Data profiling is the process of examining data to understand its structure, content, and quality before an organization uses it. Profiling helps teams find duplicate, incomplete, and inconsistent records across business data. It’s a first key step in cleaning up and improving datasets for use, and can also help identify weaknesses in data-collection processes.
Poor data quality is a cost center. IBM reported in 2025 that 43% of chief operations officers ranked data quality as their top data priority. In ecommerce, these issues can affect product discovery, inventory decisions, and customer trust.
Ahead, you’ll learn what data profiling is, how to conduct it, and what to look for in a data profiling tool.
What is data profiling?
Data profiling is the process of consolidating your existing data, removing errors and inconsistencies, and analyzing it to better understand its structure, content, and quality. Data profiling can also involve enriching the data with additional information, like geographic or demographic data.
Top use cases include:
- Preparing data for reporting and analytics: Profiling identifies the junk data that leads to expensive errors. Over a quarter of organizations estimate they lose more than $5 million annually due to poor data quality.
- Improving customer, order, and product data quality: Profiling also fixes duplicate records and invalid contact information. Email list data, for instance, decays at 23% annually, according to a 2026 ZeroBounce analysis.
- Reducing risk before migrations and AI projects: Analyzing data before migrations prevents system failures caused by low-quality data. Gartner predicts that organizations will abandon 60% of AI projects through 2026 if they aren’t supported by AI-ready data.
Data profiling vs. data mining
Data profiling checks whether data is complete, consistent, and usable. For a retail organization, that includes customer data, product details, order history, and marketing fields.
Data mining looks for patterns inside that data. A business uses it to identify repeat purchase trends, product bundles, or high-value customer segments.
The two processes take place at different stages of your data’s lifecycle. Data profiling comes first because analytics is only as reliable as the data underneath it. Oracle’s Builders Guide cites data profiling as the first step toward improving information quality and supporting better decisions, and TDWI’s 2024 “State of Data Quality Report” claims that organizations rely on trustworthy data to gain strategic and operational advantages.
You can use data profiling to improve the quality of a body of data so that it’s ready for you to analyze, using data mining and other tools.
Why data profiling matters for ecommerce businesses
Data profiling helps organizations identify gaps, duplicates, and inconsistencies in product, customer, order, and inventory data before those flaws cause issues that affect shoppers. Without it, customers will encounter incorrect information, or leave your storefront when they can’t find what they’re looking for. You might have the perfect product at the right price, but with flawed data they may never find it.
On Shopify, product categories and category metafields make products more discoverable on storefronts, in marketplaces, and in search engine results pages (SERPs). Metafield definitions also apply the same validation rules to each value, helping teams keep data consistent as catalogs grow.
Search quality now depends on that structure. Shopify’s "2025 Global Holiday Retail Report" found that 64% of shoppers planned to use AI tools during the season, while 20% planned to use AI for product discovery. When product attributes are missing or inconsistent, it’s harder for shoppers and AI-based search tools to match the right products to the right queries. AI tools in particular may not have the insight to recognize when a result looks wrong.
Data profiling also protects conversion. Baymard’s 2026 cart abandonment data puts average cart abandonment at 70.22%, and Baymard lists extra costs, slow delivery, and missing up-front totals among the top reasons shoppers leave checkout. Price, shipping, tax, and availability data have to stay accurate from product page to cart, or the checkout experience will hurt consumer trust.
Charlie Gower, cofounder of supplement company The Nue Co., sums up the benefits of high-quality data: “If you’re an early stage ecommerce business and you can start to capture data in an innovative way, it’s really going to help you as you build and scale.”
Three types of data profiling
Data profiling comes in several different forms. Which data profiling technique is most effective may depend on your business’s industry, size, and needs.
Here are three common types of data profiling used by ecommerce businesses.
1. Structure discovery
Structure discovery, or structure analysis, evaluates how data is organized and formatted in a dataset at the field level. For example, this type of profiling would ensure that a column for customer phone numbers is formatted for 10-digit numbers.
2. Content discovery
Where structure discovery evaluates what fields are included and how they are formatted, content discovery evaluates the actual data within those fields. Referring back to the previous example, content discovery would check whether each entry in the customer phone number column has the correct number of digits and is formatted per the field’s requirements. It would also check for null values, or missing phone numbers. .
