In its simplest form, it is considered that data aggregation is usually the process of collecting a large amount of information from a particular database and their organization into a more complex and comprehensive medium. Based on rich data results, data from any scale can be used to summarize information and draw conclusions. Due to the increased availability of information and the importance of personality measurement throughout the company, the implementation of data aggregation has become very relevant.
All the same, data aggregation is any process in which information is collected and summarized, for example for statistical analysis. The common goal of aggregates is to obtain more information about specific groups based on specific variables. Data is valuable by processing perceptions, which illuminates key events, results, and provides a better overview of available information. Processes in which data is analyzed, collected, and summarized, data aggregation helps organizations to achieve certain business goals or perform human processes or analyzes to almost any extent.
It has been widely used in many parts of society for countless years; however, with the development of computers and technology, such as Artificial Intelligence and Machine Learning, the scope and possibilities have increased significantly. Examples of data aggregation can be as simple as collecting the number of operations required for a job this week and as complex as using a sharing program to track your car in exactly minutes.
How Do Data Aggregators Work?
They work to integrate data from multiple sources, analyze data to gain new knowledge, and summarize the publication of aggregated data. Also, they typically can map a database and may refer to basic aggregated data.
Initially, data assortment software can get hands-on data from a range of sources and pile it as data in enormous databases. Data can be obtained from the Internet of Things (I-o-T) sources, such as:
- News headlines
- Communication on social networks
- Determine the history and personal data of internet devices
- Podcasts, call centers, etc.
It is processed after data collection. Artificial intelligence (AI), predictive analysis, or machine learning algorithms can be used to collect data for the data aggregator. They then use the necessary statistical measures to compile the results.
Users can send aggregated data themselves in a concise form containing new data. The methodological results are detailed and of high quality. However, it is generally large-scale, making manual collection inaccessible. By comparison, there is a risk that manual collection skips basic information and flows.
For many industries, it can be useful, such as financing and business management decisions, product planning, product and service pricing, operational optimization, and marketing strategy development. Consumers can be IT professionals, data experts, database administrators, and content experts.
Data Aggregation Examples – By Industry
Finance and Investment
In specific industries, companies need to keep up to date with the latest news to find the latest trends and respond to them. Data aggregation is an ideal way to achieve this, as it allows investors and financial firms to change perspectives to best suit the performance of the company or product they are investing in.
Access to such data is, of course, not possible by visiting one of the many news websites covering stories about financing and investment development. However, with so many websites available, it can often be difficult to access all the relevant data needed to manage a business. Also, database reliability can be a problem if there is no data.
Fortunately, data aggregation can help you combat this problem by allowing you much more efficient and accurate access to large amounts of data from hundreds of websites. This is made possible by the use of automated data collection tools that make it very easy to obtain accurate and relevant data.
Data aggregation in marketing usually comes from marketing methods and the various platforms you use to sell to your customers. The results of one initiative should be summarized and seen how it has changed over time and for specific groups. Ideally, you combine a comprehensive set of data to link data for each specific campaign, showing you how the product is viewed on platforms, demographics, and groups.
Whether it allows you to gain market insight, effectively research competitors, or track the prices of competitive points, it is clear that travel companies benefit greatly from using the process of aggregate data. It is now extremely important to take into account all of the factors because if they do not, travel companies can be disastrous and thus far behind competitors in an increasingly competitive market.
Without aggregated data, it would be a little difficult to obtain relevant information for all the purposes, as manually performing such a task takes an incredibly large amount of time and resources, collecting data from many different sources. This is why data aggregation is extremely useful today for travel companies that need to monitor the offers accessible by competitors. At the same time, companies must be able to offer appropriate prices for destinations that tend to attract customers and prevent them from ordering from other suppliers.
The retail industry is very competitive today. Therefore, companies need to be as attractive as possible to customers who would simply buy from elsewhere if their needs are not met. The most important use of data in this particular area is competitive price control. Also, throughout the data collection process, retail and e-commerce companies need to focus as much as possible on collecting relevant data from potential websites. It allows marketers to obtain accurate data that helps them significantly determine pricing and targeted marketing methods.
Data aggregation means you can better link all your performance to your marketing efforts and make better marketing and business decisions. Getting data from different sources can be a little inconvenient, especially if you need a large amount of data. But having data science training makes it easier, and this is where data aggregation occurs because it facilitates the processing of data that is too difficult to interpret and use.