Did you know poor data quality can drain as much as 25-31% of a company’s revenue? While there are multiple factors that can affect data quality, data duplicates are one of the most persistent culprits behind inaccurate analytics, operational inefficiencies, and lost profits. Imagine sending the same email twice to a whole bunch of clients or billing them incorrectly – all because of duplicate data. These small mistakes can snowball into major operational issues.
But businesses don’t have to accept this as the status quo.
By implementing advanced data deduplication strategies and harnessing the power of automation for data management, organizations can not only prevent duplicates but also enhance the accuracy and reliability of their databases and hence, decisions.
What is Data Duplication and Why Should You Care About It?
Data duplication occurs when identical copies of the same data are stored in multiple locations within a system. This leads to inefficiencies, increased storage costs, and potential inconsistencies in data integrity.
Datasets that contain redundant copies are inaccurate and can harm a business in several ways. These include:
Inaccurate Analytics and Reporting
Reliable data is the foundation of effective business intelligence. When duplicates skew data, the insights derived from analytics and reporting become unreliable. This can lead to poor strategic decisions based on faulty data, which ultimately harm the organization’s performance and growth prospects.
Operational Inefficiencies
Duplicates not only clutter databases and systems but also cause overrepresentation of certain segments or datasets, making it harder for employees to find accurate information quickly. This slows down decision-making processes and increases the time and effort needed to complete tasks. For example, employees might waste time verifying the correct record among data duplications and lose productivity. Datasets rife with duplicates also increase the risks of errors and, resultantly, operational setbacks. Data deduplication reduces the risks and eventually improves workflow and operations.
Increased Costs
Managing data with duplicates is expensive. Not only does it require additional storage space (which increases storage costs), but it also demands more resources for data cleaning and maintenance. Inaccurate data can lead to costly mistakes, such as sending duplicate marketing materials to the same customer or processing incorrect orders. Over time, these inefficiencies can accumulate and significantly impact the bottom line.
Poor Customer Experience
When customer data is duplicated, it can lead to confusion and frustration. Customers may receive multiple communications or face issues with account access and businesses may struggle to provide personalized services due to difficulties in tracking consumer behavior due to duplicate records. This damages the customer experience as well as erodes trust in the organization. A frustrated customer is less likely to remain loyal, which can hurt long-term revenue.
Damage to Brand Reputation
Remember the old saying a happy customer tells a friend; an unhappy customer tells the world? Never has this been more true than in the social-media-dominated world of today. Inaccurate or redundant communications with customers, errors in service delivery, and other issues caused by data duplicates can lead to negative perceptions of the brand.
Simply put, poor data quality can undermine the efforts of maintaining a positive brand image, which businesses cannot succeed without.
Compliance Risks
In many industries, maintaining accurate data is not just a best practice but a legal requirement. In such cases, duplicate records can lead to compliance violations, especially when dealing with regulations that require accurate customer data, such as GDPR. Non-compliance can result in hefty fines and legal repercussions, which can further increase the cost of poor data management.
What Causes Duplicate Data?
Duplicate data arises when information about the same entity is stored multiple times. This can happen due to:
Human Error
Human errors during data entry are a significant source of duplicates. Manual data entry often causes typographical errors, inconsistent naming conventions, and incorrect information, all of which can lead to duplicate records. Employees may also accidentally create duplicate entries if they are unaware of existing ones. This is more likely to happen in the absence of clear data management practices and centralized data storage and management systems.
Lack of Standardization
Variations in data entry formats, such as different date formats, address conventions, or naming standards, also often result in data duplication. When different departments or systems use varying terminology or formats for the same data, it can create confusion and lead to the generation of multiple records of the same entity.
System Integration Issues
When integrating data from multiple sources or systems, duplicate records can emerge if the integration process is not meticulously managed. Similarly, importing data from external sources without adequate deduplication checks can introduce duplicates into the system, further compounding the existing data quality issues.
Lack of Unique Identifiers
Unique identifiers, such as customer IDs or product numbers, are essential for differentiating between records for the same entity. Without these keys, it becomes challenging to distinguish between multiple records representing the same entity and the likelihood of duplicate copies increases.
