Data Analytics and Business Intelligence
min read

Big Data Analysis By Combining Structured & Unstructured Data

Explore the two pillars of big data analysis and how it is essential to make significant business decisions.
Pinakin Ariwala
Pinakin Ariwala
Updated on Dec 29
Data Analytics and Business Intelligence
min read
Big Data Analysis By Combining Structured & Unstructured Data
Explore the two pillars of big data analysis and how it is essential to make significant business decisions.
image
Pinakin Ariwala
Updated on Oct 14
Table of contents
Structured vs. Unstructured Data: The 2 Pillars of Big Data Analysis
Key Differences Between the Structured and Unstructured Data
The Future of Data
Best Solution For Big Data Analysis
To Conclude

The importance of data for making significant business decisions is immense. An organization’s ability to gather correct data, interpret it accurately, and work on those insights is fundamental in determining its success. The key to unlocking the value of such massive amounts of data is understanding data structure.

Data structure refers to a specific way of organizing and storing vast sets of data in a database or warehouse so that companies can access and analyze it quickly. However, organizations today are swarmed by the sheer amount of various forms of data available in a wide variety of formats, from relational databases, email logs to social media data.

All of this data available in different formats can be segregated into two main categories: structured data and unstructured data in big data. This post will explore the difference between these two types of data and how they can be integrated into extensive data analysis.

Structured vs. Unstructured Data: The 2 Pillars of Big Data Analysis

Data exists in multiple different forms and sizes, but most of this can be presented as structured and unstructured data, as discussed below –

1. Structured Data

The term structured data refers to data available in a fixed field within a file or record. So any information that is factual to the point and adequately organized comes under structured data.

Stored in a relational database (RDBMS), structured data comes in numbers and letters that fit perfectly into the rows and columns of tables. 

Example of Structured data

We all are aware of structured data as this data can comprise text and numbers, such as contacts, ZIP codes, employee names, addresses, credit card numbers, geolocations, etc.

Other typical relational database applications with structured data include airline reservation systems, sales transactions, inventory control, and ATM activity. Structured Query Language (SQL) easily enables queries on this type of structured data within relational databases.

2. Unstructured Data

As the name suggests, unstructured data in big data analytics refers to all the data that is not structured in any way. Unlike structured data, it is not structured predefined, even though unstructured data may have a native, internal structure.

Example of Unstructured Data

Among typical human-generated unstructured data includes:

  • Email:  While email has some internal structure because of its metadata and is sometimes referred to as semi-structured, its message field is generally unstructured. Traditional analytics tools cannot parse it.
  • Text files: Spreadsheets Word processing, emails, logs, and presentations
  • Website: YouTube, Instagram
  • Social Media: Data from social media platforms such as Facebook, Twitter, LinkedIn
  • Communications: IM, Chat, phone recordings, collaboration software
  • Mobile data: Text messages, locations
  • Business applications: Productivity applications, MS Office documents
  • Media: Digital photos, MP3, audio, and video files

Some of the examples of machine-generated unstructured data include:

  • Scientific data: Seismic imagery, atmospheric data, oil and gas exploration, space exploration
  • Digital surveillance: Surveillance videos and photos
  • Satellite imagery: Weather data, military movements
  • Sensor data: Weather, oceanographic sensors

Key Differences Between the Structured and Unstructured Data

Among some of the main differences between structured and unstructured data include-

1. Defined vs. Undefined

While the structured data is clearly defined in a structure, unstructured information is usually stored in its native format. Apart from this, structured data is typically present in rows and columns and can be mapped into predefined fields. In contrast, unstructured data does not have a predefined data model and is not organized and easily accessible in relational databases.

2. Ease of Analysis

One of the other key differences between structured and unstructured data is the ease of analysis. While structured data is relatively easy to search, unstructured data is more challenging to search, process, and understand.

The absence of a predefined model makes it challenging to deconstruct unstructured data. Further, unlike structured data, where there are multiple analytics tools available for analysis, there aren’t many for mining and arranging unstructured data.

