bill imnon vs ralph kiball

8/17/2019 Bill Imnon vs Ralph Kiball

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Bill Inmon vs. Ralph Kimball

Vladimir Marković, software architect at Banca Intesa Beograd



Agenda

• Facts without bias

– History

– Data warehouse paradigms

–Diversity

– Pros & Cons

– Make decision

• Make compromise

– Find the weaknesses

– Development techniques

– Take the best of the both approaches



Facts without bias



History

• 1990 - Inmon publishes “Building the DataWarehouse”

• 1996 - Kimball publishes “The Data WarehouseToolkit”

• 2002 – Inmon updates book and defines architecture for

collection of disparate sources into detailed, timevariant data store - the top down approach

– Kimball updates book and defines multiple databasescalled data marts that are organized by businessprocesses, but use enterprise standard data bus -thebottom-up approach



Data warehouse paradigms

• Bill Inmon's paradigm: Data warehouse is one part of the

overall business intelligence system. An enterprise has

one data warehouse, and data marts source their

information from the data warehouse. In the data

warehouse, information is stored in 3rd normal form.

• Ralph Kimball's paradigm: Data warehouse is the

conglomerate of all data marts within the enterprise.Information is always stored in the dimensional model.



Diversity

Ralph Kimball Bill Inmon

Build business process oriented small data

marts which are joined to each other

using common dimensions between

business process.

One centralize data warehouse which will

act as a enterprise-wide datawarehouse

and then build data mart as per need for

specific department or process

It is known as bottom-up approach It is known as top down approach

Data marts should be build on

dimensional modelling approach

Central data warehouse to follow ER

modeling approach



Inmon’s top-down approach



Bill Inmon, an early and influential practitioner, hasformally defined a data warehouse in the following terms;

– Subject-oriented

• The data in the database is organized so that all the data elementsrelating to the same real-world event or object are linkedtogether;

– Time-variant

• The changes to the data in the database are tracked and recordedso that reports can be produced showing changes over time;

– Non-volatile

• Data in the database is never over-written or deleted - oncecommitted, the data is static, read-only, but retained for futurereporting;

– Integrated

• The database contains data from most or all of an organization'soperational applications, and that this data is made consistent



Kimball’s bottom-up approach



Ralph Kimball, a leading proponent of the

dimensional approach to building data

warehouses, provides a succinct definition for a

data warehouse:

– “A copy of transaction data specifically structured

for query and analysis.“



Inmon vs. Kimball: Similar or different?

• Kimball, in 1997, stated that – "...the data warehouse is nothing more than the union of all the

data marts",

– Kimball indicates a bottom-up data warehousing methodologyin which individual data marts providing thin views into the

organizational data could be created and later combined into alarger all-encompassing data warehouse.

• Inmon responded in 1998 by saying, – "You can catch all the minnows in the ocean and stack them

together and they still do not make a whale,“

– This indicates the opposing view that the data warehouseshould be designed from the top-down to include all corporatedata. In this methodology, data marts are created only after thecomplete data warehouse has been created.



Inmon vs. Kimball: Similar or

different?



Pros and cons of both the approaches



How to decide?

• Insurance: It is vital to get the overall picture with respect to individualclients, groups, history of claims, mortality rate tendencies, demography,profitability of each plan and agents, etc. All aspects are inter-related andtherefore suited for the Inmon’s approach.

• Marketing: This is a specialized division, which does not call for enterprisewarehouse. Only data marts are required. Hence, Kimball’s approach is

suitable.• CRM in banks: The focus is on parameters such as products sold, up-sell

and cross-sell at a customer-level. It is not necessary to get an overallpicture of the business. For example, there is no need to link a customer’sdetails to the treasury department dealing with forex transactions andregulations. Since the scope is limited, you can go for Kimball’s method.

However, if the entire processes and divisions in the bank are to be linked,the obvious choice is Inmon’s design vs. Kimball’s.

• Manufacturing: Multiple functions are involved here, irrespective of thebudget involved. Thus, where there is a systemic dependency as in thiscase, an enterprise model is required. Hence Inmon’s method is ideal.

http://searchdatamanagement.techtarget.com/news/2240084024/Bill-Inmon-Kimball-methodology-ignores-the-value-of-textual-data









In a nutshell

Bill Inmon’s enterprise data warehouse approach (the top-down design): A normalized

data model is designed first. Then the dimensional data marts, which contain data

required for specific business processes or specific departments are created from the

data warehouse.

Ralph Kimball’s dimensional design approach (the bottom-up design): The data marts

facilitating reports and analysis are created first; these are then combined together to

create a broad data warehouse.



Make compromise ???



