     Permeates redundant data in every data mart
•     Perpetuates inconsistent and irreconcilable data
•     Proliferates unmanageable interfaces

In this bottom-up approach, you build your departmental data marts one by one. You would set a priority scheme to determine which data marls you must build first. The most severe drawback of this approach is data fragmentation. Each independent data mart will be blind to the overall requirements of the entire organization.

A Practical Approach In order to formulate an approach for your organization, you need to examine what exact-ly your organization wants. Is your organization looking for long-term results or fast data marts for only a few subjects for now'? Does your organization want quick, proof-of-concept, throw-away Implementations? Or. do you want to look into sonic other practical approach?

Although both the top-down and the bottom-up approaches each have their own advantages and drawbacks’ a compromise approach accommodating both views appears to be practical. The chief proponent °inns practical approach is Ralph Kimball, an eminent author and data warehouse „expert.

The steps in this practical approach are as follows:

1. Plan and define requirements at the overall corporate level
2. Create a surrounding architecture for a complete warehouse
3. Conform and standardize the data content
4. Implement the data warehouse as a series of super-marts: one at a time

In this practical approach, you go to basics and determine what exactly your organization wants in the long run. The key to this approach is that it is first planned at the business level. You meet the general requirements. The architecture is established for the complete warehouse.1 When determining the content of the data for each supermarket. Super-marts are carefully designed data marts. You implement these supermarkets, one at a time. Before implementation, make sure that the content of the data between the different supermarkets is conformed in terms of data types, field lengths, precision and semantics. Some data clement should mean the same thing in every super mart. This will prevent the propagation of disparate data through various data marts ...

A data market, in this practical approach, is a logical subset of the complete data store, a kind of pie-wedge, or the complete data store. A data warehouse, therefore, is a conformed union of all data markets. Individual data margins are targeted at particular business groups within the company, but the collection of all data markets from an integrated set called the enterprise data warehouse. When we refer to data warehouses and data marts in our discussions here, we use meanings as understood in this practical approach. For us, a data store means a collection or constituent data marts.

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