3. Relationship discovery
Organizations use relationship discovery to identify connections between data elements by analyzing how fields relate to each other. For example, relationship discovery might compare purchase history and customer location to reveal correlations that could be used to guide advertising and stocking decisions.
Data profiling techniques
Data profiling checks whether data is complete, consistent, and usable before teams use it for reporting, automation, or decision-making.
Most profiling work falls into three categories: column profiling, cross-column profiling, and cross-table profiling. Each technique answers a different question about data quality.
Column profiling
Column profiling examines one field at a time. It measures factors such as completeness, uniqueness, format, and value distribution. This is the starting point for most data quality analysis because it shows what exists in a dataset before anyone checks how the fields relate to one another.
Snowflake’s data-profiling documentation lists common outputs to check for, including:
- Null counts
- Distinct values
- Common values
Column profiling can show if a customer ID field contains duplicates, for example. It can also find issues like a revenue field that contains negative values, or when a data field is incorrectly formatted.
Cross-column profiling
Cross-column profiling checks whether fields within the same record agree with each other. A single value might look valid on its own, but related values can still conflict when viewed together.
For example, a dataset might show a start date that comes after an end date, a discount amount greater than the listed price, or a state code that doesn’t match the country field. These issues won’t always appear in column-level checks because each field on its own may still contain valid-looking values.
Cross-table profiling
Cross-table profiling checks whether records stay consistent across related tables. A team can use cross-table profiling to check whether every order links to a valid customer, whether every transaction maps to a product record, or whether lookup tables still match the values used in production data.
How to conduct data profiling
Data profiling starts with a clear business question, then checks whether the records behind that question are complete, consistent, and connected in a way that will allow the business to answer that question..
In Adverity’s 2025 research, CMOs said 45% of the data their teams use is incomplete, inaccurate, or outdated. The following steps will help you conduct data profiling to produce more accurate data.
- Define the business question and scope.
- Audit structure, completeness, and anomalies.
- Use tools to discover beneficial relationships in the data.
- Prioritize fixes and document findings.
1. Define the business question and scope
Start with the question or issue for which your business needs the data. If the goal is to reduce stockouts, you’ll want to profile product, variant, inventory, and location data. If the goal is to understand repeat purchases, profile customer, order, and line item data across a defined time range.
The time range is important because platforms like Shopify separate records by date across related objects. The Shopify Admin API order query connects customer, product, payment, and fulfillment data. Only the last 60 days of orders are accessible by default through the Admin API unless broader access is approved, so any profiling project that uses API data needs to confirm the date range before the audit starts.
2. Audit structure, completeness, and anomalies
Once the scope is set, audit the data first. Check whether fields use a single format for each field including dates, product types, vendor names, and SKUs. Then measure completeness by looking for blank values, nulls, placeholders, and partially filled records.
Look for anomalies that can distort reporting or create a bad shopper experience. In Shopify, that could mean duplicate SKUs, missing variant attributes, negative inventory counts, impossible prices, or mismatched availability across sales channels.
Product content deserves extra attention in this step. Salsify’s 2025 consumer research found that 54% of shoppers had abandoned a sale due to inconsistent product content across channels, and 71% had returned a product because it did not match the online listing.
3. Use tools to discover beneficial relationships in the data
After reviewing fields one by one, test whether related records still line up correctly.
Each line item in Shopify is associated with a product variant, and inventory items connect product variants to inventory levels at specific locations. A profiling pass will confirm whether those joins still resolve and that the values stay within the organization’s rules.
Validate that every line item maps to a valid variant, whether every tracked variant maps to an inventory record, and whether location-level quantities match the business’s inventory logic.
4. Prioritize fixes and document findings
Start with the findings tied to inventory accuracy, product content, and reporting.
Shopify’s InventoryItem object, for example, tracks SKU, whether quantities are tracked, and inventory levels across locations, and it includes a duplicateSkuCount field. This allows you to address duplicate SKUs and broken variant-to-inventory links when analyzing catalog or stock data.
Document each finding after the review. Record each failed field, table, or relationship, and which rule is violated. Also note the owner of the data, and track the status of the fix so the records can be checked again after cleanup.
How Shopify data can support profiling
Shopify data can support profiling when a business combines Google Analytics 4 (GA4) ecommerce events with Shopify customer and order data. GA4 records shopping behavior, and Shopify’s GraphQL Admin API exposes products, customers, orders, and inventory.
