When it comes to extracting data and generating a report for analysis, it is always difficult and time-consuming to do it manually. A successful technique is required for this goal in order to optimise the process and acquire correct and appropriate data as effectively as feasible. With this in mind, data aggregation is the best strategy.
This article will help you to understand Data Aggregation more clearly with the help of examples and how does it benefit the data driven businesses
➔ What is Data Aggregation?
Data aggregation is a form of data and information mining process in which data is sought, collected, and presented in a report-based, summarised style in order to fulfil certain corporate objectives or procedures, as well as conduct human analysis.
Data aggregation can be done manually or with the help of software. To gather summary data for analytics, data aggregation is used. It aids statistical analysis for a variety of purposes.
➔ Example of Data Aggregation
Companies regularly collect data from their web users and website visitors. for example, to check where the users come from? What kind of content do they prefer? etc. Google, for example, collects data in the form of cookies in order to serve targeted adverts to its users. Facebook does the same by collecting and analysing data and displaying adverts to its users.
Consumer demographic statistics and behaviour metrics, such as the number of payments or average age, will be included in the aggregate results. Customers utilise a single master’s PIN to gain access to their various accounts (similar to banking institutions). That type of data aggregation is commonly referred to as screen scraping. The sales staff would handle the aggregated data in order to personalise the customer’s digital experience with brand message, deals, and more. It improves the whole client experience.
➔ Why is Data Aggregation Important?
Data gathering, processing, and occasionally even display are handled by aggregation systems. It is a necessary component of data integration. Data aggregation aids in the consolidation of data from various, diverse, and many sources. It makes information more valuable.
Data integration solutions can provide an audit trail and track the data’s origin. You can find out where the data was collected from. It’s critical to realise that aggregate data isn’t just numbers. Data aggregation can be done manually or with software designed specifically for this purpose.
➔ What are the steps in data aggregation?
- Data Collection – A data integration platform gathers information from many sources and stores it in the cloud, on-premise, or in a data warehouse. This information might have come from anyone. When it comes to IoT or sensor data, it could have been taken from sources such as social media, eCommerce platforms, or data kept in files.
- Data Processing – A combination of advanced machine learning algorithms is used to perform this processing. Depending on the type of data being processed, the processing method may differ. Data lakes, social networks, linked gadgets, or any other source could be your data source. The type of processing varies depending on the data’s intended usage. This information can also be processed in a variety of ways. It could be a picture, a graph, a table, a vector file, audio, charts, or any other format you like
- Data Presentation – All aggregated data is presented in a summarised manner once the data has been collected and processed. The manner in which it is presented is significantly dependent on the sort of business. Textual, tabular, and diagrammatic representations of data are the most prevalent (bar charts, pie charts, line graphs, scatter graphs). To analyse the data and gain insights from it, sophisticated statistical methods might be applied.
➔ Automated Data Aggregation vs. Manual Data Aggregation
Data collection can be a labor-intensive process, especially if your company is still in its early stages. To export, simply click the button. Examine an Excel spreadsheet. Make it look like other data sources by reformatting it. Then, using maps, track the performance, budget, and progress of various marketing strategies. If you want the automatic solution, you will need to install third-party apps, also known as middleware, that can automatically pull data from the marketing tool. As a result, manual and automatic data aggregation are both possible, depending on the needs of the company.
➔ Conclusion
To conclude, Data Aggregation is an important stage because data is always necessary, regardless of the sector. This information is used to forecast and analyse future activities that the company will take. It is often used prior to conducting statistical analysis. The data aggregation and statistical analysis information can then be utilised to tell you all kinds of things about the data you’re looking at.
Read more: Reasons Why You Should Choose Data Annotation Services
