Data Aggregation is a data mining process where data is sought, collected, and presented in a summary format report to achieve specific business objectives or strategies and analysis.
What is Data Aggregation?
Data Aggregation is a method in which data or information is collected and expressed in summary form for purposes such as statistical analysis.
A general purpose is to obtain more information on particular groups of people based on specific variables, such as age, income and profession. Information about such groups can be used for website personalization when choosing content and advertising that may attract an individual belonging to one or more groups for which data has been collected.
For example, a website that sells music CDs may advertise certain CDs based on the user’s age and aggregated data for their age group.
User-based personal data aggregation services aim to acquire user personal information or data from different websites. It is known as “screen scraping,” which automatically obtains the information that the user shares on various websites, extracting it to form and enrich the database itself.
The screen scraping allows, for example, access to different accounts of a user using a unique personal identification number (PIN) on various websites. Another variant of data aggregation, a simple type, is OLAP (Online Analytical Processing), which uses an online report to process information.
Advantages of Data Aggregation
Benefits of data aggregation in metric-based reports
Quick and easy access to data is vital to make the right decisions at the right time, especially at a time like today, where every action has its origin in large amounts of data.
However, processing, collecting and analyzing large amounts of data can be complicated if you want to reduce the response time as much as possible. It is where the importance of Data Aggregation comes into its own. An example of this is working with metrics from key performance indicators (KPIs).
To recreate a real scenario, we could talk about a telecommunications company, specifically its finance area. In this case, the proposed objective would be to maintain a set of desktops presenting a group of previously defined KPIs.
The data source that feeds these dashboards is the financial module of the corporate ERP, which allows information about:
- Sales revenue: encompasses the metrics recorded from incoming revenue transactions derived from services and products contracted by customers.
- Other income contains the incoming income transactions derived from other concepts.
- Personnel Expenses: Includes individual expense transactions for each employee, from salaries to bonuses, compensation, or certain expenses, such as those related to the settlement.
- Supply expenses: contains the payment transactions to suppliers.
Establish the requirements
Once these metrics are in place, it is necessary to establish the requirements in the financial dashboards. These rules would make it possible to establish comparisons, for example, between income and expenses for a given period; identify trends, such as income in recent years or expenses in the months before the analysis; and also enrich reporting, allowing for margin reports, among others.
However, you must take into account that the process has a high risk of ending up offering a performance below the expected because without Data Aggregation:
- The large volumes stored in the data source (ERP) would reduce the agility of the process.
- The process would slow down since it requires several consultations, being necessary, and contrasting the conclusions and executing various calculations to obtain the KPI needed for each case.
The solution is to rely on data aggregation
The solution is to rely on data aggregation, which, by accumulating large amounts of detailed data at higher levels of dimension hierarchies, makes it possible that it is not necessary to store each transaction. Instead, large rows can be grouped based on the month these transactions occurred and the type of transaction. Thus, Data Aggregation can:
- Reduce the number of rows to query for KPI values (from millions to thousands).
- Minimize the time required to update dashboards.
- Achieve a considerable reduction in resource consumption and end-user waiting time.
To promote it, you can use the KPIs repository. This option eliminates the calculations when the report is updated since it is in the ETL process when they are carried out. The benefits of using the KPI repository have to do with:
- Increase in performance thanks to the reduction of the time required for the execution and the update time of the dashboard. It is achieved since the data is previously calculated, and the number of tables to be consulted has been reduced.
- Customization of calculations: The ETL process performs specific calculations (such as the variance and variation of expenses and income for a given period) and stores the results in the new fact table. The analyses performed during this process are often much more powerful and complex than the BI tool can perform at run time.
Final Thoughts:
The aggregation solution minimizes the time spent updating and reporting by reducing the number of rows to query. However, it does not solve the general problem since consulting KPIs and calculations that the reporting implies need to improve its performance.
The benefits of using data aggregation are seen in the productivity and profitability of the operations.
