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.

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.

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.

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.


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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