The collection and management of data require the right set of tools to aid the process. But not all data comes in the same form. It can be divided into two – unstructured and structured data.
Unstructured and structured data: what’s the difference?
Unstructured data is data that is not organized or has a certain form. Examples: different instances of a product; different 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.
A 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 needs to be processed 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 kind of unstructured data, but the same article in Excel can easily be transformed into structured data, something like an email list. Unstructured data is the preferred choice for a database, as it is inherently complex and needs to be processed using a pre-existing schema.
What are the main differences between unstructured and structured data?
Unstructured data is a term used to describe data that is not organized in a table or a 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 useful.
Structured data is useful for analytical purposes, but unstructured data is more easily categorized.
There are many tools that can be used to analyze and perform data analysis. After the data has been collected, one must decide how to best 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 which represent the actual data and contain columns for each variable.
With more and more emphasis on the use of cognitive computing, it is but natural that its impact will be felt on 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 the analysis of unstructured data are Machine-Learning and Artificial Intelligence (AI).
AI is beginning to play a role in the discovery of patterns in unstructured data. One of its branches, Natural Language Processing (NLP), now allows a computer to understand the language of humans, thus making sense of customer conversations, and categorizing them. This means the use of NLP in online social conversations can help recognize a sentiment on a particular subject, probably in real-time, thus allowing the brand to change course on a product, midway through its marketing campaign.
Some studies show that unstructured data weighs in at as much as 80% of the total data available today. It is to be found in social media networks, news, chat services, messaging services, niche magazines, government reports, white papers, to name a few sources. Online conversation between two or a set of people, too, is also defined as unstructured data.
So what information does unstructured data contain? It has pointers to customer requirements, feedback, emotional behavior, emerging sectoral trends, and a host of distinct information, all of which can prove to be of vital importance in executing business decisions.
Cognitive Computing Solutions
The expertise to convert these conversations or feedback from consumers, largely in real-time, into near-accurate actionable intelligence to be used for business means has only become available of late.
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 the modern-day tools even allow employees with statistical skills but no formal knowledge of data analytics to look for answers to business questions, without the need of the 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 from current attributes and use data to determine 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. Going forward, firms that will continue to rely only on structured data will miss deriving benefits hidden in unstructured data. In today’s world of intense competition, it could prove to be a costly mistake.
An Engine That Drives Customer Intelligence
Oyster is not just a customer data platform (CDP). It is the world’s first customer insights platform (CIP). Why? At its core is your customer. Oyster is a “data unifying software.”
Liked This Article?
Gain more insights, case studies, information on our product, customer data platform