Data collection refers to the near-instant extraction of data from websites with software. The data extracted from these websites may be in the form of videos, images, or text. Data collection helps remove large volumes of data from multiple sources simultaneously, which may be used on another website. Famous use cases of data scraping include web content research, online price comparison, identifying unique sales ideas, etc.
Let's discuss real-life case uses of data collection. Market research companies rely on data collection processes to gain data from social media forums. Google uses similar techniques to rank its content pages. Other companies use data collection to acquire data for digital marketing.
As the digital economy grows more significant, the importance of data collection grows more profound. However, the volume of digital data grows larger by the minute. Hence, traditional data collection techniques, such as surveys, observations, and existing datasets, which require human effort to function, have become inappropriate.
What are the Benefits of Automating Data Collection?
Traditional data collection methods require human intervention, making them inappropriate for several websites and large data sets. However, automating the data selection process has several benefits. First, this process will save you time and resources. You'll no longer need to manually sift through large datasets, which could lead to data loss. Furthermore, data collection reduces error to the barest minimum. Traditional data collection methods are prone to flaws and inconsistencies, while automated techniques boost accuracy and effectiveness.
When you choose automated data collection, it increases productivity. Your staff will not have to spend plenty of hours combing through data, and they will have more time to focus on other essential aspects of the business. Most businesses also collect data to influence how they treat their customers. This data determines how to improve products and services to suit customer tastes.
When you use automated data collection tools, you can analyse your business in real-time and make swift decisions. It may not look like much, but automated data collection is a way to stand out from other companies. Real-time client data shows customer preferences and lets you stay on top of trends.
What Are Tips to Improve and Automate Data Collection?
Automating data collection is a process that extracts large volumes of data from multiple websites without human intervention. Here are tips to improve and automate data collection:
Create a Data Governance Strategy
A data governance strategy is your company's blueprint on how it plans to store, collect, and use the data collected. By developing a sound data governance strategy, you'll ensure the continuity of data collection procedures in your business. It'll also ensure that your data is regularly updated and organised. Hence, your business can make decisions based on structured data.
Additionally, a data governance strategy helps to avoid data corruption and duplication because it will also contain strategies to deal with errors. This strategy will influence every aspect of your business, creating a consistent data collection approach. To reduce the burden of managing a data governance strategy by yourself, you can delegate roles and responsibilities to other staff. However, ensure they are well trained on data handling and management principles to avoid costly errors.
Let's say a multinational corporation operating in the financial sector decides to implement a governance strategy for its data management practices. To ensure accountability and transparency, they establish clear roles and responsibilities within their data governance framework. The Chief Data Officer (CDO) is assigned as the overall steward of data governance, responsible for defining policies, procedures, and standards.
Data stewards are also appointed within each department or business unit. Their role is to enforce data policies, resolve data-related issues, and ensure compliance with regulatory requirements.
Use Proxies and Rotating User Agents
As more websites become aware of data collection techniques, they've put in place stricter measures to block and restrict third-party access to data. When using data collection software, such as Octoparse, Beautiful Soup, Mozenda, Import .io, Parsehub, Crawlmonster, and ProWebScraper, websites may detect bot activity and deny access. By employing proxies and rotating user agents, you can mask the identity of your software.
Rotating user agents help your scraper mimic the identity of different browsers and devices, while proxies make it difficult for websites to block you. When you use rotating user agents and proxies together, websites can shut you out.
For instance, a travel aggregator website gathers data from airline and hotel booking platforms to provide users with the best deals. To avoid getting blocked or flagged by these platforms, they rotate proxies and user agents during collection, simulating different IP addresses and user behaviours to prevent detection and ensure uninterrupted data collection.
While many proxies promise unrestricted access to the internet, only a few like Live Proxies live up to their name. Live Proxies is a reliable proxy network designed to suit several use cases from eCommerce to market research and travel aggregators. Furthermore, this network is suitable for individual and business use.
Schedule Your Data Collection
Automated data collection involves the extraction of data from several web pages with the use of software tools. These tools allow you to schedule your data collection processes. Most websites regularly update their database. So, it would help if you kept up with the most recent submissions to the platform. By scheduling your data collection, you ensure you have updated data from your target website. Set the data to be extracted at specific intervals. Data collection software, such as Selenium, Beautiful Sound, and Scrapy, allow businesses to integrate schedulers into the process.
When you schedule your data extraction, you don't have to monitor the process. The software handles all the burden and provides updated information at intervals. Scheduling data collection saves time as you don't have to re-initiate the entire process from scratch.
An e-commerce company tracks competitor product launches and pricing changes by regularly gathering data from their websites. By optimising the data collection to match the volatility of product listings and market dynamics, they ensure timely updates without overwhelming the target websites or risking IP blocks, thus maintaining a competitive edge in the market.
Incorporate Error Detection and Auto Retry Systems
When designing data collection processes, keep in mind that errors will occur due to a variety of issues. Server failure, software bugs, network glitches, and changes in website structure may cause errors. These errors can be costly when they occur. That’s why implementing an error detection and auto retry system will help your data extraction tool recognise any mistakes quickly and resume the data extraction process afterward.
By designing automated data collection systems that can monitor errors in real-time, the data and analysis it generates will be accurate. When these systems detect a deviation from the regular workflow, a response is triggered. This response will vary depending on the nature of the error detected.
