What Is Web Scraping? Definition, Risks, and Better Alternatives

Web scraping has always been an option many companies and users rely on - usually because there isn’t a budget for verified data from a provider. In 2020-2023 especially, scraping was seen as one of the most efficient ways to extract data from websites. And some websites really do behave like databases: they contain huge amounts of information - detailed product descriptions (e-commerce), stock prices, people’s business profiles, statistics, company revenues, business data, and more. 
 
Web scraping can be done manually (and years ago it was mostly a manual task) but nowadays, in most cases, it’s almost fully automated using scraping tools. Website scraping isn’t an easy process, though - sites have CAPTCHAs or protect themselves from scrapers, and such scraped data often comes very unstructured. So the second big step is being able to process and read the data to get the information you’re searching for.  
 
But let’s first understand what web scraping is. 

What Is Web Scraping 

Web scraping is the process of reading and extracting information from websites. These days, in about 95% of cases, it’s an automated process. How does this work in practice? A scraper sends a request to a web page, receives the response, parses the HTML or rendered Document Object Model (DOM), and extracts specific fields such as names, prices, addresses, or operating hours. The output is stored in a file or database for analysis or integration. 

What’s the opposite of scraping? First example: an API. And scraping is very different from using an API or a licensed dataset. An API is designed for programmatic access under explicit terms and documentation. Licensed datasets are curated, validated, and distributed contractually, with provenance that supports audits - but most importantly, they contain structured, reliable data. Scraping relies on parsing presentation layers that can change without notice and may be restricted by terms of service. 

Web Scraping Market in 2025 

In 2025, the web scraping market is expected to reach about USD 1.03 billion with steady double‑digit growth through 2030, reflecting broad adoption across industries (Mordor Intelligence, June 2025). At the same time, AI and retrieval bots have surged, and publishers report tens of millions of automated requests that bypass blockers each month, highlighting how automated access continues to expand across the open web (TollBit data reported in June 2025). These trends show that automated collection remains a major force in how organizations gather online data, yet the decision to scrape brings legal, technical, and business tradeoffs that leaders should understand before building or buying. 

Further in this article, we explain how web scraping works, where it’s commonly applied, the key risks and limitations, and why many teams choose licensed, registry-based data instead. Two comparison tables summarize the differences and the hidden costs often overlooked. 

How Web Scraping Works 

A common pipeline follows these steps: 

1. Targeting: Identify sources, pages, and fields to extract. Define frequency and monitoring. 
2. Requesting: Send HTTP or HTTPS requests with appropriate headers. Some scrapers mimic browsers to avoid simple blocks. 
3. Rendering: For pages that rely on client‑side JavaScript, run a headless browser to render the DOM before parsing. 
4. Extraction: Use CSS selectors, XPath, or programmatic logic to isolate the target elements. 
5. Normalization: Clean and transform the extracted values to match a schema. Handle units, encodings, duplicates, and nulls. 
6. Storage: Write records to CSV, relational databases, data lakes, or search indexes. 
7. Monitoring: Track response codes, layout changes, error rates, and volume. Alert on anomalies and maintain change logs.

At small scale, a single script can do the job. At production scale, teams add proxy rotation, retry logic, CAPTCHA solving, concurrency controls, and observability. The cost and fragility increase with scale and with the number of target sites. 

Why Organizations Use Web Scraping 

Why do teams scrape? Speed, control, and “it’ll be cheaper.” They want data now - no procurement loops, no vendor paperwork. They want to hand-pick sources and fields, tweak logic on the fly, and spin up a crawler tonight if priorities change tomorrow. And it feels flexible: point the script at ten sites today, twenty next week, add a new attribute, done. 

Representative use cases: 

• Competitive pricing and assortment monitoring in retail and travel 

• Job posting aggregation and labor market trend analysis 

• Lead list building and company research 

• Point of interest data collection for mapping and navigation 

• Content aggregation for research and media monitoring 

These use cases can be valid for exploration. For production systems that require reliability, compliance, and broad coverage, scraping often proves difficult to sustain. 

Risks and Limitations of Web Scraping 

Legal and Compliance 

• Many websites restrict automated access in their terms. Violations can trigger takedown requests or litigation. 

• Collection may include personal data subject to GDPR, CCPA, or other privacy frameworks. Under GDPR, serious violations can bring fines of up to 20 million euros or up to 4 percent of global annual turnover, whichever is higher. 

• Lack of clear provenance and permissions complicates audits and vendor reviews. 

Data Quality 

• Website structures change frequently, which breaks extraction logic and silently reduces completeness. 

• Coverage is inconsistent across geographies and categories. Public pages may omit critical attributes or contain stale entries. 

• Duplicate and conflicting records require ongoing deduplication and validation. 

Technical Fragility 

• Bot detection, IP rate limits, and CAPTCHAs disrupt pipelines. 

• Headless rendering adds compute cost and latency. 

• Proxy networks, rotation, and observability tooling are required to keep pipelines healthy. 

Business Impact 

• Low quality inputs pollute CRMs, analytics, and scoring models. 

