Best Way to Convert Unstructured Data to Structured Data with Example

Conversion of Unstructured Data to Structured Data


These days, Big Data is described with 3 words volume, velocity and variety. The idea or concept to build the developing processes in order to manage the increasing ‘volumes’ and ‘velocity’ of knowledge nearly looks feasible. But from a method excellence purpose we are specifically curious about the ‘variety’, as this relates to two knowledge category; structured data knowledge and unstructured data knowledge. The web data extraction services are used to extract both of this data types to be applied for business and technology purposes.

Unstructured data is a generic term to describe knowledge that does not sit in knowledgebase’s and may be a mixture of textual and non-textual data. It is difficult to convert unstructured data to structured data as it usually resides in media like emails, documents, presentations, spreadsheets, pictures, video or audio files.

As the volumes of this sort of knowledge have increased through the employment of good technology the necessity to analyse this data and its awareness has also grown. This unstructured data file is processed and converted into structured data as the output by using unstructured data to structured data conversion tools. Automated unstructured data mining software will surely help in such scenarios.

Transforming Unstructured Data to Structured Data

How to convert unstructured data to structured data in Hadoop with an example

One of the immense things about Hadoop is that it provides a consistent, easy on the pocket and comparatively a simpler framework for gathering, confining and storing multiple data streams that was some years ago not feasible.

Taking an example, consider unstructured data in Hadoop as being a crude oil. Though it is one of the most valuable raw materials, however before you can extract or fetch needed gasoline from crude we require to put it across a filtering or more precise a distillation procedure in a refinery to remove its impurity, and extract the valuable hydrocarbons which can be categorised as structured data.

Structured data is relatively uncomplicated and easy to utilize

Using structured data is easy with its methodological enhancements and as they reside in databases within the category of rows and columns. It’s classified into relations or categories based mostly upon shared characteristics. The information is usually allotted attributes (data descriptions) associated with the categories inside every cluster to assist in ordering and logically grouping. Finally it is often delineated by predefined formats (string or value) with predefined lengths of characters.

This makes structured data a decent place to begin for anyone longing for sturdy knowledge to form data upon that to create significant insights. Structured data are often queried and analysed to type, group, filter, count and total so as to answer business queries or live method capability. It is used in product data intelligence as well as price monitoring software solutions.

With the account for the validity of the information it does modify comparatively with the process to verify and observe the information. Structured data forms an out-sized part of the information utilized by several in method enhancements, but this trend is quickly dynamical because the dominance of unstructured data will increase.

Unstructured data extraction involve complexities while processing the data initially

As unstructured data resides on company networks, inside collaboration tools and within the cloud these are often very troublesome to interrogate. So as to look the information, processes ought to be in place to assist tag and sort it. This step is essential to permit for linguistics looking against key words or contexts.

Unstructured knowledge is being used in an exceedingly huge approach for social media corporations needing to perceive their markets and customers in additional depth. This presents identical opportunities to several of our businesses to assist perceive not solely its customers higher, however operations inside.

A recent IDC report foretold the amount of digital content in 2012 can increase from 2011 figures by forty eighth percent to over 2.7 zettabytes (ZB) continued to associate 7.9 zettabytes (ZB) by 2015. Over 90% of this data is calculable to be unstructured data that highlights the necessity to develop sturdy strategies to know and analyse the embedded data.

Challenges with Business Processes in relation to unstructured data extraction

The challenge for businesses is to develop processes to use structure to the unstructured nature of the information for instance crucial the amount of satisfaction of consumers by analysing emails and social media could involve sorting out words or phrases. Words and phrases could also be classified into positive, negative or neutral classifications.

At this stage the unstructured data is remodelled to structured knowledge by using unstructured data mining software wherever the teams of words found based mostly upon their classification are assigned a value. A positive word could equal one, a negative -1 and a neutral zero. This unstructured data will currently be kept and analysed as you’d with structured knowledge. Rather more work is required during this space to analyse the unstructured data and plenty of the large vendors are functioning on solutions.

I believe the companies that may get the foremost of their unstructured knowledge sources are those who notice ways and unstructured data mining software tools to remodel the unstructured to structured data.


The actual value can be derived when structured and unstructured data analysis is combined for an end-to-end solution.

To know how you can grow your business results using DataCrops web data extraction software and solutions, connect for a free consultation with one of our experts today.

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Best Tips for Scraping Unstructured Data

Top 5 Tools for Unstructured Web Data Extraction

Best 5 Unstructured Data Mining Tools


Unstructured Data Extraction Tools: It is correctly said that data is money in the present business world. Also with the changeover to an app based world drives the upsurge growth and maturity of data. But still maximum amount of data in the organizations and market are unstructured and so it requires a process and methodology to fetch or extract structured information from that unstructured data and which can be further transformed it into usable business insights as well as intelligence using unstructured data analytics tools.

This is the above stage wherein unstructured web data extraction is required. There are some unstructured data extraction tools available in the market, using automated web techniques to extract valuable data.

Unstructured data in big data further assists organizations or companies at all the levels to better transform their current businesses. They execute this using the extracted data, transformed data intelligence, analytics and insights using unstructured data management solutions.

