What is the meaning of Unstructured Data?
Unstructured data doesn’t have a predefined structure and is common in sources like:
- Audio files
- Video files
- Social media posts
While unstructured data doesn't have the same organization as structured data, you can still analyze it to find trends and insights. To do this, businesses need to invest in big data technologies like OpenText™ IDOL Unstructured Data Analytics to easily process large amounts of unstructured data.
Unstructured Data vs. Structured Data
Structured data is information organized in a predefined way. This includes data arranged in tables with rows and columns. This type of data typically resides in a relational database. Structured data is oftentimes easier to access, manage, and analyze.
Unstructured data doesn't have a predefined data model or structure. Common unstructured data examples include customer information, product catalogs, and financial records. Since this type of data is not organized in a predefined manner, it’s more difficult to process and analyze using traditional methods.
How is Unstructured Data stored?
Unstructured data is usually stored in a non-relational database like Hadoop or NoSQL and processed by unstructured data analytics programs like OpenText IDOL. These databases can store and process large amounts of unstructured data.
Common storage formats for unstructured data are:
- Text files (PDFs and emails)
- Image files (JPEGs and PNGs)
- Audio files (MP3s and WAVs)
- Video files (MPEGs and AVIs)
What are the benefits of Unstructured Data?
There are many benefits to working with unstructured data. Data scientists use unstructured data to improve customer service, target marketing campaigns, and make intelligent business decisions.
Some of the most common benefits of unstructured data are:
- Improved customer service: Businesses can provide better customer service by analyzing customer sentiment in social media posts and online reviews.
- Targeted marketing campaigns: Marketing teams can use unstructured data to identify customer needs and wants. This information can then help them create targeted marketing campaigns.
- Better business decisions: Unstructured data can businesses find trends and insights that would otherwise be difficult to identify. This information ultimately helps stakeholder make accurate judgments and improve their companies.
What companies can do with Unstructured Data after parsing?
Some companies have successfully parsed unstructured data through text analytics and natural language processing (NLP). These technologies help organizations sift through large amounts of unstructured data to find the nuggets of information they are looking for. What's more, parsing through unstructured data does hold several key benefits, such as:
- Limitless use: Unstructured data isn’t predefined, meaning owners can use it in unlimited ways.
- Versatile formatting: Users can store unstructured data in various formats.
- Affordable storage cost: Enterprises have more raw, unstructured data than structured information. Storing unstructured data is both convenient and cost-effective.
- File extraction: Gain more insight from your data with support for over 1,500 file formats, and a Document file reader and file extraction with standalone file format detection, content decryption, text extraction, subfile processing, non-native rendering, and structured export solution.
- AI Digital Assistant: Once data is analyzed, natural-language dialogues are pulled from many different sources to provide highly matched answers to questions. Visitors to your site can chat with an automated, human-like natural language digital assistant.
- AI Video Surveillance & Analytics: Automatically monitor thousands of CCTV cameras in real time or retrospectively. Tag video, send alerts, review, and distribute to interested parties. Includes facial recognition, event analysis, license plate recognition, and more.
- Law Enforcement Analytics & Media Analysis: Identify and extract facts from video and image evidence during investigations. Collect, organize, classify, and secure these assets faster while reducing costs and the strain on labor.
- Natural Language Q&A and Chatbot: Accesses a variety of sources for highly matched answers and responds in a natural language format. Create a human dialog chat experience for customers through AI and ML.
What are the challenges of Unstructured Data?
Working with unstructured data can be challenging. Since this type of information is not organized in a predefined manner, it's more challenging to analyze.
In addition, unstructured data is often stored in a non-relational database, making it more difficult to query. Some of the most common challenges of unstructured data are:
- Security risks: Securing unstructured data can be complex since users can spread this information across many storage formats and locations.
- Poor indexation: Because of its arbitrary nature, indexation is usually both a challenging and error-prone process.
- Need for data scientists: Unstructured data usually requires data scientists to parse through it and make interpretations.
- Expensive data analytics equipment: Advanced data analytics software is necessary for parsing unstructured data, but it may be out of reach for companies on a tight budget.
- Numerous data formats: Unstructured data doesn’t have a specific format, which makes it difficult to use in its raw state.
How is Unstructured Data analyzed?
There are many ways to analyze unstructured data. Users can process unstructured data using NLP techniques like text mining and sentiment analysis. In addition, stakeholders can analyze unstructured data through tools that feature machine learning.
Some standard methods for analyzing unstructured data are:
- Text mining: This technique extracts valuable information from text-based sources. For example, text mining can analyze customer reviews to identify patterns and trends.
- Sentiment analysis: This technique identifies emotions in text-based sources. For example, sentiment analysis can examine social media posts to determine positive or negative sentiments about a brand or product.
- Machine learning: This technique finds patterns and insights in data. For example, tools that feature machine learning can inspect customer behavior to identify trends.
How can OpenText IDOL Unstructured Data Analytics help?
OpenText unstructured data analytics platform helps organizations analyze this type of information. OpenText IDOL includes tools and technologies that collect, process, and analyze unstructured data.
Critical features of OpenText IDOL include:
- Image analytics: This feature enables businesses to extract meaning from images. For example, image analytics can identify objects in a picture or find faces in a crowded image.
- Audio analytics: This feature enables businesses to extract meaning from audio files. For example, audio analytics can identify keywords in a conversation or detect emotions in a voice.
- Repository Data access and connectors: Users can easily connect to various data sources. This includes social media, enterprise applications, and databases.
- Cognitive search: OpenText IDOL enables businesses to find information using natural language queries. For example, cognitive search can help data scientists find documents that contain a certain keyword or phrase.
- Unstructured Data Analytics Software for OEM & SDKs: Use our software development kit to build the apps and APIs you need to take advantage of your unstructured data.
Learn more about OpenText IDOL
You deserve a cutting-edge platform to disseminate unstructured data with uncanny precision and convenience. If you want to learn more about OpenText IDOL, request your live demo today. We can answer any questions about the platform and help you make an informed decision to improve your unstructured data analysis.