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About two-thirds of Fortune 1000 firms are undertaking data migration projects at any given time. They do it for better analytics, customer service, competitive advantage, and increased efficiency. But the biggest reason for taking on this complex project – continued business success. However, most projects fail to preserve data quality before data migration.

80% of these data migration initiatives fail because of data quality, budgetary reasons, or both. According to Oracle, poor data quality remains the biggest hindrance to migration efforts.

A very complex, high-risk project like data migration requires a foolproof plan. One that involves fixing data quality as the primary challenge.

We’re going to look at how data migration is intrinsically tied to data quality. We’ll also look at how no-code solutions like WinPure can help you take your data quality up a notch, which can consequently increase the viability and reliability of your data migration plan.

How is data quality tied to data migration?

Companies collect a lot of data – approximately 328.77 million terabytes are created every day. 

As your business continues to grow, and so does your data. As more data is collected, it becomes complex and voluminous to manage in the absence of data quality processes.

A common mistake that most organizations make is moving all data from one system to another without resolving incorrect, incomplete data issues in their current data structure.

What does this mean for your business? It means:

❌ Poor Decision-Making: Inaccurate or incomplete data can lead to flawed analysis and decision-making, resulting in suboptimal strategic choices and missed opportunities for growth or efficiency improvements.

❌ Customer Dissatisfaction: Incorrect or incomplete customer data can lead to errors in communication, such as sending promotional offers to the wrong addresses or contacting customers with outdated information, leading to dissatisfaction and potential loss of business.

❌ Compliance Risks: Inaccurate data can lead to non-compliance with regulatory requirements or industry standards, exposing the organization to legal and financial risks such as fines, penalties, and reputational damage.

❌ Wasted Resources: Migrating incorrect or incomplete data consumes resources in terms of time, effort, and costs associated with data cleansing, validation, and remediation, diverting resources away from more productive activities.

❌ Erosion of Trust: Inaccurate or incomplete data erodes trust in the organization’s systems, processes, and decision-making, undermining confidence among stakeholders such as customers, partners, and investors, and potentially damaging the organization’s reputation in the market.

Preserve data quality before data migration with no-code tools

Data migration plans run the risk of not accomplishing their objectives unless you carry out a data quality assessment.

Data quality assessments can be done either manually or automatically with special software. A no-code data quality tool like WinPure can help companies, especially small to medium businesses, save a ton in costs and technical know-how with this process. It makes the lives of data analysts easy.

Here are some of the main reasons how a no-code data quality tool can ensure that your data is one step closer to data migration:

✅ Completeness

Do you have missing data entries or fields pulled across multiple data sources? Perhaps the sales department has the phone number of one James Martin but the email marketing department does not. No problem. 

Here’s how WinPure can help:

❇️ Data profiling: The tool can scan through your data to identify missing values or incomplete records. This task can take hours if done manually by data analysts, which is why a no-code tool is recommended.

❇️ Summary statistics: Data quality tools can provide summary statistics such as counts and percentages to indicate the completeness of different data fields or attributes. This makes it very easy to check if all the required information is cleaned and matched.

❇️ Data validation rules: Users can define rules to check for completeness, ensuring that all necessary fields are populated. A handy feature that can aid your data migration efforts down the line.


It is important that the quality of your source data and your target data results in accurate entries, especially as you embark on data migration later on and don’t want to be caught fixing issues in transit. 

Here’s how data quality tools can cut down on burgeoning data migration processes:

❇️ Data cleansing: The tool can automatically identify and correct inaccuracies such as typos, misspellings, or inconsistent formats.

❇️ Data matching: It can compare data across different sources to identify discrepancies or inconsistencies.

❇️ Outlier detection: Advanced algorithms can detect outliers or anomalies that may indicate errors in the data.

✅ Consistency

Suppose you’re thinking of migrating your data to a newer enterprise resource planning (ERP) platform. You run the risk of dealing with inconsistent data without fixing your data beforehand.

Here are a few of those problems that can be resolved by data quality tools:

❇️ Data Structure Discrepancies
Differences in database schemas and structures between the source and target ERP systems can lead to inconsistencies in data mapping and transformation. Fields may have different names, data types, or constraints, requiring careful mapping and conversion to ensure data integrity.

❇️ Data Format Inconsistencies
Variances in data formats, such as date formats, currency representations, or units of measurement, can result in data inconsistencies during migration.For example, one ERP system may store dates in YYYY-MM-DD format while another uses DD/MM/YYYY format, leading to conversion challenges and potential data loss.

❇️ Master Data Management (MDM) Challenges
Inconsistencies in master data, such as customer, product, or vendor information, can occur if there are discrepancies in data entry, maintenance, or governance practices between the two ERP systems. Harmonizing master data across systems may involve resolving conflicts, deduplicating records, and establishing data governance policies to maintain consistency. These things can be taken care of with tools like WinPure.

❇️ Integration and Interoperability Issues
Incompatibilities between the source and target ERP systems, such as differences in APIs, data exchange formats, or integration protocols, can hinder data migration and interoperability.Middleware or integration platforms may be required to facilitate seamless data transfer and synchronization between systems while maintaining consistency. Preserve the integrity of your data with WinPure’s support for the most popular databases.

✅ Usability

If you have an in-house data team, chances are that they need to do a lot of work from scratch. A no-code data quality tool in comparison, comes with a user-friendly interface that allows users to perform data quality assessments and improvements without needing to write code or scripts. 

With just a few clicks, you can take advantage of features to help evaluate and enhance data quality across various dimensions such as completeness, accuracy, consistency, timeliness, relevance, and integrity.

Questions to consider before doing data migration

Before embarking on this high-risk and time-consuming fixture, data managers need to come up with a workable strategy. Their strategy and planning should take into account these data quality issues:

questions data migration infographic

It used to be that these issues could require standalone tools to address them. But thanks to advanced data matching and cleaning no-code solutions like WinPure, you can take care of a lot of legwork without wasting a lot of time.

Read WinPure’s Whitepaper on preserving data quality during data migration 

While it’s true that data collection is an ongoing process, there have been great advances in how data quality and migration initiatives can be achieved. That too at a fraction of the time and money it used to take.

Read WinPure’s white paper to find out:

  • How AI can help big data analytics by combining information about an individual from different data sources
  • How custom rules can take care of menial data tasks, leaving data talent to do the truly important work
  • How automated matching and deduplication can reduce overall data migration times by 45%
  • To achieve potential savings of 60% in labor and consultancy costs
  •  The global address parsing feature eliminates address-based data entry errors massively

Access whitepaper

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To Conclude – Clean Up Your Data with WinPure Before Data Migration

In 2021, companies spent $8.2 billion migrating their data to newer systems. In 2030, the data migration market size is expected to be $33.58 billion. If you’re thinking of embarking on a data migration project, you’ll need to first plan and execute data quality initiatives. With advancements in data matching, deduplication, and cleansing, a no-code tool like WinPure can help you preserve data quality before data migration.

Did we mention that you can take WinPure for a spin with a 1-week trial period? Try it out today.

Written by Samir Yawar

Samir writes about data quality challenges faced by businesses and how it impacts their day-to-day operations. His end goal - help businesses make sense of their data with WinPure's no-code platform.

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