Together, analysts can build profiles based on product interest, purchase history, and total spend.
Google Analytics 4 and ecommerce event data
GA4 measures actions such as viewing item lists or product details, adding or removing items from cart, starting checkout, making purchases or refunds, and applying promotions.
Ecommerce events aren’t sent automatically because they need extra context, though some events are tracked through the Shopify Pixel on Shopify sites. If a Shopify store is connected to Google Analytics, some ecommerce events are collected automatically, including add_to_cart, begin_checkout, and purchase.
Shopify Admin API and ShopifyQL for custom analysis
The GraphQL Admin API can be used to give apps access to products, customers, orders, inventory, and more.
Shopify’s customers query returns information such as name, email, location, and purchase history, and you can use it to segment audiences, personalize marketing campaigns, or analyze customer behavior with filters for location, order history, marketing preferences, and tags. The customer object also tracks total spending and includes contact details, purchase history, and marketing preferences.
ShopifyQL includes built-in support for time-series analysis, date range comparisons, charts, tables, and customer segments..
Data profiling tools for ecommerce
If you’re in the market for data-profiling tools, evaluate options against a standard set of key criteria. .Focus on the capabilities that affect data quality, reporting, and day-to-day usability:
- Profiling depth: Check whether the tool identifies missing values, duplicates, outliers, format issues, and inconsistent fields across large datasets.
- Rule validation: Look for support for data quality rules that test accuracy, completeness, consistency, and conformity.
- Integration options: Confirm that the tool connects to the systems your business already uses, such as ecommerce platforms, customer relationship management (CRM) software, enterprise resource planning tools (ERP), inventory management systems (IMS), cloud warehouses, and spreadsheets.
- Export and reporting: Review how the platform shares findings through dashboards, downloadable reports, scorecards, or visual summaries.
- Scale: Measure how well the tool handles larger datasets, more users, and more connected sources as operations grow.
- Governance controls: Check for permissions, audit trails, and traceability so the right teams can track and analyze data.
- Pricing transparency: Compare whether pricing is clear, how charges scale, and which features require higher-tier plans.
Here are a few options for tools to get you started.
Informatica
Informatica offers cloud data profiling that can analyze schemas, assess data quality across sources, and evaluate completeness, conformity, and consistency. It also offers an Address Verification tool for validating postal addresses in real time.
Informatica offers a free 30-day Cloud Data Quality trial. Current official pricing is consumption-based and quote-driven.
IBM InfoSphere Information Analyzer
InfoSphere Information Analyzer helps evaluate data for consistency and quality, offers 80 configurable reports for visualizing analysis and trends, and includes a browser-based interface as an alternative to software for many common functions. Pricing is not publicly available.
Clear Analytics
Clear Analytics offers an affordable, Excel-based data-analysis tool for smaller or less technically complex ecommerce businesses. The software includes a complete audit trail that tracks where data came from, when it was imported, and who handled it. It also integrates with Microsoft Power BI to create interactive data visualizations like graphs and charts. It starts at $29 per month.
Data profiling FAQ
How does data profiling make big data easier?
Modern data profiling uses automation to organize, analyze, and provide valuable insights into large and complex datasets, saving time and money.
What are some common data profiling techniques?
Data profiling techniques include column profiling (quality and completeness of data in each field), cross-column profiling (analysis of agreement between fields within each record), and cross-table profiling (analyzing for consistency across related tables).
What are some challenges of data profiling?
Automated software can assist with cleaning data, but manual intervention may still be necessary. Data privacy is paramount and requires safeguards such as encryption and server backups to protect customer information.
What’s the difference between data profiling and data mining?
Data profiling checks the structure, content, and quality of a dataset. Data mining looks for patterns, relationships, and predictions inside large datasets.
When should a business run data profiling?
Run data profiling before a migration, integration, reporting project, or rule-building effort, and when a business adds a new data source. Profiling is a key step in any data project because it reveals strengths and weaknesses in the data.
What problems can data profiling uncover?
Data profiling can uncover missing fields, incomplete records, inconsistencies, anomalies, redundancies, and data that does not conform to expected formats or rules. It helps teams assess completeness and consistency across data sources.
