Unstructured data is information that doesn't reside in a traditional row-column database. It’s usually text-heavy but may include data such as dates, numbers, and facts.
Organizations of all sizes rely on unstructured data to make critical business decisions, determine financial projections, and engage with customers—but data scientists must successfully extract and organize unstructured data before they can put it to use.
With the right tools in place, data scientists can easily extrapolate, analyze, and use unstructured data to meet business objectives.
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Unstructured data doesn’t have a predefined structure and is common in sources like:
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 Micro Focus IDOL Unstructured Data Analytics to easily process large amounts of unstructured 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.
Unstructured data is usually stored in a non-relational database like Hadoop or NoSQL and processed by unstructured data analytics programs like Micro Focus IDOL. These databases can store and process large amounts of unstructured data.
Common storage formats for unstructured data are:
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:
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:
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:
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:
Micro Focus unstructured data analytics platform helps organizations analyze this type of information. Micro Focus IDOL includes tools and technologies that collect, process, and analyze unstructured data.
Critical features of Micro Focus IDOL include:
You deserve a cutting-edge platform to disseminate unstructured data with uncanny precision and convenience. If you want to learn more about Micro Focus 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.
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