In modern times, where businesses are dynamically evolving, data becomes the key driver. Regardless of the industry you operate in, handling tremendous volumes of data on an everyday basis is inevitable. Data flows from numerous sources – company websites, social media, and external systems. Furthermore, data may be in a raw or consumable form.
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That said, the core challenge for businesses is to collect, choose, process and present the data in a comprehensible language. Technology comes to the rescue. The simple yet efficient technique which makes this task user-friendly and resourceful is – data aggregation.
This article delves into what is data aggregation, how data aggregation works, why you should use it, best practices, steps involved, data aggregation use cases, data aggregation automation, and more.
So, let’s get started.
What is data aggregation?
At its most basic, data aggregation refers to organizing, summarizing, and compiling large volumes of information from databases and making it available in a more consumable form. Essentially, when data is aggregated, the small rows of information gathered from multiple sources are replaced with parameters like sum, totals, or aggregates. Data aggregation can be applied at any level – from pivot tables to data lakes – ensuring that the data is well-collated and useful to draw meaningful conclusions.
Data aggregation lets data analysts examine huge volumes of data easily, within a short time. An aggregate row could comprise hundreds or thousands of small data records. Once data is aggregated, it makes querying easier rather than requiring processing each underlying row of data when it is accessed in real-time.
Primarily, data aggregation is used in statistical analysis for large groups of people and creates meaningful summaries of data that can be used in business analysis. Data aggregation is usually carried out by software known as data aggregators. These tools basically collect, process, and summarize the data to present it in an easier format.
As businesses increase in complexity, the volume of data they handle also increases, which in turn makes it imperative for organizations to resort to data aggregation.
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How does data aggregation work?
Data aggregation is particularly relevant in cases where a dataset as such is of no use and needs to be fine-tuned and organized to turn it meaningful. Say, you are given a spreadsheet with thousands of rows and hundreds of columns of raw data, it may make no sense to you. Data aggregation involves converting individual values into parameters like sum, count, average and more to give them meaning.
The technique deals with finding useful information using options like summarizing the data and converting it to useful reports. You can aggregate data that is numeric or non-numeric. It is not necessary that the aggregate must be a number. It is possible to find the count of non-numeric data. Aggregation must be carried out on groups of data and not individual data.
Examples of aggregate data:
- Finding the average age of customers using certain products. You may not look for the details of individual customers but rather capture the average ages of people buying various products.
- Finding the number of customers in a geographical location. Say your company operates in multiple geographies, you can use the count option to find the number of customers in each of these locations.
- Finding the voter turnout in a state or province. You can get the total votes cast in a particular state, though the individual voter details may not be relevant to you.
Why should you use data aggregation?
With business requirements proliferating, it becomes essential to maintain data in an organized manner, more than ever before. Data is critical to any business. That said, when organizations have to sift across large volumes of data, it becomes more cumbersome if the data isn’t properly managed and classified.
Data aggregation ensures that data is compiled, summarized, and maintained in an organized fashion, which makes it convenient for the stakeholders to draw actionable insights. Data aggregation makes it easier to identify key trends, patterns, and findings – thereby making the information more usable and hence valuable. The technique enables businesses to achieve their specific goals by analyzing processes or human assets using features like searching, collecting, and presenting the data in a summarized, report-based format.
How to do data aggregation – Step-by-step approach
Data aggregation is performed using software tools known as aggregators. Data aggregators combine small rows of data from multiple sources, process them, and present the aggregate in a summarized view.
There are several steps involved in the data aggregation process. These include:
Collection: First, data aggregators extract data in small rows from various sources, storing it in large databases. The sources for collecting the data include the following but are not limited to:
- News Headlines
- Social media communications
- Call center audio, podcasts
- Personal data and browsing history from IoT devices
Before starting the analysis, data must be checked for accuracy, and that sufficient quantity is available for aggregation. This requires a data aggregation technique to be chosen. There are various techniques available, the choice of which depends on the volume of data and the software.
- In-network aggregation: This uses a multi-hop system for gathering and transmitting information.
