Have you ever asked yourself, what is data extraction? Data collection and analysis are more critical to business than ever before. Modern organizations collect information from a wide array of data sources, but these sources often have different types of data or raw data that need to be transformed before Machine Learning and data analysis tools can even begin to draw valuable insights.
Data extraction plays a critical role in the success of HiTech data analysis tools that utilize Machine Learning and AI. In addition, data extraction tools help businesses utilize more of the information they collect from their data sources.
This post will explain what data extraction is, how it is commonly done, and some of the challenges commonly associated with the data extraction process.
Understanding Data Extraction
At its most basic, data extraction refers to the action of taking data from one source and moving it to another. Typically, organizations will extract data from multiple sources and store it in a single source, either on-premises, in the cloud, or a hybrid solution. In addition, unless the data extracted is only being used for archival purposes, data extraction is only the first step of a process termed ETL (extract, transform, load), which renders extracted data usable in future mobile and web applications and analysis.
Data extraction enables organizations to consolidate, process, and refine the data they collect so that it can be stored in a unified manner for analysis. Businesses that can’t extract different types of data or raw data cannot fully leverage their data’s potential and make strategic decisions. Without good, clean data, the world’s best data analysis tools are worthless, so data extraction plays a pivotal role in analytics.
Common Ways to Extract Data
How data is extracted depends mainly on whether the data is structured or unstructured. Structured data is formatted based on standardized models and is ready for analysis. Therefore the extraction process of structured data is straightforward and either happens fully or incrementally.
Full data extraction refers to single-trip extraction of data. Typically, this is the least complicated form of data extraction, especially when the tools being used for the extraction process are good. However, full extraction is not the best data retrieval method when you have a data source that is continually being updated and changed.
In such scenarios, utilizing a system for invoice data management becomes essential, as it can allow for more controlled and frequent updates. This approach ensures that critical financial information, such as invoices, remains accurate and up-to-date, improving overall efficiency and reducing errors in data processing.
For ongoing structured data extraction, an incremental approach is the best method. Incremental data extraction entails recurring visits to the original data source to monitor changes and extract new data points. The challenge with incremental data extraction is retrieving only the new data and not extracting repeat data. However, this challenge is easy to overcome with the right data extraction tools and team members.
Extracting unstructured data is far more complicated than extracting structured data. For starters, the types of data that are considered unstructured vary wildly. For example, unstructured data can be emails, PDFs, text, audio, spool files, or anything that contains essential data your business wants to extract.
Furthermore, data extraction is not enough for unstructured data. In these situations, the ETL pipeline comes into play. Unstructured data must be transformed to match the standardized format of structured data and then loaded into a unified data source. Some tools can help businesses efficiently extract unstructured data, but in many cases, data experts do this work manually.
The Challenges Associated with Data Extraction
Data extraction plays a critical role in the long-term success of a business. However, while data extraction is important, there are common challenges that organizations will have to overcome if they want to truly leverage the full potential of their data. Common challenges associated with data extraction include:
- Data security
- Making the process more efficient
- Unifying data sources
Data Security
Data security is something that every business needs to take seriously. During data extraction, data is potentially vulnerable. Therefore, organizations must encrypt or remove sensitive data before extraction to ensure security. Unfortunately, most data extraction tools cannot automatically do this sort of task. As a result, your organization’s team members must be trained to recognize and take steps to protect sensitive data during the extraction process.
Organizations that don’t take data security seriously will likely find themselves in trouble. If you handle any sensitive user data, even a minor breach or loss of it can do irreparable damage to your brand image.
Making the Process More Efficient
Data extraction tools can aid the extraction process, but most data extraction work still needs to be done manually or requires oversight from technical professionals. For example, if your organization is extracting data from supplier invoices, the chances are that most invoices will be different in terms of layout, naming conventions, data formats, etc. Even if two invoices appear to share a similar layout, implementing a streamlined extraction process will be difficult if the text content is not formatted identically.
One of the more challenging aspects of efficient data extraction is unstructured data which looks like structured data on the surface. Trying to navigate through these difficulties makes the data extraction process more time-consuming and requires manual attention, even when your organization invests in data extraction tools to speed up the process.
Unifying Data Sources
The most significant challenge of data extraction is unifying data sources into a single comprehensive view. Businesses collect data from so many different sources they might not even realize they are completely missing data when they look at what they have collected comprehensively. For example, if you wanted to get a comprehensive view of user data, your organization would have to compile data from analytic tools, customer surveys, social media posts, images of documents, etc.
Compiling and unifying data is a momentous task, but the organizations that can successfully do it have a significant advantage over their competitors.
Final Thoughts
Data extraction is a vital part of analytics and HiTech data applications, but it is a time-consuming and difficult process when done right. If you want to learn more about how your business can effectively extract and unify data, reach out to an app development expert for guidance and support.
