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According to Zippia, for example, average companies analyze only 37-40% of their data. They’re facing two main problems:
- They can’t or don’t know how to effectively perform data-related tasks. According to one survey, the lack of necessary staff, in-house expertise, and collaboration are the biggest barriers to data management excellence.
- They can’t or don’t want to invest ample resources into data-related tasks. Data tasks can require large amounts of resources, including money, time, and expertise.
This article aims to provide insights that will help your enterprise overcome both issues.
- We’ll shed light on what data extraction is, how it works in practice, and how it can help organizations in various industries.
- We’ll then discuss how automating data extraction using specialized tools, especially AI Agents, can help you minimize your investment while ensuring high accuracy.
Let’s start with the basics.
What Is Data Extraction?
Data extraction is the process of extracting useful data from different sources and storing it in a structured format. It’s primarily beneficial to organizations that want to analyze and use collected data more efficiently — or need to do so in order to make core business decisions.
For example, loan companies need to extract applicants’ data from documents in order to approve or deny loans and store clients’ data for later.
It’s important to note that data extraction is not synonymous with data analysis. It is, however, the first step in the data analysis process; further processing can’t be done until data is extracted.
To efficiently extract data, every organization should take two preparatory steps:
- Identify relevant data sources — i.e., understand which systems or documents hold usable data. This can include anything from customer-submitted documents to internal databases, website forms, or PDFs. Most organizations will need to extract data from multiple data sources.
- Define data requirements — i.e., specify the types of data that are valuable to the organization, such as customer names, income amounts, or contract dates.
What Are the Benefits of Data Extraction?
Data extraction helps organizations get more value from their existing information assets, such as documents and databases. It can help them access previously untapped data, make better, data-based decisions, and improve customer and employee experiences.
The exact benefits will, of course, largely depend on how enterprises use data and why they’re extracting it. However, in either case, data extraction can help organizations improve their internal knowledge and obtain better business outcomes.
What Does a Data Extraction Process Look Like?
A typical data extraction process consists of two major steps:
- First comes planning. As mentioned, this involves defining data requirements, scope, sources, etc.
- Then comes the actual data extraction. This task can be done either manually or automatically via a specialized data extraction tool. Organizations with a lot of data usually choose the latter option.
As mentioned, data extraction involves retrieving data from a source. This source can be internal, i.e., native to an organization, or external, such as a website, third-party database, or third-party document.
The data extraction process is completed once you’ve, well, extracted data. However, as we’ve already said, extraction is only the first step in a data analysis process. There are other steps you’ll need to take in order to actually make your data valuable and usable — such as:
- Checking data quality — i.e., validating aspects like completeness, accuracy, and data type constraints. This helps identify data issues, such as inaccuracies, missing information, and incorrect formats, as early as possible.
- Data cleaning — i.e., applying techniques like data deduplication and normalization to remove duplicate entries, standardize data formats, and ensure that organizations are dealing with consistent and accurate data.
- Data transformation — i.e., transforming data to ensure it aligns with specific business or technical requirements via complex processes like data aggregation. It ensures that the data is in a usable and meaningful format before it’s presented to decision-makers.
- Loading — i.e., loading the extracted data into a central repository, such as a single database or data warehouse, for storage, easy querying, and easy analysis.
In summary — quality checking and data cleaning make the raw data usable; transformation structures it; and loading into a central system makes it easily accessible. This process is usually a part of a larger data integration strategy.
