Which of the below technologies help to transform the unstructured data to a structured format to analyze the data efficiently?

Let’s first begin by understanding the term ‘unstructured data’ and comprehending how is it different from other forms of data available.

Data that also contains meta-data (data about data) are generally classified as structured or semi-structured data. Relational databases – that contain schema of tables, XML files – that contain tags, simple tables with columns etc. are examples of structured data.

Image credit: Dileep Govindaraju

Now consider data like a blog content, or a comment, email messages, any text document – say legal policies of a company, or an audio file, or video file or images, which constitute about 80 to 90% of all forms of data available for analysis. These forms of data do not follow any specific structure nor do they contain information about the content of the data. These are all classified as unstructured data.

Having talked about the proportions of structured and unstructured data, old school database analytics methods on only structured data will limit the access to just 0.5% of the information available for analysis. With technologies like Hadoop growing fast, the focus is shifting towards tapping information from this unexplored chaotic realm of unstructured data that is available in huge volumes.

How is Hadoop suitable for analysing unstructured data?

1. Hadoop has distributed storage and distributed processing framework, which is essential for unstructured data analysis, owing to its size and complexity.
2. Hadoop is designed to support Big Data – Data that is too big for any traditional database technologies to accommodate. Unstructured data is BIG – really BIG in most cases.
3. Data in HDFS is stored as files. Hadoop does not enforce on having a schema or a structure to the data that has to be stored.
4. Hadoop also has applications like Sqoop, HIVE, HBASE etc. to import and export from other popular traditional and non-traditional database forms. This allows using Hadoop for structuring any unstructured data and then exporting the semi-structured or structured data into traditional databases for further analysis.
5. Hadoop is a very powerful tool for writing customized codes. Analyzing unstructured data typically involves complex algorithms. Programmers can implement algorithms of any complexity, while exploiting the benefits of the Hadoop framework for efficiency and reliability. This gives flexibility for users to understand the data at a crude level and program any algorithm that may be appropriate.
6. Hadoop being an open-source project, in numerous applications specific to video/audio file processing, image files analysis, text analytics have being developed in market; Pivotal, pythian to mentioned a few.

Let’s take an example of unstructured data analysis:

Consider the Video data feed from a CCTV surveillance system of an enterprise. Currently monitoring of these videos is done by humans. Detecting incidents from these videos will not only require the monitoring person to be noticing multiple video feeds, but also be attentive all the time.

Assume this monitoring process needs to be automated. The amount of data that will be fed in is huge – few Terabytes every hours. Processing close to real-time is required to detect incidents at the right time. Clearly, this will require a system that has the capability to store really heavy volumes of streaming data, very high processing speed and also the flexibility to be configured to perform any customized algorithm on the data.

Clearly Hadoop has all the capabilities listed and can be used in this scenario effectively. However, in many cases of unstructured data – mainly video/audio analysis, designing optimized algorithms to extract useful information for analysis is still a challenging problem under research. But with the way innovations are constantly being seen in the data space, we are sure to see new and improved techniques and tools in the very near future. Watch this space as the team at Jigsaw will be sure to update you on all new updates and more as and when they happen.

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Unstructured data can be thought of as data that’s not actively managed in a transactional system; for example, data that doesn’t live in a relational database management system (RDBMS). Structured data can be thought of as records (or transactions) in a database environment; for example, rows in a table of a SQL database.

There is no preference as to whether data is structured or unstructured. Both have tools that allow users to access information. Unstructured data just happens to be in greater abundance than structured data is.

Examples of unstructured data are:

Until the advent of object-based storage, most, if not all, of this unstructured data was stored in file-based systems.

The way to think about how to deal with the challenges of unstructured data is to ask: What do enterprises face with traditional approaches to managing unstructured data?

It’s common in many enterprises to encounter unstructured datasets at the scale of tens or hundreds of billions of items. These items, objects, or files can be anything from a few bytes (for example, a temperature reading from a production-line instrument) to terabytes in size (for example, a full-length 8K resolution motion picture). Managing this scale with traditional file approaches rapidly moves from difficult to impossible as more and more resources are required just to maintain a “balance” of servers, file systems, arrays, and so on.