3. Qualitative vs. Quantitative Data

In most cases, structured data is quantitative, meaning it consists of complex numbers or things that can be assessed or counted. Among the critical methods for analysis include regression, classification, and clustering of data. 

Unstructured data, on the contrary, is often categorized as qualitative data and is not easy to process and analyze using conventional tools and methods. 

4. Storage

Structured data is typically stored in data warehouses, which is the endpoint for the data’s journey through an ETL pipeline. On the other hand, it is stored in data lakes-which is a kind of limitless repository where data is mainly stored in its original format. 

Besides, structured data requires much less storage space as compared to unstructured data. When it comes to databases, structured data is usually stored in a relational database (RDBMS), whereas unstructured information is stored in NoSQL databases

The Future of Data

The global data has shown no signs of slowing down since it started to grow exponentially (a decade ago). While the data structures will evolve in the future, the future will be unstructured as unstructured data is fundamental to the next generation of a wide array of intelligent systems, information primarily based on cognitive analytics and artificial intelligence (AI)-based applications.

It is predicted that by 2025, 80% of all data would be unstructured, and an increasing number of organizations have reached that estimate already. While it offers a massive opportunity to the organizations, it also poses a unique challenge in systematically accessing and analyzing it. Further, organizations won’t be just using unstructured data but a combination of structured, unstructured, or semi-structured data. However, the key concern here will remain accessing, preparing, and combining this data to make sense of it.

Best Solution For Big Data Analysis

When it comes to big data analytics, most analysts wonder about this- how does big data handle unstructured data?

However, the need here is to integrate both structured data and unstructured data. Examples of this could be mapping client addresses and audio files or mapping customer and sales automation data to social media posts.

Irrespective of the complexity and variance of structured and unstructured data, analysts need to use appropriate preparation, visualization, and analysis techniques to leverage all the available data for better business decision-making.

However, one of the critical challenges that analysts face in combining structured and unstructured data for extensive data analysis is the varied types of databases/ systems both these types of data exist in. Due to this, many analytics professionals are compelled to navigate multiple systems and move massive amounts of data, which is not too desirable.

Efforts are being put to unify traditional RDBMS tools and big data stores through a common data analytics platform, to resolve this issue. It will help enable analysts to access massive data sets for any analysis at any time.

Maruti Techlabs utilizes both SQL and NoSQL technologies for building an efficient, extensive data analytics ecosystem. This is how our data experts do it:

  • We have developed a logic to convert data collected from clients in RDBMS databases to NoSQL.
  • This new NoSQL database is then analyzed by Elasticsearch – a tool for querying written words – which offers textual results that resemble a given query and satisfy the search needs of all users. Find out in detail What is Elasticsearch?
  • Elasticsearch converts data from the RDBMS form to the NoSQL form to make it searchable instantly once uploaded in RDBMS. 
  • Another distinct feature of Elasticsearch is that it returns search results for both logged-in users and public repositories. It can also see search results for any private repositories that it can access.

To Conclude

Irrespective of the business specifics, the goal of every business today is to make sense of structured data and unstructured data for better and more productive decision-making.

Since both these types of data hold a great deal of value, good big data analytics in business requires integrating variously structured and unstructured data stores and systematically acquiring intelligence across them. Businesses looking to make the most sense of their data should use multiple tools that utilize the benefits of structured and unstructured data.

At Maruti Techlabs, our data analytics services are oriented towards drawing maximum value. We deliver analytics, reports, BI, and predictions of superior accuracy to solve your unique business problems, sometimes even before they crop up. Big data analytics, data management, predictive analytics, data visualization, and more – we do it all. You can reach out to us here for all your big data analytics requirements.

contact us - Maruti Techlabs

Pinakin Ariwala
About the author
Pinakin Ariwala


Pinakin is the VP of Data Science and Technology at Maruti Techlabs. With about two decades of experience leading diverse teams and projects, his technological competence is unmatched.

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