In Data Aggregation, value is derived from the aggregation of two or more contributing data characteristics.  

Aggregation can be made from different data occurrences within the same data subject, business transactions and a de-normalized database and between the real world and detailed data resource design within the common data architecture.

Reporting and data analysis applications that work closely to tie together company data users and data warehouses need to overcome problem on database performance. Every single day, the amount data collected increases at exponential proportions. Along with the increase, the demands for more detailed reporting and analysis tools also increases.

In a competitive business environment, the areas that are given more focus to gain competitive edge over other companies include the need for timely financial reporting, real time disclosure so that the company can meet compliance regulations and accurate sales and marketing data so the company can grow a larger customer base and thus increase profitability.

Data aggregation helps company data warehouses try to piece together different kinds of data within the data warehouse so that they can have meaning that will be useful as statistical basis for company reporting and analysis.

But data aggregation, when not implemented well using good algorithm and tools can lead data reporting inaccuracy. Ineffective way of data aggregation is one of the major components that can limit performance of database queries.

Statistics have shown that 90 percent of all business related reports contain aggregate information making it essential to have proactive implementation of data aggregation solutions so that the data warehouse can substantially generate data for significant performance benefits and subsequently open many opportunities for the company to have enhanced analysis and reporting capabilities.

There are several approaches to achieving an efficient data aggregation. Having robust and high powered servers will make the database perform incrementally better. Another approach is to do partitioning, de-normalization, and creating OLAP cubes and derivative data marts. Report caching and broadcasting can also help boost performance. And another method is having summary table.

But while these approaches have been proven and tested, they may have some disadvantages in the long run. In fact those approaches have already been lumped among the traditional techniques by some database and data warehouse professionals.

Top data warehouse experts recommend that having a good and well define enterprise class solutions architected to support dynamic business environments have more long term benefits with data aggregation. The enterprise class solutions provide good methods to ensure that the data warehouse has high availability and easy maintenance.

Having a flexible architecture also allows for future growth and flexibility and most business trends nowadays tend to lean towards exponential growth. The data architecture of data warehouses should use standard industry models so they can support complex aggregation needs. It should also be able to support all kinds of reports and reporting environments. One way to test if the data warehouse is optimized is if can process pre-aggregation with aggregation on the fly.

Data warehouses should be scalable as the amount of data will definitely grow very fast. Especially now that new technologies like RFID can allow gathering of more transactional data, scalability will be important for the future data needs of the company.

Data aggregation can really grow to be a complex process through time. It is always good to plan the business architecture so that data will be in sync between real activities and the data model simulating the real scenario. IT decision makers need to make careful choice in software applications as there are hundreds of choices that can be bought from software vendors and developers around the world.