The amount of information out there and all around us is beyond comprehension. Even when we need to deal with but a tiny fraction of this information that specifically interests us, it is impossible to go through all the data units manually. That is why we need data aggregation – the process of compiling and summarizing data into datasets that could be analyzed for valuable insights.
Summary of what matters the most
Information really matters. This is true for all aspects of life and this truth keeps getting more and more important for businesses. This means that huge amounts of various data are constantly being gathered in different databases.
If we were just to pile all the data from different databases on top of each other, it would not yet mean that we know a lot. Knowledge comes from structuring the data and compiling it into information that tells us something about whatever we were researching.
This is what data aggregation does. Instead of just accumulating atomic data it merges the information in different databases to form a summary of statistics from which important conclusions can be drawn. It is done by data aggregators – software tools that collect and process data, aggregating it into compact summaries, that make it easy to see what the data is really showing.
Thus, a few rows of aggregate data sum up the underlying information that may consist of very many individual data units. This makes the information much more accessible and usable in a much shorter period of time, as it is not necessary to go over every unit to arrive at the same insights.
Key things to know about data aggregation
It is easy to see why data aggregation is a very important aspect of handling the information that businesses possess or wish to possess. Below are the four important things about data aggregation for every manager and analyst who aims to bring the most value for their company out of the available data.
- It is better when automated. Doing data aggregation manually is possible but not advisable. Manual gathering and processing of huge amounts of data units from many different data sources would naturally require a lot of manpower and time. Usually, even if the company has a big IT department, it will mean that its attention is completely drawn to this task, leaving other matters hardly attended if at all. It would also significantly raise the probability of error, making data less trustworthy and of lower utility. Data aggregation tools are made for avoiding these hazards and should be used to their fullest potential.
- Data can be aggregated at different levels. This means that when we have different metrics for a certain dimension, data can be aggregated by any of the metrics. For example, in finances, certain types of data, like a number of invoices, could be aggregated by day, month or year. In education, data, like enrollment rates can be aggregated at the level of the whole school district, particular school and grade level either in the school or in the district. Different levels of aggregation allow to filter the information in various ways, make important comparisons and analyze proportions as a particular level is compared to the aggregated data in its entirety.
- Data aggregation is conducted over different time periods. Sources for data gathering and analyzing are themselves usually subject to updates. This means that it is important to pay attention to the temporal parameters of the data aggregation tools. One of these parameters is how often data is gathered from a particular source. The other is how long does it take for a particular tool to aggregate data from a source or a set of sources. Naturally, being able to present summaries of aggregated data often and fast gives a great advantage to the receivers of the reports.
- Different aggregation models for different needs. There are various ways to aggregate data. This means that the outcomes of the aggregation process may be summarized and presented in different forms. This makes it applicable in various situations, as the aggregation model can be chosen by the particular needs. When the purposes of the aggregation process are clearly defined, is easy to choose the best model that will provide the kind of insights that are expected.
Seeing the big picture
Data aggregation is the process by which various information is summarized as statistics. This is great because private information is protected. Aggregated data remains highly anonymous. Yet, these reports help us see and understand the big picture in the field that interests us. And this is exactly what we need to see to make the right decisions.
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