What is data extraction & how does it work?

What is data extraction & how does it work?

Hanna Kleinings
Content Queen

Despite the availability of highly valuable data, one survey found that organizations ignore up to 43% of accessible data. Worse yet, of the data they do collect, a mere 57% is actually utilized. Why is this a reason for concern?

Without a way to extract all varying data types, including the poorly structured and disorganized, businesses aren't able to leverage the full potential of information and make the right decisions.

In the following article, we’ll discuss what data extraction is and mention the top challenges businesses encounter in the process. We’ll also cover the prevalent types of data extraction software and provide viable alternatives. Let’s start off with defining data extraction.

What is data extraction?

Data extraction refers to the process of procuring data from a given source and moving it to a new context, either on-site, cloud-based, or a hybrid of both. There are various strategies employed to this end, which can be complex and are often performed manually. Unless data is extracted solely for archival purposes, it is generally the first step in the ETL process of Extraction, Transformation, and Loading. This means that after initial retrieval, data nearly always undergoes further processing in order to render it usable for future analysis.

How is data extracted: structured & unstructured data

Virtually all data extraction is performed for one of three reasons:

  • To archive the data for secure long-term storage
  • For use within a new context (during domain changes for example)
  • In order to prepare it for later stage analysis (the most common reason for extraction)

Let’s start off by taking a look at how structured data is commonly derived.

Understanding structured, full, incremental and unstructured data extraction
Understanding structured, full, incremental and unstructured data extraction

Structured data extraction

Structured data refers to data formatted according to standardized models, making it ready for analysis. It can be extracted via a relatively straightforward method known as logical data extraction. Structured data extraction is itself broken down into two subtypes, i.e., full and incremental extraction.

Full extraction

As the name might already suggest, this method refers to a single-trip retrieval of data from a given source. It is extracted without any supplements in the form of additional logical information from the system. This is relatively uncomplicated when performed with the right data extraction tools.

That being said, if it is vital to know which changes to the data are continually being made within the source system, the second extraction method is required.

Incremental extraction

Extracting incrementally is an ongoing and more complex logical process, as it’s not limited to the initial retrieval. Recurring visits to the source system are required in order to monitor for and extract any recent changes the source has made to the data. Determining which changes have occurred while avoiding repeated extraction of the entire data set is where the additional logic is required. This is termed change data capture (CDC) and is the preferred practice.

Now, how different does the process of extracting unstructured information look? Let’s explore below.

Unstructured data extraction

Without a doubt, extracting unstructured data is more complex than in the case of its structured counterpart. No wonder – the types of data that constitute this group are highly varied. Examples of data sources include web pages, emails, text documents, PDFs, scanned text, mainframe reports, or spool files. However, it’s crucial to remember that the information contained within them is no less valuable than that found in structured forms!

The capacity to extract and process unstructured data is equally as important despite the process’s challenging nature. To render the data ready for analysis, further work is required, and it goes beyond mere extraction. Examples of this are removing whitespace, symbols, and duplicate results, or filling in missing values.

What is the difference between data extraction and data mining?

Before moving on, it’s important to clear up the differences between the often confused terms data extraction and data mining.

As previously described, data extraction, also referred to as web scraping, is the act of taking data from one source and transferring it to another. Data mining, also termed knowledge discovery in databases (KDD), knowledge extraction, or information harvesting, is a fundamentally different process.

The terminology used for the two separate processes already points to their differences. While extraction is the movement of data, mining entails qualitative analysis. Through mining, stored data is methodically surveyed to find otherwise overseen insights, patterns, relationships, and even fraudulent activity.

Another difference is that for data to be effectively mined, it first needs to be structured and cleaned up. Extraction on the other hand can be done with data in all forms. The more labor-intensive nature of mining requires mathematical methodology and comes with a higher price tag. Data extraction software, in comparison, is based on programming language and can be simple and cheap but less insightful.

Common business challenges with Data Extraction

Reducing manual work with traditional data extraction tools & methods

With most data processing being performed manually, it requires regular human oversight and know-how. Even greater challenges come when, for example, companies must deal with varying types of invoices. Their suppliers frequently have differing layouts, formats, and field naming conventions or text. Layouts can appear similar, but if the text is not identical as well, a streamlined extraction process will remain elusive. Also, data can even look structured at a superficial level, veiling its true unstructured format. Navigating it can pose a significant drain on resources and can be unattainable for teams without a strong technical background. For a more efficient and reliable alternative to data extraction, it’s worth considering AI-driven tools like Levity.

Connecting data from various sources

Another common challenge companies face might be gaining a comprehensive view of the hundreds (or, in some cases, thousands) of customers on an individual level. Let’s take calculating churn risk, for one. In order to accurately identify it for each client, we need access to a wide variety of per-account information. Compiling all of it into a single functioning digital environment can be a momentous task, especially when dealing with numerous types of files. For instance, there can be customer satisfaction survey results in plain text mode, documents in non-readable PDFs, or photos of text files in a JPG/PNG. All of which could be holding critical insights.

