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Product Data Cleansing

Thoroughly cleansing data ensures high-quality product data when syndicating your product information. Clean data indicates accurate and complete data.

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Importance of product data cleansing

Harness the Power of Product Information Today with Data Cleansing

Clean product data is essential for effective PIM. A “golden” data record must consist of accurate points; otherwise, it lacks value and may harm.
Product data cleansing is crucial in PIM projects to ensure clean data that adds value to product information.
Start with Data emphasizes the importance of cleansing, and we have dedicated professionals to deliver it.
Cracking the code of product data cleansing

Empower Your Business with Accurate & Valuable Data

Data cleansing is detecting and fixing incorrect or corrupted data points in a dataset by replacing, modifying, merging or deleting them.
The focus of product data cleansing is to eliminate irrelevant, useless, and inaccurate values such as:
Duplicate values
Non-compliant or inappropriate data formats
Missing or inaccurate values that need to be populated or reconciled.
Demystifying the magic of data cleansing

Polishing Your Product Data to Perfection: Enhanced Customer Experience & Sales Growth

Measure your product information's accuracy, completeness, conformity, and uniqueness, including de-duplication, using rule-based classification, matching, linking, and normalization to ensure content quality. Gain an overview of the product data's status with KPI reporting and flag any errors for correction.
Three benefits of cleaning product data are:
Speed and ease
Easy Data Feed Management if standardized, complete, and consistent. Less time for channel-specific tailoring. Easy search and filter for analysis, insight extraction, and adjustment.
Visibility, click-through, and conversion
Having a product feed that includes clean and accurate data makes it easier for buyers to find products through searches and make informed purchasing decisions.
Customer satisfaction
Accurate and enriched product information leads to higher satisfaction among individual consumers and B2B buyers, as they rely on it to make decisions.

US government figures indicate that the economy loses a minimum of $3 trillion annually due to the mismanagement and utilization of insufficient or unclean data, highlighting the significance of clean data.

In terms of internal organizational efficiency:

When CX issues arise, identifying and resolving errors is more manageable when the product data is well-maintained and clean. Businesses can develop more accurate strategic plans, and managing multiple channel needs becomes faster and more feasible.

Cleansing data silos and lakes eliminate errors.

Product data cleansing challenges

Lost Sales No More: The Power of Data Cleansing to Boost eCommerce Success

Product data changes frequently based on its attributes and metadata. Provenance alone can’t ensure data cleanliness, especially when sourced from numerous suppliers. Neglecting legacy data in PIM/MDM projects leads to postponed dirty data problems.

Product data cleansing challenges

Lost Sales No More: The Power of Data Cleansing to Boost eCommerce Success


These can arise through various means, such as data migration, data exchange via integrations, third-party connectors, manual entries, and batch product data imports. Failure to deduplicate may result in:

-Poor workflow and data retrieval efficiency
-Substandard software adoption due to inaccessible data
-Reduced ROI in CRM and marketing automation


Outdated data sets may be years old, irrelevant, incorrect, and in non-compatible formats, making them useless. Yet, why do such data persist?

-Important stakeholders enter or leave
-rebranding and acquisitions
-Legacy systems evolving from earlier versions constantly can render data incompatible.

Today's digital ecosystem undergoes constant changes, so product information must be updated and current before being used for insights, syndication, or decision-making.


Data security and privacy laws are evolving due to technological progress, digital shopping dominance and geopolitical shifts. In a consumer-focused business environment, insecure data is seen as the most damaging form of dirty data regarding the company's potential financial and reputational costs.


Data is considered incomplete if it lacks key fields necessary for processing before sales and marketing action. Missing key feature fields in new or existing product data can't be included in catalogues or eCommerce sites, leading to missed revenue opportunities.


The main issue with incorrect data is incorrect storage (e.g. a text field holding a numerical value). In contrast, inaccurate data is a field filled with wrong information (e.g. incorrect product dimensions).

Incorrect data can lead to inaccurate targeting and segmentation, non-personalized messaging, and storage/display problems.


A common challenge in PIM implementation projects is finding multiple versions of the same data elements across different databases, leading to inconsistent, non-standardized data.


Some companies simply dislike data culling, which leads to

-Slower data exchange
-Inflated record counts
-Non-compliance with storage limits.

Maintaining "lean" databases is essential for achieving complete product data hygiene and organizational agility.

Data cleansing methods

Techniques that Improve Decision-Making and Gain Valuable Insights

Data cleansing methodology addresses the challenges of cleaning product data, which has unique attributes, no set syntax, and limited standards across selling and content-providing organizations.

To clean product data effectively, with its multitude of variables, methods often utilize the following approaches:

Data cleaning tools utilize algorithms, rules, and lookup tables to analyze data and detect errors. Rules-based validation adds an extra layer of data quality control that can be reused for future raw data inputs.
Various solution providers on cloud platforms offer data cleansing services, including those specializing in PIM implementation.
The rapidly growing trend toward data-driven commerce means that organizations will need to use Data Governance frameworks to unite product data management and streamline challenges. So, data cleansing will be integrated into an overarching business strategy for managing product data quality, lineage, and cataloging tasks.
Data cleansing strategy - success factors

Unleash Your Business's Full Potential & Achieve Your Goals

The necessary business reasons for data cleansing are:

Identifying and removing significant inconsistencies and errors when working with a single product data source, as well as combining multiple sources to improve and contextualize data for export.
Utilizing tools that minimize manual inspection and programming, streamlining the entire process.
Deploying the cleansing project with a well-defined and strong data governance framework, ensuring the whole organization understands and follows the protocols and standards for cleansing data.
Data cleansing best practices

Ensuring Accuracy & Consistency in Your Data

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Develop a plan for Product Data Quality
First, establish standards and expectations for product data quality to determine the data quality KPIs:
What causes dirty data?
How can the root issues be addressed?
How do you monitor the health of your data?
Which KPIs and metrics are used to measure cleanliness?
How to sustain high levels of data hygiene continuously?
Standardize at the point of entry

Verifying incoming data at the point of entry to standardize it before entering the database simplifies duplicate detection. Implementing a standard operating procedure ensures that only quality data is accepted into PIM, CRM, ERP, etc., at the point of entry.

Validate accuracy
Tools like list imports are available to clean data and provide real-time validation of the accuracy of product data.

Begin data profiling early and regularly once the scope of the product data management solution's business case has been defined. Establish a benchmark of the initial quality level before cleansing to objectively show the impact of poor-quality data on business value and justify continued funding.

Is Data Mining the same as Data Cleansing?

Data mining is a part of data cleansing but not a substitute. It involves extracting valuable information from data sets for analysis, gathering insights and strategic decision-making.
Data quality mining is a modern approach that applies data mining principles to identify, correct, or eliminate problematic data from large databases. This technique is increasingly used as part of data cleansing initiatives to improve overall data quality.
From cost savings to improved insights:

The advantages of outsourcing data cleansing and management

Outsourcing data cleansing is cost-effective compared to investing in new technology and hiring specialized data professionals. Partnering with a data cleansing service brings additional resources at a low cost and low risk.
At Webelocity, we partner with vendors offering integrated data cleansing in their product information management solutions. Our experts help establish a solid case for cleansing and advise you on the best solution for your specific needs.

Connect with us and discuss how we can ensure your data remains clean and reliable for your present and future needs.