A Quick Guide to Easy & Efficient Data Cleansing

Data is inherently dirty, caused by many factors that are often out of one’s control. It can occur for various reasons, including but not limited to:

❌ human error: when data is entered manually leading to duplicates, typos, and poorly structured data.

❌business processes: when new systems, software, or tools are introduced and data is merged or exported without a data quality strategy in place.

❌poor data collection: when data collection is not governed or monitored, such as a web forms where there are no data controls.

In a spreadsheet or database, dirty data would look like this 👇

In this table, you can see the data fields have incomplete information, names with multiple spellings, duplicate entries, and invalid postal codes. You could spend weeks sorting this data, leading to delayed outcomes and poor organizational productivity.

Additionally, the consequences of ignoring these data problems can be dangerous. Your organization could face legal suits, and security risks, be penalized for missing sanctions and GDPR compliance, and lose money because of inaccurate insights or predictions, among many other problems.

All of this stresses the need for organizations to prioritize data cleansing. But the traditional way of handling dirty data through manual scripting, using complex regular expressions, or Excel is not scalable and cannot be applied to large datasets that are needed for AI, ML, or big data projects.

Data cleansing, therefore, is the process of treating raw, dirty data and transforming it into records that are reliable, accurate, and fit for business.

In theory, data cleansing is similar to a water treatment process. Water must be cleansed of impurities before it can be directed to water systems for residential use. Without the cleaning process, you’d be unable to use water safely. Similarly, without data cleaning, your data would be unfit for insights, analytics, reports, campaigns, and any other data-driven business purpose.

If you value information quality, you need to prioritize data cleansing as a strategic activity consisting of several steps. Contrary to most practices, data cleansing must not be treated as a simple “error-fixing” activity. You must understand the context of your data before you can treat or clean it. Once you know the context and end goal, you can follow a step-by-step process to clean and make the data fit for purpose.

The data cleansing process has five key steps: 

✅ Data analysis or profiling: The first step is analyzing the data to identify the errors and inconsistencies in the database. For example, records with text in numbers (or vis-a-vis), with a mix of upper and lower case, incomplete fields, etc are detected using data profiling functions.  

✅ Create transformation workflow and mapping rules: define the detection and elimination of anomalies through a sequence of steps that involves correcting typos, removing odd characters, and defining mapping rules such as combining First Name and Last Name into a Full Name field for marketing purposes. 

✅ Data match: use fuzzy, exact, and numeric matching algorithms to identify duplicates in different data sets. Data match can also be used to consolidate disparate sources: a critical step in removing redundancies and merging/purging data as needed.

✅ Verification & validation: using verification modules on phone, address, or email data to ensure its validity. 

✅ Create master records: Once the data is verified and validated, an updated master record can be accessed by users within the organization.

For any sizable data set, accomplishing these tasks manually is expensive & time-consuming, especially when organizations are crammed with a massive volume of information, collected at an unprecedented scale. This causes a catch-22 situation where data is needed, but it’s not fit-for-purpose until it is cleansed, but because data analysts don’t have the time, they end up doing batch fixes or cleaning the data only when there is a business request. The data is treated in isolation, devoid of context and business understanding. 

In an era where digital transformation is happening at breakneck speed, companies need to adopt faster methods to clean, prepare, and process data.

While there are plenty of data cleaning tools, most can only handle a limited amount of data at a time with limited functionality. You would have to use a combination of tools, methods, and scripts to clean, match, deduplicate, and consolidate data. 

Modern data matching and cleaning solutions, however, are significantly more advanced as they incorporate machine learning algorithms allowing them to automate numerous tasks that were once done manually, such as sorting, filtering, standardizing, and removing duplicates from data sets. This enables greater accuracy and faster turnaround times than traditional methods. In addition, modern solutions are often cloud-based and easily scalable, so they can handle larger datasets with less cost and fewer resources. 

To summarize, here’s a table highlighting the difference: 

One of the biggest benefits of using data cleaning tools is the fact that they are no-code. You can clean large sets of data by simply ticking off options on a dashboard, or you can use pre-defined expressions to transform your data as many times, in as many ways as you like. What would generally take a data analyst five days, can easily be achieved in five hours! 

Let’s look at how much time and money you’re actually saving if you choose to go with a no-code data cleaning solution.

Traditional methods have lengthy implementation times, which can add up to additional costs in terms of labor and resources. This can lead to delays in project completion, resulting in decreased customer satisfaction and reduced ROI.

