--- title: "Web Crawling Is So 2019" slug: web-crawling-is-so-2019 date: 2021-11-24T13:53:43+00:00 modified: 2025-08-26T10:36:24+00:00 permalink: https://brightdata.com/blog/leadership/web-crawling-is-so-2019 type: blog --- [ Blog ](https://brightdata.com/blog "Blog") / [Leadership](https://brightdata.com/blog/leadership) [Leadership](https://brightdata.com/blog/leadership) # Web Crawling Is So 2019 Datasets are delivering ready-to-use snapshots of entire websites, or smart subsets in a matter of minutes: lenders are receiving alternative loan applicants’ data, Venture Capitalists are being served startup accelerator info, while other companies are having social media influencers’ engagement scores fed directly to algorithms 5 min read [ ](https://brightdata.com/blog/authors/aviv-tal) [Aviv Tal Director of Data Partnerships ](https://brightdata.com/blog/authors/aviv-tal) ![Web Crawling Is So Last Decade](https://media.brightdata.com/2021/11/Web-Crawling-Is-So-Last-Decade.svg) In this article we will discuss: - Pre-collected Datasets are more effective and create more value than web crawling - How Datasets are being leveraged across different industries: - Social media Datasets ## **Pre-collected Datasets are more effective and create more value than web crawling** Since Bright Data’s introduction of ready-to-use [*Datasets*](/products/datasets), many companies are moving away from in-house [web crawling](/products/web-scraper) to having a snapshot of entire sites, or smart subsets that are tailored to their data needs, delivered directly to teams. This option is helping businesses become more efficient in terms of their: - **Agility** – *Datasets* enable high levels of workflow, and budgetary flexibility as you have no ‘ongoing commitment’ to your data collection operations. This means that you can custom order a Dataset for a specific project one month, then take a break, and order another for a Proof of Concept (PoC) later down the line. Access to data takes on a supportive role instead of constraining you. - **Resources** – *Datasets* do not require maintenance/upkeep, or any in-house hardware/software, nor do they require maintaining teams of IT, engineering, and DevOps personnel. - **Time** – *Datasets* can shorten the time span between ‘ideation stages’ and the roll out of a new product, feature or capability. This is because there is no collection time, meaning the data your algorithms need can be delivered in a matter of minutes. Additionally, *datasets* are regularly refreshed ensuring that you are relying on information that is up-to-date. - **Cost-efficiency** – *Datasets* are a more cost-effective option as the cost of scaling, accessing, and upkeeping is spread among multiple corporations. This ‘data sharing model’ reduces the costs for each individual participant. ## **How Datasets are being leveraged across different industries** ### **Business/finance *Datasets*** Industries such as insurance, investment, and lending are all part of very regimented industries that can benefit from *datasets* as a whole, and alternative *datasets* in particular. For example, institutional lenders try to mitigate risk by creating a profile on the company or person requesting a line of credit. Typically they use ‘classic data’ such as: - Credit history/scores - Income to debt ratio But being able to feed algorithms an additional layer of information with which decisions can be made about applicants can open institutions up to new previously overlooked low to mid-risk customers. When evaluating the financial strength of a company, *datasets* such as industry ranking, job posting, employees’ reviews, or the more “traditional” data points such as revenue, company size, and investment rounds can provide relevant insights into a given company’s strengths and credit ratings while widening one’s scope of understanding of a specific corporation. For individuals, lenders can utilize social media profiles in order to gain a better understanding of who the person is and how that might influence a loan’s level of risk (do they skydive? Party every night? etc). Also, they can order a ready-to-use *dataset* pertaining to the average time it takes target audience applicants to fill out online loan applications. [The First Bank of Omaha](https://alt-data.org/finance/the-importance-of-alternative-credit-data-for-institutional-lenders-in-the-wake-of-coronavirus/#:~:text=The%20First%20National%20Bank%20of%20Omaha)’s compliance team, for example, collects this information, taking a closer look at applications with an unusual time lag. This is due to their internal statistics which show that there is a higher probability of these applications fitting one of many fraud profiles. As far as investors are concerned, Venture Capital firms are leveraging *datasets* in order to get in on companies at an early stage. This is due to [a huge rise in investment capital while the pool of startups remains stagnant](https://alt-data.org/finance/data-driven-sourcing-screening-is-creating-meaningful-value-for-vcs-as-competition-becomes-increasingly-cutthroat/#:~:text=A%20lot%20of%20available%20investment%20capital%20vs.