How to Improve The Success of Data Extraction
Data analytics, data science, and data engineering are important organizational roles. The growth of these roles indicates the continued relevance of data to business development and sustenance.
Businesses need accurate data to make the right decision. Data is everywhere, so there’s no excuse. However, many businesses need to optimize their data extraction processes. In many cases, businesses are doing data extraction wrong.
Hence, to derive maximum value from data extraction – which is the start of data analytics – it’s important to put some things in place. This article will look at measures that can help improve your data extraction success.
What is Data Extraction?
Data extraction involves collecting data from varying sources. Data collection is the first step in data analytics, and it is the most disorganized step of the process. The data is highly unstructured and most likely in several formats.
After collection, data is forwarded to central storage, which can either be on-site or in the cloud. Some organizations use hybrid databases.
There are numerous data extraction techniques, of which the most popular is web scraping. Web scraping involves collecting information from websites in related industries. The data collected during scraping can be used to make business, marketing, branding, and competition decisions.
Why is Data Extraction Difficult?
Data extraction is a necessary business process. But it’s more challenging than it seems. Things can get quickly frustrating and difficult for the following reasons.
One difficult fact about raw data extraction is the stark lack of structure. And without structure, collected data is nothing but useless. The need for structure adds a couple more complexity to the data processing process. There’s the need to structure data, which consumes human, technological, and processing resources.
Data extraction processes are automated, which means you get data faster than you can process or store them. This leaves a problem to be solved.
One of the easiest ways to extract data is web or SERP scraping. However, website servers are oftentimes behind several defense mechanisms, which make the extraction process difficult. When websites notice unusual traffic from a source, they flag the traffic and ban the source. Some websites also contain pages that human users will not click. However, bots can click them. Such pages exist to detect bot action on the servers. When the bots click, the website bans them. So, in the end, your web scraper may get little or no data from the website.
Not all data is useful. Most of it won’t be useful whether you’re collecting web or social media data. This poses a huge problem as you’d waste resources and time collecting information you’d delete.
Since data comes from social media and websites, verifying the quality before storage is impossible, leaving your facility inefficient.
Diversity of Sources
Data is everywhere. However, as much as getting data from anywhere is good, it doubles as a downside. Diverse data sources complicate storage, processing, and usage. It becomes even more difficult to figure out how to merge the data obtained. Too many sources also mean dedicating diverse manpower and resources to data analytics, which can cost more in the long run, especially if the data obtained isn’t high-quality.
How to Improve Data Extraction Success
Data extraction can be improved by doing the following:
Enhancing common HTTP headers
Web scraping is a major data extraction process that can be improved by common HTTP headers. HTTP headers facilitate the transfer of details in the connection requests and connection responses sent between users and web servers. Improving the common HTTP header improves the quality and quantity of data sent between the two parties. The following HTTP headers will improve web scraping data extraction:
- HTTP header user-agent
- HTTP header accept-language
- HTTP header accept-encoding
- HTTP header accept
- HTTP header referer.
Find more info about HTTP headers and how to optimize them.
As much as having multiple data sources can be inefficient, it’s still your best shot toward eventually arriving at quality data. The more data you provide to your analytics engine, the more accurate the insight it gives at the end of the analysis.
Classifying data according to the similarities is the next step after data extraction. Irrespective of where your data is coming from, grouping them according to relevance and similarity helps your analytics engine work better. This data enrichment process helps you narrow down, supply your data with more context, and put them in more accessible formats.
For instance, if you have large data sets, you can group and turn them into high-quality PDF files, which you’d feed into an automated Optical Character Recognition (OCR) software. The software scans the doc, recognizes them, and converts them into data consumable by machines.
To make your data extraction process easier, you must cater to its subsequent processes. Hence, it’s important to automate every other stage after extraction. Automating subsequent data processing stages eliminates backlogs and sluggishness, thereby accelerating organizational workflow.
Data extraction is an important process needed in every organization of all industries. Not handling data effectively can cause a business to fall behind its competitor, which costs more in the long run. Following the tips above will certainly improve the data extraction success rate.