What Is Data Aggregation Tool – Everything You Need To Know

by | Oct 19, 2019 | Analytics, Data Aggregation, Marketing Analytics | 0 comments

Data aggregation refers to processes and methods in which information is gathered, compiled as required, and expressed together with a purpose to prepare combined datasets used in the data processing. It is used to analyze the data statistically.

Talking about another purpose of data aggregation includes collecting information about a certain group based on age, profession, and similar others. The information available after grouping accordingly is then used to personalize websites and make choices regarding content. 

The collected data is also used to form advertising strategies that can appeal to individuals belonging to groups for which the data has been collected. For instance, a site that sells stylish earphones will look forward to forming advertising strategies based on parameters such as age groups.

A type of data processing is OLAP (Online analytical processing), a simple data aggregation. Basically, an online reporting mechanism, which processes the information, is brought to use.

Data aggregation can be based on user preferences. A single point for the collection of personal information is provided to the users by some websites. There is a type of data aggregation, which is sometimes referred to as “screen scraping.” In this kind of aggregation, a single master personal identification number (PIN) is used by the customer to access their accounts.

Going into the detail

The United States of Geological Survey puts it as, “when data are well documented, you know how and where to look for information and the results you return will be what you expect.”

Data Aggregation

The information is gathered by referring to public records and databases. This is then converted into documents containing reports, and then as a next step, it is sold to businesses, local or state agencies.

This information comes as a use for marketing purposes as well. Different versions of records for the gathered data is made available for agencies and individuals. Individuals can request records as per their preferences.

A look at the components

The data aggregation process has a set of components, which are as follows:

  • The raw data which acts as the source for aggregation.
  • The aggregate function which is required to perform computation on the raw data.
  • The aggregate data is the result of the aggregation that the raw data was subjected to.

Steps to be considered

Three steps are ordered in a sequence in a general data aggregation process. Each of the steps covers a series of activities on these constituents, which are as follows:

  • Preparing the raw data: As a first step, one is supposed to prepare the raw data required for aggregation. This is collected from the source into an aggregation unit termed as an aggregator. Locating, extraction, transportation, and normalizing raw data are some of the basic steps involved in the process.
  • Moving forward to the aggregation of raw data: A function used for aggregation is implemented on the raw data, which transforms it into aggregate data.
  • Aggregate data getting post-handled-Now, the aggregated data is subjected to different ways of handling it. It can either be saved into some storage, made available to fulfill other purposes, or turned into a part of some project.

The vitality of the Internet

The Internet plays a major role as an application in the huge cycle of data aggregation. Owing to its potential to consolidate information together in bundles and manipulate information has a major application in data aggregation.

This is commonly known as screen scraping. Passwords and usernames are consolidated in the form of PINs. This enables users to gain access to a wide variety of websites that have been protected by PINs. Even by having access to merely one master PIN line can access several websites containing personal information.

Online account providers include people from a variety of fields. Some of them are stockbrokers, financial institutions, airlines, frequent flyer, and e-mail accounts.

Based on an account holder’s request, information can be made available to the data aggregators by using the account holder’s PIN.

Such services are offered in different forms. These can be offered on a standalone basis or can be concatenated with other services such as those in the finance departments.

Mostly these are available in conjunction with services such as portfolio tracking or bill payment, which is made available by certain specialized websites.

These are also available as additional services that can augment the online presence of enterprises that have been established somewhere outside the virtual world. 

Many pioneering companies that have been established and have an internet presence consider the value of offering an aggregation service as an important aspect. Mainly the fact the web-based services are enhanced, and visitors are attracted taken into consideration.

Also, there is this possibility that users of the services will be drawn to the website, which is acting as the host quite frequently. This is one such factor that keeps aggregation services in demand.

Data aggregation for businesses on a local scale

When compiling information regarding the location for businesses that have been set up on a local scale is taken into consideration, several major data aggregators exist out there that make a collection of descriptions, hours of operation, and other relevant and significant information.

data aggregation

Then a series of validation methods are implemented to validate the accuracy of the same. Basically, this is done by taking information from different sources and comparing it with what is currently available in the database. Accordingly, databases are updated.

Approaches to solving ineffective data aggregation which no longer exist

The traditional approaches which were considered for solving data aggregation tools are no longer sufficient.

New server hardware: Introducing additional hardware to the BI applications, which mostly rely on RDBMS infrastructure, perform only minutely better.

