Data duplication is a data quality problem where records are unintentionally duplicated. As we gather increasingly complex data from multiple sources, there is always a chance for that data to be duplicated. Let’s take for example, the case of a simple web form. Any time a user enters details with a new email address, phone number or address, a duplicate record is created. As this happens, the organization assumes it has 100 unique records, but in actuality, it has 30 duplicated records and only 70 usable records.
Data duplication is a serious data quality problem that affects an organization’s trust in its data.
How do you fix it? Here’s what you need to do.
What Can You Do to Control and Fix Data Duplication
As our demand for data increases, so does the complexity and quantity rise. While companies are employing several front-end data controls in place, data duplication is not possible to control a 100%. Due to multiple instances, such as user input, system faults and other human errors, data duplication cannot be prevented.
That said, you can implement a data quality framework that will ensure duplicated data do not ruin your analytics and reporting efforts. Here’s how.
Analyze a Segment of Your Records to Understand the Extremity of Duplicates
Take 100 records of your recent data (treated and untreated), and check for the extremity of duplicates. For every 100 rows, if you have 10 – 20 duplicates, you have a serious problem. Now here’s the tricky part – it might be possible that you don’t have duplicates in these 100 records, but may be in the other 100. The point being that you will have to run an analysis of your entire data source or data set to weed out duplicates. It’s easy to do that via Excel or Oracle if you have just a few hundred rows of data, but for complex data, you’ll need a data matching solution, which leads to the next important point.
Invest in a Top in Line Data Matching Solution
A top-in-line data matching solution or deduplication software is necessary to implement a much-needed data duplication check; especially if your organization is a large enterprise that deals with a large amount of data. Moreover, the nature of duplicated data today is way more complex. As data is acquired from multiple sources, it may have different formats and variations in details that can cause duplicates.
For example, an entity named John Doe can be a duplicate record if he writes his name as John Doe in one instance, but John D. in another instance. In the second instance if he also changes another critical data token such as a phone number, email or address, it may be difficult to sort through the duplicates using traditional data duplication tracking methods.
This is why a top-in-line data solution is needed. These solutions use a combination of fuzzy matching algorithms to weed out even the most complex duplicates that would otherwise take development teams months if not years to accomplish.
Understand the Source or Reason of Data Duplication
While you can’t prevent duplication from happening entirely, you can find out the source or reason of duplication. It could be a glitch in a system, a lack of data governance, unintentional human error or even a complete lack of data management. It’s not surprising that many companies still don’t have a data management plan in place.
In fact, a majority of companies don’t have a data management plan in place and are failing in their efforts to be data-driven, reports HBR.
You can only identify the source of your data duplication if you have a data management plan in place because data duplication is just one facet of a larger data quality problem. Once you discover the source, you might be inclined to pursue a complete data transformation plan.
Get Executive Buy-ins
If you don’t have a data management plan in place and your data is being duplicated while also suffering from severe quality issues, it’s time you alert your upper management and get their buy-in. It’s important to remember that data duplication will affect every aspect of your business – from your operational efficiency to your company culture itself. People may even lose their jobs because of bad data.
The good news is today’s executives are open to data improvement plans. CEOs know they have a data management problem; they just don’t know how to fix it. If you’ve got a plan, you can get executive buy-ins to support your plan and set your organization on a path to being data-driven.
Implement a Data Quality Framework
A large part of data management includes data quality. When you’re making the data management plan, make sure to incorporate the data quality framework. Your goal isn’t just to remove duplicates – your goal is to ensure that data is: consistent, clear, accurate, valid, complete and is usable for its intended purpose.
Many organizations collect data, but they are unable to manage or sort this data because they do not have a data quality framework in place. You start from data quality; you end at digital transformation. Most companies start at digital transformation only to realize they never had good data in the first place. Therefore, it’s imperative to create a data quality framework with your data transformation plan.
Data duplication is just part of a bigger data quality issue. When you set out to tackle data duplication, you may end up with a bigger problem than you intended. Hence, it’s imperative to focus on the quality of your data as a whole and implement solutions that will help you get the data you need for accurate analytics.