Levity analyzes customer feedback using AI
Analyze customer feedback using Levity's no-code AI

If you choose AI-powered software instead, it will eliminate the need for code writing skills in your team. Not to mention skipping all the costs you’d have to dedicate to developing your own ETL solution.

With the appropriate technology, data analytics is no longer an ivory tower activity. Setting clear extraction paths can be undertaken by a wider selection of company staff, which increases their productivity and authority.

Ensuring data security

Lastly, while dealing with data extraction, data with sensitive information is a matter to be taken seriously. Sensitive data requires either encryption or removal prior to extraction. This is another process that exceeds the capability of traditional data extraction software. And companies that do not ensure data security do, in fact, ensure their own failure.

Let’s now take a look at some examples of data extraction software.

Data Extraction tools: a short guide

Below are viable options for tools whose functionalities exceed that of mere extraction. They can be categorized according to whether they are:

  • batch processing,
  • open source,
  • cloud-based.

Batch processing organizes data in groups during off-hours in order to avoid the consequences of excessive daytime computing. Open source tools, respectively, are great for organizations with limited budgets, but require the appropriate know-how and infrastructure. Cloud-based are the newer tools, utilizing off-site storage and real-time extraction.

Scrapestorm

Scrapestorm is just one data extraction tool that you can consider. It is an AI-powered software for web scraping or data extraction. It features simple and intuitive visual operation and compatibility with Windows, Mac, or Linux operating systems. This tool automatically recognizes entities such as emails, numbers, lists, forms, links, images, and prices. Its flowchart mode affords users with high ease-of-use when producing complex scraping rules.

It can utilize various types of export strategies when transferring extracted data to Excel, CSV, TXT, HTML, MySQL, MongoDB, SQL Server, PostgreSQL, Google Sheets, or WordPress.

Scrapestorm for AI powered web scraping - product screenshot
Source: Scrapestorm

Altair Monarch

Monarch is desktop-based and self-service, requiring no coding. It can connect to multiple data sources such as structured and unstructured data, as well as cloud-based and big data. By utilizing more than 80 built-in data preparation functions it connects to, cleanses, and processes data with high speed and no errors. Less time is wasted making data readable in order to focus on higher-level knowledge generation instead.

Altair Monarch screenshot - data processing tool
Source: Altair

klippa

klippa offers cloud-based processing of invoices, receipts, contracts, and passports. They boast a conversion speed of between 1 and 5 seconds for most documents. The data manipulation and classification can be done online 24/7, is capable of processing PDF, JPG, PNG amongst other formats, and can convert into JSON, PDF/A, XLSX, CSV, and XML. The software also manages invoices, processes payments, manages expenses, and performs custom branding and file sharing.

klippa product screenshot - cloud based document processing tool
Source: klippa

NodeXL

NodeXL Basic is a free, open-source add-on extension for Microsoft Excel 2007, 2010, 2013, and 2016. It specializes in social network analyses and because the software is an add-on, does not perform data integration.

NodeXL Pro offers the extra features of advanced network metrics, text and sentiment analysis, and powerful report generation.

NodeXL Product screenshot - open source excel extraction tool
Source: smrfoundation

How to use data extraction on qualitative data?

The specific nature of qualitative data extraction can pose numerous obstacles to procuring and utilizing accurate samples. Either a minimum of proficiency in coding and data manipulation or personnel who are capable of performing the work at scale is required. Manual tagging of data is a possible customized answer, but this can be a labor-intensive method with lower chances for accuracy when working without large enough datasets. Smaller and medium-sized companies may have a harder time accessing and extracting these adequate amounts of data needed to provide accurate analyses. Why is data extraction so hard with small datasets?

Analyzing data from small data sets can result in:

  • overfitting,
  • outliers,
  • high dimensionality.

Overfitting refers to the difficulty in deriving reliable trends from small data batches. In mathematical terms, it means that any predictions or assumptions gleaned from the data will have high variance and skewed interpretation. Outliers are data lying too far outside the average, thwarting any of the already slim chances of detecting useful patterns. High dimensionality is an issue of ratios between feature volume and dimensionality of feature representation, ultimately causing reductions in statistical significance.

Needless to say, extracting small sets of qualitative data without the proper data extraction software for subsequent classification and processing can result in wholly inaccurate interpretations.

In a few words...

Effective data analytics is an indispensable aspect of business intelligence optimization. To this end, automated data processing is ideal, but only when a given company has a clearly defined path to the desired outcome. Mere data extraction is only an initial step along this path and can be insufficient as a stand-alone solution (unless performing strict transfers for archival purposes). In order to competently and effectively utilize the data retrieved, extraction must be coupled with data classification, modification, or more sophisticated analysis.

Fortunately, this can be achieved by choosing the right AI software, with fewer resources required of a company to successfully solve their problems. These technologies do not stop at extraction, such as OCR devices, but are moving in the direction of the burgeoning field of AI and ML.

If you’re looking for a solution that will help you in proper data classification (especially after your scraping 😉) and will relieve you of repetitive, mundane tasks, Levity might be a fitting choice for your business.

Now that you're here

Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.

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