For instance, if a connection timeout occurs when a market research company while scanning the content of a website for product prices, the system logs the error, adjusts the data collection parameters, such as timeout duration or request frequency, and retries the request after a specified interval. If the retry is successful, the newly collected data is processed and integrated into the research database.
Detect Content Changes
When extracting data from a platform that regularly updates its content, it can be plenty of work to download the same set of data all over again constantly. A practical method to handle data collection in this way is by ensuring your automation detects content changes. So, the software will access the edited pages and download new data. Regardless of the purpose of your data collection, implementing congruent change detection is a much-needed improvement. Instead of having multiple versions of the same data in your business's database, your automation will keep a single version and regularly update it.
News aggregation platforms collect articles from various online sources to provide users with up-to-date news coverage. To ensure the accuracy and reliability of the information presented, they implement content change detection mechanisms in their data collection process. Using sophisticated algorithms, they continuously monitor the gathered data for any changes, such as updates to article text, addition of new images, or modifications to headlines.
When a change is detected, the platform automatically flags the article for review by human moderators to verify the accuracy of the updates. This proactive approach not only ensures that users are provided with the most recent and relevant news but also helps maintain the platform's credibility and trustworthiness in delivering accurate information.
Automated CAPTCHA Solving
Captchas, which stand for ‘Computer Automated Public Turing Test to Tell Computers and Humans Apart,’ are short tests used to determine whether the person accessing a website is a human or a bot. Their job is to prevent automated tools from accessing websites. However, with tools like Anti-Captcha and 2Captcha, it's possible to automate the captcha-solving process. Therefore, your automated data collection can occur without interruptions.
Automated CAPTCHA solving is your best bet to beat these short tests that may deny your data collection software access to specific websites. Optical character recognition is a software capable of recognising and extracting text from images. This tool helps decipher text-based CAPTCHAs. An automation script will capture the Captcha image and process it through an OCR engine before submitting it to the website.
Let's review an example of an online marketplace that relies heavily on data collection to gather product information from competitor websites for price monitoring and market analysis. However, many of these competitor websites employ CAPTCHA challenges to prevent automated data collection.
To overcome this obstacle, the marketplace implements an automated CAPTCHA solving system. They integrate a third-party CAPTCHA solving service into their data collection procedure. This third-party service utilises advanced algorithms and machine learning to automatically solve CAPTCHA challenges in real-time. As a result, the marketplace can seamlessly collect data from competitor websites without manual intervention, significantly improving the efficiency and reliability of their data collection process.
Collect Only What You Need
One of the biggest challenges of traditional data collection techniques is the volume of data that it generates. This large volume of data is more challenging to analyse and organise and often wastes resources. So, it would help if you were strategic about what data you extract from a website. Ensure to only focus on what’s essential to your business's goals.
This approach does not mean you shouldn't collect additional website data. However, don't lose track of why you're extracting that data. You can set your automation to focus on specific aspects of the website or webpage.
Let's look at this example of a healthcare organisation conducting a research study to analyse patient outcomes and treatment efficacy for a specific medical condition. Instead of collecting vast amounts of irrelevant data, they meticulously define the variables and parameters necessary for their research objectives.
They focus on gathering essential patient demographics, medical history, treatment regimens, and outcome measures directly related to the study's goals. By collecting only what they need, the organisation reduces data clutter, minimises the risk of data breaches or misuse, and streamlines the analysis process.
Focus on Efficient Data Storage
After extracting data from multiple websites, the next thing to do is organise and store it. Automation is crucial to how extracted data is stored. You can set your automation software to store data in databases, JSON, or CSV formats. Proper storage of extracted data reduces the occurrence of data corruption or loss.
Now, a common challenge faced by most businesses is how to store a large amount of data, which may include customer transaction records, sensor data, etc. Traditional data storage methods have proven inappropriate for storing this quantity of data. The better alternative is to opt for columnar stores, NoSQL databases, and distributed file systems. These new storage systems are scalable, flexible, and boost performance. Hence, they are an essential aspect of your data automation kit.
If a healthcare provider implements a system to improve their data collection efficiency by focusing on efficient data storage. The provider must find a way to deal with a vast amount of patient information, including medical records, test results, and treatment plans. Instead of storing all data in a single database, they opt for a distributed storage system, utilising cloud-based solutions like Amazon S3 or Google Cloud Storage.
Each type of data is stored in a specific storage solution optimised for its characteristics. For instance, structured data such as patient demographics and medical history are stored in a relational database for easy querying and retrieval. Meanwhile, unstructured data like medical images and doctor's notes are stored in object storage for scalability and cost-effectiveness.
FAQ
How can you collect data more effectively?
To collect data more effectively, you must define your goals and outline why you need data collection. Next, choose an appropriate method to accomplish your goals and use standard processes. Additionally, use technological tools where appropriate and regularly review the process to determine how to improve data collection.
How can I improve my data-gathering skills?
To improve your data-gathering skills, master how to craft survey questions and use data analysis tools. You should also master critical thinking to identify relevant data and pick appropriate data collection methods.
How do you ensure high-quality data collection?
If you want high-quality data collection, plan the entire process meticulously and use only standardised procedures. You may also adopt verification mechanisms and constantly update the data collection process.
What is an effective strategy for data collection?
An effective strategy for data collection will have clearly defined objectives and adopt standard procedures. It also features automated data collection tools to extract the required data from multiple sources without human intervention.
