In data management, a seemingly straightforward question frequently emerges: To match data first and then clean, or to clean first then match? While a seemingly innocuous question, it carries with it drastic consequences affecting the overall success of any data quality initiative.
If you find yourself grappling with this decision, this article will guide you through the advantages and disadvantages of each approach.
Let’s dive in!
How Did We Get Here? Exploring the Match vs. Clean Question
There are several theories to explore to understand why this question has become a dilemma. Some of the most common causes we’ve encountered include:
Fragmented approach to data quality:
As companies become inundated with vast amounts of data, ensuring its quality has become an overwhelming task. The rapid rate of data collection often outpaces the ability of many businesses to manage and process data effectively. Consequently, companies resort to addressing data quality issues in fragmented ways: some just want to batch clean certain data segments to meet an immediate business goal, others want to simply standardize the data to make it look clean, and others want to invest in expensive architectures to manage data. Few delve into the basics of data quality to prioritize clean, error-free data.
Data matching is considered more important
Data matching is generally considered a higher priority because of the complexity involved. Professionals have to decide on the type of datasets to match, the type of algorithm to use, and the threshold and weightage to assign; all of which take the focus away from other necessary activities like data cleaning and data standardization. In fact, data cleaning is seen as a backburner activity that can be done later by a junior data analyst or IT assistant. There are almost no strategic efforts for data cleaning.
Customers are unaware of dirty data challenges
In our experience, we have observed that most customers are unaware of the data quality issues plaguing their datasets. They attempt to achieve key goals like data consolidation and data deduplication (all of which require efficient data matching capabilities), with incomplete, invalid, dirty data. It is only when they use a solution like WinPure that are they able to see the flaws affecting their data. When confronted with the reality of the state of their data, customers then prefer to clean the data before matching.
Vendors package data matching as a standalone function
Enterprise data matching vendors package data matching as a standalone function that is powerful enough to match raw data. This is a false promise that leads customers into believing data cleaning is not as essential. With this narrative, data cleaning becomes a secondary choice. Most customers need data cleaning more than they do data matching!
The Consequences?
Errors are still unresolved and are carried forward into a new database or a new record. Over time, the records become bloated with poor data quality again, and once more professionals have to treat the data. Instead of a refined dataset optimized for business, what companies are left with is a trove of flawed information, limiting its real-world applicability.
Data cleaning is only prioritized when data-driven business objectives fail. By that time, leaders and managers begin to panic and resort to quick fixes to make the data useful. Hence, organizations remain stuck in the data quality loop for a long time, repeating the same mistakes over and over again.
WinPure’s data clean & match sequence
WinPure’s interface follows the standard data quality process. Customers start by profiling their data to detect errors, then move on to cleaning, standardizing, and finally matching and validating results.
Here’s a step-by-step preview of how you can clean and match data in the right order.
1. Import the data
Whether you want to match or clean data, the first step is connecting the right data. With advanced compatibility, WinPure can let you connect your CRM, SQL databases, spreadsheets, and other data sources directly into the solution.
2. Profile the data
WinPure’s data profiling feature allows you to assess the statistics of your data health, identifying problems like odd characters in data fields, numbers in text fields, and vice versa.
3. Clean the data
Once you know the kind of errors affecting your data, select the built-in options to fix the errors, run the clean matrix, and see your data transformed! No code or long-winded formulas are required to clean data!
4. Standardize the data
Implementing consistent standards before matching will help with greater accuracy. For example, you would want all city names to be in their full form instead of NYC or DC. This way, when two similar data fields with NYC are matched, they can be identified as duplicates if you decide to run exact matches.
5. Match the data
Now, is the time you would want to match the clean data to weed out duplicates or to simply consolidate different tables. If you have clean data to work with, you can make efficient use of exact, numeric, or fuzzy matching algorithms to match multiple data columns.
As you can see, data match is the final process. If you cut through all the other steps and match first, you risk inaccurate results, a higher number of negatives, and false positives.
Why Does WinPure Recommend this sequence?
WinPure’s step-by-step sequence represents the industry’s best practices based on the experiences of data scientists, IT professionals, and businesses grappling with data quality issues. Regardless of your business goal, following this sequence allows you to have confidence in your data.
Other than best practices, WinPure’s clean and match sequence is based on a simple logic – you need to understand your data landscape (profiling), to know what to fix (cleaning). Once done, you can then match the clean records to weed out duplicates and build a master record. With this sequence, you can prioritize data quality goals and ensure that even the most complex datasets can be treated if the task is broken down into a sequence of doable activities.
And the good part? You can perform this whole sequence without having to use a single line of code. You just need strategic and contextual knowledge of the data to get the results you want.
benefits of clean before match approach (with industry examples)
Before the internet era, data collection was limited, allowing manual and straightforward data clean-up row by row. However, today’s data landscape is far more intricate than a basic spreadsheet. Through our years of assisting various industries, we’ve recognized the benefits of the clean-before-match approach. This method has saved companies countless hours and provided a clear, manageable process without overwhelming complexity.
✅Avoiding Costly Errors
💡Example: In the healthcare industry, patient records must be accurate to ensure proper treatment. If data isn’t cleaned, you could match a patient to the wrong treatment history due to a slight discrepancy in name spelling. Cleaning ensures that John D. and John Doe are recognized as variations of the same entity before matching.
✅Avoiding Reputational Damage
💡Example: An e-commerce company aiming to target past customers with a new offer might have a list of emails and another list with user profiles. Cleaning data to remove typos in names and email addresses before matching ensures that marketing campaigns reach the intended audience on one address instead of multiple addresses of the same recipient. These mistakes are an instant hit on reputation and can cause significant damage to your marketing goals.
✅Improving Efficiency
💡Example: In inventory management, items might be cataloged under different codes or names across databases. Cleaning data to standardize item descriptions ensures that when matching with supplier databases, there’s no over-ordering or under-ordering due to misidentification.
✅ Improving Customer Relationships
💡 Example: A bank with multiple service channels might have customer data scattered across them. If a customer’s contact details are outdated in one database but updated in another, cleaning ensures the most recent and accurate details are used. By doing this before matching, the bank avoids situations like sending sensitive information to old addresses.
✅ Enhancing Predictive Analytics:
💡Example: In real estate, when predicting housing prices, data from various sources (like historical sales, neighborhood demographics, and property features) is used. Cleaning datasets to correct outlier values or standardize unit measurements (e.g., sq. meters vs. sq. feet) before matching ensures that predictive models are built on accurate, consistent data, resulting in more reliable forecasts.
Even if you have robust data governance, it is always a best practice to clean and standardize data before merging, matching, or deduplicating. Data cleaning must be prioritized as a key activity in any data quality initiative. It cannot and should not be a back burner or an afterthought activity because dirty data is far more dangerous than duplicate or disparate data.
Clean and match your data in minutes
Want to know how to profile, clean, and match data in minutes? Download the WinPure free trial to clean and match without any code or scripting knowledge.
