Data duplication is a serious problem that has long plagued organizations and complicated the data analytics efforts of internal audit. At best, it hogs up expensive storage space and frustrates communications with customers; at worst it can lead companies to make flawed business judgements.
Business users, data analysts, IT managers, and internal auditors are well aware of data duplication because they deal with the problem every time they require extracting data for a project. A company-wide concern, however, is created when dirty and duplicate data becomes the basis for poor business decisions and poor data governance becomes the norm.
Data deduplication, also known as “deduping,” is a technique of compressing data with the aim to remove duplicate information from datasets. With the help of deduplication, a considerable amount of storage can be freed up, specifically when deduping is carried out over sizeable volumes of data. The bigger benefit, however, is better insights into customers and trends and more accurate data analytics.
What Causes Data Duplication?
In many cases, the root cause of data duplication is difficult to ascertain through exact data matching techniques. This is because these techniques rely on fields having exact values to detect matching data. Evidence of where those duplicate records came from is often not captured in the information. What’s worst, even if there’s a protocol in place to avoid data duplication, duplicates will still most likely occur. Below are the main reasons that duplicate data occurs:
Mergers & Acquisitions: When organizations merge, data extracted from several sources during mass migration, the level of data duplication can get increasingly complicated. Both companies’ data structures might differ despite them having the same customers’ information.
Lack of Data Entry Protocols: Companies that fail to implement a strict data governance policy or don’t follow strategic data entry protocols are more than certain to have duplicated, dirty data. It’s common for several members of an organization to access the CRM, filling and customizing data at their own will. This leads to a lack of traceability or accountability of who’s responsible for maintaining data entry protocols.
Use of Third-Party Data: Third-party data, such as the data taken from networks, communities, partner portals, or even from web registration forms can cause a significant amount of duplication.
System Errors and Software Bugs: Administrative errors and software bugs in the CRM as well as associated apps can cause numerous duplicate records. Data or system migration activities often are the leading causes of this occurrence, and while this is easily rectified, it still poses a serious data quality concern.
Which Method Makes Sense?
There are two primary methods of data deduplication: source deduplication and target deduplication. The distinction between the two is that source deduplication takes place near the space where data was created, whereas target deduplication takes place near the space where data was stored.
Source Deduplication follows the process of eradicating redundant data at the source. It usually occurs within the file system, where new files are scanned periodically. The hashes found as a result are sent across to the backup server so they can be compared. If the chunk is found to be unique by the server, it is written to the disk after being transferred to the backup server. In case there are identical hashes found, the chunk is declared not unique and halted from being transferred to the backup server. As a result, bandwidth and storage are both saved.
Target Deduplication, on the other hand, follows the process of removing duplicates at the target storage. Once data has reached the target device, deduplication can take place before or after backup has completed. This means the server is not involved with the deduplication process as data chunking and comparison occurs at the target device. This method is more popular among the two, although it has certain drawbacks when compared to the former.
The Data Deduplication Process
Data duplication is defined as the process where duplicates are compared, matched, and removed in order to set a consolidated record. The three steps of data deduplication can be explained as follows.
Comparing & Matching: This step revolves around comparing and matching different records and lists to detect exact as well as non-exact duplication. A common example is that of CRM lists matched with internal database lists to avoid uploading the same records twice in the central database.
Handling Outdated Records: Outdated duplicate records end up being removed or updated with new information. Another aspect of this step is the consolidation of data, after which new columns or rules are made to store the additional information.
Creating Consolidated Records: After the removal of duplicates, a consolidated record having clean and treated data is made, which can be utilized as a “golden record,” on the basis of which existing records should be modeled after.
Deduping Made Easy
One easy way, which is yet to be universally accepted by system engineers, is to automate the above process through the use of a data deduplication software. Most data management platforms are yet to introduce such automation abilities, which leaves data analysts to carry out the entire process manually. The problem lies in data management platforms, which lack robust data matching capabilities to help users in duplicates identification.
This mainly translates in significant loss of time for engineers and analysts during the manual data deduplication.
Data deduplication software that utilizes advanced fuzzy matching algorithms can be the best way out. This type of tool can match data at a deeper level, which is something that not all data management platforms are capable of.
A Methodical Approach to Data Deduplication
With the nature of data records growing more complex with every step, the complexities related to data quality seem to be increasing further as expected. This means manual processes will only become more challenging and create additional hurdles when it comes to tackling duplicates. Moreover, businesses these days demand real-time insights, and as such, spending weeks in order to come up with the perfect code just seems impractical.
In short, it is essential for organizations to consistently update their data management protocols to ensure data integrity and accuracy. For this purpose, data duplication needs to be avoided at all costs, and it’s best to consider investing in a data deduplication software that can take care of these issues. 
Javeria Gauhar is an experienced B2B/SaaS writer, specializing in writing for the data management industry. She is also Marketing Executive at Data Ladder, an enterprise data-quality solutions provider, where she is responsible for implementing inbound marketing strategies.