Find the weaknesses



Development techniques

• User oriented

• Operational oriented

• Business process oriented



User oriented warehouse

development

• the significant metrics are based on the company’s

critical success factors and these factors depend on the

company’s management strategy and goals

•

the corresponding dimensions relate to influences on thecritical success factors

• use interviews in gathering the informational

requirements

• problems: – potential ignorance of the users

– most users have only a slight idea of their demands



Operational oriented warehouse

development

• to analyze the data models of the underlying

operational sources and to identify the relevant

transactions• the major problem with operational oriented

warehouse development is the restriction of data

models.

• data models specify the business of a company

incompletely



Business process oriented warehouse

development (Kimball)

• the business process model contains a formal

description of the informational requirements of the

users• possibility to identify informational requirements

that could not be satisfied with the actual

information offerings of the source systems



How to design dimensional model

-Kimball approach-

• DW gathering and documenting enterprise-

level business requirements

• Analytic themes

• Report bus matrix

• Data quality review – gap analysis

• Design DW data model

• Data dictionary



What would the sponsors like to see?

• Decrease startup cost

• Speed up initial set – up

• Transparent business value of the deliverables

– how to measure the investment

• Decision making support

• …



What would the BI users like to see?

• Covered requested analysis

• Understandable and easy to use structures for

ad hoc reporting

• Predefined reports and dashboards

• Excellent BI tools

•

…



What would IT folks like to see?

• Big budget

• Clearly functional specification

• Enough time for developing without stress

• Transparent UAT

• Easy to maintenance

• Easy to enhancement

• Easy to enrichment

• …



Take the best of the both approaches• Use Inmon approach to design EDW

– design EDW data model for specific industry if you have time or buy if you have money

– try to identify all entities (dimensions) without insight in particular data mart (DM)

• Spend time for ETL conceptual design & strategy – Create two type of tables for each entities : staging table with SCD Type1 attributes and

row version table with SCD Type2 attributes

– Establish view layer if it possible

– Establish metadata repository and try to make code generator

• Use Kimball approach to design DMs – cover all requested analysis

– use shared dimensions and star schema

– make dimension model understandable and easy to use for business users

• Populate EDW with all dimensional data – serve improvement

• Populate EDW with fact data necessary to feed DMs

• Populate DDMs using EDW as staging

• Develop BI applications – try to get more without redesignDM and developing ETLs



Books:

• Building The Data Warehouse. Third Edition – Bill Inmon

• The Data Warehouse Toolkit, Second Edition - Ralph

Kimball, Margy Ross

•The Data Warehouse Lifycycle Toolkit, Second Edition – Ralph Kimball, Margy Ross, Waren Thornthwaite, Joy

Mundy, Bob Bacer

• The Microsoft Data Warehouse Toolkit - Ralph Kimball,

Waren Thornthwaite, Joy Mundy



Articles:

• Data Warehousing: Our Great Debate Wraps Up

• Can't Inmon and Kimball just get along?

• Inmon vs. Kimball data warehousing: the debate over DWarchitecture

• How to choose between the Inmon vs. Kimball approach for data

warehouse design • Bill Inmon: Kimball methodology ignores the value of textual data

• Data Warehouse Architecture: The Great Debate

• Ralph Kimball vs. Bill Inmon approaches to data warehouse design

• Alternative data warehousing frameworks

• Resources for Oracle DBA learning about DW principles

• Is an Inmon-modeled BI system, like Madison, the future of datawarehousing?

http://searchdatamanagement.techtarget.com/news/2240086048/Cant-Inmon-and-Kimball-just-get-along

http://www.computerweekly.com/news/2240016833/Inmon-vs-Kimball-data-warehousing-the-debate-over-DW-architecture



http://searchdatamanagement.techtarget.com/answer/How-to-choose-between-the-Inmon-vs-Kimball-approach-for-data-warehouse-design





http://searchdatamanagement.techtarget.com/news/2240111062/Data-Warehouse-Architecture-The-Great-Debate

http://searchdatamanagement.techtarget.com/answer/Ralph-Kimball-vs-Bill-Inmon-approaches-to-data-warehouse-design


http://searchcrm.techtarget.com/answer/Alternative-data-warehousing-frameworks

http://searchoracle.techtarget.com/answer/Resources-for-Oracle-DBA-learning-about-DW-principles


http://searchdatamanagement.techtarget.com/answer/Is-an-Inmon-modeled-BI-system-like-Madison-the-future-of-data-warehousing









http://searchcrm.techtarget.com/answer/Alternative-data-warehousing-frameworks





http://searchdatamanagement.techtarget.com/news/2240111062/Data-Warehouse-Architecture-The-Great-Debate

















http://searchdatamanagement.techtarget.com/news/2240111065/Data-Warehousing-Our-Great-Debate-Wraps-Up

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