Inadequate Data Validation Rules
Weak or insufficient validation checks during data entry or import processes may allow duplicated data to slip through, creating duplicate entries. This lack of rigorous validation can further complicate data management efforts.
Sometimes, you can end up with duplicates in your database even with the presence of unique identifiers, such as when the unique identifiers do not conform to valid patterns (for example, AAA-GG-SSSS for SSN), or do not have strict integrity constraints (for example, 11 char limit for SSN).
This happens due to inadequate data validation measures.
Weak or insufficient validation checks during data entry or import processes may allow duplicate data to slip through, creating duplicate entries. This lack of rigorous validation can further complicate data management efforts.
Data Synchronization Issues
Synchronizing data across different systems or platforms without adequate deduplication processes can lead to multiple copies for the same entity. Timing differences in data updates or synchronization can also cause discrepancies that result in data redundancy in the system.
Legacy Systems
Older systems often lack the sophisticated tools needed to manage and prevent data duplicates effectively. They may not support advanced deduplication or validation features, which can contribute to the persistence of duplicate data.
Customer or User Behavior
Duplicate records in databases can also result from user actions, such as when customers create multiple accounts or profiles, particularly if they are not prompted to log in or if they forget their credentials. Additionally, frequent or duplicate transactions in financial or transaction-based systems can result in duplicate records and further complicate data accuracy and integrity efforts.
How to Remove Data Duplicates from Your Database? The Process of Data Deduplication
The data deduplication process involves a series of systematic steps aimed at identifying and eliminating duplicate values from databases. Here are the steps involved in it:
1. Data Preparation
The first step in the deduplication process is to ensure uniformity across all databases in terms of data structure and fields. Data preparation reduces the structural heterogeneity across the databases – at least to some extent. It involves following two steps:
i. Data Cleansing
In this stage, the goal is to identify and correct erroneous data, fill in missing fields, and remove incomplete or irrelevant records. Cleansing helps prevent false positives (where two distinct records are incorrectly identified as duplicates) by ensuring data is accurate.
ii. Data Standardization
The purpose of data standardization is to minimize differences in data formats and structures and ensure uniformity across databases. This can be achieved by:
- Parsing long strings to identify important data components. An example of this is when you have the entire address in one field. Parsing the Address field will give you its subsequent data components, such as Street Name, Street Number, ZIP Code, City, State, and Country. Matching becomes quite easier on these parsed data chunks, as compared to matching on the entire field.
- Transforming data values to achieve similar data types, naming conventions, etc. This can be done by converting data types (e.g., string to number), renaming column names, or merging fields together.
- Standardizing patterns for all values contained in a data field, so that every value is expected to follow the specified pattern. An example of this is when you standardize the pattern of the Phone Number field to XXX-XXX-XXXX. Hence, making comparisons and matching easier and more accurate.
2. Data Field Mapping
Once the databases are standardized (as much as possible), the next step is to map fields that represent the same information across different datasets, such as mapping Contact Name to First Name and Last Name or Contact Number to Phone Number. This is either done manually (for example, Address to Address, Phone Number to Phone Number, etc.), or running checks to identify which field’s values overlap with the other database’s fields. For smaller datasets, the former technique can be useful, but if you have large datasets where columns are named differently, the latter is quite helpful.
3. Computing Similarity Using Field Matching Techniques
Once fields are mapped, the data is in relatively better shape to compare and identify duplicate records. But misspellings, human typographical errors, and conventional variations still exist. This is why exact matching techniques of data deduplication are not useful here, and we need techniques that consider these aspects of data while computing scores to assess similarity between individual values, and hence, the entire record. The available options here include:
a. Character-Based Similarity Metrics:
These are useful for text fields where typographical errors may occur. Common techniques include:
- Edit Distance
This algorithm calculates the distance between two strings, computed character by character. The distance is calculated by counting the number of edits required to transform the first string into the second string. Then a threshold is defined which classifies two strings as a match (if distance < threshold) or non-match (if distance > threshold). There are three types of edits allowed to calculate the distance: inserting a character into the string, deleting a character from a string, replacing a character with another in the string.
