Data Aggregation: A Comprehensive Guide In 2021

Introduction

There is an undeniable fact that data surrounds us on every corner. However, extracting meaningful data for multiple purposes is still a problem, which makes data aggregation important. Knowing all about data aggregation is certainly important for the people serving in these fields, and this is what this article seeks to achieve. Read on to explore the various facets of data aggregation and have a better understanding of it. 

The data aggregation meaning has evolved over time due to many reasons. To understand what is data aggregation, one must keep in mind the simple phrase ‘summary’.

Data aggregation is a process where data is collected and expressed briefly in a summarised format. Here, observed aggregated groups are simply replaced by the summarised statistics. Aggregate data are found in a data warehouse, as they can provide answers to analytical questions and also reduce the time to query big data sets. Data aggregation is used to form summarised data for business analysis and to provide statistical analysis for groups of people. By using software tools known as data aggregators, aggregation is usually done on a large scale.

These aggregators involve aspects of the collection, procession, and presentation of data. Data aggregation is a critical part of effective data management. The most significant and frequently accessed data can benefit from aggregation, making it feasible to access efficiently as the amount of data storage by organizations continues to expand. Data aggregation generally works on big data that do not provide much information value as a whole. Data aggregation may be performed manually or through specialized software.

In this article let us look at:

1. What does Data Aggregation do?
2. How do Data Aggregates work?
3. Uses of Data Aggregates 

1. What does data aggregation do?

Data aggregation simply summarises data from various sources. They layout capabilities for various aggregate measurements such as counting, sum, and average. Some data aggregation examples include the following-:

• The average age of the customer through the product. Each customer is not identified, but the average age of the customer is saved for each product.
• A number of customers by count. A count of the customers in each country is presented instead of examining each individual customer.

Data aggregators usually provide the ability to track data lineage and can trace back to the underlying atomic data that was aggregated. Aggregate data does not need to be numeric. For instance, you can count the number of any non-numeric data element. Data aggregation can also result in a similar effect to individual data elements where personally identifiable details are combined and replaced with a summary representing a group as a whole. Before aggregating any data, it is significant that the atomic data is analyzed for accuracy and that there is enough data for the aggregation to be useful.

2. How do data aggregates work?

Data aggregators are able to work by collecting data from various sources, then processing the data for the latest insights, and lastly, presenting the data in a summarised form. This is explained by the following-:

• Collection-: First and foremost, data aggregation tools extract data from various sources then store that in huge databases. This data can be simply extracted from internet sources, namely – social media communications, browsing history, and other personal data from internet devices, news headlines, call centres, etc.

• Processing-: Once the collection process is done, data is processed. Atomic data which is to be aggregated will be identified by the data aggregators. These aggregators can apply various algorithms to this collected data for the latest insights.

• Presentation-: Once processing is done, aggregated data can be presented in a summarised form that will itself provide the latest data. These statistical results are highly comprehensive and of high quality.

3. Uses of data aggregates 

• Data aggregation can prove to be helpful for multiple disciplines, such as business and finance strategic decisions, service and product pricing, product planning, optimization of operations, and creation of marketing strategies that require multiple data aggregation tools and distinct data aggregation techniques.

• This can also be used for multiple purposes in travel industries. These involve competitor research, competitive price monitoring, gaining market intelligence, etc. 

• Data aggregation is used to form summarised data for business analysis and to provide statistical analysis for groups of people. This can be performed by experts who may be data scientists, data analysts, data warehouse administrators, and subject-related experts.

• Aggregation of data into summarized form helps leaders to make well-informed decisions for business analysis purposes.

• Aggregated data is also used to gain information about specific groups, which is based on specific behavioural or demographic variables, such as profession, age, level of education, or income for statistical analysis.

• Data aggregation can also be used to provide companies with critical insights into consumers by aggregating data from various sources such as social media communications, browsing history, and other personal data from internet devices.

Conclusion

Thus, it is concluded that nowadays, business decisions are based on huge amounts of data, making quick access to data important for making the right decisions at the right time. The advent of big data and data sources explosion provide organizations and data scientists with a wealth of information. However, extracting meaningful data is still a problem, which makes data aggregation important.

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