Dark data makes up over 80% of all data. Its share of the pie is increasing and the pie expanding. It is projected to grow to 175 zettabytes [Source: Researchworld] by 2025. In colloquial terms, that is 175 trillion gigabytes—or about as many bytes as there are atoms in a cheeseburger.
Dark data, otherwise known as unstructured data—email, blogs, social media posts, video, audio, etc.—are a valuable source of insights. They can be used to understand customer behavior, identify trends, and improve decision-making. Yet, only 18% of organizations reported being able to tap the advantage provided by unstructured data, according to a survey by Deloitte [Source: Deloitte]. And 90% of this data is unutilized [Source: Saxon], says IDC.
This calls for a need to develop and improve existing data extraction techniques so that unstructured data can be used more advantageously.
The Need for Extracting Unstructured Data
Data has become the primary driver of growth and transformation. However, to suitably utilize data, they first have to be extracted from their sources and refined.
As technology advances and data becomes more pivotal, utilizing unstructured data to derive meaningful information becomes increasingly vital for staying competitive and driving innovation. Extracting insights from unstructured data has multiple significance.
- They help us understand complex real-world scenarios: Real-world data that billions of internet users generate daily contains invaluable information. But they are mostly in unstructured form. This makes it difficult to analyze and derive insights. Mining data can help us analyze and derive insights from unstructured sources and can provide a more comprehensive understanding of complex real-world scenarios.
- They can give us a competitive advantage: Unstructured data hold crucial details about customer sentiments, preferences, and market trends and developments. Effectively harnessing these data can give a competitive advantage, enable better decision-making, enhance product offerings, and improve customer experience.
- They are training feeds for various technologies: Non-textual unstructured data such as audio recordings, images, and videos also provide a wealth of information. These can be used to train machine learning algorithms and improve technologies such as voice and facial recognition. Training image recognition systems requires lots of, well, images. For all this, the raw data have to be scraped before they can be used for training various models.
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Challenges in Extracting Unstructured Data
For all their abundance and usefulness, unstructured data are difficult to extract and tedious to work with. They are a disparate lot and lack a predefined, consistent format or pattern, unlike structured data. This makes it complex to process and extract meaningful information.
Another barrier is the diversity of languages in which unstructured data occur. Different languages have different syntaxes, grammatical rules, and structures. The open nature of vocabularies, abbreviations, and domain-specific dictionaries presents further challenges. Moreover, unstructured data are fraught with noise and dirt (inaccurate, improper, and incomplete data).
Scraping unstructured data may also encounter legal and ethical hurdles. Since these data can, and generally do, contain sensitive and private information, mining them without consent is ethically and legally suspect.
And extracting data is not the end. The scraped data have to be sanitized and transformed so that they can be used for analysis or other purposes. This may include removing white spaces, symbols, duplicates, and completing missing values.
Data Extraction Techniques for Unstructured Sources
The importance of unstructured data and their increasing proliferation necessitate extracting information efficiently and reliably. Here are five data extraction techniques for unstructured sources.
Manual data entry and extraction
Manual data entry is a basic data extraction technique that involves human operators inputting data from various sources into digital systems. This technique involves entering data by typing, copying, or pasting them from physical documents, websites, or other sources into a designated digital format.
Manual data extraction, though old-fashioned and inefficient, can be useful in certain scenarios. When information exists in physical forms such as paper documents or handwritten notes, manual entry is the most viable option to extract the necessary details and digitize them. Manual data extraction is also more suitable for cases where the data from a variety of sources do not have a standardized format, making automated extraction challenging.
Furthermore, manual data extraction can be a cost-effective alternative, especially for small-scale uses. Besides, there are outsourcing companies that provide affordable data mining and extraction services with results that are often more reliable and consistent than automated extraction.
Optical character recognition
Optical character recognition (OCR) is another technique for extracting data from unstructured sources. OCR is a form of computer vision that is used to extract text and characters from images and handwritten information and convert them into a machine-readable, structured format.
The technique is used in a variety of applications such as scanning documents, converting scanned images into digital formats, and extracting text from images. It can be used to extract a variety of information such as names, addresses, phone numbers, dates, and quantitative data like prices and quantities from unstructured documents, images, and web pages.
It allows you to automate data entry tasks, digitize historical records, convert printed material into digital formats, and extract information from scanned documents. You can then effectively classify, categorize, and analyze unstructured data. This makes the data searchable and therefore more accessible.
Text mining using natural language processing
Natural language processing (NLP) is a subset of artificial intelligence that gives machines the ability to read and understand human languages, identify patterns, and derive meaning from unstructured data such as text. Text mining uses NLP to identify facts, patterns, relationships, and assertions that would otherwise remain buried in the mass of textual data. The desired information can then be extracted and converted into structured data.
Text mining using NLP can be used for various data extraction tasks such as named entity recognition, sentiment analysis, text classification, keyphrase extraction, text clustering, and, part-of-speech tagging. This technique allows organizations to extract valuable insights from their unstructured data and make informed decisions.
Web scraping is another technique for extracting data from unstructured sources on the web. It allows you to collect specific data from websites in a structured format, which can then be processed, analyzed, stored, and used for various purposes.
Using the web scraping technique, you can extract various types of information from a website. You can, for example, scrape product information such as names, prices, descriptions, and images. You can also retrieve the metadata of web pages that are not directly visible.
Scraping the web and analyzing the data can help you make informed decisions, conduct market research, track trends, and monitor competitors among other things.
Regular expressions (regexes) are a powerful tool that can be used to extract data from unstructured text based on some matching criteria. Regexes are sequences of characters that define a search parameter. They allow you to define patterns that match the data you are interested in and ignore the rest.
Regular expressions can be used to match patterns in text, such as phone numbers, emails, addresses, and dates. For example, regexes can be used to extract all the mentions of product names in customer reviews or to extract the dates mentioned in news articles.
They can also be used for data cleansing. They can help remove unwanted characters, white spacing, or formatting from text. Regular expressions are however only suitable for syntactic pattern matching and are not well-suited for data extraction tasks that involve both syntactic and semantic components.
Data extraction, especially from unstructured sources, is a tedious process. Each of the techniques outlined here is suitable in certain scenarios but not in others. Which technique to employ depends on the type of unstructured data and the goals of data extraction.
And since data extraction requires specialized expertise and a dedicated team, which not many organizations have at their disposal, outsourcing data extraction services to third parties that have the requisite resources is a viable option.