The modern world runs on data. Whether it’s marketing research, health records, or a company tracking its accounts payable, there’s data to be extracted. Taking data from disparate sources, cleaning it up, and analyzing it can reveal enlightening metrics or simply keep your company running smoothly.
But what exactly is meant by “data extraction?” In this article, we’ll walk you through what data extraction is, why it matters to a business, and give an example of how it can be used to improve something as fundamental to a company as its accounts payable department.
What Is Data Extraction?
Data extraction is a process that retrieves information from multiple sources, typically so that it can be used in a structured, analytical way. This information could be anything from performance metrics, to client or patient records, to internal business data like invoices and receipts. Any time you fetch information—especially from disparate sources—in order to move it to another location, you’re extracting data.
The Different Types of Data Extraction
There are three different ways to go about data extraction: bulk extraction, incremental extraction, and update notification. Which of these different forms of extraction will work best for you will depend on how up-to-date you need to keep your data, how big the datasets are, and what resources you have available.
- Bulk extraction, also called full extraction, is the process of downloading, reading, or otherwise extracting an entire dataset from its source. This can result in time-consuming data transfers and unwieldy datasets, but in general, it’s the most straightforward form of data extraction.
- Incremental extraction is an extraction process that allows a dataset to be updated as changes are made at the source, for instance, as new data is added. This eliminates the need to re-extract the full dataset every time there’s a change or update.
- Update notification is a form of incremental extraction that, rather than automating updates, notifies the user of any changes or additions that occur in the source data so they can choose if and when to extract those updates.
Why Is Data Extraction Important?
Data extraction is a meaningful part of any business’s operations because it’s the first step toward understanding patterns in any dataset. Whether the information relates to clients, customers, patients, employees, or sales, analyzing trends and quantifying patterns are vital for making sure a company is on track to meet its goals and its customers’ needs.
Data extraction can also be used for maintaining regular internal operations like accounts payable, accounts receivable, and managing accrued expenses. Some of the benefits of data extraction for internal company processes include:
- Reducing data entry errors. Humans are fallible, and that extends to their data entry skills. Estimates for the frequency of human error during spreadsheet data entry range from one to four percent. And when it comes to numbers, little mistakes add up quickly. For instance, AT&T has years’ worth of invoicing errors that led to years of overpaying vendors and likely millions of dollars in lost funds.
- An increase in productivity. Time spent entering data is time employees could spend on other tasks with higher value added for your company. Plus, automated data entry is faster, so you have data in hand sooner. For example, a company might automate the extraction of information from incoming vendor invoices that the accounts payable team needs to process, increasing the efficiency of the department.
- Saving time and money. Automating processes like accounts payable with data extraction saves time, which means it also saves money. A 2019 report by Levvel Research found that companies with little to no automation spent, on average, $15 per invoice. Meanwhile, companies that automated their account payable processes only spent $2.36 per invoice.
How Data Is Extracted: The Process
Now that you know what data extraction is and why it’s important, we can dive a bit more into the process of how data extraction actually works. It occurs in three main steps:
- First, check the data structure. Some data is already structured, meaning it’s in a format like a spreadsheet or data table and is ready for extraction and use. But other data is unstructured, like information embedded in social media feeds, emails, PDF files, or other complex sources. Understanding the structure of the data will allow you to choose the right method for extracting it.
- If data is unstructured, structure it. This involves transforming it into a format usable for analyses. For instance, OCR, or optical character recognition, can transform images of text and PRDs of printed or handwritten text into a digital, computer-readable format.
- Retrieve and extract the data. Finally, you’re ready to either do a full or incremental extraction, depending on whether you’ll need to log changes to the source data in the future.
Data Extraction and Your Business
Data extraction may sound technical, but it’s a straightforward process that can be used to benefit any sector of your business, from your marketing department to accounts payable. By now, it’s clear that improving your company’s AP invoice processing workflows with data extraction can reduce human errors and save time and money.
Interested in seeing how data extraction can help streamline your operations? MHC offers automation solutions that can help your AP department extract critical invoice data and automate the invoice processing process, for instance by using OCR technology to quickly and effectively digitize invoices. Contact MHC today to find out how to optimize your AP department with data extraction.
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