What Is Data Aggregation?
The most valuable asset any company has is their data. But, if that data isn’t properly gathered, cleaned, and used, then the analysis performed on that data isn’t going to get you anywhere.
Data aggregation, which consists of compiling and organizing data into a more comprehensive form, is essential for organizations of all sizes and industries to perform high quality, accurate data analysis. It’s a common text mining process that can be performed on datasets of all sizes, from a few gigabytes to many terabytes.
When performed correctly, it can help reduce noise in data, condense the amount of data, and change the scale of a dataset. All of these factors make the resulting data easier to work with—for example, displaying the average time a user spends on a webpage, rather than showing each individual time on page as its own attribute in a dataset. Marketers often rely on data aggregation to compile information from different campaigns and channels to maximize the impact of their customer outreach.
On the surface, data aggregation is just a technique to summarize data, but its impacts can be monumental. It can be the difference between an incoherent mess of data and a streamlined dataset that’s rich with relevant insights.
Why Is Data Aggregation Important?
Most organizations understand the value of data analysis and quality, but they often fail to gauge the importance of aggregation. Through proper data aggregation and modeling, your organization can leverage the resulting data to improve your operations, products, services, and much more. You can get deep insights by aggregating data from siloed, disconnected sources like your CRM, social platforms, and even website analytics.
Apart from this, aggregating data is used to help organizations study and learn about their customers, leading them to identify trends in behavioral patterns, spending actions, and responses over time. Through the results of this process, organizations can make better decisions, implement meaningful changes, and plan for future campaigns.
Examples of Data Aggregation
Data aggregation is nothing new, but with the emergence of advanced technologies like artificial intelligence and machine learning, its capabilities and use cases have grown immensely.
Here are a few examples of how it can help your organization:
Achieve the best outcomes
As we mentioned, one of the biggest benefits of data aggregation is that it reduces unnecessary noise in your data. This means the data you present to decision-makers is easier to understand, with no nonessential data points. You can use this ultra-efficient method to generate more valuable insights and achieve your top business objectives, like increasing ROI and reducing costs, much faster.
Understand high-performing channels
Marketers constantly utilize various channels for promoting their company’s products and services to potential customers. In the past, it’s been difficult to determine exactly which methods were yielding the best results. When you implement data aggregation, you can identify the source of traffic and establish a better understanding of the customer journey, resulting in optimization of your marketing budget to the right channels.
Data aggregation can also be used for risk assessment and mitigation, which is particularly crucial for financial institutions and other companies that have highly regulated operations. By combining critical, sensitive data into a more comprehensive form, organizations can optimize their response to any potential threats, internal or external.
Facilitate KPI reporting
Apart from risk management, data aggregation makes measuring and reporting key performance indicators, or KPIs, much easier. This helps companies identify clear areas of improvement while setting better goals. For example, a quick-service restaurant can leverage aggregated data to benefit from competitor research, market intelligence, price monitoring, and average order time so they determine more realistic and pertinent KPIs.
Aggregating data can help your marketing team surface metrics about an image placed in multiple ads and determine how well certain CTAs are performing across campaigns. You can use that data to produce more relevant ads comprised of high-conversion, effective elements.
Challenges Associated with Data Aggregation
While data aggregation is a staple of quality data analytics, it’s not without its challenges. Here are a few roadblocks organizations can run into when executing data aggregation.
Disparate data sources
If you don’t have a data warehouse or repository, data has to be collated from across various sources. Since each source has different ownership, they have to be validated individually, making the process complex and time-consuming.
Each time data is aggregated, the extraction code, or logic, has to be rewritten. If a different person writes the logic, there can be unexpected results.
Lack of system balance
Most organizations don’t have a solid balance between manual and automated systems. If an engineer makes a mistake in a manual process, the accuracy of the output could be compromised.
Choosing the Right Data Aggregation Solution
Data aggregation is a must for any organization looking to analyze its data, derive valuable insights, and make winning business decisions. While aggregation processes can be time-consuming and tedious, data aggregation solutions can help organizations become more efficient by quickly and accurately reducing unnecessary data points and noise in datasets.
When looking for the right tool for your organization, be sure to prioritize a platform that can support changing business requirements with a flexible architecture. Your aggregation tool should also be cost-effective, scalable, and easy to implement.
With RapidMiner, data aggregation is just the tip of the iceberg. Our data science platform empowers you to address your organization’s top pain points and generate valuable insights from data you already have.
Ready to get started? Request a demo to see what the impact RapidMiner could have on your team.