On the contrary, modern data cleaning tools offer a significant reduction in implementation times, reducing it by up to 50%. This leads to faster project completion and improved customer satisfaction rates. Additionally, organizations can benefit from increased productivity due to the streamlined nature of no-code solutions.

A simple cost-benefit analysis shows: 

Not only are you saving valuable time, you’re also reducing labor costs and improving ROI with better accuracy as you transform your data!

We encourage you to read some of our data cleansing case studies to see how our customers save time, human resources, and money by opting to use our no-code solution to clean, deduplicate, and treat their data. In all of these case studies, you will see how replacing manual methods with an automated solution like WinPure improved organizational efficiency and helped teams achieve their business goals faster than they would normally get.

WinPure is an all-in-one, no-code data match and cleaning solution that can clean, match, standardize, verify, and validate data within a single, user-intuitive interface. With WinPure, analysts can quickly find, identify, and correct any errors without the need to code scripts to identify errors. 

The software can check a million records in minutes while still maintaining a high degree of accuracy and speed. It uses fuzzy, numeric, exact logic algorithms with proprietary algorithms to spot patterns in your dataset so you can quickly fix any inconsistencies or typos you might have missed when editing your files manually. 

Some of the key features of WinPure include: 

✅ No-code: Match complex data, define custom rules, and perform extensive data standardization and cleansing tasks without the need to code a single line! It’s so easy that even your business users could merge, purge, and clean their departmental data. 

✅ Multiple views: You can see the health of your data, and errors affecting the data, and perform key data standardization and cleansing activities in a single dashboard with multiple views. You don’t need to switch between systems or data sources to treat your data.

✅Advanced profiling and statistics: Get clarity on the percent of fields affected by data quality issues. Track how many fields are affected by odd characters, how many have numbers or alphabets mixed up, how many have incomplete information, and so on. Access advanced statistics you’d never have with a traditional method.

✅ Advanced cleaning & customization: The cleaning matrix has sections that let you simultaneously perform cleaning operations on multiple columns. You can also use Word Manager to create custom dictionaries and labels, or even set specific standardization rules (for example, from NYC to New York City). 

✅ Data match using match definitions: Match names and addresses, zip codes, and phone numbers using a combination of fuzzy, exact, or numeric matching. You can match between tables or within tables as required. 

✅ International address verification:  The Verify (Address Verification) module will allow you to check the validity and deliverability of every physical address on your mailing list. It will automatically correct and add all missing address elements, comparing it to the latest country data, adding ZIP+4 info, Latitude/Longitude,  Carrier Route info, LOT Codes, County Names and Codes, Congressional Districts, and much more.

WinPure’s no-code, intuitive interface makes it easy for non-technical users to take full advantage of its features without extensive training or coding knowledge. The platform contains a library of ready-made rules which can be applied to any dataset to reduce errors and inconsistencies; this helps improve data quality within minutes instead of days or weeks. 

Despite the best tools and technologies, data cleansing is mostly a process performed by specialists. If you’re new to data cleaning and are attempting to clean data using a software like WinPure, here are a few best practices to remember. 

✅Always save a backup of the data before beginning any cleaning process. That way, if something goes wrong, it can be reversed in order to resume working from where you left off without having to start from scratch. Better yet, always use sample data before treating your actual data. 

✅ Use data validation techniques such as range checks and format checks to ensure that only valid values are included in the dataset. This will help to identify errors more quickly and it also prevents inconsistencies which can cause issues further down the line.

✅Use fuzzy matching algorithms when dealing with text-based data containing spelling mistakes or typos. Fuzzy matching algorithms can help reduce noise and improve accuracy by recognizing variations on similar words in meaning and context.

✅ When dealing with missing values, always opt for the most reasonable approach depending on the context of the dataset and how it is being used (e.g., imputation vs deletion). Making an informed decision in this regard can ensure that results aren’t skewed due to incorrect assumptions made while handling missing values. 

✅ Take into account outliers when performing exploratory analysis or predictive modeling tasks by either discarding them (if deemed appropriate) or applying data transformations such as logarithmic scaling, min-max scaling, etc., so as to minimize their influence on the model’s performance metrics like accuracy, recall, etc.

Always review the quality of match results. You will need to manually review any false negatives or positives before making final changes to your master record. 

In Conclusion 

Data cleansing is necessary if you prioritize information quality. However, the traditional methods of data cleaning is no longer effective, efficient, or even scalable. You need modern data cleaning solutions like WinPure that allow you to match, clean, and consolidate data without having to waste 80% of your time – which could be used for more strategic activities. In an age of AI and real-time information processing, you cannot afford to mull over scripts and formulas.