Unstructured Data to Structured Data Tools

Here are Top five unstructured data extraction tools available:


1. Upstream Commerce:

Upstream Commerce offers retailers solutions to boost business margins through competitive data insights, pricing intelligence and product assortment. It has assisted organizations across retailing categories to enhance pricing, promotion and merchandising management. It allows business users to build intelligence by using patterns and relationships with both structured and unstructured data information extractions.

2. DataCrops:

DataCrops is a scalable software platform that intelligently extracts information from numerous websites and complicated online data sources by using a robust self-enhanced technology. It extracts data, transform and load it, ensuring the delivery of correct information at correct time and in a required and correct format.

DataCrops offers web data extraction solutions and analytical tools to extract data using both structured and unstructured data sources. It extracts unstructured information and convert it into business insights to help retail, travel, hotels, flight, tyre, research, finance, data management and online market intelligence companies. Along with web data extraction software solutions, it also offers price intelligence tools, product intelligence, online market intelligence, social media intelligence, brand intelligence and channel intelligence solutions.

3. ParseHub:

ParseHub is a visual data extraction tool for getting web data. It manages interactive calendars, maps, search, nested comments, dropdowns, forms, infinite scrolling, authentication, forums, Ajax, JavaScript, and much more with ease. You can create APIs from different sites using this tool.

ParseHub provides multiple plans for web data extraction.

4. Talend Data Fabric:

Talend Data Fabric is a data integration platform that lets clients operate between streaming, batch, and real-time data. It runs on-premises, in Cloud and with Big Data. Talend offers its users a unique design interface for all data integrations and the master data management requirements.

5. ABBYY FlexiCapture:

ABBYY FlexiCapture is a data capturing, extraction and document processing software tool for unstructured data analytics. It is well designed to transmute streams of documents of structured, unstructured and complexity into business data. It offers automatic data extraction from different invoices and export it to online sources.

It offers programmed document classification, with a scalable and customizable architecture, assisting companies of any size to modernize their business processes, upsurge efficiency and decrease costs. It extracts data from documents, text-heavy papers, structured forms and the surveys.

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Best Tips for Scraping Unstructured Data

Know how to scrape Unstructured Data?


Web scraping also known as web data extraction is an automated web technique of fetching or extracting required data from the web. It transforms unstructured data on the web into structured data that can warehoused to your database.

Here are Top Six Tips for Scraping Unstructured Data

1. Have a scalable solution to scrape unstructured data

Old-fashioned technical approaches to scraping of unstructured data isolate “moving parts” of a solution to make it simpler for programmers to solve an issue. They are inaccessible from the runtime usage set-ups. However, when a non-programmatic methodology builds the code, it opens up the probability to accept indications about proposed usage of extracted data. An automated Web data extraction software and monitoring solution can, for example:

  • Avoid useless links and reach at anticipated data more quickly
  • Put away less hardware resources
  • Build an agiler load footprint on the targeted sites

This will help extract unstructured data at scale using unstructured data extraction tools. Addition to non-programmatic methodology this will better capture knowledge about targeted websites and influence it to speediness of learning through multiple sites, addition to the capability to scale competently and excellently while extracting unstructured data.

2. Capability to work across dynamic unstructured data

The web scraper software’s are reliant on HTML delimiters, which breakdown when the original HTML changes and the necessity for fixes have to be manually tracked. An automated Web data extraction and tracking solution perceive changes and additions with accuracy, providing only the preferred data by using unstructured data analysis techniques.

3. Effectively generate as well as manage scripts and agents for unstructured data

An automated Web data extraction solution specially for data extraction tools for retailer, can assist to rationalize processes and workflows at scale, effortlessly generating productivity gains. These include:

  • Shared schemas and request lists to handle different projects with reliable team practices
  • Tools that effortlessly raise mass adjustment activities
  • Data mining tools and techniques for unstructured data
  • Automatic deployment and load handling
  • Bulk operations with job and task scheduling
  • Agent Migrations and user subscriptions amongst the systems
  • Consistent testing and better quality assurance

4. Convert Unstructured data to functional structured data

Unstructured data is intended for human eye whereas structured data is intended for computers. A traditional web scraper and an automated web data scraping software solution will both transform unstructured data into structured data providing analysis to execute better business decisions. However, the automated solution integrates and utilize data normalization methodologies to make sure that the structured data can effortlessly be turned into key actionable data insights.

5. Minimize errors in fetching structured data by automation

Visual abstraction is a methodology which utilizes machine learning to create well-organized code we term it as an agent. Visual abstraction understands each web page as a human observes the page visually. But an automated Web data extraction and tracking solution can better support an advanced level of abstraction without using HTML structures. And, it also does not break when it perceives page variations.

6. Integrate data results with business processes and operations

In the current data-driven business environment, multiple teams at work frequently interact with data collection and the analysis processes. Organizations looking for web scraping of unstructured data must communicate and support the data requirements, for multiple purposes. As the requirements are diverse, built-in features supportive to the variety of needs are key for scaling to higher volumes and frequencies of data gathering.

Learn more about DataCrops accurate, better accessible and result oriented solutions.

Contact DataCrops and find out how an automated Web data extraction and data intelligence solution can advance your organization’s efficiency, productivity and overall workflow.

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