Normally, the count of one edit operation is considered to be ‘1’. But different models propose a different cost for each edit. For example, the Levenshtein distance considers the cost of each edit as 1, while Needleman and Wunsch explained that the cost of each edit depends on the nature of the edit (replacing O with 0 has a smaller cost, then replacing T with M).
- Affine Gap Distance
The edit distance algorithm does not work well with strings that have initials or short forms. For example, the edit distance might classify Jennifer Lily Stevens and Jen L. Stevens as a non-match. This is where the affine gap distance may be useful as it introduces two more edit operations called:
- Open Gap: It refers to adding a gap (or space) to a string where there was none.
- Extend Gap: It refers to adding a gap (or space) to a string where there was already a gap.
It is obvious that the cost of opening a gap (where there was none) is more than extending a gap (where there was already a gap). This variation of edit distance lets you calculate the similarity between shortened strings as well.
- Smith-Waterman Distance
This is another variation of edit distance and affine gap distance. This model lowers the cost of mismatches found in the beginning or ending of strings, since the prefixes and suffixes commonly differ. For example, matching these two strings with S-W distance makes more sense: Dr. Jennifer Lily Stevens and Jennifer Lily Stevens, Doctor at Nat Medical Center.
- Jaro Distance
Jaro introduced a formula for comparing the similarity between first and last names. The algorithm that calculates Jaro metric is as follows:
- Compute the lengths of both strings to compare (S1 and S2).
- Identify the number of characters that are common in both strings (C).
- Compare each character of the first string to the corresponding character of the second and calculate each nonmatching character as a transposition (T).
- Evaluate Jaro metric as:
Jaro = 1/3 * [ (C/S1) + (C/S2) + ((C-(T/2))/C)]
The lower the value of Jaro metric, the more similar the two strings are.
- N-Gram Distance
This algorithm creates N-lettered substrings from the matching strings, and compares the substrings, rather than the entire word. This helps detect typographical errors or variations. Let’s take the words Guide and Guode as an example. To calculate 2-gram distance between them, following substrings are created:
- Guide = {‘gu’, ‘ui’, ‘id’, ‘de’}
- Guode = {‘gu’, ‘uo’, ‘od’, ‘de’}
The similarity is then calculated by assessing the number of substrings that are the same. This evidently shows if the user meant to type the same word and if it is just a typographical error.
b. Token Based Similarity Metrics:
Token-based similarity metrics come into play when you want to compare strings that are rearranged differently but mean the same thing. This is helpful when fields contain information in different orders. For example, Jennifer Stevens can be written as Stevens, Jennifer, but both mean the same things and hence, would be identified as a match through Token-Based Similarity metrics. Character-based comparison will not be effective for such scenarios.
- Atomic strings
This is the most common token-based similarity metric. In atomic strings’ algorithm, the entire string is divided into words delimited by punctuations, such as space, comma, full stop, etc., and then the words are compared to each other, rather than the entire string.
- WHIRL
The atomic strings algorithm does not assign any weights to the words during comparison. Because of this, Doctor Jennifer Stevens, and Amanda Tates, Doctor will be considered somewhat similar (since one word is a complete match). WHIRL fixes this problem by assigning relatively lower weights to commonly used words and computes similarity accordingly.
- N-grams with WHIRL
The original WHIRL metric did not consider misspellings in its similarity comparison algorithm. It was later extended to include N-grams comparison technique, so that n-grams were compared instead of whole words (or tokens).
c. Phonetic Similarity Metrics:
The character- and token-based algorithms were designed to compare strings that reflected similarity in their character composition. But there are also cases where we need to compare strings that may not look the same at all but have a very similar sound when they are pronounced. This is where phonetic similarity metrics come in handy. Let’s take a look at the most common techniques for computing phonetic similarity metrics.
- Soundex
Soundex is commonly used to identify surnames that may be different in spelling but are phonetically similar. This helps in catching any typographical or spelling mistakes that occurred while entering the data. But the algorithm mostly performs well with only English surnames and is not a good choice for names of other origins.
