# Know if your web-scraped data is good enough to rely on

## **A data quality framework built for scraped data**

Most data quality advice assumes you control the source. Web data does not work that way, the sites change without warning and the collection happens outside your systems. This framework is built for that reality. It scores a dataset across five dimensions you can check on any sample:

### **Completeness**

Are all the records and all the fields actually there, or are rows and columns silently dropping out? Completeness is the first thing to break when a page layout shifts.

### **Accuracy**

Do the values match what is really on the source page, in the right format and units? Accuracy errors are the hardest to spot because the data still looks plausible.

### **Freshness**

How recently was each record collected, and does that match how fast the source changes? Prices and listings that are a week old are often worse than no data at all.

### **Consistency**

Is the schema stable across every run, so field names, types, and structure do not drift and break downstream systems? Consistency is what makes a feed safe to automate on.

### **Deduplication**

Are the same records appearing more than once and inflating your counts? Duplicates skew every aggregate you build, from pricing averages to market sizing.

## **Why data quality breaks silently**

The most damaging quality problems do not throw errors. When a source site quietly changes its structure, a scraper can keep running and keep delivering data that is subtly wrong, missing a field here, mislabeling a value there. The ebook shows how these silent failures happen and what monitoring should catch them before they reach your analysis.

## **Score any dataset or vendor sample**

At the center of the ebook is a scoring framework you can apply to any dataset in front of you. Rate each of the five dimensions, weight them for your use case, and turn a vague sense that the data feels off into a concrete, comparable score. Use it to audit your own pipelines, or to compare vendor samples on the same objective standard before you sign.

## **What good QA looks like from a managed provider**

If you are buying data rather than building it, quality is something you should be able to expect, not hope for. The ebook lays out the QA processes a serious managed provider runs behind the scenes, automated validation, anomaly detection, coverage monitoring, and clear SLAs, so you know what to ask for and what a strong answer sounds like.

## **Why PromptCloud**

PromptCloud delivers reliable, large-scale web data on a recurring basis, without the scraping complexity your team would otherwise own. Quality is built into the delivery: automated validation, anomaly detection, coverage monitoring, and human QA, so the data you receive already clears the standards in this framework. ISO 27001 certified and GDPR compliant.

## **Frequently asked questions**

### What is a data quality framework?

A data quality framework is a structured way to define, measure, and score how good a dataset is, usually across a set of dimensions such as completeness, accuracy, freshness, consistency, and deduplication. Instead of judging data by gut feel, a framework gives you repeatable criteria you can apply to any dataset and compare over time or across vendors.

 

### What are the dimensions of data quality?

The five most useful dimensions for web-scraped data are completeness (all records and fields present), accuracy (values match the source), freshness (data is recent enough for the decision), consistency (stable schema across runs), and deduplication (no repeated records inflating your counts). Together they cover the ways scraped data most often goes wrong.

 

### How do you measure data quality?

You measure data quality by scoring a dataset against each quality dimension using checks you can run on a sample, for example the percentage of populated fields for completeness, spot-checks against the live source for accuracy, and collection timestamps for freshness. This ebook includes a scoring framework that turns those checks into a single comparable score.

 

### How do you check the quality of web-scraped data?

Take a representative sample and test it dimension by dimension: look for missing rows and empty fields, verify a set of values against the source pages, check how recent the records are, confirm the schema matches previous runs, and search for duplicates. The ebook provides a checklist so you can run this audit on your own data or a vendor sample before committing.

 

### What data quality metrics should you track for a recurring feed?

For a recurring feed, track field-level fill rates, accuracy sampled against the source, record freshness, schema-change and error rates, and duplicate rates over time. Watching these metrics run to run is what surfaces silent breakages early, which is exactly why the QA a managed provider runs matters.