What you need to know about Data Aggregation
The retail industry, manufacturers, and marketers all need relevant data in order to do their job. Products need improvement, services need to evolve, and the goal is always to get as many users as possible. However, any business-to-community model faces a challenge, and that is to figure out what the audience wants and how to get them excited about the product.
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You don't exactly have the time to talk to each of your potential users individually. You need a more effective way of compiling opinions, feedback, and negative experiences in order to learn from them. This is done through data aggregation, and for the purpose of maximizing the efficiency of this aggregation, businesses often rely on data aggregation tools. Considering how all of this sounds pretty vague, we will take a deeper dive into data aggregation, explain what it is, and how it helps.
What is Data Aggregation
Data aggregation is the process of bringing or collecting data from multiple sources and summarizing it in a unified form. It is an important step that precedes data analysis or statistical analysis. Once aggregated data is analyzed, it can be used to create actionable business intelligence or guide a decision-making process.
Sources usually vary depending on the purposes of aggregating data. For example, customer data for marketing purposes is aggregated from prior marketing campaigns or maybe campaigns that were used by competitors.
In an ideal scenario, you are gathering business data from multiple sources, and you are comparing their performance. Also, if you want to target a specific demographic, you will also have to cleanse the data and isolate only relevant datasets.
Process of Data Aggregation
As mentioned, companies that need to gain market intelligence usually rely on tools of software for aggregation data processes. These tools tend to perform three basic actions:
- Data extraction - targeting or isolating relevant data from aggregated data in a way that corresponds to the company's needs.
- Transformation - transforming or adjusting the data so that it corresponds to the prescribed template or format for data analysis.
- Data analysis and visualization - creating a summary form or a visual representation of analyzed data and KPIs that can be used for business intelligence.
Data aggregation tools like Whatagraph can play a crucial role in optimizing these processes, considering how you can monitor interaction on multiple channels and aggregate data from those channels. Other features include data extraction and also for that data to be transformed and formated and displayed in a neat report.
Automated vs. Manual Data Aggregation
Data aggregation can be extremely tedious, especially if you are running a startup. This is because the information is gathered manually during these initial stages of business. A start-up company rarely invests in data aggregation tools right from the get-go, and another reason is that they are still figuring out what type of data is relevant to their line of work.
However, this is an important process regardless of whether we are talking about the retail industry or travel companies, or news websites. Tracking how audience or buyers interact with your content, products, or website are all valuable inputs that can lead to useful realizations.
Thankfully data aggregation tools allow us to automate those processes and to compare information from different sources. This is a first step to improving services or products, and considering just how streamlined it is, it comes as no surprise that we have new models of products on an annual basis.
Data aggregation tools also have multiple integrations, which allows them to connect with different data sources or software you are using. In essence, it frees up a lot of user's time and allows the marketing team and leading board to come up with better business strategy decisions.
Levels of Data Aggregation
There are a few levels of data aggregation that we can typically notice that differ depending on the resources you are using or the data aggregation process you rely on.
This is when companies are making data-informed decisions but are not collecting data or viewing it within the right context. An example of this is looking at research results for testing a vaccine to decide which one has the highest efficiency. You only see the results or size of the sample but do not regard the conditions under which the testing was conducted or what was the likelihood of receiving infection, and so on. In other words, you are technically making an informed decision, but the information you are basing it on requires a larger context or additional inputs.
We also see this frequently when a company is trying to improve its website. They go to Google analytics to see their traffic or bounce rate, so to figure out how to get more website visitors. So they decide to either market more aggressively or add additional features that could result in visitors spending more time. Whereas a more informed decision would be to see if the traffic you are getting from certain sites results in bounce rate because users who come to the website found the ad misleading.
So, it is a good thing that someone relies on data prior to making a new decision, but focusing on data alone without more relevant context means they are missing out.
At this stage, people decide to create a dashboard where they can make relevant inputs, track information, and see how their assets are performing. They can also make necessary comparisons and potentially find relevant correlations in those performances.
Marketing teams who rely on the in-house dashboard to generate reports and manage their database are fully aware of just how time-consuming it can be. It also needs to be updated frequently in order to retain its value, and that can drain resources. This is a good sign, however, as it indicates that a company sees value in data analysis and that it can be crucial for business intelligence.