Soundex algorithm computes a code for each string and compares how similar the codes for two separate strings are. The Soundex code is computed as:
- Keep the first letter of the name.
- Ignore all occurrences of w and h.
- The letters a, e, i, o, u, and y are not coded and are only kept temporarily (as they’ll be dropped completely in the latter step).
- Replace following letters with these digits:
- b, f, p, v → 1
- c, g, j, k, q, s, x, z → 2
- d, t → 3
- l → 4
- m, n → 5
- r → 6
- If two or more identical digits are present in the code, only keep the first occurrence and drop the rest.
- Drop these letters: a, e, i, o, u, and y.
- Keep the first letter (from step A.) and the first three digits created. If there are less than three digits, then append zeros.
For example, these two strings ‘Fairdale’ and ‘Faredayle’ output the Soundex code F634, since they are phonetically the same. Soundex proves to be 95.99% accurate while locating similarly sounding surnames.
- New York State Identification and Intelligence System (NYSIIS)
As the name suggests, this algorithm was devised for the New York State Identification and Intelligence System in 1970 – which is now part of the New York State Division of Criminal Justice Services. Its accuracy rate is 98.72% (2.7% increase from that of Soundex) as it retains details about vowel position in the code (maps them to the letter A). Moreover, consonants are mapped to other alphabets and not to numbers, thus creating a complete alpha code – no numbers involved.
- Metaphone, Double Metaphone, and Metaphone 3
Lawrence Philips developed a better rendition of Soundex called Metaphone in 1990. It performed remarkably well as it considered the details of variations and inconsistences that are in English pronunciation and spelling. In his algorithms, he made use of 16 consonant sounds that are used in pronouncing a large library of English and non-English words.
Later on, Philips published a newer version called Double Metaphone, in which he also incorporated details of a number of languages – in addition to English. Finally, in 2009, he developed Metaphone 3, that proved to be 99% accurate for English words, other words familiar to Americans, and first and family names commonly used in the US.
d. Numeric Similarity Metrics:
There are many methods for calculating string-based differences, but for numeric datasets, these methods are limited. Simple numeric differences are usually assessed by calculating how far values are from each other, but for complex computation, the distribution of numeric data can also be considered. Algorithms such as Cosine Similarity can help locate numeric differences by measuring the numeric distance between two records.
Which field matching technique to use for data deduplication?
As we just witnessed, the process of finding similarity between two data fields is quite complex. We reviewed multiple data matching techniques but noticed how each of them solves a specific data deduplication problem, and there’s not a single technique that promises to perform well for all data types and formats.
Selecting a matching technique highly depends on these factors:
- Nature of your data – or the data type. For example, Jaro distance performs well for strings, but cosine similarity is broadly used for numeric datasets in data deduplication.
- Kinds of duplicate record that are present in your dataset. For example, typos and spelling mistakes are better tracked using character-based similarity metrics, while differently formatted fields are better matched using token-based similarity metrics.
- Domain of your data. For example, if you are matching English first or surnames, then Metaphone works well, but if non-English names are also involved in your dataset, then using Double Metaphone or Metaphone 3 makes more sense.
4. Similarity Scoring of Redundant Data
Once the data has been compared, the algorithm used by your data deduplication software assigns a similarity score to each pair of records. This score quantifies how close two records are to being duplicates. A threshold value is set, above which two records are considered duplicates, and below which they are considered unique. For example, two customer records with a similarity score of 0.85 (on a scale from 0 to 1) will be classified as duplicates if the threshold is set to 0.8 but considered distinct if the threshold is 0.9.
- Score-Based Threshold: The systems typically allow users to adjust the threshold to fine-tune the matching accuracy. However, this needs to be done carefully as a higher threshold will result in fewer duplicates being detected, while a lower threshold may identify false positives.
5. Data Deduplication
Once the duplicate copies are identified, the next step is deduplication, where they are either removed or merged into a single record, depending on the business needs and rules.
