What is Data Aggregation?

Data MiningDatabaseData Structure

Data aggregation is a process in which data is gathered and represented in a summary form, for purposes including statistical analysis. It is a kind of information and data mining procedure where data is searched, gathered, and presented in a report-based, summarized format to achieve specific business objectives or processes and/or conduct human analysis.

Data aggregation can be implemented manually or through specialized software. The objective of Aggregation is to get more data about specific teams based on specific variables including age, profession, or income.

The data about such teams can then be used for website personalization to select content and advertising possible to appeal to a single belonging to one or more teams for which records have been collected.

Example − A site that sells music CDs can advertise specific CDs established on the age of the customer and the data aggregate for their age group. Online analytic processing (OLAP) is a type of data aggregation in which the trader uses an online documenting mechanism to process the data.

Data aggregation can be user-based − Personal data aggregation services provide the user a single point for a set of their personal data from other websites. The user uses an individual master personal identification number (PIN) to produce access to their several accounts including those for financial institutions, airlines, book and music clubs, etc. This type of data aggregation is defined as “screen scraping.”

As a data aggregator, data is your business not a byproduct of your business. It can buy data, transform it, scrub it, cleanse it, standardize it, match it, validate it, analyze it, statistically project it, and sell it. It can require a rock-solid data aggregation solution as the foundation of your operations.

The data aggregation solution is as follows −

  • It fully automates key steps of the data aggregation process so that your IT team can boost productivity, accelerate delivery times, and dramatically reduce costs. That automation lets your company focus on what it does best your proprietary data sampling, data projecting, and data assembling core competencies that distinguish you from your competitors.

  • It can transform any data, in any format, from any source so your company can create new products for new markets.

  • It is used to detect data quality problems early so your company can synthesize high-quality marketing research, point-of-sale analysis, and other data products more quickly and at a lower cost.

raja
Updated on 23-Nov-2021 07:42:57

Advertisements