Increasingly, these massive unstructured datasets deliver value as they are shared (for example, researchers at multiple hospitals who share a common massive bank of genomic sequences). With traditional approaches, the ability to share massive sets of unstructured data across geographies, corporate entities, and so on, has required extremely expensive replication and governance.

Today’s object storage solutions meet the challenges of scale and collaboration by delivering a geo-distributed active namespace. This namespace enables a user at any location to retrieve an object or a file from any location with a simple GET command (without having to specify a data center, server, file system, or director). Similarly, PUT commands enable the ingest of data so that all locations can easily have access.

The simplicity and scalability of a single global namespace combined with a simple stateless data management protocol (for example, Amazon S3 and Swift) help organizations deliver a scalable and collaborative environment across geography, organization, and application boundaries.

You can store and manage unstructured data at scale by using NetApp® StorageGRID® technology for secure, durable object storage for private and public clouds. With StorageGRID, you can build a massive (multilocation) single namespace, and you can also integrate a unique information lifecycle policy into that data. With the StorageGRID integrated policy engine, you can be confident that your data is available:

• In the right geographic location
• At the right level of performance
• At the right level of durability and protection
• At the right time and changing over time automatically as business needs evolve

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 Data

Semi-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

• Markup language XML This is a semi-structured document language. XML is a set of document encoding rules that define a human- and machine-readable format. (Although saying that XML is human-readable doesn’t pack a big punch: anyone trying to read an XML document has better things to do with their time.) Its value is that its tag-driven structure is highly flexible, and coders can adapt it to universalize data structure, storage, and transport on the web.

• Open standard JSON (JavaScript Object Notation) JSON is another semi-structured data interchange format. Java is implicit in the name, but other C-like programming languages recognize it. Its structure consists of name/value pairs (or object, hash table, etc.) and an ordered value list (or array, sequence, list). Since the structure is interchangeable among languages, JSON excels at transmitting data between web applications and servers.

• NoSQL Semi-structured data is also an important element of many NoSQL (“not only SQL”) databases. NoSQL databases differ from relational databases because they do not separate the organization (schema) from the data. This makes NoSQL a better choice to store information that does not easily fit into the record and table format, such as text with varying lengths. It also allows for easier data exchange between databases. Some newer NoSQL databases like MongoDB and Couchbase also incorporate semi-structured documents by natively storing them in the JSON format.

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. NoSQL

SQL (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.

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Structured Vs. Unstructured Data: Next Gen Tools Are Game Changers

New 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:

• Analyze digital communications for compliance. Failed compliance can cost companies millions of dollars in fees, litigation, and lost business. Pattern recognition and email threading analysis software searches massive amounts of email and chat data for potential noncompliance. A recent example in this area is Volkswagen, who might have avoided huge fines and reputational hits by using analytics to monitor communications for suspicious messages.
• Track high-volume customer conversations in social media. Text analytics and sentiment analysis lets analysts review positive and negative results of marketing campaigns, or even identify online threats. This level of analytics is far more sophisticated than simple keyword search, which can only report basics, like how often posters mentioned the company name during a new campaign. New analytics also include context: was the mention positive or negative? Were posters reacting to each other? What was the tone of reactions to executive announcements? The automotive industry, for example, is heavily involved in analyzing social media, since car buyers often turn to other posters to guide their car buying experience. Analysts use a combination of text mining and sentiment analysis to track auto-related user posts on Twitter and Facebook.
• Gain new marketing intelligence. Machine-learning analytics tools quickly work on massive amounts of documents to analyze customer behavior. A major magazine publisher applied text mining to hundreds of thousands of articles, analyzing each separate publication by the popularity of major subtopics. Then they extended analytics across all their content properties to see which overall topics got the most attention by customer demographic. The analytics ran across hundreds of thousands of pieces of content across all publications, and cross-referenced hot topic results by segments. The result was a rich education on which topics were most interesting to distinct customers, and which marketing messages resonated most strongly with them.

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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 Analytics

No 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:

• Apache Hadoop
• Tableau (Salesforce)
• KNIME
• Microsoft Power BI
• Oracle BI
• RapidMiner
• SAS Viya and TextMiner
• Cogito Semantic Technology
• Zoho Analytics
• CVAT

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.