Structured Vs Unstructured Data : Role Of ML/AI In Deriving Insight

Structured Vs Unstructured Data : Role Of ML/AI In Deriving Insight

November 25, 2022

Data science connects the world by condensing previously dispersed information into small components. But, with all the talk about big data and how businesses use it, you might be wondering, "What types of data are we talking about?"

To begin, it's important to recognise that data is critical to business decisions. It is the lifeblood of any company and comes in a wide range of formats, from well-structured relational databases to your most recent Instagram post, or the recording of a meeting held a few weeks ago. The ability of an organisation to obtain relevant data, evaluate it, and act on its findings is what determines its level of success. However, the amount and types of data available to businesses are ever-expanding. Secondly, all data isn't created equally. As a result, data generated by social media apps differ significantly from data generated by supply chain systems. In all its different formats, all of this data can be divided into two main categories: structured data and unstructured data, and the way this data is collected, processed, and analysed depends on its format.

Before we get into the unique differences between structured and unstructured data, it's vital to understand that using versus does not imply that one is better than the other. Instead, it simply implies that organisations are debating whether to invest in unstructured data analytics and if it is conceivable to combine the two for better business intelligence.

What is Structured Data?

Structured data is most commonly classified as quantitative data, and it's the form of data with which most of us are familiar. It is the data in relational databases and spreadsheets that fits nicely into specified fields and columns. Examples include names, dates,addresses, credit card numbers, stock information, geo location, and more of the like. 

Machine language and artificial intelligence tools can easily understand structured data because it is well-organised. For example, those working with relational databases can use a relational database management system to quickly input, search, and manipulate structured data. 

It's crucial to remember that while this data may not appear relevant at first glance, it's crucial to remember that using analytic tools can reveal patterns and trends that can benefit the business. While structured data has effectively replaced paper-based systems for corporate intelligence for decades, more firms are turning to deconstruct unstructured data for future potential.

What is Unstructured Data?

Unstructured data is frequently classified as qualitative data since it cannot be handled or analysed using traditional tools and methodologies. Examples include text files, emails,social media, websites, mobile data like geolocation or text messages, media files, satellite imagery, sensor data like traffic or weather, etc. 

Because unstructured data lacks a pre-defined data model, it cannot be organised in relational databases, making it difficult to deconstruct. Non-relational or No SQL databases, on the other hand, are ideal for handling unstructured data. Unstructured data can also be managed by flowing into a data lake in its raw, unstructured form. 

While organised data provides a bird's-eye view, unstructured data can provide a far more in-depth understanding of the subject. However, it's not easy to find the insights buried in unstructured data and to make an impact, complex analytics and a high level of technological competence are required. For many businesses, data analysis is a costly transition. Those who can use unstructured data, on the other hand, have a competitive advantage.

Structured Vs.Unstructured Data

There are some notable differences between structured and unstructured data that data workers should be aware of.

Artificial Intelligence and Machine Learning: The Next-Gen Game-Changing Tools

Analysts could search unstructured data using keywords and key phrases a few years ago and get a good idea of what was there. However, today unstructured data has grown to the point that data workers require advanced analytics and tools that run at computer speeds and learn from their actions and conclusions. As a result, new methods for analysing unstructured data came into existence.

 Artificial intelligence (AI) and machine learning (ML) are at the heart of most of these tools. Natural language processing (NLP), pattern recognition and classification, text mining methods,document relevance analytics, sentiment analysis, and filter-driven web harvesting are just a few examples. AI and ML play roles that are of utmost significance in operating, processing, and analysing unstructured data.

Roles of AI and ML in Deriving From Unstructured Data

1. One of the most  significant roles that Artificial Intelligence and Machine Learning play  in analysing unstructured data is overcoming the major challenges that   have been concerning knowledge workers for decades. These challenges  include:

  • Automating data workflow
  • Standardising unstructured data formats using traditional object-relational mapping techniques
  • Complying governance
  • Evaluating new technologies to understand human-generated data
  • Increasing accessibility

2. Among the different  sources of unstructured data, texts become of the most challenging kinds. This is so because one can never be absolutely sure of what they will  receive, it’s never the same as shared before, and the most important  reason is that it is in human language that has ambiguities and incomplete, inconsistent cases. However, AI and ML techniques like pattern recognition  and sentiment analysis make it possible to not only understand but also  make use of such complex unstructured data.

3. AI and ML run  complicated analytics on unstructured data analytics and help:

  • Identify data points from millions of unformulated data formats
  • Categorise data and rectify errors and redundancies
  • Study data relationships, conduct data modelling
  • Implement data visualisation
  • Use digital imaging technologies and pattern recognition to interpret images and video content
  • Implement Natural Language Processing (NLP) to obtain insights into human-generated,natural language unstructured texts and queries that traditional machine languages cannot comprehend
  • Empower data analysts with greater automation, better efficiency, enhanced scalability, and high accuracy in processing and understanding unstructured data.

Organisations will improve existing products, increase the efficiency of internal processes, and enable informed decision-making by incorporating AI and ML and analysing the insights obtained from structuring unstructured data.

Unstructured Data is the Future

For the foreseeable future, structured data will undoubtedly continue to play a role in most company activities.However, as data management improves and user data gets more complicated, the additional context added by unstructured data will become increasingly important. In addition, the capacity to store, query, and analyse data from any source will offer new possibilities, and it will be the place where businesses will find future success.

 With Needl, you can learn to unlock the hidden potential of unstructured data as we bring together category leaders in discrete apps, productivity tools, office suite platforms, communication apps,and workflow management tools that manage unstructured data and build uniform processing, collaboration, and information workflows. Moreover, we assist you in leveraging AI and ML to deal with unstructured data, streamline your workflow, and improve your experience.

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