Data Aggregation brings together the data from a variety of sources, processes them, and makes them eligible to undergo analysis. From simple clicks to complex transactions, anything that happens online turns into data. The Internet produces tons of data every passing second. Statista says global data creation is expected to grow more than 180 zettabytes by 2025.
Until this abundant data is left as it is, it is of no use. With some valuable operations, like data collecting and processing, this data qualifies as valuable input for business insights. This article will guide you to make use of the data effectively using Data Aggregation techniques.
Data aggregation is the process of unifying data from multiple sources. The sources may be social media, historical databases, data warehouses, datasets, RSS feeds, web services, or flat files. The data from these sources are not just text, they may also be images, graphics, statistical data, complex functions, binary values, and IoT signals. All this data is a worthy resource for Data Marketers. They perform statistical analysis on the aggregated data to design business insights from them. Marketers extract data from multiple sources and perform the data aggregation process.
Data Aggregation is the key process that benefits common users and business people to make decisions based on the results of the historical data. Data Aggregation can help users to handle multiple types of data. Raw data with no further processing is of no use. Raw data should undergo a cleaning process to remove unnecessary noises and convert them into a standard format. Apart from just collecting data, the data scientists who use the data aggregation technique perform business intelligence techniques, like predictive analytics, and visualize the results through a marketing dashboard.
Data aggregation is the process of summarizing and condensing widely collected data into a simpler form, making it easy for data scientists to develop critical insight from it. Based on when and on what the aggregation takes place, people categorize the aggregation service in two ways:
Time aggregation collects multiple data points of one resource over a while. For example: Consider you run a shopping complex, where you collect sales data on one shopping complex at the end of the day. Here, the aggregation takes place on one resource (the shopping complex) at a regular interval (end of the day).
Spatial aggregation collects data from multiple resource groups at regular intervals. Here, data collection depends on more than one factor. For example: Consider, you own a shopping complex. You perform spatial aggregation to view the sales data of all the stores at regular intervals. Here, they work on multiple resource groups like individual stores of a complex.
There are a few concepts that address how often and under what conditions the data is aggregated or collected.
Reporting period denotes the time period over which the data is gathered. The data of a particular device or circumstance are collected over a period of time for presentation purposes. For example, let us consider a toll booth that records the vehicle details that cross their way each day. Here, one day is the reporting period.
Granularity is slightly different from the reporting period. In this case, the data are collected over a period of time for the aggregation process. Granularity helps in performing aggregation operations over the collected data. Example: A toll booth records the vehicles that pass their way. If the data are collected every 10 minutes, the granularity is 10 minutes and the granularity range may vary from 1 minute, 2 minutes, and 10 minutes to 1 month.
The polling period is an extended process of granularity. As granularity is the time period over which the data is collected. While the polling period is the time taken for data creation. Assume if the toll system takes 10 minutes to generate data of the vehicles crossing by. Then 10 minutes is the polling period. And if we prefer collecting data every 5 minutes the granularity is 5 minutes.
Data aggregation is all about unifying data from multiple sources. Though it sounds simple, data aggregation involves multiple processing cycles in the proper order of execution.
The primary step of data aggregation is data collection. The collection phase extracts data from multiple sources. The sources are not necessarily always static, they may be dynamic, too. The data warehouse and historical data records are a few of the static data sources. They don’t change. But, there may be dynamic sources, like social media, as well. Social media communications are the most interactive data sources, where data may keep on changing with every passing minute.
Example: The likes, comments, and share counts of social media posts and the traffic on a website may change with time. In this case, the data aggregation process should work with the streaming data.
Collecting data is the primary phase, so data aggregation tools proceed with the process in this processing phase. This phase is responsible for converting raw data into a format that is suitable for the data analysis process. Data processing includes multiple operations, like cleaning the unnecessary noises from the data, performing logical or arithmetic operations, like MIN, MAX, AND, SUM, and other complex data transferring operations.
Example: A business marketer is trying to find out the demand for his product through social media. He makes a post on social media and keeps track of his users’ reactions. From this, he can analyze the demand for the product in the market. Initially, data scientists will perform arithmetic operations to count the likes and dislikes of the posts. Then they will handle complex operations, like the sentimental analysis. This focuses on people’s comments and finds people’s sentiments or opinions on the product. They also track what sort of catchy words or links attract people to their product.
The last step of data aggregation is presentation. Data aggregators typically visualize the results in a marketing dashboard that displays the business insights of their success and failure rates. In this presentation phase, the data aggregation tools display the factors that positively impacted the business as charts or tables. This comparison of multiple trial and error methods can finally help users predict a design pattern from successful trials and build a business intelligence report.
Example: Social media posts are not only a way of advertising, but it also helps data analysts predict human behavior and their interests. The business analysts come up with a report that highlights the methods or approaches that worked on customers.
Proxy servers act as intermediate servers between the nodes of communication in the network. The proxy server acts on behalf of the client and hides the identity of the client from the server and the network. This anonymity helps users access geo-blocked sites and prevents IP bans. These special features of the proxies ease the data aggregation process by automating the data extraction with high speed. The data aggregation process can make use of multiple proxies from rotating proxy pools.
The manual data aggregation takes quite a long time and requires a lot of effort. Manual data aggregators can find it tedious to have to repeat the collection, processing, and presentation phase for as much data as they have. This is why people prefer automated data aggregation software or data aggregation tools that can speed up the aggregation process. Choosing the right data aggregation system can enhance the quality and standards of the process. Here are some of the factors to consider before deciding on a data aggregation system.
Cost efficiency – Cost is the main factor to focus on. The data aggregation tools you choose should not exceed your budget for installation.
Compatibility – Make sure the data aggregator supports all data formats and is compatible with all data sources. The system should be efficient enough to handle different data formats.
Scalability – Business people expand or reduce their business scale as needed. In this case, the data aggregation system they choose should adopt the scalability changes.
Residential proxies may be the proper choice for the data aggregation process. As their proxy address is associated with a physical system, they appear like a real address. This reduces the suspicions on the IP addresses. Also, with the residential pools, people can find proxies of various locations and protocols to access specific sites.
A proxy is not the primary component of the data aggregation process. Data scientists have many automated data aggregation tools that can aggregate the data collected and present aggregate data. But, a proxy can add value to this system. Though a proxy is not the major requirement of data aggregation, efficient data aggregation requires a proxy as it simplifies the scrapping process through its features.
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Both are similar in that they collect data from various sources, but the integration focuses more on presenting the aggregate data in a summarized format.
Data scientists utilize this data aggregation technique to handle atomic data records. If you are expecting to collect data from various sources and convert them into valuable insights, make use of this data aggregation technique. To simplify the data aggregation process, consider factors like cost, compatibility, scalability, and other factors to choose a suitable data aggregation software. Also, configuring a suitable proxy type can improve the efficiency of the data aggregation process.