What is Data Aggregation?
Data aggregation is the process of summarizing a large pool of data for high level analysis. At its most basic level, it involves compiling information from a range of prescribed databases and organizing it into a simpler, easy-to-use medium, usually utilizing sum, average, mean, or median references. But it is important to note that data to be aggregated does not need to be specifically numeric, for example, non-numeric data elements can be counted as well.
Why is Data Aggregation Important?
Data collected by enterprises is vital to help them make better decisions, understand consumer behavior, improve process efficiency and finally, understand performance, be it of the company or its products. To this end, simply collecting high quality and reliable data is not enough. Consistent findings, consistent evolution, and the usability of the data plays a vital role too. Business decisions related to activities such as strategic planning, pricing, and marketing campaigns rely heavily on the insight obtained from aggregated data.
To give an example, a social media platform could collect data from social media interactions, browsing history from connected devices and other personal data. It uses this data to obtain insight into specific demographics or behavioral variables, such as education levels, career choices, age, and income brackets. This in turn could be used to help position products and services to specific groups. In this example, data aggregation could be used to summarize the collected information into insightful statistics. Maybe the data revealed that fifty percent of all women in the age group of 35 to 45 are mothers, or the average age of mothers in a specific geographical region is 30. This data would be extremely useful for baby products marketing, healthcare providers, or women-oriented product advertisements.
Laypeople often wonder why they keep seeing advertisements for resorts in the Maldives after once searching for honeymoon destinations. That is data aggregation at work. Data aggregation helps advertisers curate and target their campaigns to specific audiences. If someone searches for honeymoon destinations, advertisers know they are looking for a luxurious romantic escape.
Some other specific examples of aggregated data can include things like:
- Voting information, when individual records are not relayed, just the total votes for each candidate for a specific region
- Average consumer age, when for each product, the average age of the consumer is recorded and not each individual customer
In today’s world of big data and the internet of things, data aggregation is an important component of successful business strategy.
How Does Data Aggregation Happen?
A row of aggregate data could be representing hundreds of thousands, or even millions of data records. Extracting data from the individual records and preparing an analysis report can be a time-consuming, complex task, especially if done manually. Instead, data aggregation is best completed using advanced tools known as automated data aggregators. These software tools optimize the aggregation method by capturing the appropriate and accurate data as effectively as possible. This allows the scalability needed for vast screeds of data, which manual processing simply cannot cope with.
An aggregation tool typically includes components for collection, processing, and presentation of the aggregate data. Automated data aggregator tools also provide the ability to track the lineage and history of the data, tracing back to the source and authenticity of the data.
The Data Aggregation Process
In order to create useful and accurate outcomes, it is important that a few basic steps are followed.
The data aggregation process begins with the actual collection of data. The aggregation tool will extract data from multiple sources and store it in large databases as atomic data. Before analysis, data should be analyzed for accuracy and checked that there is sufficient quantity available before aggregating it. For example, making predictions on an election when only ten per cent of voting results are available is likely to produce an incorrect prediction.
Then, a data aggregation technique must be chosen. There are a range of techniques that may be used. The technique used depends on the volume of data and the software.
- In-network aggregation: Uses a multi-hop system for the process of gathering and routing information
- Tree-based approach: An aggregation tree is constructed, mapping put the data from leaves to roots (source and sink nodes respectively)
- Cluster-based approach: This approach is used to collate larger amounts of data on the entire network
- Multi-path approach: In this approach, partially aggregated data is sent to the root or parent node which then can send the data down various paths
Then, there are data collection intervals that need to be defined, and aggregation only applied to data within those intervals.
- Reporting period: This is the period over which the data is collected. For example, a summary table may contain data collected from a particular network device over a period of one week. The reporting period may include raw data or aggregated data points. The reporting periods include daily, weekly, quarterly and annually.
- Granularity: This is the period over which data points for a resource or set of resources are collected for aggregation purposes. For example, if an organization wishes to find the average of the data points for any resource over a ten-minute period, then the granularity is ten minutes.
- Polling period: The polling period refers to the duration of time that determines how often resources are used for sample data. For example, a set of resources may be polled every ten minutes, meaning that a data point for each resource is generated at ten-minute intervals.
Mathematical functions speed up and optimize data aggregation. Some commonly used functions include:
- Average: This computes the average value of a given set of data
- Count: The function tallies the total number of datasets in any given data set
- Max: This function obtains the highest value in any given set
- Min: The function displays the lowest value in a set or category
- Sum: This adds all the specified data to identify the total of the set or specified category
The extraction of data is followed by its processing. The tool identifies the atomic data which will be aggregated and applies processes as required, such as predictive analysis, machine learning algorithms and artificial intelligence. Specific statistical functions such as means, medians and averages, are thereafter applied to obtain aggregated data.
Aggregated data is thereafter presented in a summarized format for analysis, insight and overview purposes.
