The implications of bad, inaccurate, and outdated data, particularly from a finance perspective, are well known. When data is a commodity amongst industries worldwide, every shred of information is understandably valuable; however, it shouldn’t be at the cost of high-quality data.
Poor data quality leads to poor decision making, and the ripple effect within organizations can be enormous. Data is only considered useful if accurate and standardized. No matter the size of the dataset or the data storage tools used, cleansed and quality data is fundamental, and duplication only increases costs, promotes inaccuracies, and leads to poor decisions.
Therefore, it is essential to ‘de-dupe.’ A deduplication tool becomes valuable when a smooth and efficient solution is required to remove duplicate data sets and significantly reduce your data size.
Performing this with the Data Deduplication Tool requires four simple steps to follow to make sure data is deduplicated efficiently based on custom-defined business rules. The steps are defined by StrategicDB, a leading supplier of data deduplication tools.
#Step 1 – Automatic Normalization – Data Deduplication automatically normalizes fields in a structure that allows for simplified searching of exact matches. Also, normalizing enforces a data field to accept recognized descriptions for multiple variations of that same value. When adding normalization, there are three fields that you normalize:
- Website – Accepting multiple domain name variations to be transformed as a single domain, e.g., http://www.datadupingtool.com, www.datadupingtool.com, or www.datadupingtool/strategicdb will all convert to datadupingtool.com.
- Company Name – Accepting normalized business structures that form into one, e.g., DataDeduplication Corporation and DataDedup Corp., would be normalized into DataDeduplication Corporation.
- Address – States, provinces, and country names can be aligned to a single normalized record, e.g., if you type ENG, UK, or United Kingdom, this will normalize as England.
Defining the normalized structure is the first step.
#Step 2 – Define Business Rules for Duplication Identification
You can identify and remove duplicates according to your own business rules. Set up the deduplication process to reduce time-lapse and promote better deduplication accuracy. As part of your business rules, you can combine more than one field, such as Country Name, Address, Company Name, and Phone.
An example of a field combination may include:
- Last Name and Company Name
- First and Last Name
Where there is more than one record of data within multiple identical duplicate groups, they can become combined within one duplicate group to speed the deduplication process.
#Step 3 – Selecting The Master Record
Set up your rules to identify your master record. The master record is one that isn’t considered as part of the deduplication. Where business rules are not applied accordingly, a record is chosen to be the master record at random. You can add multiple master records as separate rules. There is no restriction.
For example, if you have records that contain the word “customer,” you want to ensure that all records representing customers are marked as “master.” Where there are multiple records of the same duplicate group, for example, for “customer,” they will be merged as one record.
Master and merge are two separate business rules.
Another example is “No of Contacts.” Where a single account is associated with multiple contacts, that account will be considered a “master.”
When applying the business rules, here are some selection considerations to be aware of:
- If a single account has multiple “active” contacts, this should be a master.
- If a record has the most complete and accurate data, this should be a master.
Always consider the master and merge business rules as part of your criteria.
#Step 4 – Review the Final File
When all of your selections have been made, and the first 50 rows of data have been deduplicated, download the final file.
Once the deduplication is complete, expect to see the following:
- All selected fields will be normalized
- Each duplicate will have a unique identifier (ID) for each duplicate record group
- Each record will have a “Master” or “Merge” against it. One master record is assigned to each duplicate group.
- Where a duplicate group contains more than five records, this will be flagged for your attention.
Data cleansing can be established swiftly using the data deduplication tool. It helps cut out the middleman and allows for better control of the deduplication process, with the tool performing the identification of master records that survive deduplication. This means records not considered a duplicate, merging duplicate groups (multiple data records) into a single group, and providing a confidence level to show the number of records that match precisely to your business rules criteria.