Here are five things to know about processing unstructured data:

  1. The majority of data is unstructured data, according to one study. 

  2. Unstructured data comes from files, photos, spreadsheets, emails, and social media posts.

  3. Unlike structured data, unstructured data doesn't have a predefined schema, making it difficult to move to a target system without the right tools. 

  4. The easiest method for processing unstructured data is to move it to a data warehouse or lake via ELT.

  5. helps you move large amounts of unstructured data for big data analytics without lots of complicated code or engineering. 

Unstructured data is big. Really big. According to an MIT Sloan School of Management study, 80-90 percent of data is unstructured information such as photos, text, audio, emails, and social media posts. This should come as no surprise to e-commerce retailers who collect and store unstructured data from invoices, online product reviews, sales presentations, and chatbot conversations with customers. There’s huge potential for processing unstructured data. E-commerce retailers can use it to generate valuable insights about their customers and inventory processes and use technologies like Hadoop to handle all this big data. 

So, what are the best ways organizations can deal with their unstructured data sets? is here to break things down.

Table of Contents is a data warehouse integration solution built for e-commerce. It moves unstructured data to a warehouse or data lake via low-code/no-code ELT data pipelines, letting you operationalize and analyze the e-commerce data that flows through your organization. Email to learn more. 

Recommended reading: Structured Data vs Unstructured Data: 5 Key Differences

Processing Unstructured Data Methods

Unlike structured data, which comes neatly organized in relational databases, unstructured data does not have a predefined schema and isn’t available in a specified format. How can your e-commerce organization process this? First, you need to know about the two different groups of unstructured data:

Processing Unstructured Data: Logs

The first group of unstructured data consists of application logs, stored as files that list events such as page visits, button clicks, logins, exceptions, and so forth. You can structure part of these loglines and have it contain the date, log type (info/warning/error), and URL, while the other part may be fully unstructured with any info the app’s developers choose to include. Log data could also include newline characters, which may further complicate processing, to determine where one log starts and another one ends.

Say your manager needs analytics about web app logs in your e-commerce organization. She wants to know which e-commerce transactions took place, how long they took, and what errors occurred. What tools can you use to provide these numbers? Unfortunately, the solution is to write custom code that checks for sequences and extracts values using regular expressions. You could use Hive or Pig to deal with large amounts of data, but you’d still need to find or write UDFs (user-defined functions). Logs are a critical part of the data processing and data storage pipelines, providing keen insights into some of your vital data. Make sure you have the technological capacity (and proper tools) for processing unstructured data like log data.

Fully Unstructured Data

Data such as social network statuses, emails, documents, images, and videos make up the second group of unstructured data. In actuality, this label may be misleading, because emails and binary file formats have well-defined headers with metadata. However, their content is fully unstructured and may appear in the form of free text or binary bits and bytes, either raw or compressed.

Processing unstructured data means extracting structure from it. Let's look at the example of sentiment analysis, which is also known as opinion mining. It determines judgment, evaluation, and even emotional state by processing unstructured data in text and analyzing how the words fit together before assigning a polarity that identifies the text to be positive, negative, or neutral.

Biometrics is another field that uses unstructured data, more specifically images. Biometrics works by processing fingerprints and facial images to extract structured attributes. For example, ink smears turn into lines and polygons. From there, a biometric comparison uses structured attributes rather than raw data.

Your e-commerce organization's "fully unstructured data" is another important element of your overall data gains. The right type of processing can "bring order to chaos" by bringing out critical and concrete insights from data that is anything but.

Processing unstructured data such as files provide your e-commerce organization with real-time insights for decision-making, data management, scalability, and problem-solving. However, getting unstructured data to a target system can be a challenge. That's why has simplified the entire data integration process with its jargon-free platform for e-commerce enterprises of all sizes. Email to learn more about processing unstructured data.

Recommended reading: What is a Data Warehouse and Why are They Important?

Structured Unstructured

Actually, unstructured data already exists in structured data. Take BLOBs (binary large objects)—collections of binary data saved in a database entity. BLOBs can store text, documents, videos, images, and other kinds of unstructured binary data.

As for textual data, some features are already available in relational databases: LIKE operators with regular expressions, full-text search, entity extraction, text classification, and text similarities. Processing XML is also possible via XQuery support on Oracle and SQL Server, with extra support for JSON on PostgreSQL. SQL Server also has the ability to map file systems to database tables.

"Structured Unstructured" might seem like a bit of a confusing term, but it's really quite important to your data processing. Elements like BLOBs should not get overlooked when it comes to planning out your data processing solutions.

Utilizing Unstructured Data

There are two major groups of unstructured data for processing: The first is application logs that need handling via custom code and regular expressions; The second is fully unstructured data from which advanced algorithms extract structured attributes.

Considering the huge amount of unstructured data and that only a tiny bit of it is being analyzed, adventuring in this dense information jungle with the right tools and methods could lead to some amazing discoveries.

Many successful e-commerce retailers use unstructured data from social media platforms, for example, to predict future purchasing trends. By measuring social sentiment on websites like Facebook and Twitter, these retailers can determine the success of future product launches after processing unstructured data.

Recommended reading: ELT vs. ELT: 5 Key Differences

How to Use Unstructured Data for Analysis

The best way to use unstructured data for e-commerce analytics is to move it from its original data source to a warehouse or lake. From here, you can run data through business intelligence (BI) programs and generate insights and visualizations about your business such as:

  • Churn rates

  • Conversion rates

  • Customer lifetime value

  • Revenue growth

  • Revenue per customer

  • Customer retention 

  • Supply and demand

  • Demand forecast accuracy

Extract, Load, Transform (ELT) is the most effective method for processing unstructured data in a warehouse or lake like Snowflake, Amazon (AWS) Redshift, or Microsoft Azure. This data integration method involves extracting unstructured data from its source, loading it to a target system like Snowflake or Amazon Redshift, and then transforming the data into a suitable format for analytics. This entire process can be complicated for e-commerce companies with little data engineering experience. Thankfully, data warehouse integration solutions like make it easier to move, store, and analyze structured, semi-structured, and, of course, unstructured data. 

How Helps With Processing Unstructured Data is a data warehouse integration solution that helps e-commerce retailers with processing unstructured data such as files in one centralized location. Now you can seamlessly move unstructured data from applications, NoSQL (non-relational) databases, and other data platforms without any data engineering experience. You can also transfer semi-structured data such as JSON files to a target location.'s range of native out-of-the-box connectors syncs data with the most popular data warehouses and lakes without the need to build complex data pipelines. After using an connector, you can generate incredible insights about e-commerce operations such as sales, marketing, inventory management, customer service, customer retention, and the customer experience. has a simple philosophy: To remove the barriers associated with moving unstructured data from data sources to a target location. With more than 100 native connectors for CRMs, ERPs, relational databases, transactional databases, and SaaS tools, you can ELT unstructured data to a warehouse or lake without the stress. No longer will you have to worry about data mining, hierarchies, data structure, unstructured data analytics, and other complicated tasks. also performs Extract, Transform, Load—a more suitable data integration method for moving structured data and semi-structured data to a warehouse—ReverseETL, and super-fast Change Data Capture, which lets you sync two or more databases. Other features include exceptional customer service, simple pricing, world-class security, and adherence to all major data governance principles. is a new data warehouse integration platform for e-commerce that ELTs unstructured data for analytics. Now you can transfer data without lots of complicated code in a jargon-free environment. Schedule an intro call or email to learn more about processing unstructured data.