Data sourcing is widely used among companies operating in almost all industries. When it comes to choosing between acquiring vs. scraping data, it’s important not only to know the resources you have but also current data trends and technologies in data acquisition.
Buying or scraping data: what to choose for your business?
The choice between buying or scraping data depends on your specific project requirements, budget, and technical capabilities. Many times, a combination of these methods can be the best solution for you. You might buy foundational data from reputable sources and supplement it with scraped data to create a customized dataset that meets your needs.
Buying data offers you accuracy and reliability if you choose a trustworthy data provider. You can also be sure that reputable providers comply with data protection laws, so you don’t need to worry about legal issues. These are great advantages, but it’s worth noting that there are also costs and certain limitations.
Scraping data may be a cost-effective solution, especially for a small-scale project. You have full control over data and how you structure it. However, there may be some technical challenges to deal with as well as legal risks and data quality gaps.
The latest trends in data sourcing to follow
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There are certain trends in data sourcing that should be considered when choosing the best method for your company.
Growing adoption of data extraction across industries
According to Google Trends, the search frequency of web scraping grew 3x over the past few years. With the rise of big data and the use of data for well-informed decision-making, data extraction has become a key tool for market research, trend analysis, and competitive advantage.
Hedge funds and asset managers, for instance, utilize web scraping to obtain market knowledge, watch news feeds, and monitor sentiment on social media to power investment insights.
To learn more about the spread of diseases and promote advances in public health, healthcare researchers take data from open-source websites. To better understand consumer behavior and stay competitive, e-commerce companies and marketing agencies also rely on data sourcing. Data extraction is becoming an essential skill in the digital age as its significance for generating corporate value is being recognized.
Higher demand for top-quality data from external sources
The ability of data to facilitate effective decision-making and foster growth is still being underutilized by many businesses.
Organizations must accept external data sources if they want to remain competitive by utilizing comprehensive market knowledge. However, when it comes to using outside data, most businesses are falling behind. According to MIT Sloan, 92% of data analytics experts agree that businesses need to leverage more external data.
There is a vast amount of external data available online, from competitor websites to public data, that can serve as a valuable asset to insights for business. As more and more organizations want to integrate external data into their decision-making process, the demand for top-notch data quality will only continue to grow.
Companies need to make an investment in data extraction and web scraping skills to address the rising demand for external data. Additionally, businesses need to set up strong data validation capabilities to ensure rigorous data quality assurances that adhere to the business's exact requirements.
The use of artificial intelligence and machine learning
Businesses are embracing AI and ML technology as essential tools for gaining a competitive advantage. Businesses are making greater use of the potent AI and ML tools to automate data analyses and gain access to previously unavailable beneficial information.
According to Accenture, unstructured data accounts for a massive 80% of all data generated. Yet this type of raw data has some limitations and doesn’t bring much value to businesses. However, with the advancement of big data technologies, businesses are now able to restructure this raw data and overcome the potential challenges and pitfalls of analyzing unstructured information.
Businesses are using AI and ML more and more to accurately and precisely provide insights from semi-structured and structured training data. Scaling AI and ML across businesses has obvious advantages.
Access to insightful data made possible by using artificial intelligence and machine learning empowers companies to tackle problems more efficiently and cost-effectively, which promotes revenue growth.
Conclusion
To receive better results from your newest projects, you need to use the power of efficient data sourcing. Whether you can buy or scrape data, you should keep in mind the quality and relevance of this gathered data for your business needs.
Knowing current trends in the market may also help you get ahead of your competition and use more advanced AI-powered data scraping tools for faster and more efficient data acquisition.