All organisations have data. If you want to use this data, you need to organise it. You need to collect the data, you need to put it somewhere, and you need to organise it in a way that you can easily turn it into useful information that will help you make important decisions. This is all harder than it sounds.

Yes. Some examples to illustrate:    If you have poor quality customer address data, letters go missing and you can no longer contact the customer for sales or marketing purposes.    If you have poor quality information on customers’ ages, you may call retired widower Ethel Thompson to congratulate her on her 21st and ask her if she would like assistance on planning the party.    If you have poor quality information on customers’ ages, you may spend $20,000 on a marketing campaign based on the incorrect assumption that most of your customers are younger than 35.    You could spend 2 days pursuing an opportunity that is actually not as profitable as you think (and you would not know it)

Structured Query Language (SQL) is a programming language which is used to get data and update data in a database. A query pulls relevant pieces of data out of a database and organises it in a way that makes sense. The query 'organises' the data by matching database fields up, grouping them, or summing up their content to get a total. The results can then be used in graphs, tables and reports that managers use to make their day-to-day decisions.

A data cube is a set of structured stand-alone data. Data cubes hold data that has been pre-organised into specific dimensions of information (eg - region, sales person, quarter), and a limited number of data items to fit within the dimensions (eg - # number of sales, $ value of sales).

Online Analytical Processing (OLAP) automates the querying process to allow you to easily and quickly explore the data in your cube. OLAP provides you with easy access to different views of your data, allowing you to identify trends and other statistics about your business. For example, using an OLAP tool and a 'cube' of sales data a person could quickly view their sales data from the following points of view:    Yearly sales by region    Yearly sales by sales person    Yearly sales by territory by quarter    Percent difference between this quarter sales, and last quarter sales, per sales person