Data aggregation is a type of processing in which data is gathered, structured, and presented to achieve a specific business goal or process and for analysis by humans. Data aggregation may need to be performed iteratively to reveal new clues regarding the nature of the investigated data. The goal of data aggregation is to collect and organize all relevant data points to be presented in a helpful way that informs decisions.
What do you mean by data aggregation and analytics platforms?
Data Aggregation and Analytics platform manages and delivers your data as trusted, timely and relevant information by aggregating your data in one centralized analytics warehouse and automating the extract, transform, and load (ETL) processes. Data Aggregation and Analytics scale to the biggest jobs with a unique massively parallel processing (MPP) architecture that lets you run multiple queries against hundreds of terabytes of data using industry-standard SQL. The dashboard allows you to generate reports and filters to create the exact information you need for better business decision-making. The data aggregation tool outweighs every manual analysis process regarding both time and accuracy. Moreover, they are very fast and can result in just a few minutes. So, decision-making of the financial institutions is eased, and many risks are averted.
The necessity of Data Aggregation
Data aggregation is extensively used in areas for high-risk management, for measurement of key performance indicators (KPIs), strategies used in product development, various strategic decision making in business, marketing management strategies, allocation of budget, and many more.
How do the Data Aggregation and Analytics platform work?
The aggregator is a processing element that generates complex objects from more direct inputs. It takes data provided by individual nodes and produces aggregated information using algorithms like Mean, Median, Sum of values, etc. This consolidated information is sent to the head node for further processing and reporting. It can also be aggregated across dimensions using specific methods, such as time-bound data aggregation, product bound aggregation, and dimensions-bound aggregation, through different tools. Depending on your needs, the aggregated data can be displayed as a chart, a table format, or anything else.
Different types of Data aggregation
- Time /spatial aggregation. Time aggregation occurs when you view data points at different periods from one source, which could be a calendar year, quarter, month, week, or day. Spatial involves multiple sources within a particular time frame.
- Real-Time Data Aggregation is quickly becoming the norm due to the continuous nature of customer engagement, which requires constant updating to dashboard data.
- Manual and automated approaches differ primarily in their speed, but automation offers more flexibility in design and formatting.
Risk factors in Data Aggregation tools
How is it helping financial institutions?
Data aggregators automate manual processes and enhance customer experiences. Most financial institutions have the same essential services: auto loans, mortgages, and checking account balances. These data aggregation tools gather information from multiple sources and combine it into a single unified view of the customer or prospect that can tell financial institutions about consumers’ other payment obligations. Data aggregators also do clerical operations. For example, they collect the necessary documentation to underwrite a loan on behalf of the consumer. They can even use autofill applications for customers looking for a new one. One beneficial use of data aggregators is in cross-selling existing customers.
What points should be considered before buying software?
- Easy implementation– Software today costs two to three times more to implement than to buy. It mandates that companies should evaluate software’s implementation times. Therefore, the solution should be simple enough to implement and not require too much training. Also, it should be less complex in its usability.
- Straightforward pricing– The pricing should be done in a very well-structured and transparent way. There should not be any hidden costs while buying it out from vendors.
- Performances – Financial institutions are adding these types of software to their daily uses to ease the lending processes and do it in just a few minutes. So, speed and accuracy are essential for these types of data aggregation software. A bit of inaccuracy, and you can count a heavy loss of some good customers. Users do not have to compromise their valuable time for query performance in excessive numbers if they are using these types of aggregating tools.
- Business intelligence solutions share several characteristics for any serious company interested in data aggregation. The platforms support dynamic business environments and ensure actual availability and easy maintenance. The tools can accommodate multiple servers and provide various backup/recovery functions.
- The interface architecture must be very user-friendly. As an aggregation solution, the solution should support standard industry models.
Perfios data aggregation platform aims to enable better assessment of data sources, intelligent extraction that automatically learns preferences over time, and easy-to-use, intelligent, interactive, visual analysis that improves the understanding of data for your entire organization. Thus, the overall size of the dataset is reduced for sharing with others within the institution. Analyzing performance can also be done using these tools. It adds value to the existing dataset instead of reducing its size, which the device is made to do.
Summary – Big data analytics can provide a competitive advantage to lending FIs and help them save money, generate revenue, and improve efficiency. The most effective strategies use data aggregation solutions, omnichannel strategy, and targeted marketing campaigns. In the long run, this will make your FIs more agile in their operations and more capable of making critical decisions.