With the explosion of data in recent years, organizations are sitting on petabytes of unstructured data within documents, emails, social media posts, videos and more. However, much of this data remains unused due to the difficulty of extracting insights from unstructured formats.
Advanced data extraction techniques provide solutions to unlock the value within these archives. By utilizing techniques like natural language processing, machine learning and computer vision, organizations can now automatically extract useful information such as entities, keywords, sentiment and more from various unstructured data sources at scale.
This report explores several cutting-edge data extraction methods and how they can be leveraged to gain previously hidden insights. It discusses tools that facilitate advanced information extraction and highlights best practices for application across different industries and use cases.
What is Data extraction?
Data extraction is the crucial initial step of retrieving information from diverse sources and transforming it into a usable format for downstream analytics and storage. Data can originate from databases, spreadsheets, websites, APIs, logs, sensors and more, either structured in organized tables or records or unstructured as non-tabular text or multimedia.
While extracting structured data like Excel sheets or databases can be straightforward, leveraging specialized data extraction software is preferable for unearthing insights from less structured formats including PDFs, emails, images and videos, given the inherent difficulties in handling their variabilities.
What is unstructured data?
Unstructured data is the raw, disorganized information that floods companies daily. From emails and pictures to social media conversations, it comes in many forms without a clearly defined model or structure. As organizations create around 2.5 quintillion bytes of data every day, upwards of 90% is unstructured data.
This presents challenges for businesses seeking to extract value from the reams of information in their possession. New advanced data extraction techniques using artificial intelligence and optical character recognition are helping to process and leverage this unstructured data treasure trove by converting unorganized material into structured, analyzable insights.
What are the examples of unstructured data?
Wondering what defines unstructured data? It refers to information without a specific organization structure, covering textual documents, images, videos, social media posts, emails, sensor data and more, whether originating from people or machines.
Text data
Extracting value from vast amounts of unstructured data poses challenges but offers great opportunities. New advanced techniques are helping process emails, documents, social interactions and more by converting them into structured insights through artificial intelligence, optical character recognition and other innovative tools. This unlocking of information allows organizations to leverage their full data assets.
Multi-media messages
Visual and audio content such as photos, videos and sound files contain complex unstructured data as encrypted binary codes without organized formatting or categorization. Messages in JPEG, PNG, GIF, audio and video formats defy patterns through their inherent multimedia natures, making extraction of value from them an ongoing challenge.
Website content
The web holds vast amounts of potentially useful information across sites in the form of lengthy, scattered and disorganized pages of content. However, due to the lack of any predefined arrangement or structure, this online data remains under-leveraged, as transforming the raw material into properly composed insights proves challenging.
Sensor Data – IoT devices
The Internet of Things consists of physical objects equipped with technology to gather surrounding data and relay it to cloud systems. As IoT traffic monitors and voice assistants like Alexa and Google Home transmit sensor-based readings, the volumes of unstructured information they produce present new opportunities but also difficulties in extracting tangible insights.
Business documents
Businesses handle many document forms such as PDFs, emails, invoices and orders that need more uniformity. To unlock valuable insights within these varied paper-based files, intelligent document processing programs exemplified by Nanonets leverage machine learning to retrieve operational data from the wealth of unstructured documentation.
Top 10 Data Extraction Techniques and Methods
Regular expression extraction – Use patterns to extract specific data types from text.
Optical character recognition (OCR) – Extract typed or handwritten text from images.
Computer vision – Extract useful information from images using visual processing techniques.
Audio fingerprinting – Identify audio files, songs, and speakers from sound clips.
Natural language processing (NLP) – Extract semantics, and sentiments from documents using linguistics.
Web scraping – Extract structured data from web pages using scripts.
Database queries – Extract data from structured databases using SQL, queries.
API calls – Extract data from external APIs and services using requests.
Robotic process automation (RPA) – Automate extraction from desktop apps using software robots.
Machine learning models – Train models on large datasets to extract patterns autonomously.
Common Challenges for Businesses
Businesses often struggle with effectively extracting value from their data sources due to various common challenges. A major issue is disparate systems – many organizations have accumulated different programs, databases and file types over time without standardization.
This makes uniform data extraction difficult. Other frequent problems include unclear data structures without field definitions, inconsistent or invalid data values, and inaccuracies introduced during manual data collection. Unstructured formats like documents and emails also pose obstacles.
Data protection regulations add further complications, requiring precautions around sensitive customer information. To address these widespread obstacles, businesses are increasingly adopting specialized AI-powered data extraction tools that can interpret diverse file types, clean inconsistent data, and scale to handle high volumes more seamlessly.
Top Data Extraction Tools
- A fully managed data extraction service.
- Unified engine for large-scale extraction and processing.
- Open source tool for real-time data routing and transformation.
- Cloud-based integration platform handling extraction.
- AI tool for enriching and resolving dirty extracted data.
- Visual workflow builder and automation of extraction processes.
- End-to-end ML platform including data preparation and extraction.
- Cloud software for extracting data from different sources.
- Metadata-driven design of extraction workflows.
- Extracts, transforms and loads repositories.
FAQ’s
What is unstructured data?
Unstructured data refers to data that does not have a predefined data model or is not organized in a predefined manner, such as text documents, emails, videos, images, audio etc.
What are some common techniques used to extract information from unstructured data?
Common techniques include natural language processing, text mining, computer vision, and optical character recognition.
What is natural language processing?
Natural language processing (NLP) refers to a set of techniques that allow computers to understand, analyze and derive meaning from human language text.
What is text mining?
Text mining is the process of analyzing text to extract meaningful information by techniques like subject identification, and relationship identification.
What is one challenge of extracting information from unstructured data?
One major challenge is the unstructured and inconsistent nature of the data which makes it difficult to apply standard relational databases and data models to extract useful information from them. Proper parsing and interpretation are required.
Conclusion
As data volumes continue climbing, with unstructured formats constituting much of the growth, businesses require increasingly sophisticated techniques for extractable useful insights. Traditional rules and Regex-based methods struggle at scale. Advanced natural language processing, computer vision, audio fingerprinting and machine learning are now playing a larger role in intelligently interpreting messy text, images, video and audio files.
Deep learning models can identify patterns and relationships within vast unlabelled datasets, learning representations and extract semantics without explicit programming. Cloud platforms additionally power these approaches by providing immense computing power and virtual resources on demand. Going forward, continued progress on methods like self-supervised learning and transfer learning promise even better comprehension of diverse multimedia content. With the right tools, the promise of big data can be fully realized by liberating intelligence from humanity’s wealth of digital outpourings.
