Transaction Applications Vs Data Warehouse Application

There is an everlasting question about the fundamental differences between Transactional applications and data warehouse applications. The definition of a data warehouse according to Inmon is "a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process". Now raises the question "If a transaction based application never deleted any data and stored history and is the only application used by the company can it be treated as a data warehouse?". It depends. 

I recently came across an article that outlines the differences between the two. Though many understand the differences, I felt the article gave a textbook answer to this question which I think every data warehouse professional should know.

Project Domain	Transaction/Production Business System	Data Warehouse System
Business Benefits	Tangible benefits in terms of functional capabilities, business processes that will be automated, number of headcounts and reduction etc. Typically, the process getting automated is being done manually, and there is enough visible pain at the ground level and customers.	There is a lesser proportion of initiatives where there is 'heaven will fall', if the project is not done. The benefits can be appreciated by fewer people and much fewer at a ground level.
Usage	A business system once implemented, drives the usage as it typically automates a business process.	Data Warehouse platform has a lesser compulsion for usage. Unless there are critical operational reports required.
Measure of Usage	One can specify the measure of usage for a business system in terms of processed unit, number of users.	While number of users and the number of queries does represent the level of usage, but it no means suggest that the usage is resulting in delivery of final outcome.
Skills and Expertise Requirements- Business	Business system requires the expertise on business process knowledge	More knowledge is required horizontally and vertically. One needs a much higher domain experience as well as cross-functional knowledge for an effective business role fulfillment in Data Warehouse project. The domain expertise also includes all three levels (strategic, managerial and operational).
Business Requirements	Ability of defining the business requirements, prioritization is easier as a business system automates an existing process and/OR a severely needed business functionality.	What analysis one needs, why and what one will do post its availability are questions, which demand/challenge the management and strategic thought process. Unlike a business process, analysis for any problem can be done in hundred different ways. Therefore, business requirements tend to change throughout a Data Warehouse project.
Business users availability and engagement	Business users are more available and engaged.	Its easier to provide and confirm the requirements of a business process automation, and difficult to define the information and analysis needs. Business users are too busy doing day-to-day work to dwell upon these questions.
The demands on the Database	The queries and data access is predictable as they are driven by the mapping of type of transaction, instances etc. A typical transaction touches only certain tables and certain records. Mostly the large and all-encompassing processing happens end of the day processing.	Data-Warehouse cannot predict the kind and incidences of queries on the system. A query can access all the tables and records.
Variety of front-end applications	A business system has a pre-defined back-end and front end applications accessing the back-end Database	A data-Warehouse could be having new front-end applications being added on the ongoing basis. This includes OLAP tools, Data mining applications, business performance management applications, online user query and reporting applications.
Expectations of flexibility to enhancements	A typical business system has an ever increasing list of enhancements, However, it is expected that the enhancements will take time and system will go through well-spaced out releases.	A Data-Warehouse is expected to provide granular enhancements for most cases. It has to have its design flexible enough to be able to incorporate new dimensions, measures and system sources without unsettling the foundations.

 ( the main article is here http://www.bipminstitute.com/data-warehouse/challenge-issues.php)