The collection and management of data require the correct set of tools to aid the process. But not all data comes in the same form. It can be divided into two types: unstructured and structured data.
Unstructured and Structured Data: What’s the Difference?
Unstructured data refers to information that is not organized or lacks a predefined structure. Examples: different instances of a product; other people’s contributions to a blog post.
Structured data is obviously data that can be processed like any other kind of data: in terms of consistency, structure, and production—examples: different versions of the same article, or a product’s specs.
Comparison of Structured and Unstructured Data
Structured data is data that is in a defined format, such as an Excel spreadsheet. Unstructured data is typically raw data. Structured data is the best choice when the data is not in a database.
Raw data is more complex and, therefore, requires processing using a framework. Unstructured data is what you get when you do not process it with a pre-existing schema.
A blog post, for example, is always a type of unstructured data, but the same article in Excel can be easily transformed into structured data, similar to an email list.
Unstructured data is the preferred choice for a database, as it is inherently complex and requires processing using a pre-existing schema.
What are the main differences between unstructured and structured data?
Unstructured data refers to information that is not organized in a structured format, such as a table or spreadsheet. Examples of unstructured data include emails, documents, videos, and audio files.
Structured data is data that has been organized in a table or a spreadsheet. Data in a table is often arranged in rows and columns, with the columns labeled with different fields.
There are key differences between structured and unstructured data. Unstructured data is easier to categorize, but structured data is far more helpful.
Structured data is helpful for analytical purposes, but unstructured data is more easily categorized and analyzed.
Numerous tools are available for data analysis. After the data has been collected, one must decide how best to organize and summarize the data.
Data modeling is the process of structuring data into simple, hierarchical forms. This can be done by using a data model or an object model.
There are many advantages of using a data model and an object model. The main advantage is that it allows the data to be easily and quickly searched. This allows for more efficient use of existing storage and retrieval mechanisms.
The most common types of data models are relational and object-oriented. Relational databases provide a structure for tables that represent the actual data and contain columns for each variable.
With an increasing emphasis on the use of cognitive computing, it is natural that its impact will be felt in today’s data analytics landscape.
For the longest time, Enterprises chose to mostly ignore unstructured data since the tools and skills required to derive meaning from it were not sophisticated nor flexible enough. No longer so.
Today, among the solutions offered for analyzing unstructured data are Machine Learning and Artificial Intelligence (AI).
AI is starting to play a role in identifying patterns in unstructured data. One of its branches, Natural Language Processing (NLP), now enables computers to understand human language, allowing them to make sense of customer conversations and categorize them accordingly.
This means that the use of NLP in online social conversations can help recognize sentiment on a particular subject, likely in real-time, thus allowing the brand to adjust its product strategy midway through its marketing campaign.
Some studies indicate that unstructured data accounts for as much as 80% of the total data available today.
It can be found in social media networks, news, chat services, messaging services, niche magazines, government reports, and white papers, among other sources.
Online conversations between two or a group of people are also considered unstructured data.
So what information does unstructured data contain? It provides pointers to customer requirements, feedback, emotional behavior, emerging sectoral trends, and a wealth of distinct details, all of which can prove vital in informing business decisions.
Cognitive Computing Solutions
The expertise to convert these conversations or feedback from consumers, largely in real-time, into near-accurate, actionable intelligence for business purposes has only become available recently.
This includes the techniques offered by cognitive computing technologies. As we had mentioned in an earlier post, cognitive computing is exhibiting all the signs of changing the way the world does business.
It can be dubbed the “mother of all computing” (so far) – a superset of analytics, AI, Business Intelligence (BI), and machine learning.
A simplistic explanation would be – it’s a computer trying to make a copycat version of the human mind.
Such is the rapidity of progress being made in the interpretation of unstructured data that modern-day tools even allow employees with statistical skills but no formal knowledge of data analytics to find answers to business questions, without requiring IT personnel.
To start, pattern discovery is the core of cognitive computing’s ability to make sense of unstructured data. Machine learning algorithms, they say, can establish precedents based on current attributes and use data to predict future ones.
Cognitive computing technologies, including flexible machine learning algorithms, can cleanse the data, add “structure” to such unstructured data, decide their integration, and determine the future course of business actions. All of which also makes an organization more agile.
Thus, if your company is one of those that has been ignoring unstructured data so far, it can no longer afford to do so.
From now on, firms that continue to rely solely on structured data will miss out on deriving benefits from the hidden insights in unstructured data. In today’s world of intense competition, it could prove to be a costly mistake.