- Tree-based approach: An aggregation tree is created and mapped in a way that data is put from leaves to roots, as the source and sink nodes
- Cluster-based aggregation: This involves collating large volumes of data on the entire network
- Multi-path aggregation: Under this method, partially aggregated data is sent to the root or parent node, from where it sends the data to different routes
Next, data collection intervals have to be chosen, and data is aggregated within these intervals.
- Reporting period: This refers to the period over which the data is collected. It could be weekly, monthly, annually, etc. Besides, the reporting period may include raw or aggregated data.
- Granularity: This is the period for which data points for a resource are collected for aggregation. For example, if an organization wants to find the average of data points over a 15-minute interval, then the granularity is 15 minutes.
- Polling Period: It refers to the duration of time that specifies how often resources are used for sample data. Say a resource is polled every 15 minutes, that means the data point is generated for the resource every 15 minutes.
Mathematical functions play a significant role in data aggregation. Some of the commonly used functions include:
- Average: This function computes the average of all the data points in a set
- Max: This function returns the highest value in a given set or category
- Min: This function returns the lowest value in a given set or category
- Sum: This function provides the sum or total of all the data points in a set
- Count: This function returns the total number of datasets in a given data set
Processing: Next, data aggregators process the collected data. Data aggregators first identify the collected data to be aggregated. They use techniques like predictive analytics, artificial intelligence, and machine learning algorithms on the data collected and draw meaningful insights. Finally, the aggregators apply specific statistical functions to the data to aggregate it.
Presentation: The last stage in data aggregation is presenting the processed data in a consumable form. The high-quality statistical tools let users show the data in a more summarized, organized, and comprehensible manner.
Data aggregation can be conducted manually or using aggregators. However, the process of data aggregation usually occurs on a large scale involving huge volumes of data. That said, applying a manual procedure may not be feasible as it is prone to the accidental omission of important data patterns or sources.
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What are the best practices for data aggregation?
Some of the best practices that you can adopt for data aggregation include:
Understand your company’s short-term and long-term analytics objectives
For effective data aggregation, you must clearly understand your goals, short-term as well as long-term. Say, today, you are interested in collecting and analyzing customer data on their interest, preferences, and such, however, in the future, your focus may need to shift to building long-lasting customer relationships through fostering trust and crafting rewarding customer experiences.
Furthermore, your data may be in structured or unstructured form. That said, you must aim to enhance customer experience and learn more about the company’s product manufacturing processes to improve product quality. In any case, it is quite possible that your present and future goals would affect your data aggregation requirements.
Your data aggregation strategy must include these pointers. You may not need customer buying pattern data today. However, there may arise a need for other inputs from new sources in the future.
Ensure that data purchased from outside complies with your governance regulations
Most industries, especially finance and healthcare need to consider privacy and governance compliance aspects seriously. For example, say you get the external patients’ sensitive data like genetic structures for certain diseases from an outside vendor, you need to ensure that these remain confidential. Likewise, you need anonymization when you have to ensure that the data of your patients remain private.
Determine the process of storing data and ways for users to access it
You need to ascertain if your aggregated data needs to be shared with specific groups of people in the company, certain departments, or everyone. Depending on your requirement, you may need to store the aggregated data in a large repository, granting access privileges to specific user sets. Alternatively, you may create small data marts that cater to the needs of specific user groups.
Automate data integration to the maximum extent
Regardless of the outside sources – call center audio, website text, or anything -, you want to aggregate data from, you certainly need an easy way to validate and integrate them with your target repository. You would prefer some user-friendly method over manually writing the code for every integration interface. With regard to data aggregation, you can use standard APIs or other automated integration options which do most of the integration tasks for you.
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What are the different use cases of data aggregation?
While data aggregation is useful across most industry verticals, some of the major data aggregation use cases in various business domains include:
Investment and Finance
Usecase/Example: Aggregating data from headlines, expert opinions on financial and stock markets, etc.
The finance industry has a lot of dependence on data aggregation. As investors need to keep track of financial trends and stock market changes, the finance space particularly gets its data from the news. These industries gather news headlines that contain information on trends, opinions from experts, or events on the finances of the products that they track.
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Data aggregation enables them to collect, process, and present this information which is scattered across various marketing websites and other sources. This indicates the need for a robust data aggregation strategy.
