Data Deduplication: Key Concepts, Use Cases & Benefits
What does data duplication mean? Here's a detailed guide on data duplication meaning, working, types, key concepts & important duplications use cases. Click to read now!
Duplicate records are one of the biggest sources of identity fraud and theft. One of the most effective prevention countermeasures to check such activities is data deduplication. Deduping means the deduplication of data to remove extra copies and decrease the storage requirements. Data duplication can occur at multiple levels. According to IDC, 64.2ZB of data was created or replicated in 2020, and its impact will be felt for several years.
Data deduplication is not only a necessity in today’s digital footprint-heavy enterprises, it also acts as the first step to abate redundancies and reduces the burden on disk storage, costs, and data security. In this blog, we will take a closer look at various facets of data deduplication.
Why Do You Need Data Deduplication?
Data deduplication helps to eliminate duplicate data blocks and store unique blocks of data. As per some estimates, organizations today are storing nearly double the amount of data every two years, than previously.
These cause redundancies, consuming up to 10-20 times more than the actual capacity needed for storage.
Moreover, enterprises can effectively stem the frauds that emanate due to data duplication with dedupe techniques. Considering the fact that data compromise leading to identity theft and frauds in the US jumped 68% in 2021, hitting an all-time high with a record 23% more than the previous year, enterprises need to step up their vigil with data deduplication. It is helpful in:
- Securing against identity fraud
- Preventing fraudulent payments made multiple times
- Maintaining financial and regulatory reporting integrity
- Saving storage, archival, and maintenance costs
- Enhancing customer experience by inspiring trust
How Does Data Deduplication Work?
This whole process depends on the metadata database with inimitable hash storage of fingerprints of the unique blocks. These blocks are then matched and compared to filter and find duplicates.
There are various ways data duplication can be done, though primarily, two methods are followed: inline processing and post processing. In the first, data is filtered simultaneously as it is fed, and redundancies are captured and eliminated as data is stored.
While the post processing data deduplication method follows an asynchronous backup method to eliminate redundant data post storage. Duplicates are removed at the block level in the hash store.
Other types of data deduplication are:
- Fixed length deduplication
- Variable length deduplication
- Local deduplication
- Global deduplication
Since deduplication techniques vary from vendor to vendor and implementations, the results differ significantly.
What Can Data Deduplication Be Used For?
Every enterprise today is grappling with mammoth digital data. The way data deduplication frauds are perpetrated presents one of the biggest challenges for enterprises to identify fraudsters proactively. The scope is expanding to include a broad range of use cases from insurance, healthcare, technology, and financial services to governments and law enforcement agencies.
- Finding and initiating action against duplicate claims
- Raising red flags on duplicate vendor bills and invoices
- Checking if duplicates are reported in a timely manner
- Validating ID documents before engaging in business and financial dealings
Using data deduplication, a financial institution was able to plug loopholes and cut down bad loans. The bank observed that fraudsters were repeatedly applying for loans by changing identity cards. It decided to deploy data deduplication software to identify duplicates. Within a year, the bank was able to avoid USD 10 million equivalent of fraud. Where e-KYC and manual verification systems proved insufficient, data deduplication worked wonders.
How Can You Get Started With Data Deduplication?
There is no right or wrong data deduplication strategy. It is better to assess the suitability with respect to the existing IT ecosystem and determine the framework that can offer better integration with minimal changes. Here are a few tips to help you get started by choosing the right deduplicating approach.
Step 1: Analyse Your Existing Data Backup Framework
Every enterprise faces different external and internal factors. The deduplication ratio, too, may vary accordingly. Analyzing the complete data backup process accurately may take some time, but it’s advisable not to hasten the process.
The main factors influencing the deduplication ratio are:
- Data type for deduplication
- Data variability rate or change rate
- Size and amount of redundant data
- Backup method employed (full, incremental, or differential)
- Size of backup data
Step 2: Understand The Scope Of Alteration In The Backup Environment
The approach selected for backup storage influences the deduplication ratios. A faster backup will result in a higher ratio. Questions you can ask are:
- What is the scope of changes that can be made in the current backup environment?
- How can the deployment of data deduplication be done with minimal changes?
- Can the software be rolled out across regional and global offices, if needed, with the existing hardware and IT infrastructure?
Step 3: Review Performance During Backup And Integration
The backup using process, especially, the first time, can be challenging. It may take a significant amount of time to perform the backup and integrate it with the current IT systems. You may observe that:
- The total amount of data and changes will influence both backup and integration
- Usage of additional hardware and software with the data deduplication software will also have a bearing.
Choosing HyperVerge: Better RoI And Superior Tech
HyperVerge is the market leading AI company that has helped enterprises to improve and automate their processes, provide better customer experience, and digitally adapt at a quicker pace. Ranked by the world’s top agencies for advanced AI capabilities to identify frauds, we have helped several global financial institutions to reduce risks and identity theft while maintaining the efficient processing of documents and services.
Leading lenders in different territories in India, Vietnam, Malaysia, Singapore, and the United States have been able to save $50 million from fraud annually with HyperVerge. Some of the world’s largest entities like CIMB, Home Credit, and Grab, trust us to help them combat fraud risks.
Data deduplication is more than a space saving feature. It yields cost savings and efficiencies, in the long run, not to mention thwarting fraud risks significantly. It also cuts down compliance and regulatory risks. Though, the lack of standards for implementation and technology varies, making it tough to compare vendors.