Establishing Making-it-Happen as ‘Formal & Measurable’ Business Discipline

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Customized Communication to Roles and Levels	Structure of Cascading

Execution-MiH Encyclopedia → Enterprise Intelligence → SECTION - BI End-to-End → CHAPTER - BI Architecture Components →

De-normalized DW- Data Warehouse vs. Data mart

Data Warehouse/ Data Mart form the sanitized repository of Data which can be accessed for various purposes.

Data Warehouse

A Data Warehouse is the area where the information is loaded in under-normalized Dimensional Modeling form. This subject has been dealt in fair degree of detail in Data Warehousing/Marting section. A Data Warehouse is a repository of data, which contains data in a under-normalized dimensional form ACROSS the enterprise. Following are the features of a Data Warehouse:

A Data-Warehouse is the source for most of the end user tools for Data Analysis, Data Mining, and strategic planning .
It is supposed to be enterprise wide repository and open to all possible applications of information delivery.
It contains uniform & standard dimensions and measures. The details of this can be referred to Dimensional Modeling Concepts.
It contains historical as well as current information. Whereas most of the transaction systems get the information updated, the data warehouse concept is based upon 'adding' the information. For example if a Customer in a field system undergoes a change in the marital status, the system may contain only the latest marital status. However, a Data Warehouse will have two records containing previous and current marital status. The time based analysis is one of the most important applications of a data warehouse. The methods of dined this is detailed in special situations in Dimensional Modeling.
It is offline repository. It is not used OR accessed online business transaction processing.
It is read-only: A Data warehouse platform should not be allowing a write-back by the users. It is essentially a read-only platform. The write back facility is more reserved for OLAP server, which sits between the Data Warehouse and End-user platform.
It contains only the actuals data: This is linked to 'read-only'. As a best practice, all the non-actual data (like standards, future projections, what-if scenarios) should be managed and maintained in OLAP and End-user tools

Data Marts

Data Marts are a smaller and specific purpose oriented data warehouse. Data Warehouse is a big a strategic platform, which needs considerable planning. The difference in Data Warehouse and Data Marts is like that of planning a city vs. planning a township. Data Warehouse is a medium-long term effort to integrate and create single point system of record for virtually all applications and needs for data. Data mart is a short to medium term effort to build a repository for a specific analysis. The differences between a Data Warehouse vs. Data mart are as follows:

Data Warehouse

Data Mart

Scope & Application

Application Independent

A Data Warehouse is single point repository and its data can be used for any foreseeable application

Specific Application

Data-Mart is created out of a specific purpose. This means that you will have a data mart created to analyze customer value. This means that the designer of the data-mart is aware that the data will be used for OLAP, what kind of broad queries could be placed.

Domain Independent

The Data Warehouse can be used for any domain including Sales, Customer, operations, finance etc.

Specific Domain

A Data-mart is specific to a given domain. You will generally not find a data mart , which serves Sales as well as operations domain at the same time.

Centralized Independent

The control and management of data warehouse is centralized.

Decentralized by User Area

Typically a data-mart is owned by a specific function/sub-function.

Planned

Data Warehouse is a strategic initiative, which comes out of a blueprint. It is not an immediate response to an immediate problem. It has many foundation elements, which cannot be developed in an ad-hoc manner. For example the standard sets of dimensions & measures.

Organic, possibly not planned

Data-Mart is a response to a critical business need. It is developed to provide gratification to the users, and given that it is owned & managed at a functional level, it grows with time.

Data

Historical, Detailed & Summarized

A good data warehouse will capture the history of transactions by default; even of there is no immediate need. This is because a data-warehouse always tries to be future proof.

Some history, detailed and summarized

It's same with Data Warehouse. However, the level of history that is captured is governed by the business need. For example, a data warehouse will capture the changes in the Customer marital status by default. A Data Mart may not do it, if Data Mart is created to profile/segment a Customer on the basis of his spending patterns only.

Sources

Many Internal & external Sources

This is an obvious outcome of the Data Warehouse being a generic resource. That is also the reason why the staging design for a data warehouse takes much more time compared to that of a data mart.

Few Internal & External Sources

Self Explanatory- A limited purpose leads to limited sources.

Life Cycle

Stand-Along Strategic Initiative:

A Data Warehouse is an outcome of a company's strategy to make data an enterprise resource. If there is any other trigger, chances are that it may not achieve its objectives

Typically part of a Business Project:

A Data Mart comes into being due to a business need. For example Risk Portfolio Analysis data mart could be a part of Enhancing Risk Management Initiative.

Long life

A Data Warehouse is a long-term foundation of an enterprise.

Can have any life span

A Data Mart starts with a given objective, and it can have a life span ranging from one year to endless. This is because some applications are core and business as usual to an enterprise. The life a data mart could be shortened, if a Data Warehouse comes into being.

Customized Communication to Roles and Levels

Structure of Cascading

All Topics in: "BI Architecture Components" Chapter
BI Data Warehouse Source System → Data Warehouse BI Staging Area → De-normalized DW- Data Warehouse vs. Data mart → OLAP Server Layer and capabilities- Why is OLAP needed? → ODS- Operational Data Store → BI & Data Warehouse- End User Tools → Business Intelligence Metadata Model →