Techniques for Efficient Data Aggregation

 We require different data sources to identify potential answers to business queries. Some sources are more reliable than others, but you can always address this risk through validation. Data aggregation services enable businesses to improve their data collection efforts strategically. This post will discuss different data aggregation techniques for corporate usage. 

What is Data Aggregation? 

Data aggregation means collecting data from multiple sources and expressing it in summary form to facilitate more reliable statistical analysis. Therefore, many data analytics solutions include data aggregation techniques to help their clients acquire new insights. 

Statistical hypothesis verification becomes easier when you increase the sample size data. After all, a smaller sample size increases the risk of biased conclusions. Data aggregation services help commercial entities arrive at neutral or balanced interpretations. So, business leaders can create objectively more resilient strategies. 

Business strategy affects the rate of your company's growth and how it builds new, mutually beneficial relationships with industry peers. Besides, gathering data from multiple sources increases your capacity to innovate conventional workflows. 

Types of Data Aggregation Services 

1| Time Aggregation 

It is time-based data aggregation if you evaluate data points originating at a single resource over a fixed period. Consider the analysis of sales data corresponding to a single product. Otherwise, you can focus on the performance of one of the sales channels in the last two business quarters. 

2| Spatial Data Aggregation 

When data aggregation services emphasize a group of multiple data resources, this category is called spatial data aggregation in data mining operations. For example, a company could summarize the performance of various sales channels in the same quarter to create a data-driven sales strategy. 

3| Manual Aggregation 

Smaller companies with a limited scale and budget often hire people to conduct manual data aggregation. However, you will require automated data aggregation and analytics solutions as you start scaling up your business. Moreover, manual aggregation distracts your workers from more critical activities due to the mundane nature of operations. 

4| Automated Data Aggregation 

Innovative data aggregation services eliminate the drawbacks of manual data aggregation by implementing automation-friendly integrations. Machine learning (ML) models utilized by data analytics solutions assist companies in partially automating data aggregation techniques. Likewise, artificial intelligence (AI) serves international enterprises through more holistic automation. 

5| Real-Time Data Aggregation 

Social media listening, new sources, and research journals generate large quantities of data with each passing moment. Therefore, corporations want unique data aggregation techniques that provide real-time summaries without straining computer resources. Real-time data aggregation services benefit from the virtualization of computing operations through cloud platforms. 

Techniques in Data Aggregation Services 

1| Tree-Based Aggregation 

Trees have branches, and humans get inspiration from trees in their data aggregation techniques. Intermediate nodes help professionals who want to use data analytics solutions create secondary and tertiary branches in the data aggregation workflow. Therefore, this system requires less energy compared to structureless clusters. 

2| Cluster-Based Data Aggregation Techniques 

Cluster heads facilitate large-scale data aggregation using a massive network of clusters. Each cluster head ensures stability for every node in the cluster. Cluster-based data aggregation helps in dynamic data acquisition techniques. However, this type of data aggregation service requires tremendous energy and computing resources to minimize downtime. 

3| Chain-Based Aggregation 

Data collection and transfer nodes communicate with each other instead of reporting to the cluster head frequently. Since they communicate with their neighboring sensors, this type of data aggregation helps you use data analytics solutions without using too much energy. After all, you will observe significant energy savings by avoiding frequent communication with the cluster heads. 

4| Structureless Aggregation 

Imagine an extensive network of sensors that remains available all the time. They listen to the events and triggers for every kind of data emerging from an identified array of sources. While event-based or structureless data aggregation techniques allow real-time tracking and updating of databases, these services consume way more energy than other data mining methods. 

5| Multipath Data Aggregation 

What will happen if a node fails in a data aggregation network? If you use a finite number of different pathways, the failure of nodes in one pathway will not interfere with overall data aggregation efforts. This principle is at the core of multipath data aggregation services. Simultaneously, you will convey the same information to the root node several times. This routine can increase data duplicity unless you engineer mechanisms to prevent unnecessary overwriting. 

Example: Power Query in Microsoft Excel for Data Aggregation 

Using power query in Microsoft Excel for data aggregation is one of the most straightforward data mining techniques. Simple functions, like sum or maximum values, can help you get the general summary for preliminary data analytics solutions. 

Power query in MS Excel is a self-service ETL (Extract, Transform, and Load) technique. It supports database solutions from Microsoft and Oracle. Also, you can use simple formats like CSV files, PDFs, or JSON. Power Query allows small firms to perform ETL operations for PostgreSQL and Salesforce data. 

Likewise, you can use the query functions in Google Sheets and any SQL-friendly data aggregation services. SQL, or structured query language, is integral to relational database management systems (RDBMS).

 

Conclusion 

Data aggregation techniques empower businesses, and data analysts provide flexibility in data mining models. You can also combine multiple data-gathering techniques suitable to your organization. Real-time aggregation requires significant computing resources. However, tree-based data aggregation services are more energy efficient. 

You have learned about different types of data aggregation techniques, and hopefully, you understand their advantages and disadvantages. Tools like the power query feature in Microsoft Excel facilitate simple data aggregation methods. Yet, a thriving business must consult experienced professionals to optimize its data collection activities. 

A leader in data analytics solutions, SG Analytics, supports organizations in their data acquisition and insight extraction efforts. Contact us today if you want automation-friendly data processing capabilities for impressive strategy innovations.