Last Updated on November 8, 2022
Decisions in business are based on data. A company’s level of performance is frequently determined by its capacity to obtain the appropriate data, analyse it, and take action in response to those insights. But both the quantity and variety of data that businesses can access are constantly growing. There are many different forms available for business data, ranging from strictly constructed relational databases to your most recent tweet. There are two basic categories that can be used to categorise all of this data, namely structured data and unstructured data.
While semi-structured and unstructured data are more complicated and challenging to arrange and extract, structured data is generally easier to work with. Every form of data has great value for businesses, and knowing how to manage data effectively helps firms reduce errors and boost productivity.
We’ll examine these ideas in more detail in this post, as well as how they differ from one another.
What is Unstructured Data?
Most frequently classified as qualitative data, unstructured data cannot be handled or evaluated using standard data tools and techniques. Text, audio and video files, social network posts, mobile activity, satellite photography, surveillance imaging, and more are examples of unstructured data.
Since unstructured data lacks a predetermined data model and cannot be arranged in relational databases, it is challenging to deconstruct. For managing unstructured data, non-relational or NoSQL databases are preferable. Allowing unstructured data to flow into a data lake and remain there in its unprocessed state is another approach to handling it.
It might be challenging to glean the wisdom buried in unstructured data. To truly make an impact, it takes cutting-edge analytics and a high level of technical proficiency. For many businesses, data analysis can be a costly change.
However, those who can utilise unstructured data have a competitive advantage. Unstructured data can give us a much deeper knowledge of client behaviour and intent than structured data, which only gives us a top-down picture of customers.
Using data mining techniques on unstructured data, for instance, businesses might discover client purchasing trends, timing, attitudes regarding a certain product, and much more.
For predictive analytics software, unstructured data is equally essential. For instance, sensor data from industrial machines might notify producers in advance of unusual activity. With this knowledge, a fix can be made before the device experiences an expensive breakdown.
What is Structured Data?
Relational databases (RDBMS) are often where structured data is located. Data with defined lengths, such as phone numbers, Social Security numbers, or ZIP codes, are stored in fields. Records even include text strings with varying lengths, such as names, making searches easy. As long as the data is produced within an RDBMS framework, it can be generated by either humans or machines. This format is entirely searchable with human-generated queries and algorithms that use data types and field names like alphabetical or numeric, currency, or date.
Applications using relational databases frequently use structured data, such as inventory management, sales transactions, and ATM activities. Within relational databases, queries on this kind of structured data are possible thanks to structured query language (SQL).
Some relational databases, such as those used by customer relationship management (CRM) programmes, hold or refer to unstructured data. Since memo fields do not lend themselves to conventional database queries, the integration may be at best problematic. However, the majority of CRM data is structured.
Structured vs. Unstructured Data
1. Defined vs Undefined DataÂ
Data kinds in a structure that are well described as structured data. Structured data exists in rows and columns and can be mapped into pre-defined fields, but unstructured data is typically stored in its original format.
Unstructured data is thought of as being undefined since it lacks a predefined data model, in contrast to structured data, which is arranged and simple to access in relational databases.
2. Qualitative vs Quantitative Data
Quantitative data, or data that can be tallied, is what structured data frequently consists of. Regression is a technique for analysis that predicts the relationships between variables. Classification is a technique for estimation of probability (based on different attributes).
Conversely, unstructured data cannot be handled and evaluated using traditional tools and methods and is frequently labelled as qualitative data. Qualitative data can, for instance, originate from consumer surveys, interviews, and social media interactions in a commercial setting. Data mining and data stacking are two sophisticated analytics approaches needed to extract insights from qualitative data.
3. Ease of Analysis
How easily information can be analysed is one of the key distinctions between organised and unstructured data. Searching through structured data is simple for both people and algorithms. Contrarily, unstructured data needs processing to make it understandable and is inherently harder to find. Due to the lack of an established data model and consequent incompatibility with relational databases, it is difficult to deconstruct.
While there are many sophisticated analytics tools available for structured data, most analytical tools for mining and organising unstructured data, such as NLP and ML, are still in the research and development stage. Data mining is difficult since there is no predefined structure, and it is difficult to build best practises for handling data sources including rich media, blogs, social media data, and customer communications.
4. Storage in Data Houses vs Data Lakes
Unstructured data is frequently kept in data lakes, while structured data is frequently kept in data warehouses. The destination of the data’s journey via an ETL pipeline is a data warehouse. On the other hand, a data lake is a kind of virtually infinite repository where data is stored either in its original format or through a simple “cleaning” procedure.
Both have the capacity to be used in the cloud. Unstructured data needs more storage space than structured data, which requires less. For instance, even a tiny image consumes more space than numerous text pages.
Structured data is often kept in a relational database system (RDBMS), whereas unstructured data is better suited for so-called non-relational, or NoSQL, databases.
5. Predefined Format vs Variety of Formats
Text and numbers are the most typical formats for organised data. A data model has previously defined structured data.
On the other hand, unstructured data is available in a range of forms. It can include anything, including email and sensor data as well as audio, video, and picture. The unstructured data is kept natively or in a data lake without any need for transformation; there is no data model for it.
Semi-Structured Data
Additionally, semi-structured data exists. This type of data primarily consists of unstructured text but is informally categorised using “meta tags.” Email is a good illustration of this because you can search it by Inbox, Sent, Drafts, etc. Additionally, there are social media platforms that can be divided into Friends, Messages, Public Posts, Private Posts, etc.
Although the information contained in these predetermined categories is itself unstructured, semi-structured data is easily broken down into its predefined categories.
Intent categorization can be useful for automatically assessing business emails to determine whether a consumer is responding to a query with real interest or not.
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