The importance of data aggregation
What is data aggregation?
Data is all around us. This makes it essential to understand what data aggregation is. Let’s explore the different facets of data aggregation to get a better understanding.
Data aggregation is a type of data and information mining process where data is searched, gathered, and presented in a report-based, summarized format to achieve specific business objectives or processes and/or conduct human analysis. Data aggregation may be performed manually or through specialized software.
Data aggregation is used to get summarized data for analytics. It helps provide statistical analysis for different objectives.
How does data aggregation help?
Aggregation platforms take care of the collection, processing, and sometimes even the presentation of data. It’s an essential part of data integration. Data aggregation helps summarize data from different, disparate and multiple sources. It increases the value of information.
The best data integration platforms can track the origin of the data and establish an audit trail. You can trace back to where the data was aggregated from. It’s important to understand that aggregate data is not limited to numbers. Data aggregation may be performed manually or through software expressly written for such purpose.
What are the steps in data aggregation?
To accomplish data aggregation, you need a data integration platform that helps in collecting data from multiple sources. The next step is data processing to enrich the data and finally, we arrive at the data aggregation stage. This is where the data is statistically analyzed for the latest insights. We explain the steps involved in the data aggregation process below:
A data integration platform extracts data from different sources and stores it either on the cloud, on-premise, or data warehouse. This data may have originated anywhere. It could have been extracted from sources such as social media, eCommerce platforms or it may be data stored in files when it comes to IoT or sensor data. The advantage of a data integration platform is that it makes it easier and seamless to bring all the information from all the different data silos together.
During the data processing stage, the data that has already been collected is processed for interpretation. This processing is done using a combination of smart machine learning algorithms. The method of processing can vary depending on the source of data being processed. Your data source could be data lakes, social networks, connected devices or any other source. The type of processing can also vary based on the intended use of this data.
This data can also be processed in different forms. It could be in the form of an image, a graph, a table, a vector file, audio, charts or any other format of choice.
Once your data has been processed, developing and delivering an effective data-driven presentation is crucial. Data analysis tools first analyze and process the raw data, after which the aggregated data is presented in a summarized form. How the data is presented will vary based on the business.
Once the data is collected and processed, all the aggregated data can be presented in a summarized form. How it is presented depends highly on the type of business. Data is most commonly presented either in textual, tabular or diagrammatic forms (bar charts, pie charts, line graphs, scatter graphs).
Sophisticated statistical algorithms can be used to analyze the data and derive insights from it.
What makes data aggregation so useful
Making sense of the avalanche of data isn’t as easy as it may seem in the age of big data. This is where data aggregation can prove helpful. Data aggregation brings together data from multiple sources and summarizes all this data in a uniform manner. Companies that need to gain market intelligence rely on software tools for data aggregation. These tools can be used to perform the following actions:
Data extraction- This involves the targeting or isolation of relevant data from the aggregated data to ensure that it meets the needs of a particular business.
Modification- Modifying or transforming the data in such a way that it corresponds to the prescribed format for data analysis.
Data visualization and analysis– A visual representation of analyzed data and KPIs which can be used for actionable business intelligence.
Once this aggregated data is analyzed, it can be used to create actionable business intelligence or guide you through the decision-making process. You can easily evaluate where your business needs to improve with these metrics.
Consider the case of the restaurant industry. A quick-service restaurant can leverage the data it collects through a data integration platform to benefit from competitor research, price monitoring, market intelligence, average order time and more.
Used this way, data aggregation is a boon for business analytics. The summarized aggregated data can help leaders make well-informed decisions. It is an effective means for companies to gain critical insights into customers. Enterprises can gain deep insights by aggregating data from disconnected, siloed sources such as social media, your CRM and even web analytics.
Embrace the data-driven future with DataChannel
Business analytics and intelligence require huge amounts of data for real-time decision-making. The world is embracing the latest paradigm of data-driven enterprise.
If businesses want to stay competitive, they need data that holds the answers. It’s time to become more innovative, agile and uncover new growth.
Disparate data integration platforms and point solutions lack cohesion and are limited in their data aggregation capabilities. While they provide ETL and some integration capabilities, they lack the enormous range of integrations, data sources and destinations DataChannel empowers you with.