August 15, 2025
Data extraction is a process where data is retrieved from a variety of sources, which are described as either structured or unstructured. Data extraction, of both structured and unstructured data, can be useful because it allows the information to be run through analytics tools that can help unveil patterns and trends about a specific customer or customer base.
Some data is structured. It’s highly-organized and formatted in a way so it’s easily searchable in databases. Structured data examples include names, addresses, credit card numbers, stock information, geolocation, and more. SQL, structured query language, is the programming language used for managing structured data and was developed by IBM in the early 1970s. It’s useful for handling tricky relationships in databases.
Most data – more than 80% of all data created today – is unstructured. It has no predefined format or organization. It’s much more difficult to collect, process, search and analyze. Unstructured data examples include text, video, audio, mobile activity, social media activity, satellite imagery, surveillance imagery, and more. These data types cannot be organized in relational databases, meaning that NoSQL (non-relational) databases are the best way to manage this data. This is the very reason that we launched DAS Analytics, a sister company that allows us to dive deeper into the issue of managing unstructured data in the best way possible. Click here to learn more about DAS Analytics.
During the data extraction process, for both structured and unstructured data, difficulties might arise. It’s important to plan ahead and prepare for these difficulties. One potential problem is that the data might be attacked while it’s in transit from the source to where it will be analyzed. You should encrypt your data while it’s in transit as a security measure. Another potential problem is when the data contains personally identifiable information (PII) that could be revealed during the extraction process. You can remove this sensitive information during the extraction process. One last common challenge that sometimes occurs during data extraction is when the necessary data comes from two different types of sources: unstructured and structured. You have to plan in advance for how to extract different types of data at the same time.
ETL stands for Extract, Transform, and Load. This process helps organizations combine data from multiple sources and is often used to build a data warehouse. ETL is useful for analysis. You can use ETL to pull data from multiple sources and run an analysis on it together, without having to combine and compare manually.
At the extraction (E) stage, data is taken from a source system. At the transformation (T) stage, data is converted into a universal format that can be analyzed. Finally, at the loading (L) stage, data is stored in a data warehouse or other system.
There are many benefits of the ETL process, including the popular use cases described below.
Using ETL software can improve the three following factors: marketing data integration, database replication, and business intelligence.
When companies apply the ETL process to web analytics, social media information, and consumer data, they will gain a wealth of information that can be used to inform future marketing and product decisions.
As for database replication, the ETL process can help by taking data from many different databases including MySQL, PostgreSQL, and Oracle. The ETL process can transfer that data into a cloud warehouse. Cloud-based operations can benefit a company in many ways - from the reduction of operational and management costs to greater security and a shift in focus for internal resources in your organization.
Lastly, business intelligence can be improved as a result of the ETL process. Once the data is extracted, transformed, and loaded onto a destination database, there is an opportunity for analysis leading to actionable insights. Ultimately, these insights can be used to inform future marketing and product decisions.
PDFs have become the new paper, which means they contain lots of information. As a result, extracting data from PDFs can help companies learn more about their customers.
However, there’s a catch. Because PDFs are like paper, it’s difficult for machines to extract information from them. Furthermore, PDFs are oftentimes scanned images of paper documents – and computers can’t interpret the scanned image text as well as humans can. Many people believe that copying and pasting data from PDFs into Excel is the only way to extract this data. However, this process is extremely monotonous and often results in countless errors.
Instead, it’s wise to invest in an ETL tool that can extract data from PDFs and transform it into a format that provides useful insights.
There is one tool that stands out among the rest when it comes to document capture and ETL solutions: IBM’s Datacap. While there are countless reasons that Datacap is the cream of the crop, don’t just take our word for it:
“IBM Datacap, recognized by Harvey Spencer and Associates as the #1 Capture provider in the Worldwide Software Vendor Capture Report, enables enterprises to automate the digitization, classification, and extraction of important structured and unstructured data from business documents to reduce or eliminate manual entry and errors, increase efficiency, productivity, and business insights.
IBM Datacap supports the next generation of data capture using artificial intelligence to reduce mundane tasks, reducing the need for manual intervention, allowing enterprises to redeploy knowledge workers to higher-value projects. Datacap uses natural language processing, text analytics, and machine-learning technologies for complex tasks to enable enterprises to significantly accelerate processes, reduce labor costs, deliver meaningful information and improve the responsiveness of customer service. Data security is increased using role-based redaction to automatically redact captured documents, based on the role of the requester, blocking out information according to a user's specifications to ensure the protection of sensitive data.
Datacap provides flexibility for capturing data from documents from a variety of sources with support for multiple-channel capture by processing paper documents, application images, and digital files (such as PDF) from scanners, mobile devices, multi-function peripherals, and fax. Enterprises have found IBM's Datacap software can reduce labor and paper costs, helping workers to quickly identify insights resulting in faster, more accurate, decision making.”
Learn more about how Datacap can transform your organization here.
Extracting data from other sources is possible, too. Many different data types, including text, images, video, audio, mobile activity, social media activity, and surveillance imagery, can all be transformed from their raw state and loaded into a system where pattern analysis can happen.
When it comes to extracting data from images, DAS has tools that can reverse engineer images of data visualizations to extract the underlying numerical data.
There is another DAS tool available which enables you to fetch social media content from across the web. Through natural language processing (NLP), businesses can extract critical metadata through an easy-to-use and comprehensive admin console. Additionally, if you need to look at audio or video data, deep learning tools are being developed that can support this type of extraction. These tools can help transform a variety of different types of unstructured data into useful data. Insights from these types of data are especially useful when it comes to making informed business decisions.
The experts at DAS will identify and run the tools your business needs to gather data that will help you make the most informed business decisions possible.
There are many different advantages to using ETL tools, and many different businesses rely on these tools every day to make informed decisions. ETL software is easy to use and efficient. It empowers organizations to build resilient and resourceful data warehousing systems. This is particularly helpful when companies are migrating data from legacy systems to modern systems in different data formats. This is also useful in the case of a merger, and when joining data from external partners. ETL tools both improve the quality of data and free IT staff from some of their duties, which can seriously boost their productivity. Above all else, ETL provides easier access to data that helps companies drive growth and innovation.
Setting up a Capture and ETL process for the first time can be complex. When you partner with the experts at DAS, you alleviate the burden of making the right choices and running the operation flawlessly.
DAS has the knowledge and experience needed to implement the right ETL tools for your business. By expertly using the right tools for the right job, DAS works in harmony to achieve your goals.
If you’re ready to learn more about how DAS can help you use ETL tools to gain access to more customer insights, contact us here today!
.avif)
