Data Fragmentation: Definition and Solutions
Updated June 24, 2022
When data becomes fragmented, it means it is stored in separate locations. Processes like data fragmentation may adversely affect your resources, but it's possible to improve how you handle this type of data. In this article, we discuss what data fragmentation is, the importance of addressing it, what causes it and how to solve it, along with the benefits of these solutions.
Related: What Is Data Management?
What is data fragmentation?
Data fragmentation is data that's stored in multiple locations, creating huge caches of secondary data that aren't essential to business operations and affect storage capabilities. Examples of data fragmentation are:
This data may include duplicated data, or versions that were created for specific circumstances. You can store this data in a variety of locations, causing it to take up space in your storage centers. The variety of systems and uses for each data point often means duplicating data or separating it from its context, leaving it to be stored in multiple locations that aren't connected. If companies don't address their data fragmentation, it can become difficult to find relevant data in the mass data stored in their systems.
Importance of addressing mass data fragmentation
Mass data fragmentation can drain your resources and stall your employees' productivity. Addressing your data fragmentation and developing a system where you organize your data into a cohesive and comprehensive infrastructure can simplify tasks for your employees and create more storage space in your servers. This increase in time, space and information technology resources can help you establish effective data usage plans across all departments.
Companies can use data collection in several aspects of their business strategies. They may use it to streamline communications with their customers, refine their target markets and improve their sales conversions. Addressing your mass data fragmentation allows you to access those tools more efficiently. It can open new opportunities for your business and help increase employee productivity.
Related: What Is Customer Data?
What can cause data fragmentation?
With the increase in data analytics and technology usage in business, data fragmentation can be a byproduct of business operations. Here are some common operational factors that contribute to data fragmentation:
Data silos are management systems or programs that store data but don't connect to other programs or systems. When other programs can't access data, it can lead to inconsistencies. It can also lead to increased work to enter data more than once or update data in multiple locations.
For example, if your sales team stores client contact information in one database and your marketing team stores the same information in another database, they've created two sets of data. When one team alters the contact information, the other team's data doesn't update, so they have incorrect data saved in their system. If the marketing team and the sales team communicate the change, they'd complete the same task twice by updating the data into two different systems. Both teams can eliminate these silos by sharing a database or by using compatible systems.
Copied data is data that someone has purposely duplicated. This can happen when using data silos or when testing data. For example, someone may copy data to test manipulating or analyzing it without altering the original data. However, if you don't manage copied data properly, you can create secondary data. Then, that data becomes inaccurate and takes up storage space. Teams can avoid this by always sharing linked data, meaning data that's linked to the original, to ensure everywhere the data is being used reflects the alterations. They can also delete test data when it serves its purpose.
File-sharing is when someone shares a file with someone else. Many database programs can host files that multiple people can alter at once. However, sometimes sharing a file leads to duplicated data being shared and saved on the same server. For example, if you save a document on your work desktop, and you email it to a coworker who saves it to their desktop, your server has two copies of the same file. You can minimize secondary data created by file-sharing by using file-hosting technology and deleting unused files.
Related: How To Send Large Files
How to solve data fragmentation
There are many ways to solve data fragmentation, depending on what operations you can implement. Creating a process that works for you may involve using some or all of the following strategies :
1. Organize your data infrastructure
Companies might have multiple programs and systems collecting, storing and analyzing their data throughout different departments. Your company may have implemented these systems at different times. You may also use programs from different brands, which can make it difficult to share data. Sometimes these systems are a necessary element of business, but they can lead to data fragmentation.
Examine what parts of your data infrastructure you can organize, combine or eliminate. Consider if it's possible to implement pathways between the systems. Organizing your infrastructure into one system that communicates with its different programs can save time and storage space.
Related: What Is a Data Architect?
2. Delete duplicates
After you've examined your data infrastructure, you may find that there are duplicates of information in your servers from the development or testing of different systems and databases. You may also notice that there are multiple copies created from recovering corrupted or deleted data. Maybe these copies were necessary when you created them, but you can delete them after they serve their purpose. When you're reorganizing your data infrastructure, notice how many data copies you have on your servers and determine which ones are necessary.
3. Refine cloud usage
The cloud is a term for software and services that run on the internet, including storage and data management programs. The cloud has created a lot of agility and accessibility for businesses by allowing their employees to access data from more locations. However, some companies may not be using this technology to its full potential, resulting in duplicated data and wasted space. Many companies have multiple clouds, separated by department or purpose, which can further isolate data.
These different cloud accounts can create data silos that separate your data and make it hard to access. However, they can also help you organize your data infrastructure. Consider establishing a single cloud management system. This can help you visualize your data and organize it amongst its various locations for maximum efficiency and minimize duplication.
Read more: 11 Benefits of Cloud Computing
Benefits of solving data fragmentation
Here are some benefits of implementing new processes to minimize data fragmentation:
Addressing mass data fragmentation can help you save resources that you have diverted to organizing and storing secondary data. For example, you may have to pay for additional storage, backup servers and space in your digital storage systems. You can minimize these costs by minimizing the amount of unnecessary data accumulated in these data management systems. You can also save money on IT services used to manage secondary data by diverting those resources to activities that increase productivity.
Data can be an effective asset for businesses, helping with activities like analyzing analytics, storing customer information, or monitoring production. However, it can be hard to know what version of the data is accurate when different departments store information in multiple locations. Addressing your mass fragmentation can help your employees easily access the data they need so they can focus on their tasks.
Related: 10 Jobs That Involve Technology
Speed up processes
With your data easily organized and linked to reflect updates regardless of where it's being stored, you can save time updating data in multiple locations. Your employees can avoid checking multiple systems or questioning the accuracy of their data. By minimizing time spent searching or double-checking their data, they can speed up their processes, which can lead to increased productivity for their department.
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