%20a%20stagnant%20pool%20of%20startups.%C2%A0). Relevant ready-to-use *datasets* in this context include: - **Scanning entire startup accelerator sites** in search of companies with stats that yell ‘monetization opportunity’ (such as growth in the number of employees over a short period of time, rise in number of job postings, heightened activity in industry forums or a recent successful launch of a product) - **Crawling full app store sites** for applications with high performance, downloads, and star ratings which can all be indicative of a company’s growth/adoption rates among target audiences. ### **Social media *Datasets*** Many companies have business models and digital services that are heavily reliant on social media input. A good example of this are [fitness apps, wearables, and ‘health tracking as a business model’ companies](https://alt-data.org/ecom-retail/fitness-apps-wearables-and-health-tracking-as-a-business-model-companies-are-leveraging-data-to-maintain-and-build-stamina/). In this context, businesses are ordering *pre-collected datasets* such as: - **Top-followed influencers in the health, beauty and sports industry** – This may include entire profiles or just trending posts with high engagement metrics. These can serve as very real indicators of target audience interest, sentiment, and workout routines. For example, there may be multiple posts discussing a desire ‘*to get rid of belly fat*’ which may be indicative of a market need for a new product that targets this issue specifically or shed light on advertising messaging that may work well for existing product lines. - **Secondary wearable or app achievement data** – Many people use fitness apps, and wearables such as smart watches to track their workout sessions. This information is private and cannot be collected but many people choose to share their achievements on social media, which is where this alternative/secondary *dataset* can be picked up on. This information can be extremely important in understanding what type of workout routine people are doing (running? yoga?) as well as the location (in a gym? Or in the park?). This data can inform ad campaigns, product lines, new fitness app features, and a host of other insights which can help your company become a consumer-first market leader. ## **The bottom line** Actively crawling the internet for the *datasets* your company needs in order to make smarter business decisions is ‘*passé*’. It is a resource-heavy, timely, and clunky way to run a business. *Datasets* allow you to focus on your core business, and order the data you need, whenever, and however (parsed JSON, CSV, or Excel) you need it. Contact usStart free trial No credit card required Aviv Tal Director of Data Partnerships Aviv Tal is the Director of Data Partnerships at Bright Data. His background is in the retail, IT, payment, and automotive market segments. He mainly focuses on defining our company’s vision, formulating an agile roadmap, and orchestrating deliverables through internal development, acquisition, and partnerships. [ View all articles ](https://brightdata.com/blog/authors/aviv-tal) Table of Contents [ ](https://news.ycombinator.com/submitlink?t=Web+Crawling+Is+So+2019&u=https://brightdata.com/blog/leadership/web-crawling-is-so-2019) [ ](https://www.linkedin.com/shareArticle?mini=true&title=Web+Crawling+Is+So+2019&url=https://brightdata.com/blog/leadership/web-crawling-is-so-2019) [ ](http://www.reddit.com/submit?title=Web+Crawling+Is+So+2019&url=https://brightdata.com/blog/leadership/web-crawling-is-so-2019) ## You might also be interested in [ ](https://brightdata.com/blog/ai/openhuman-with-bright-data "Production-Ready Web Access in OpenHuman Through the Bright Data CLI") [AI Antonello Zanini Technical Writer ### Production-Ready Web Access in OpenHuman Through the Bright Data CLI Integrate Bright Data CLI with OpenHuman to enable production-ready web access and data collection for AI agents. 09-Sep-2026 12 min read ](https://brightdata.com/blog/ai/openhuman-with-bright-data) [ ](https://brightdata.com/blog/ai/minimax-m3-with-bright-data "Giving self-hosted MiniMax M3 agents live web access with Bright Data") [AI Satyam Tripathi Technical Writer ### Giving self-hosted MiniMax M3 agents live web access with Bright Data Self-hosted MiniMax M3 agents get live web access using Bright Data’s search and scraping tools. 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