Potential Issues in Data Deduplication
While data deduplication is essential for maintaining clean and efficient databases, it is not without its challenges. Several potential issues can arise during deduplication that can complicate the process, impact data accuracy, or even introduce new problems if not carefully managed. Below are some of the most common issues associated with data deduplication:
Difficulty in Data Comparison Due to Data Heterogeneity
To get rid of redundant data in your database or deduplicate data, you need to compare the records and evaluate which ones belong to the same entity. But when you compare data records (either in the same database or belonging to different databases), you will notice that they have some systematic differences, which make it difficult to exactly compare them. This is known as data heterogeneity and can be classified into two types:
1. Structural Heterogeneity: This type of difference occurs when fields of different databases represent the same information in a structurally different manner. For example, one database could be storing a contact’s name as Contact Name, while in a second database, it is stored in multiple columns such as, Salutation, First Name, Middle Name, and, Last Name.
2. Lexical Heterogeneity: This type of difference occurs when fields of different databases are structurally the same, but they represent the same information in a syntonically different manner. For example, two or more databases can have the same Address field, but one can have an address value 32 E St. 4, while the other can have 32 East, 4th Street.
System Integration Challenges
When organizations integrate data from multiple sources, discrepancies between systems can exacerbate deduplication issues. Each system might have its own way of storing and organizing data, making it difficult to establish a consistent method for identifying and removing duplicate data. The integration process itself can introduce errors, especially if data is not properly cleaned and standardized beforehand. These challenges are particularly prevalent in mergers and acquisitions, where disparate databases need to be combined into a single, cohesive system.
Integration challenges may also arise when working with legacy systems. These older systems might lack modern data management capabilities and processing power needed for effective data deduplication. They may also not support the advanced algorithms used to remove duplicates effectively.
Performance and Scalability Concerns
Data deduplication, especially in large datasets, can be resource intensive. As the volume of data grows, so does the computational effort required to identify duplicates and eliminate them. This can lead to performance bottlenecks, especially if the deduplication process is not optimized for scalability. Organizations must balance the need for thorough deduplication with the potential impact on system performance, particularly in real-time or near-real-time data processing environments.
Data Loss Risks
If the algorithms used by your data deduplication technology are too aggressive or poorly configured, they might mistakenly remove records that are not true duplicates. This can result in the loss of critical information, which could have serious implications for business operations and decision-making. Ensuring that deduplication processes are accurate and that there are safeguards in place to prevent unintended data deletion is essential.
False Positives and Negatives
False positives happen when the system mistakenly identifies non-duplicate records as duplicates, leading to erroneous merging or deletion. For example, records with similar names but different addresses or contact details might be incorrectly categorized as identical files and merged. This can happen when the matching criteria used in the deduplication process are too broad or not sufficiently accurate.
Conversely, false negatives occur when duplicate records are not recognized as such, allowing them to persist in the database. For instance, “Hillary Brown” and “Hilary Brown” might be considered different records despite being the same person. This can happen if the matching criteria are too strict or do not account for variations in data entry, such as typos, different formats, or abbreviations.
Both situations can undermine the integrity of the deduplication process and the quality of the resulting data.
While eliminating redundant copies is essential for maintaining clean, accurate datasets, it is a complex process that requires careful planning and execution.
Simplify Data Deduplication Process through Automation
Deduplicated data is vital for organizations. However, understanding the internals of data matching techniques and choosing an appropriate one for your dataset is a difficult task. In many situations, one technique is not enough, and a combination of deduplication methods are used to accurately dedupe data. For this reason, the need for digital tools is increasing; tools that not only optimize time and effort, but also intelligently select the best data deduplication methods according to the nature of your data structure and values.
DataMatch Enterprise is one such tool that handles your entire data quality process from beginning till end. It offers a range of modules that support data coming in from different sources, enable field mapping, and suggest a combination of match definitions that are specific to your data. You can use the suggested matching fields and algorithms or override by selecting your own. The tool can also be used to assess match accuracy of different match techniques on your dataset, and conclude which algorithm performs well for your data deduplication goals.
To know more, sign up for a free trial today or set up a demo with one of our experts and start deduping your data!