Retail & E-commerce
Usecase/Example: Aggregating data for competitor research, tracking, and predicting market trends…
In the post-pandemic era, the retail and e-commerce space is booming. The companies operating in this domain are mostly interested in competitive research. There is stiff competition among the players and it becomes imperative for any business to stay updated on the competitor prices, products, promotions, and more. While there are many sources to obtain this data from including competitor websites, the challenge remains in deriving actionable insights from them.
This is where data aggregation helps. The data aggregation technique collates and processes all the competitor data gathered from multiple sources and presents the information in an easy-to-consume format. Businesses can leverage this technique and explore where they need to improve in order to surpass their rivals.
Banking
Usecase/Example: Introduction of PINS obviating the need for remembering usernames and passwords – Screen Scraping
The banking sector has greatly leveraged data aggregation and invented a new technique known as Screen Scraping. Under this method, all the usernames and passwords of users are replaced with generalized PINS. This means users accessing banking websites no longer need to remember their usernames and passwords.
Instead, each user will have 1 PIN, a combination of alphabets, numbers, and special characters which they can use to access all the websites of a particular bank. The system authenticates the user when they make the request and data aggregators validate the information using the account holder’s PIN. It is worth mentioning that these systems can operate as standalone or in tandem with other systems that perform authentication.
These data aggregation use cases have revolutionized banking procedures and made the lives of customers easier. At present, banks use screen scraping for bill payments and tracking products.
Healthcare & Pharma
Usecase/Example: Aggregating patient data to monitor their lifestyle, and behavior to various drugs, etc, tracking doctor-nurses interactions with patients, etc.
In the healthcare sector, data aggregation finds various use cases. For example, it helps to monitor patients’ lifestyles and how they consume various medicines and drugs. Besides, the interaction between doctors and nurses with patients can also be tracked. Data aggregation also helps maintain records, history of transactions, and reports.
Data aggregation is of considerable importance to the pharma industry as well. From accurate inventory to prescription management, data aggregation use cases are several in the pharma sector.
Digital Marketing and Advertising
Usecase/Example: Aggregating data to understand customer preferences, competitor strategies, etc. to craft content accordingly
Data aggregation use cases in digital marketing and advertising space are many. These industries use the technique to gather news headlines, competitor research, predict market trends, and others. Through these ways, they can develop and market content, reach prospects, and sell their products profitably.
Data aggregation helps these companies to analyze customer data by providing personalized advertisements and offering remarkable customer experiences through focused marketing efforts.
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Data aggregation for enterprises
Data aggregation is an integral component of enterprises in the era of growing business needs. Data aggregation offers multiple benefits to enterprises. For example, you may have much data. However, not everything needs to be analyzed. That said, data aggregation first checks which data needs analysis and chooses them.
This ensures that only relevant data is taken into consideration for aggregation. In addition, data aggregation improves the marketing and sales of companies as it facilitates competitor research and helps organizations leverage business intelligence to derive useful outcomes.
Data aggregation automation
While data aggregation could be performed manually or automated, the latter is usually preferred. This is because data aggregation usually deals with huge volumes of data, making it prone to errors, in the case of a manual approach. In automated data aggregation, a third-party interface known as middleware collects data from various platforms such as marketing, human resources, SaaS, products, etc. Once automated, the area of interest for the data gets increased, resulting in better focus on other segments of the analytical process.
Let’s say you conduct the data aggregation automation for marketing purposes. Then, the aggregation solution gathers data from various sources. These include Ad platforms, social media, website analytics, and more. Next, the system normalizes the data using harmonization techniques. This means the system eliminates duplicates, reduces redundancies, aligns distinct indicators, and removes data discrepancies.
Finally, the data aggregation system stores data in a distinct warehouse. It is highly recommended to opt for analytical databases as they can handle huge datasets. Such centralized storage makes it relatively easier for data analysts to gain meaningful insights.
Closing thoughts
Data aggregation is a potent tool that validates, organizes, summarizes, and presents data in an easily understandable view. The technique finds applications across healthcare, finance, banking, advertising, and more. Users, data analysts, and data scientists can gain a lot of information from the views presented by data aggregation, which can help to make useful business decisions. The primary aspects of data aggregation, its best practices, data aggregation use cases, and data aggregation automation are discussed in the article.
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