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Structured Vs Unstructured Data-Understanding the Difference

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Data provides information about a particular subject and this can be used for business analysis. Data has different formats. We see great rise in data size in this digital era. Inorder to effectively analyse vast amounts of data to help with decision making, a good knowledge of the differences between various types of data is necessary.

Let us understand the basic difference between Structured and Unstructured data.

What is Unstructured data?

Data which does not have a pre-defined data model and has no easily identifiable structure is called Unstructured data. This type of data cannot be used by a computer program easily. Unstructured data sources are diverse in nature hence managing it requires specialized tools and data science talents. The ever-expanding unstructured data collection has resulted in the growth of data lakes and Hadoop platform.

Textual analysis of written blogs and books, scanning different communications such as emails to detect spam etc are all examples of Unstructured data.

Most common forms of unstructured data are as follows: 

  1. Text files, word documents, PDF files which includes books, and other written documents, audio/video transcripts.
  2. Powerpoint Presentations and Slideshares.
  3. Audio files which includes voicemails, 911 phone calls.
  4. Videos which include YouTube uploads.
  5. Messaging which includes Instant messages and text messages.
  6. Images which includes memes, illustrations and pictures.

Unstructured data if analysed using the right type of tools can provide compelling and meaningful insights for your business. 

What is Structured Data?

Any form of data which resides in a fixed field within a record or file is called structured data. This includes Data enclosed in relational databases and spreadsheets. Structured data can be easily entered, stored, queried and analysed.

The most common example of structured data is a customer records database. We find customer first name, last name, customer ID, address etc in this database. All these are data in a uniform format and can be easily organized so that the user can quickly access this data and analyse it to derive business insights.

Most common relational database applications with structured data include airline reservation systems, sales transactions, inventory control, ATM activities. SQL often enables queries on structured data which resides within relational databases.

Key differences between structured and unstructured data

  • Structured data is simple and can be easily searched and analysed mainly because of its uniform format. Unstructured data is quite complex and difficult to analyse and search because of the diversity of its many formats.
  • Structured data is composed of letters, numbers and symbols which makes it easier to be stored and organized in databases. Unstructured data don’t have a basic text format, instead it comes in file types such as videos, audio files etc. and hence consume more storage.
  • Structured data is comparatively easier for Big Data programs to grasp while the countless formats of unstructured data is a greater challenge. 
  • Structured data analytics is a stable and mature process and technology. Unstructured data analytics is a burgeoning industry with lots of investment into R&D.

Convert unstructured data to structured data using Hadoop

Hadoop is a magical platform which would help you convert trillions and zillions of bytes of unstructured data into structured data format. This is a data storage and processing platform which is designed to scale to numerous compute nodes and petabytes of data. Hadoop was primarily used by leading search engines to create page rank based on keywords from the text on pages.

Processing unstructured data is crucial in this era of growing data breach incidents. This has special relevance when it comes to Compliance – GDPR. Securing sensitive customer data which gets stored in CRM databases, emails, chats, and other log files is extremely important. When there is a data breach, we can easily detect the specific location of PII (Personal Identifiable Information) and secure it quickly.

Here is an outline of the process commonly followed to convert unstructured data to structured data:

  • Text Extraction

Hadoop basically supports text file format. A custom input file format is required to be written to process different kinds of files, for example html, pdf, word etc. You can extract the text from these different file formats using many open source solutions.

  • Parsing or Tokenization

Once text is extracted, you must garner the sentences first and then words from the paragraph. Machine learning is helpful in this aspect. Use some Java based open source libraries for text parsing.

  • Phrase Recognition

To separate phrases from text you can use rules to check different word combinations from a dictionary or you can use machine learning models.

  • Named Entity Recognition

You can separate nouns, proper nouns, address and city from a text. Identify if a specific word is a city, address or state. For this, you can create a machine learning model to understand if a word is within a specific category. With the help of open source model, you can identify name and city. But to identify anything other than these you must create a model to learn from structured data and apply the model automatically. Once you identify names, index them for analytics and searching.

You can continuously generate value by structuring unstructured data and this helps you to understand customers and their behaviour through Sentiment Analysis. This would strengthen your customer support team by classifying, analysing and solving customer problems with greater ease and precision.

How Structured data can be managed in Azure?

Structured and Unstructured data can be stored securely using Azure Data Lake. Choosing the right data repository is always crucial. A strong database solution (like Azure Cosmos DB) which makes data searchable for analytics purposes is also vital for any business. Cosmos DB provides well-defined consistency models for fine-tuning performance, single digit millisecond latencies at the 99th percentile anywhere in the world. Apache HBase which is an open source, NoSQL database modelled after Google BigTable and built on Hadoop which scales to handle petabytes of data on numerous nodes is another solution. This provides random access and robust consistency for huge quantities of unstructured and semi-structured data.


Big data analytics revolves around efficient usage of structured and unstructured data. In the past, structured data was the only means to effectively manage data mainly because of limited processing capacity, huge data storage costs and inadequate memory. Using unstructured data for analysis was pretty expensive in the past. Technologies like Hadoop makes unstructured data analysis efficient and affordable for businesses. Now, enterprises are increasingly using unstructured data due to increased availability of storage and complex data sources.

An organization’s data laboratory often contains a balanced mix of structured and unstructured data. Both these types of data are quite valuable in our modern digital enterprise. All you have to ensure is that these types of data should be managed and analysed differently to arrive at useful business decisions.

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