Improve Article Show Save Article Big Data includes huge volume, high velocity, and extensible variety of data. These are 3 types: Structured data, Semi-structured data, and Unstructured data.
Differences between Structured, Semi-structured and Unstructured data:
The most inclusive Big Data analysis makes use of both structured and unstructured data. Structured vs. Unstructured Data: What’s The Difference?Besides the obvious difference between storing in a relational database and storing outside of one, the biggest difference between structured and unstructured data is the ease of analysis. Mature analytics tools exist for structured data, but analytics tools for mining unstructured data are nascent and developing. Users can run simple content searches across textual unstructured data. But its lack of orderly internal structure defeats the purpose of traditional data mining tools, and the enterprise gets little value from potentially valuable data sources like rich media, network or weblogs, customer interactions, and social media data. On top of this, there is simply much more unstructured data than structured. Unstructured data makes up 80% and more of enterprise data, and is growing at the rate of 55% and 65% per year. And without the tools to analyze this massive data category, organizations are leaving vast amounts of valuable data on the business intelligence table.  Structured data is traditionally easier for Big Data applications to digest, but today’s data analytics solutions are making great strides in the unstructured data area. How Semi-Structured Data Fits With Structured And Unstructured DataSemi-structured data maintains internal tags and markings that identify separate data elements, which enables data analysts to determine information grouping and hierarchies. Both documents and databases can be semi-structured. This type of data only represents about 5-10% of the data pie, but has critical business usage cases when used in combination with structured and unstructured data. Email is a very common example of a semi-structured data type. Although more advanced analysis tools are necessary for thread tracking, near-dedupe, and concept searching; email’s native metadata enables classification and keyword searching without any additional tools. Email is a huge use case, but most semi-structured development centers on easing data transport issues. Sharing sensor data is a growing use case, as are web-based data sharing and transport: electronic data interchange (EDI), many social media platforms, document markup languages, and NoSQL databases. Examples of Semi-structured Data
In big data environments, NoSQL does not require admins to separate operational and analytics databases into separate deployments. NoSQL is the operational database and hosts native analytics tools for business intelligence. In Hadoop environments, NoSQL databases ingest and manage incoming data and serve up analytic results. These databases are common in big data infrastructure and real-time Web applications like LinkedIn. On LinkedIn, hundreds of millions of business users freely share job titles, locations, skills, and more; and LinkedIn captures the massive data in a semi-structured format. When job-seeking users create a search, LinkedIn matches the query to its massive semi-structured data stores, cross-references data to hiring trends, and shares the resulting recommendations with job seekers. The same process operates with sales and marketing queries in premium LinkedIn services like Salesforce. Amazon also bases its reader recommendations on semi-structured databases. SQL vs. NoSQLSQL (structured query language) and NoSQL (“not only” structured query language) particularly showcase some of the key differences between structured and unstructured data. SQL almost always comes in the form of a database because the structured data it contains can easily be displayed in a way that shows relationships between data entities. NoSQL, on the other hand, cannot easily be displayed in a traditional table or another relational database format, because the mix of unstructured and semi-structured data cannot be laid out according to any pattern or schema. While SQL and other structured language setups are often easier to comprehend and manage manually, they don’t always have as much potential energy for data analysis and manipulation. NoSQL and other instances of unstructured data are difficult to comprehend and analyze, even with some of the strongest tools, but the outcome gives you a wider variety of data types for business intelligence practices. Ultimately, you need both structured and unstructured data, as well as the different formats that they can be displayed and organized into, in order to develop a full picture of your corporate data. Read Next: Best Data Analysis Methods 2021 Structured Vs. Unstructured Data: Next Gen Tools Are Game ChangersNew tools are available to analyze unstructured data, particularly given specific use case parameters. Most of these tools are based on machine learning. Structured data analytics can use machine learning as well, but the massive volume and many different types of unstructured data requires it. A few years ago, analysts using keywords and key phrases could search unstructured data and get a decent idea of what the data involved. eDiscovery was (and is) a prime example of this approach. However, unstructured data has grown so dramatically that users need to employ analytics that not only work at compute speeds, but also automatically learn from their activity and user decisions. Natural Language Processing (NLP), pattern sensing and classification, and text-mining algorithms are all common examples, as are document relevance analytics, sentiment analysis, and filter-driven Web harvesting. Unstructured data analytics with machine-learning intelligence allows organizations to:
Read Next: What is Data Annotation? In eDiscovery, data scientists use keywords to search unstructured data and get a reasonable idea of the data involved. Tools to Use for Structured and Unstructured Data AnalyticsNo matter what your business specifics are, today’s goal is to tap business value through both structured and unstructured data sets. Both types of data potentially hold a great deal of value, and newer tools can aggregate, query, analyze, and leverage all data types for deep business insight across the universe of corporate data. Check out these top business intelligence tools for structured and unstructured data analytics, and start growing your data capabilities across all types of data:
Next steps: to fully understand the enterprise IT infrastructure that hosts today’s structured and unstructured Big Data tools, read What is Cloud Computing? The Complete Guide Originally published March 28, 2018. Republished with updates on May 21, 2021. |
A class of database technology used to store textual and other unstructured data is called:
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