1.

Gain in Data Storage, Maintenance, or Retrieval Efficiency

A database is immediately useful if a large volume of data is involved. In those cases, efficiency gains in data storage, maintenance and retrieval are already significant and obvious benefits.
 

2.

Data Access Flexibility

Great value is returned if the data can be accessed by many users with different access devices regardless of location. One example is serving dynamic web content taken from a database to web browsers on both PCs and wireless devices.

3.

Workflow Automation or Productivity Boost

Any series of repetitive or time-consuming tasks is a prime target for automation. Besides direct productivity boosts, databases may also enable users to do things not quite possible otherwise, such as searching for individuals meeting certain conditions and printing out address labels for special mailings or exporting email addresses for mass emailing.
 

4.

Structuring and Relating Data

When RDBMS (Relational Database Management Systems) were not as pervasive as it is today, we used 'flat files' to keep data. Such files often result in duplicated efforts in order to use the data for different purposes or adapt them to other needs. They also make it very hard to relate associated information together. This shows that raw data without structure and application logic is often not very useful. A good structure makes possible the interpretation of seemingly meaningless data, and it brings to the surface patterns, trends, and tendencies in the data. RDBMS makes it a lot easier to structure data and build application logic to extract valuable information. One obvious benefit of an RDBMS is that you only need to enter the same information once and related tables can all use it.

5.

Decision Support and Data Warehousing

A relational database system can be critically important to the success of an organization if tools and facilities are available to extract information essential for formulating strategies and making important decisions. Such strategic analysis of the data typically focuses on trends, factors driving those trends, and insights gained by different perspectives in looking at the data.

At the enterprise level, decision support systems often have very different design requirements from OLTP (online transaction processing). Design for the latter centers on analyzing and automating business processes to provide consistent performance for a known set of transactions and users. The typical solution then is to separate OLTP servers and the associated operational data store from 'data warehouse' servers which form the decision support system.

6.

Data Mining

It is the use of mathematical algorithms and techniques to discover relationships in the data that wouldn't be made apparent by other tools. This needs to be done only when analysts believe there are still undiscovered relationships that may affect the business, and all current data warehousing tools have been used.

Areas in which data mining has been most successfully used include fraud detection and micro-opportunity marketing. Discovering clusters of unusual buying patterns within certain small groups might drive marketing campaigns aimed at small audiences with a high probability of purchasing products or services.