This article gives a thorough understanding of what is data duplication, hurdles faced by organizations due to data duplication, and how the solution in this article can be a tough contender to put such challenges to rest.
Why Today’s Companies Need to Invest in Data Deduplication Software
Data is a precious commodity in today’s technologically advanced world. However, more data does not always mean more accurate results. This challenge of maintaining and making sense of data from multiple sources is enough to give IT teams sleepless nights.
Understanding Data Duplication
If you are responsible for transferring large amounts of data, you might have heard about the term “data duplication”. If not, here’s a clear definition of what it means.
Data duplication is a common problem in databases where due to multiple instances, data is duplicated – meaning there is more than one version of the information on a specific entity. For example, Entity A’s data may be repeated at least five times within a data source each time each time they sign up to a service using a different email. This kind of data duplication results in skewed reports and affects business decision making. Where an organization may believe it has 10 unique users, it may actually be just 4 unique users.
Data duplication is costly as it affects business processes, causes flawed statistical data, and forces employees to spend their time resolving mundane data problems instead of focusing on strategic tasks.
Data duplication is considered to be the root cause of poor data quality as it can significantly increase operational costs, create inefficiencies and reduce performances.
Left unattended, data duplication can be a severe bottleneck in your digital transformation efforts. Imagine this, you’re all ready to move to a new CRM when you realize your data is inaccurate, invalid and mostly redundant! While you would be tempted to migrate to the CRM anyways, you know that your staff will have to spend time fixing these problems on the new system instead of making use of the CRM for what it was intended.
So what causes poor data quality? Some of the common reasons are:
Reasons for poor data quality include:
- Multiple users entering mixed entries
- Manual data entry by employees
- Data entry by customers
- Data migration and conversion projects
- Change in applications and sources
- System errors
Why Data Duplication is inevitable? Below are some instances.
- A typical email system might contain 100 instances of the same copy that demands extra storage.
- The same user can enter multiple entries in different places through a form by which we can experience performance issues.
- A more complex example could be of an organization that is linked to a billing invoice that comprised of multiple call records. This could lead to bad and unreliable connections.
- A transactional source system may present multiple instances of a record that are duplicates (or triplicates) can increase the risk that data can be misunderstood within a dataset and count of the data will be incorrect.
- Duplicate records of patients can be generated by the hospital’s technical staff that can reflect cost, such as time spent on locating the original record and problems with billing.
Implementing a Data Deduplication Process
Data Deduplication is a process by which duplicate copies of data are eliminated. Usually, a data deduplication software is used to analyze data sources and find duplicates through a data matching function. Once data is deduped it can be made ready for its intended use.
Data Duplication and Deduplication Examples
Let’s take the example of an e-commerce retailer that maintains an enterprise-level database. The company has hundreds of employees entering data on a regular basis. These employees work with an ever-growing network of suppliers, sales personnel, tech support, and distributors. With so much going on, the company needs a better way to make sense of the data they have so that they can do their job efficiently.
Suppose there are two agents – one in sales and one in tech support, who are dealing with one customer – Patrick Lewis. Due to either human error or the use of multiple data systems, both employees in different departments end up entering two pieces of data.
It’s important to note that names suffer the most from data errors – typos, homographs, abbreviations, etc., are the most common problems you’ll find with the [name] field.
Bad Data (One individual, two entries):
House C 23, NYC, 10001
C-23, Blueberry Street, New York City
Data after Deduplication (One Individual, one entry):
C-23, Blueberry Street, New York City, 10001
As you can see, various type of errors can occur as a result of the human error via manual data entry:
- Misspelled names – Pat, Patrick, Patrik, etc.
- Variation in Addresses – House C 23, C-23, House No. C 23, etc.
- Abbreviations and Cities – NYC, New York City
- Missing zip codes – 10001
- Missing values – one entry has an email and the other doesn’t
- And more
You need to transform this dirty data (data that is inaccurate and duplicated) into usable data that can be accessed by all departments without having to hand over the task to IT every time. Not having access to the correct data can prove costly to your business.
Solutions to Data Duplication Problems
How can you solve data quality issues, especially as your business continues to grow and scale? There are two ways to go about this:
As mentioned before, there are two options to clean up dirty data.
Hire a team of developers/data talent in-house to manually clean your data
Businesses that are hesitant in investing in technology prefer the first option. These firms’ operative thinking is informed by a need to save costs in the short run, and in thinking that data quality can be maintained periodically. In such a scenario, data matching and cleansing becomes a time-intensive process, requiring tons of manual work to fix data.
In addition, it has become increasingly difficult and time-taking to find someone who is a good fit for your business, this means certain part of the process might be put on hold till a professional is hired.
In the long run, these manual, temporary and periodic quick-fix solutions require developers and data specialists who are, spoiler alert, not cheap as they thought.
Invest in a commercially available data deduplication software
Data deduplication software (also called data matching software), has proven to have a higher match accuracy (85-96%) than an in-house team of data specialists (65-85%). These solutions are tested in a variety of scenarios and feature intelligent algorithms that clean up data rows in a fraction of the time it could take human eyes to peer through them all. What could typically take months can be resolved in a matter of minutes.
Moreover, the most popular data deduplication software today allows for integration with your databases, meaning you can automate the cleansing of your data in real-time using workflow orchestration features.
To sum it up, data deduplication is a technique that:
- Removes copies of similar data from various other databases and sources.
- Ensures a streamlined and proper database.
Today’s firms need to realize that improved data quality results in better decision-making across your organization. To be relevant and competitive, you need to invest in the right data deduplication software.
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