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Before deduplication platforms could emerge, data was manually checked for errors. It was humanly impossible to maintain an error-free database even after multiple checks. But with the help of most modern technological advancements, it is now easy to maintain error-free databases. At its simplest definition, data deduplication refers to the process of eliminating redundant or outdated data from a data set. In the process of deduplication, extra copies of the same data are deleted, leaving only one copy in the database. Data is analyzed to identify duplicate byte patterns to ensure the single instance is indeed the single file. Then, duplicates are replaced with a reference that points to the stored chunk.

Evolution of Data Deduplication:

By the early 2000’s, business data was moving global, real-time, and mobile. IT teams were challenged to backup and protect massive volumes of corporate data across a range of endpoints and locations with increased efficiency and scale. Various applications were developed, which analyzes data at the file object level to identify file duplicates in attachments, emails, or even down to the folder from which they originate. This approach added significant gains in accuracy and performance for data backups, lowering the barrier for companies to efficiently managing and protecting large volumes of data.

A simple example:

Let us now, for example, consider an email server that contains 100 instances of the same file attachment of 1 MB; say, a campaign template containing graphics, which was sent to everyone on the marketing staff. Without data duplication, if everyone backs up his/her E-Mail inbox, all 100 instances of the template are saved; requiring 100 MB storage space. With data deduplication, only one instance of the attachment is actually stored; each subsequent instance is just referenced back to the one copy that is already saved, reducing storage and bandwidth demand to only 1 MB.

Marketing professionals often see E-Mail and data appends as mediums that can help grow new email subscribers, and improve the information about existing ones. These measures can improve your E-Mail marketing campaigns drastically.

E-Mail Appends

E-Mail appending is the process of adding a valid email address to a contact list in your database. These contacts typically fall into one of four categories:

  • Contacts for which you have no email address but you have their first and last name and physical address.
  • Contacts with old or outdated email addresses.
  • Contacts that have become dormant and you are not sure if their email address is valid.
  • Contacts that are not responsive to the email address on file, but they may have a more current one.


Although E-Mail appending might seem like an excellent approach towards building a strong database, there are certain disadvantages to it. E-Mail addresses can be appended only to individuals that:

  • Opted-in to receive emails from a particular brand,
  • Have a prior business relationship with a certain brand, such as a purchase or an inquiry for more information, and
  • Have not opted out of any email programs from a specific brand.


Data Appends

Data appending is nothing but reverse E-Mail appending; you add information about the subscribers based on their email address. It is quite normal to miss out relevant information such as; postal addresses, dates of birth, occupation, income etc, while E-Mail appending. Through data appending, developers can append such information into their database.


In simple terms, Data Management is defined as an administrative process; that includes collecting, validating, storing, protecting, and processing required data, to ensure the accessibility, reliability, and timeliness of the data for all of its users. Organizations and establishments are utilizing Big Data more than ever before; to inform business decisions, and gain deep insights about customer behavior, contemporary trends, and opportunities for creating extraordinary and satisfying customer experiences.

Most of the times, bulk data that is collected by organizations does not make any sense to them at all. In order to make sense of the vast quantities of data that the organizations are gathering, analyzing, and storing today, they turn to data management solutions and platforms. Data management solutions make gathering, validation, and other essential functions simpler, and less time-consuming. Data management platforms are essential, as we are creating and consuming data at unprecedented rates. Top data management platforms give enterprises and organizations a 360-degree view of their customers and the complete visibility needed to gain deep, critical insights into consumer behavior that give brands a competitive edge.

Hurdles faced along the way:

Although some companies are good at collecting data, they do not managing it well enough to make sense of it. Collecting data just for the sake of it is not good enough; enterprises and organizations need to understand from the start that data management and data analytics only will be successful when they first put some thought into how they will gain value from their raw data. They can then move beyond raw data collection with efficient systems for processing, storing, and validating data, as well as effective analysis strategies.

Another challenge of data management occurs when companies categorize data and organize it without first considering the answers they hope to glean from the data. Each step of data collection and management must lead toward acquiring the right data and analyzing it in order to get the actionable intelligence necessary for making truly data-driven business decisions.

Benefits of Data Management and Data Management Platforms

Managing your data is the first step toward handling large volumes of data; both structured and unstructured, that floods businesses daily. Organizations can harness the power of their data, and gain the insights they need; to make the data useful only through data management practices. In fact, data management via leading data management platforms enables organizations and enterprises to use data analytics in beneficial ways, such as:

  • Personalizing the customer experience.
  • Adding more value to customer interactions.
  • Identifying the root causes of marketing failures and business issues in real- time.
  • Reaping the revenues associated with data-driven marketing.
  • Improving customer engagement.
  • Increasing customer satisfaction and loyalty.