Automated Versus Manual Aggregation
This age-old debate between automated versus manual processes still rages today. Often businesses will try to carry out data aggregation through manual processes, which requires a considerable reliance on humans. Manual execution of the aggregation process could involve the structuring of hundreds, if not thousands, of unique data sets from as many sources. Not only is this quite complex, but extremely time intensive as well. The manual approach, while still in use, is considered quite the outdated approach and can lead to several negative consequences, such as:
- Slower time to market: Manually standardizing data sets and sources to suit the needs of an organization is not efficient and will result in loss of agility of the business.
- Expense on internal resources: A manual task requires human oversight, and with aggregation being a continuous, ongoing task, it will take up considerable time of employees, adding onto the daily workload. Additionally, a manual data aggregation process usually requires considerable support from the IT team, which leads to key individuals being tied up performing manual tasks instead of using their valuable time to innovate and coming up with new opportunities.
- Inability to scale: As a business grows, so does the data. Relying on people does not scale up well.
- Error: Humans are prone to error. Even having one decimal point missing could result in an entire calculation being incorrect.
- Loss of revenue: The above points all translate into loss of revenue for the enterprise.
In today’s competitive environment, manual data aggregation is simply not sustainable for enterprises looking to grow continuously or implement new ideas and products. Furthermore, manual aggregation means taking on the risk of an accidental omission of vital data sources or patterns.
The most efficient approach to data aggregation must include some or complete automation. The elimination of manual tasks effectively removes the possibility of human error, frees up valuable resources, hastens time to market and enables the enterprise to become agile – a vital factor for success.
How to Choose the Right Data Aggregation Tool
This will vary from enterprise to enterprise, depending on what exactly is required. As is often the case, one specific aggregation tool will rarely give an organization all the features they require. A healthcare enterprise’s needs will vary from an e-commerce business or even a restaurant setup. Some tools may be extremely technical while others may be more user-friendly. Some may work with cloud while others may not. Knowing exactly what is needed and how the tool needs to be used beforehand will enable an organization to make the right decision. It is therefore vital to evaluate requirements carefully before investing in the tool.
For example, a small nursing home may need to combine only a few data sources for its data warehouse, but a major hospital or health insurance company will need a tool which offers forecasting features as well as powerful predictive modeling.
Spending time with data professionals that understand the raw data and the outcomes required will help in ensuring the correct product is used.
Use Cases of Aggregated Data
Data is vital for making accurate decisions, across all industries and departments. Data aggregation can not only assist in making strategic decisions but contribute greatly toward product innovation, future planning, and operations optimization. It is particularly helpful in formulating marketing strategies, a process which usually requires distinctive data aggregation techniques as well as multiple aggregation tools. Other uses include price monitoring, market intelligence and competition monitoring. Aggregated data helps in making business leaders well informed decisions through the analyses presented and can provide critical information and insight into consumer behaviors.
The retail sector is fast paced and challenging, especially with the advent of e-commerce. Competitive analysis in the retail sector becomes paramount, bringing to play data aggregation. Having targeted marketing is important, to ensure marketing spend is utilized fully and results in sales.
Another highly competitive market, travel companies need high quality reports to keep abreast of changing travel preferences, rates, and new destinations and markets. For example, post COVID, every aspect of travel changed. National travel became popular in order to avoid flights or testing, and rates changed to reflect a drop in custom and suppliers. As COVID became less prevalent, international travel started to resume. The travel industry needed to cater to a market, noting the changes in supply and demand.
An important area where aggregating the data obtained from marketing campaigns- for example a google advertisement run, or a website banner- over a period of time, allows deep insight on how effective the campaign actually is, across different segments and audiences.
The COVID pandemic is a classic example of the importance of data aggregation for government health facilities as well as any monitoring bodies. It is crucial to monitor developments and trends in the healthcare sector so that innovative solutions can be created (for example, a covid-vaccine for children) as well as ensuring correct diagnosis of patients. Data aggregation will also allow for better maintenance of trust and transparency between healthcare providers and patients.
Risks with Data Aggregation
As with any tool or process, data aggregation also comes with its own set of risks, the most central one being that of breaching data privacy. Data aggregation, for obvious reasons, includes elements of personal information, so consent of the individual is vital.
Data privacy laws have evolved considerably over the past decade, and websites today must be quite transparent on what personal information can be captured using cookies. Legal issues can occur if there are security breaches or personal data is not adequately safeguarded. Particularly in finance and healthcare, data privacy and security is imperative.
The other way data privacy can be managed is by stripping personal information. While the name or gender of the individual may be required, the name or phone number is not. Stripping identifying details takes away much of the risk.
The Future of Data Aggregation
Data aggregation is vital for enterprises today. By summarizing data from various disparate and multiple sources, data aggregation is essentially increasing the value of information. While automated aggregation tools take care of the collection and processing, they can also be used to establish an audit trail through the tracking of data origins and sources.
Fully automated and holistic aggregation tools will be increasingly deployed to analyze massive amounts of data. Organizations cannot afford to miss out on the insights and information that data aggregation provides.
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