While data deduplication is a commonly heard term to those in the business of big data, many light users of data quality tools within their business may not be as familiar with data deduplication and what it can do to boost a computer’s power.
Broadly defined, data deduplication is a process that gets rid of multiple instances of data and reduces storage. There are various ways to deduplicate data; one way is through inline deduplication, which removes duplicate data after it is written to storage. Another way is at the source, which gets rid of multiple blocks of data before it is sent to a backup destination. This method reduces the amount of storage used.
Data deduplication tools have now become a critical part of any company’s data quality toolkit.
Data Deduplication is Critical for Mail Campaigns
Duplicate emails and direct mailings are annoying to anyone. It tells your customer you aren’t paying attention to details. If this is done often enough, it could be seen as bullying, and you might even be deleted as a vendor from their list.
Even worse, you’re throwing revenue away by mailing duplicate hard copies of a catalog or postcard. While this may be through no fault of your own, per se, you do have the power to deduplicate!
Without appropriate data deduplication tools to manage your customer lists, it could be costing you revenue and the loss of customers. Let’s face it, duplication of data happens. With causes stemming from spelling errors to the use of different servers, the list of causes are too numerous to name.
Let’s take an example. As part of a recent mailing (to find new customers, new students, etc) a letter is sent to Jenny Jones at 555 Main Street and J. Jones at 1 Mn St.
This is a very common occurrence, as between 5 to 15% of all records in a database are typically duplicates. The result is wasted time, money, and customer confusion. Deduping a database can be time consuming, as databases grow in size over time, and the need to link to outside databases increases.
With estimates that poor quality customer data costs U.S. businesses $611 billion a year in postage, printing and staff overhead, the need for high quality data deduplication tools is at an all-time high. The true cost may be much higher as customer satisfaction, repetitive pricing promotions, and missed opportunities are factored in.
Manually deduping a list quickly becomes unrealistic, and simple exact match deduping solutions are exhausted.
DataMatch Enterprise is one of the most comprehensive, affordable data deduplication software tools on the market. It will cleanse, match and deduplicate your email lists, databases, and Excel spreadsheets.
There is a huge difference between a well-orchestrated email campaign and a campaign riddled with duplicate emails, so don’t confuse the two. After all, with duplicates, too much of a good thing may do you harm.
Householding Data Can Also Help
Many companies also get their data in order through householding. Simply put, householding consists of grouping like data from numerous sources. This can be identifying a set of data records from one source system, such as order entry, and how they are related to another set of data records.
The concept of householding your data is very straightforward. Imagine a specific item of data needs to be in a specified household for that type of data. Just like you have a home address, so should the data item. But, just like you, you have a home address, work address and addresses of relatives you spend time with. All of these addresses are your households. The data item should be considered in the same way and it can reside in other households also. This logic gives your data flexibility and true meaning. Let’s start with discussing some tips on “householding” your data:
- Determine the level of accuracy of your records. This can be increased, thus directly
related to your data quality and the sophistication of your matching rules.
- Determine what makes up a group or household. The quality will vary depending on the
grouping rules and quality of the data.
- Determine grouping rules. The level of accuracy achievable when grouping common
records will also vary based on the sophistication of the grouping rules and the quality of
- Consider data confidence factors. The assumption is that every item of data used in the
householding process may be invalid.
Implement an Effective Data Integration Program
What can also help in managing your data and avoid duplicate records? Implementing a strong data integration program. Integrating different customer data sources with different field types, address standards, and naming conventions is a large task, but helps keep data streamlined. Data integration is especially important when you consider how valuable the information is. The comments and history associated with these records are needed to ensure a flawless customer experience.
Keys to effective implementation of a data quality program include:
- Find someone who has done it before. Data Ladder’s customer integration specialists have completed hundreds of customer data integrations.
- The right tools make all of the difference. Every data integration is unique. Data Ladder’s DataMatch Enterprise suite has; multiple customizable match definitions so you can identify duplicate and matching customers effectively (Same email, or same address, or same company name, etc.), the ability to merge data into a single golden record that combines duplicate/matching customer data with no data loss, and the ability to save your work for use in future customer data integrations.
Get started quickly with an affordable solution. Why waste time waiting for large companies to return your phone calls? Why spend hundreds of thousands of dollars on a solution that takes months to approve, and weeks to implement? Whether you need to deduplicate your data or implement a data integration program, call on Data Ladder’s specialists to help you with your needs.
Guest Author: This Article is contributed by Mohd Sohel, He is Technical Writer; And writes for all latest and helpful software and tech related content, Now He is writing for site Data Ladder, providing helpful data quality software, He also writes and participate for all high quality blogs like TechSparkle