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Feb 24, 2022
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An Intro to Unstructured Data Processing and Analysis Software

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super.AI
Chief AI for Everyone Officer
SUMMARY

2.5 quintillion bytes of data are created each day, and 90% of the world’s data was created in the last two years. That number is forecast to double every two years for the foreseeable future. But for every 10 pieces of new data, nine are inaccessible due to unstructured data.

Unstructured data is data that lacks any kind of predefined data model or schema. This lack of structure prevents it from being stored in a traditional database. Examples of unstructured data include:

  • Email messages
  • Audio files
  • Social media content
  • Digital photos

If in doubt whether data is unstructured, check if it has any organized attributes. For example, semi-structured data has some organizational aspects like tags and metadata. This makes it easier to categorize semi-structured data according to a hierarchy. If you’ve ever downloaded a JSON file or viewed videos on your smartphone, you’ve managed semi-structured data.

By contrast, structured data has a strict format that allows it to be easily organized. Let’s take the example of an online purchase. Each purchase has several types of unique data associated with it, from each item purchased to the order’s confirmation number. If you’ve ever wondered how big tech companies serve so many ads tailored to your interests, structured data is a big reason why. It’s easier for algorithms to analyze all of the data associated with purchases because each type of data has a pre-defined purpose.

But between 80 to 90% of data produced today is unstructured. That means you’ll need unstructured data processing software to use the vast majority of available data. This post explores the features, use cases, and things to look for in unstructured data processing software.

What does unstructured data processing software do?

Unstructured data processing software performs four main tasks that help you gain value from unstructured data. It:

  • Integrates data by allowing you to upload datasets to the software from your laptop, from a web integration tool like Zapier, via the command line, or other sources.
  • Selects assignees per task. Whether you want an automated AI or human colleague to do specific tasks, you can assign ownership of each task.
  • Extracts structured attributes from unstructured data. For example, biometrics extract structured attributes from fingerprints and facial images. Unstructured data processing software might analyze ink smears as lines and polygons. This allows users to analyze the fingerprints and facial images to a degree they can’t achieve without unstructured data processing software.
  • Improves your data quality over time by spotting patterns, training models, and adding new workers (both human and AI). The more data it’s given to analyze, the easier it is for unstructured data processing software to find patterns in the data.

Key features in unstructured data processing software

Unstructured data processing and analysis software uses a range of AI techniques to accomplish the tasks above, from natural language processing (NLP) to machine learning (ML). This software isn’t industry-specific: It helps users in industries ranging from retail to agriculture.

Regardless of sector, UDP software processes unstructured data to build reusable, automated, data processing workflows. It achieves this using some key features that let you integrate, select, extract, and improve unstructured data at scale:

  1. Set data quality standards: Your team knows your data best, and what data quality looks like. Unstructured data processing software lets you input those data quality standards. This ensures that all forthcoming work is assessed for and against data quality as your business defines it.
  2. API integration: It’s important for your unstructured data processing software to integrate into the software stack your business already uses. This keeps all your data in one ecosystem, rather than storing it in several different places. (Which poses risks to data quality, security, compliance, and ethics).
  3. PII redaction: Personally identifiable information (PII) like faces, addresses, or license plate numbers poses security and compliance risks. Unstructured data processing software automatically anonymizes PII to keep your business in compliance and user data safe. It’s especially important in highly regulated industries, like healthcare and insurance.
  4. Data classification: Data cleaning and classifying takes a lot of time and money to achieve by hand. Unstructured data processing software uses graphical user interfaces to automate data classification by recognizing patterns and correctly categorizing data at scale. This classification makes it easier for software to recognize patterns in large datasets over time, including as you add more data.
  5. Extraction: Whether you need to get data from chatbot conversations or customer support emails, this feature lets you automate extraction from any data type, including the wide range of unstructured data.
  6. Natural language queries: Unstructured data often contains multitudes, like showing several animals in one image. Unstructured data processing software automates image counting and other questions users might have about unstructured data. This saves hours of manual work.
  7. Output review: Dashboards let you review UDP software’s outputs to ensure they meet your quality standards. When you mark outputs as correct or incorrect, you give the software labels that allow it to improve performance. You can also change your own task design and improve the way you write instructions.

More unstructured data analysis and processing software resources

Unstructured data processing software turns data into process-ready insights, no matter how unstructured it is. If you’re curious how it works, learn more using the links below:

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