Data hygiene, from a hybrid workforce perspective, aims at the structuring of information and the elimination of dirty data from your network. It involves the implementation of sound policies to check records for accuracy and the removal of errors. Data hygiene is vital for businesses because if you base your decisions on dirty data, it could lead to undesirable outcomes such as loss of productivity, reputation, revenue, and more.
One of the most problematic elements of dirty data is duplicate data. It slows down employees, especially in a hybrid workforce where they cannot communicate as freely and quickly as they would if they were sitting next to one another in an office. It is often difficult for an employee to find the correct data necessary for a task from a vast database if it isn’t properly organized.
Duplicate data makes this situation worse. If colleagues want help to find accurate information, lack of organization and communication due to hybrid work environments can stand in the way of doing so quickly. That’s why you must eliminate duplicate data at the earliest opportunity.
Data deduplication refers to the process of eliminating duplicate data in a data set by deleting an additional copy of a file and leaving just a single copy to be stored. It divides data into smaller chunks and identifies patterns to deduct duplicate files for removal. Apart from eliminating multiple copies, it helps minimize the network load since less data is transferred, thus leaving more bandwidth for other tasks.
Types of Data Deduplication
Some popular deduplication techniques include:
Source deduplication is the removal of multiple copies of data before transmission to the backup server.
This process occurs on the backup medium, which can be the server hosting the backup software, a deduplication device attached to that backup server, or a backup appliance.
Inline deduplication is the removal of redundancies from data while being written to a backup device.
Also known as asynchronous deduplication, this process filters out redundant data after transferring it to a data storage location.
5 Data Deduplication Best Practices
Always remember that duplicate data is something every business must deal with regularly. If neglected, this data gets accumulated over time and takes up valuable storage space, leading to wastage of resources. Excessive amounts of duplicate data can even cause poor data quality and inaccurate analytics.
Here are some deduplication best practices to follow:
- Identify the best-suited deduplication type
Although different deduplication techniques remove duplicate files by identifying patterns within chunks of data, they all perform differently. While selecting the one that best suits your business, consider factors like cost and storage requirements. You must go for a deduplication type that makes sense for your business instead of just copying a competitor. When in doubt, seek out expert advice.
- Sort files by data type
Deduplication may not be very effective with some media files such as MP4 and JPEG. Always remember to sort the data types that you handle. Otherwise, deduplication efficiency can be significantly affected and the outcomes may disappoint you.
- Do not focus on reduction rates
If someone promises you that they can help reduce your data size by 50%, 80%, etc., don’t blindly accept it. Actual reduction rates will depend on the type of backup, type of data and frequency of change in the data. It’s important to make sure your expectations are based on facts.
- Decide deduplication locations
You need not deploy a deduplication solution on every storage media since it will not be cost-effective. In most cases, only secondary locations like backup, where cost is a concern, need deduplication. Also, deployment of deduplication in primary storage, like data centers, affects storage performance.
- Consider all expenses
To avoid sticker shock, consider the full range of expenses needed for deduplication, i.e., remember to consider factors such as maintenance and management costs along with the cost of physical storage.
Article curated and used by permission.