So it’s quite clear that the equipment acquisition costs are merely additional expenses and do not yield the desired improvements or even a minimum of the exponential performance required by the latest operational applications.

Creating derivative data marts, OLAP cubes, de-normalization, and partitioning: These are some of the techniques which have been tried, tested, and have been in usage over all these years and are more important as compared to other major quick fixes.

These have been in existence for a long time to perform query performances. But looking at it from the rational aspect, the tuning requires time and is a continuous process.

Stats say that it will not improve query performance in a suitable way of delivering timely reports as required by businesses.

Report caching and broadcasting: Caching works to provide some relief in the performance. But, global organizations that are in service for geographically dispersed users find difficulty allocating enough blocks of time to process these reports.

The caching and broadcasting reports are stale, and there are canned reports and are old by some hours or days. These provide benefit up to certain limits in environments where ad hoc and on-demand reporting is required.

Summary Tables: There has been anecdotal evidence that has suggested that organizations build summary tables in only a limited number that are suitable for covering a tiny percentage of the user requests that can be possible.

The existence of several dozens of summary tables introduces a maintenance burden that quickly outweighs their incremental benefits.

What can be the legal implications?

Financial institutions have raised concerns about the liabilities that arise from data aggregation activities, and possible security problems, infringement on intellectual property rights. There is also a possibility of traffic to the site getting diminished.

Though an Open Financial Exchange (OFX) standard can be used to activate data feed agreement if requested by the customer and the aggregator and financial institution agree on it.

Open Financial Exchange

This can also be used to deliver requests and information to the site selected by the customer, as that will be the place where they will view their account data.

Agreements facilitate negotiations among institutions, which is necessary to protect their customers’ interests and to make it possible for the aggregators to offer robust services.

Aggregators who agree with information providers about extracting data without using any OFX standard can reach some level of a consensual relationship. For business reasons, the aggregator will have to decide to obtain consent on the data which is made available.

Screen scraping, it happens without the consent of the content provider, has an advantage according to which subscribers can view almost any or all accounts that have been opened by them anywhere on the Internet through a single website.

Selection of Effective aggregation solutions: Criteria Flexible architecture: Exponential growth and flexibility are allowed by introducing a flexible architecture.

In this case, the one who is providing solutions is supposed to be ultra-responsive according to the shifting needs of its customer, which is an essential aspect because the business environment is always changing.

The solution should be able to use standard industry models to support complex aggregation needs.

The solution should also be supportive of varying needs and reporting environments. The ideal architecture should be able to optimize the steps in pre-aggregation with aggregation.

Fast implementation: Implementation costs these days are two to three times the price of the software. Therefore, its evaluation of implementation time becomes imperative, and also, the determination of the product’s reliance on expensive IT resources becomes difficult.

The methodology and approach used by the system should be proven. Development speeding tools should be provided to users.

The solution should be easily implementable, and so it should not require heavy training. Control processes and management of utility should be in place.

Price/performance: The criteria used for technology selection purposes must be effective enough to fulfill the value desired by the implementation. This will justify its worth. Forming responsible decisions in finances is not only a goal, but rather it is a necessity.

The solution should be priced as per the needs of the business. There should not be any long-term costs hidden with the solution that is being supported.

Performance: Speed, responsiveness, and quality of the application come underperformance. Time taking queries no longer come under the category which is acceptable to business users.

Also, the data that is received by them must be fresh. Recent information is in demand in the market in seconds and minutes. This is required to make decisions that are judicious in nature. 

Virtually, the query performance will be instantaneous. Users do not have to trade build times in excessive numbers for good query performance.

The performance of the same should not be dependent on the users. It should be predictable. It also should be independent of variations according to time of the day.

Enterprise-class solution: Enterprise-class solutions share several characteristics required by any company that is serious about business intelligence. These solutions are architected in a way to support dynamic business environments.

These solutions provide high availability and easy maintenance, ensuring mechanisms. Multi-server environments are allowed by these.

These environments also support activities such as backup and recovery. Also, these have multiple ways through which these can be interfaced into the system. 

After designing, the solution is easy to maintain. Little or almost no management is necessary for this. Solutions should have the ability to support hierarchies and structures which are ever-changing and are easy to adapt.

The system must leverage existing investments in BI environments and DB infrastructures. Existing applications should also be integrated, and simplicity is a major factor that should be considered. The minimum requirement is a set of published APIs.

Efficient use of hardware and software resources: Solutions need to use the hardware and software resources efficiently. A significant amount of resources is required by systems that deliver vital improvements.

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