As the UK's leading companies embrace the Internet as a core part of their business, suppliers are introducing applications and toolkits that can extract data from Web-enabled business processes and make it accessible for decision making. In some cases they can even make the business decisions without human intervention. In the area of customer or Web analytics, aggregation and interpretation routines are used to create a much clearer view of each customer and/or group of customers as well as track, profile, and illustrate the habits of individual visitors to a Web site.

Data plays a crucial role in most companies, but there are some industries that need to analyse very large amounts of data from different sources quickly and accurately. Modern manufacturing plants generate and store huge amounts of data from ERP (enterprise resource planning systems) and other transaction-based systems. Often many different systems store and analyse data relating to the production of various items or substances, for example stock levels, delivery schedules, customer orders, prices paid, product return rates, product development schedules and the like.

If these systems do not communicate with each other, or if there is no single point from which to approach all of this data, it is impossible to find accurate answers to questions such as 'How will a 5 per cent drop in production of a particular product in a specific month affect company profits?' and 'Which are our most profitable customers?'

The majority of the data in most manufacturing companies covers manufacturing, logistics and financial areas, rather than market information and customer data (although both the latter are important). Financial services companies, however, see service operations and customer information as mission-critical. Retail banks and insurers usually have millions of customers. It is not easy to predict how changes in areas such as consumer behaviour, the performance of financial markets and government policy will affect business. To benefit from all the data they have collated and stored, these companies need to:

1. Extract the data they have from its different and varied sources.

2. Transform it into a consistent format.

3. Load it into a repository, for example, a data warehouse.

4. Find a way to analyse the data so as to give decision makers at all levels and in different units the support they need to make better business decisions more quickly than their competitors. (Typically this entails using business intelligence software, ranging from advanced reporting suites to statistical packages.)