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Fun fact – data cleansing is often ignored. Executives and decision-makers aren’t aware of data quality problems while mid-level managers and juniors find it irksome to tackle.

Research by the Alation State of Data Culture Report, states:

“Two-thirds of C-level executives at least sometimes ignore data and make decisions based on intuition.”

No one wants to deal with the tiring task of matching millions of rows, identifying duplicates, and cleaning their data of typos. But when flawed analytics, inaccurate results, misinterpreted facts, and false positives, affect decision-making, it becomes everyone’s problem.

Whether you’re a small business or a bank, you’ll need to identify the tools or data cleansing service you’ll need to solve the problem faster.


Let’s find out.


Data cleansing starts with accepting there is a data quality problem.

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Start asking the right questions.

  • Do I have accurate & complete data?
  • Is my data management infrastructure updated?
  • Does my team understand data quality?
  • Do I have data quality standards in place?
  • Am I facing inefficiency, flawed reports, and poor projections frequently?
  • How confident is my team with the analytics and insights?


Data cleansing isn’t simply fixing typos or errors. It requires a data management strategy where you’ll need to perform time-consuming tasks like data matching & deduplication. These tasks take ages if you do them manually through code and scripts. Moreover, you’ll also have to hire special talent to get the job done. Hence, it’s always a best practice of data quality initiatives to have a strategic approach.

Some aspects to cover in your strategy include:

  • Assess Your Business Requirements 

Small teams with CRMs can benefit from a data cleansing tool that helps remove duplicates & removes errors easily, with no code or steep learning curve involved. Ideally, you need a solution that can get the job done fast. You can use WinPure’s free version as part of your tech stack.

On the other hand, if you’re a mid-level organization, you may probably need a tool that can give your individual users and departments the ability to clean data without dependencies on other sources.

P.S: Coding scripts to clean duplicates is old school, and time-consuming. 

Every business’s data cleansing or data governance needs is different. The key to finding a solution that works is by first identifying the specific problem you’re dealing with & the kind of solution you need.

  • Building a Data Quality Management Workflow

A data quality management workflow starts with reviewing and diagnosing issues, such as identifying the cause of errors (for example, fat finger syndrome caused by manual typing, or duplicates caused by a data migration error), and the frequency with which they occur.

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Common steps involve:

  1. Planning a data quality initiative: Assess your goals, timeline, the current availability of resources, and your budget to resolve key issues.
  2. Defining data quality standards: Data should be accurate, complete, reliable, relevant and validated. Most importantly, it must be able to deliver information that can be used in business initiatives.
  3. Identifying technology & talent resources: Do you need a data engineer or a data analyst? Is your data stored in a legacy system that is compatible with modern data cleansing tools? These questions will help you understand how much time, effort, and money will go into initiating a data cleansing initiative and what kind of tool you’ll require to get the job done.
  4. Setting a communications standard: You might think there’s no connectivity between communication and data but you’d be surprised at how many conflicts occur at a workplace simply because a business user and IT user are not able to communicate effectively. Having all employees play a role in better data handling leads to better coordination. Any time an employee sees a discrepancy in data, they should be able to communicate that matter easily with the acknowledgment that it will be resolved by concerned parties.
  5. Implementing processes: Data quality isn’t a one-person job or a one-team responsibility. For instance, everyone must be responsible for quality control of incoming data and for ensuring duplicate entries and records are avoided. More importantly, a clear logical design of data pipelines at the enterprise level must be created and shared across the organization to prevent duplicates.

Related: What Is A Master Data Management Architecture Framework?

  • Choosing a Data Cleansing Service 

Your choice of a data cleansing service depends on several factors:

  1. Ease of use for non-tech users: Anyone in your organization should be able to use a data cleansing tool without the need for a steep learning curve.
  2. Fast and accurate data match: The tool should be able to quickly & accurately match multiple sources of data to weed out duplicates & hard-to-detect errors.
  3. Maintenance & automation: Data cleansing is an ongoing process, which means the tool of choice must be able to help you automate all future cleansing activities easily.
  4. Advance Data Quality Rules: During the data cleaning process, data validation rules help with maintaining and ensuring data integrity. The tool must allow you to easily create and integrate these rules into your data quality workflow.
  5. Connectivity with Multiple Sources: A data cleansing tool must have support (called data connectors) for commonly-used data sources like XML, JSON, EDI and BI tools like Tableau & PowerBI, as well as CRMs and other platforms.

The purpose of choosing a data cleansing service is to make the job easier, so if the tool is complicated and requires a steep learning curve, you’ll likely face more problems.


Implementing a solution is the first step, but should not be the last. You’ll need a regular monitoring schedule to ensure the data remains complete, valid, relevant, and accurate at all times.

Some of the key things to monitor include:

  • Levels & frequency of errors
  • Scrubbing of duplicate data
  • Verification of accurate data
  • Merging and purging of data 
  • Outliers & false positives 

Data cleansing is not a one-time process. It’s an ongoing task that requires consistency.


Companies make a big mistake when they limit data quality to the IT department where IT users are held accountable for dirty data. In today’s complex work environments, where business users are data stakeholders, data literacy must be accessible to everyone.

Moreover, business users have to be as integrated and involved in the data quality initiative as other tech users. If not, the business side will always be struggling with data while the tech side will always have to do the cleanup – resulting in unnecessary conflicts and a decrease in productivity.

Equally important, communicate with your team and create processes that make it easier for people to mutually work towards ensuring data is accurate, valid, complete, and reliable for actionable insights.


Do not ignore your data quality problems. You’re in a time where user-friendly, no-code solutions exist to help you do data cleansing faster, better, and with accurate results. All you need is strategic planning.

Test the waters with a free trial of our data cleansing service & see how we can help you take control of your data quality problems.

Written by Farah Kim

Farah Kim is a human-centric product marketer and specializes in simplifying complex information into actionable insights for the WinPure audience. She holds a BS degree in Computer Science, followed by two post-grad degrees specializing in Linguistics and Media Communications. She works with the WinPure team to create awareness on a no-code solution for solving complex tasks like data matching, entity resolution and Master Data Management.

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