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.
5 Data Deduplication Best Practices
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 rate: 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.
5 Elements of Data Duplication
- Data Standardization: Most businesses use data from multiple sources such as data warehouses, cloud storage and databases. But data from distinct sources may not be in a consistent format, leading to trouble down the line. This is where data standardization helps. It is the process of converting data into a consistent format.
- Data Normalization: It is the process of organizing data within a database. This involves making data tables and identifying relationships between those tables based on the rules designed to reduce data redundancy and improve data integrity.
- Data Analysis: Data analysis is the process of analyzing data using logical and analytical reasoning to get valuable insights. The derived information helps make sensible decisions.
- Quality Check: Businesses need good quality data to make the right decisions. Therefore, quality checks are essential.
- Data Deduplication: 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.