Data Warehouse

Author: Insert your name here


"a large store of data accumulated from a wide range of sources within a company and used to guide management decisions."


A Data Warehouse is the collection point for all data in a business which can then be used to be analysed for different purposes. Data Warehouses permanently stores data unlike transaction devices which may only store a limited amount of storage at once due to either software restrictions or storage restrictions.

Explanation and application

There are 4 different types of Data Warehouses'

Data Mart
A Data mart is the most simple form of Data warehouses available, data marts are designed to only analyse one aspect of the company for example the companies revenue, unlike the other types of Data Warehouses, Data Marts only gets their data from a few sources such as internal software or external results etc.

Online analytical processing (OLAP)
The OLAP type is much quicker compared to the Data Mart method (1 day opposed to a few hours), this is because less data is being analysed, the main difference between this method and the Data Mart method is that this method gets more in-depth with the data and looks for patterns in the data. The main focus on OLAP data is analysing historical data.

Online Transaction Processing (OLTP)
OLTP is even quicker than OLAP, OLTP is aimed to provide the most online transactions per second and most query process' per second, this allows people to view live data of ongoing process' or any small business related data such as sales.

Predictive analysis
Predictive data is about finding about the future using maths theories and models. This is practically the opposite to OLAP which looks through historical data as this only "tries" to look for futuristic data. This type tries to find predictable data in your data warehouse and then applies complex maths models to find the future of that data. This type can be seen in marketing, financial services, insurance, telecommunications, retail, travel, healthcare etc.

Social & Ethical Issues Relevant to Case Study

You do not have to complete the whole table - please just add the most relevant issues.
Examples relevant to the case study
1.1 Reliability and integrity

1.2 Security

1.3 Privacy and anonymity

1.4 Intellectual property

1.5 Authenticity

1.6 The digital divide and equality of access

1.7 Surveillance

1.8 Globalization and cultural diversity

1.9 Policies

1.10 Standards and protocols

1.11 People and machines

1.12 Digital citizenship

References and resources