Some businesses realize early on that an in-house solution might not be the most optimal way to go. So they opt for a third-party provider who already has well-developed data management software. This accelerates the whole process and even saves time on developing future in-house capabilities that might become a necessity for a more detailed data aggregation or analysis.
Quality data aggregation tools allow you to track different parameters when examining how customers behave. In the travel industry, this can be really helpful, for example, when a certain location is in high demand or if a sudden spike in demand for a certain location is related to an event. Many platforms that want to improve the browsing experience of their customers can use this as well. You can tweak your algorithms or use A/B split testing just to see if the amount of time spent on the platform changes. In other words, any changes you make can result in meaningful increments of knowledge on how to improve.
Why is Data Aggregation Useful
In addition to improving products, functions, tracking tends, price monitoring, and studying customers, data aggregation can be used for studying society in general. It can help us determine who is more likely to get sick or how a particular demographic will react to certain events.
In other words, thanks to data aggregation legal institutions, and policymakers can create better plans on how to address problems in society or how best to act in the case of emergency. Having reliable inputs in how events can unfold in an economic crisis, for example, helps governments formulate contingency plans and strategies to help citizens weather the crisis. Moreover, we can improve medical treatments for existing and adapt quickly to newly discovered diseases.
The basic idea is that increments of knowledge can easily add up, and thanks to the means of information sharing across the world, more lives can be saved. All of this may seem too difficult to grasp, so let's explain and break down the process of data aggregation.
How Data Aggregation Tools Work
Data aggregation tools also referred to as data aggregators, capture data from multiple sources. They are also calibrated to process that data or new insights and present them in a neat summary. However, to avoid missing relevant context, these tools can also track data lineage, which can be crucial in some cases, especially when you encounter outliers.
The collection is, therefore, the first phase of data aggregation, and it can be extracted from the IoT (internet of things) sources like:
- Social media
- Browsing experience or history
- Call centers
- News and podcasts
Processing collected data would be the next phase. This implies that newly formed databases or datasets need to be scrubbed or transformed. It is a process that allows you to target specific insights relevant to desired market intelligence.
Finally, all of your findings need to be presented in summaries. This can take time because data needs to be analyzed, and findings need to be accurate and relevant.
Aggregated vs. Disaggregated Data
Since we covered what it means to collect or aggregate data, let's see what it means to disaggregate data. It's pretty self-explanatory since disaggregating data means breaking it down into smaller but still meaningful units. This can be useful if you want to improve average scores or values.
For example, you aggregate data to find out the graduation rate for a particular school or college. Then you disaggregate it to find out if there are different graduation rates among certain ethnicity, race, or gender. If an interesting finding emerges from studying these new subgroups, you can take it as an indicator that either curriculum needs to be adjusted or that there are other problems in your institution. Once again, a wider context will always be necessary, but you still need to postulate new theories and collect data in order to test those theories.
This is particularly useful in marketing, as you can either find new ways to advertise products to particular demographics or find ways to double down on your loyal customers.
Risks Related to Aggregation Data Processes
What is data aggregation?
Data aggregation is an activity that includes collecting or gathering data from multiple sources or datasets using software, tools, or aggregation service providers. That data is then cleaned, transformed, and in some instances disaggregated and analyzed. After the analysis is complete, the findings are presented and summed up as key points of interest.
What is aggregated data example?
Much like its name suggests, aggregated data is data that is only available in collected or aggregated form. Examples of this can be the estimated number of tourists during a particular month, expected turnout in the upcoming election, number of citizens in either town, country, or county that has a right to vote, or number of students that are expected to sign up for a particular course.
What is data aggregation used for?
Data aggregation is used to capture relevant data, which is later processed and analyzed for the purpose of creating an insightful summary. The goal of data analytics is to provide stakeholders with enough relevant insight and value that can guide their decision-making process and allow them to come up with a new strategy for marketing. It is also used for compiling user feedback so that products can be improved or to improve medical treatments. In essence, it has a wide range of uses, and it drives the technological advancements of modern society.
Published on Apr 12 2021
WRITTEN BYIndrė Jankutė-Carmaciu
Indrė is a copywriter at Whatagraph with extensive experience in search engine optimization and public relations. She holds a degree in International Relations, while her professional background includes different marketing and advertising niches. She manages to merge marketing strategy and public speaking while educating readers on how to automate their businesses.
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