• Engineering time shifts from product value to scraper maintenance. 

• Stakeholder trust erodes when downstream errors surface. 

Scraping vs Licensed and Registry‑Based Data 

The table below summarizes differences between ad‑hoc scraping and licensed or registry‑based data delivered under clear contracts and provenance. 

Who Is Affected by Scraping Risk 

Scraping is seldom only a technical concern. The consequences extend across functions: 

• Compliance and legal. Difficulty proving permissions or lineage during audits. 

• Marketing and operations. Targeting inefficiency and CRM hygiene issues. 

• Product and mapping. Gaps in points of interest degrade user experience. 

• Data and analytics. More time spent on cleaning and reconciliation, less on analysis. 

Real‑World Dynamics That Complicate Scraping 

• Growth in automated access. Publishers report large volumes of automated requests each month, including retrieval bots and crawlers. This leads to more aggressive defenses and shifting HTML structures. 

• Market variability. Estimates for the web scraping market vary by methodology. Some analysts place 2024–2025 software revenue around the USD one billion mark, while others forecast multi‑billion ranges over the next decade. The consistent theme is growth, but the underlying assumptions differ. 

• Operational fragility. When a target site rolls out a redesign, fields move or vanish. Pipelines degrade quietly unless monitoring is robust. 

Hidden Costs of Web Scraping and the InfobelPRO Perspective 

Scraping often appears less expensive because there is no vendor invoice. In practice, total cost accumulates across engineering, infrastructure, compliance, and remediation. 

Typical hidden cost categories 

• Engineering maintenance. A significant share of developer time goes to break‑fix and selector updates instead of product value delivery. 

• Data cleaning and QA. High rates of duplicates, missing values, and inconsistent formats drive ongoing normalization work. 

• Infrastructure and proxies. Headless rendering, CAPTCHA solving, storage, and bandwidth add up, especially at enterprise scale. 

• Compliance exposure. Unclear permissions and missing lineage complicate audits and can delay deals. 

• Opportunity cost. Time spent repairing pipelines delays launches and reduces the impact of customer‑facing initiatives. 

Table. Hidden Costs of Web Scraping 

How InfobelPRO approaches the problem InfobelPRO sources and reconciles data from verified registries and trusted providers, attaches lineage metadata, and maintains refresh schedules that are fit for audit. The focus is on coverage, comparability, and compliance rather than page‑level scraping. For buyers, this reduces maintenance burden, shortens legal review, and supports consistent enrichment quality. For a deeper discussion of operational tradeoffs and cost drivers, see our write‑up on the hidden costs of scraping data. 

Sustainable Alternatives to Web Scraping 

• Licensed or registry‑based datasets. Contracted access with transparent provenance, coverage definitions, and refresh schedules. 

• APIs. Structured endpoints with rate limits, documentation, and versioning. Prefer official APIs to reverse‑engineering HTML. 

• Official registries and open data. Use authoritative sources where permissible and pair with enrichment to fill gaps. 

• Data partnerships. Establish data‑sharing agreements with clear rights and responsibilities. 

• Hybrid approaches. Use scraping for limited exploration, then migrate to licensed sources for production. 

Why InfobelPRO Instead of Scraping 

When teams need dependable global company data for marketing, compliance, product, or analytics, we prioritize verifiable sources over page parsing. Our model is built for auditability, disciplined refresh, and apples-to-apples comparability across countries and categories. 

Audit-ready lineage: We source from verified registries and trusted providers. Each update carries provenance so reviewers can trace fields back to their origin. This shortens vendor risk reviews and supports formal audits. 

Coverage and comparability: We define scope by country, region, and category, then reconcile formats into a common schema. This improves match rates and makes cross-market analysis possible without custom fixes. 

Refresh discipline: Updates follow scheduled cycles with versioning. Changes are visible and testable, which reduces silent drift and downstream surprises. 

Quality controls: We apply validation rules for entity resolution, deduplication, and field normalization. The goal is consistent enrichment quality rather than best-effort parsing. 

Compliance by design: Access is governed by contracts and documented rights. This reduces uncertainty around terms of use and privacy obligations. 

Predictable total cost: Licensing clarifies what you pay for coverage and refresh. Teams spend less time on break-fix work and proxy management, and more time on product and go-to-market priorities. 

Integration fit: We deliver in formats that slot into your stack. CRM hygiene, POI enrichment, UBO resolution, and location analytics benefit from standardized attributes and stable identifiers. 

Result: fewer interruptions, faster approvals, and higher confidence in decisions that rely on the data. 

Conclusion 

Web scraping can be useful for exploration, but it is brittle at scale and introduces legal, quality, and operational risk. Leaders who need reliable inputs for marketing, compliance, product, or analytics should favor sources that provide contractual clarity, provenance, and refresh discipline. Licensed and registry‑based data offers a clearer path to accuracy, auditability, and predictable cost. 

By understanding how scraping works and where it breaks, teams can set a higher standard for data quality and reduce surprises downstream. When the goal is dependable decisions, sustainable sourcing wins over short‑term shortcuts.