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 data processing. It is used to statistically analyze the data.

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

Also, the collected data is used to form advertising strategies which can appeal to individuals belonging to groups for which the data has been collected. For instance, a site which 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) which is a simple kind of data aggregation in which, basically, an online reporting mechanism, which processes the information, is brought to use.

Data aggregation can be based on the 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 allow access to 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.”

The information is gathered by referring to from 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

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 which is the result of the aggregation that the raw data was subjected to.

Steps to be considered

There are three steps which are ordered in a sequence in a general data aggregation process. Each of the steps cover 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 that will be required for aggregation. This is collected from the source into an aggregation unit termed as aggregator. Locating, extraction, transportation and normalization of 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 to 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 sort of storage, made available to fulfill other purposes or can be turned into a part of some project.

The vitality of Internet

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

On an account holder’s request information can be made available to the data aggregators by making use of the account holder’s PIN.

Such services are offered in different forms. Either of 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.

Also, these are available as additional services which can augment the online presence of enterprises which have been established somewhere outside the virtual world. 

Many pioneering companies which 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 is taken into consideration.

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

Data aggregation for businesses on a local scale

When the task of compiling information regarding location for businesses which have been setup on a local scale is taken into consideration, there are a number of major data aggregators existing out there which make a collection of descriptions, hours of operation and other relevant and significant information.

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 solve 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 that is 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 into existence for a long time in order 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 way that is suitable for delivering timely reports as required by businesses.

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

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

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

The existence of several dozens summary tables introduce a maintenance burden which quickly outweighs their incremental benefits

What can be the legal implications?

Financial institutions have raised concerns about the liabilities that have 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 it is requested by the customer and the aggregator and financial institution are agreeing on it.

This can also be used to deliver requests and information to the site selected by the customer as that will be the place from 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 are in agreement with information providers about extracting data without making use of any OFX standard can reach some level of a consensual relationship. For business reasons, the aggregator will have to decide in order to obtain consent on the data which is made available.

Screen scraping, if happens without the consent of the content provider, has an advantage according to which subscribers can view almost any or all accounts which 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 is something that is 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 extremely important aspect because the business environment is always changing.

The solution should be able to use standard industry models in order to support aggregation needs which are complex.

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 software. Therefore, it evaluation of implementation time becomes imperative and also, the determination of the product’s reliance on expensive IT resources become 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 that are 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 under performance. 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 which are judicious in nature. 

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

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: A number of characteristics that may be required by any company that is serious about business intelligence are shared by enterprise-class solution. These solutions are architected in a way so as to support business environments which are dynamic.

High availability and easy maintenance ensuring mechanisms are provided by these solutions. Multi-server environments are allowed by these.

Activities such as backup and recovery are also supported by these environments. 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.

Existing investments in BI environments and DB infrastructures must be leveraged by the system. Existing applications should also be integrated and simplicity is a major factor which should be considered. The minimum requirement is a set of published APIs.

Efficient use of hardware and software resources: Solutions need to be able 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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