Difference between structured semi structured and unstructured data

In today’s expanding world, the one thing that’s increasing at a really fast pace isn’t only the temperature, it is also DATA! Data is the fuel to most digital innovations of today, be it from Facebook to webshops, everything is data.

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Table: Diference between structured and unstructured data
What is the difference between semi structured and structured?
What are the differences between structured and unstructured data?
What are the examples of structured semi structured and unstructured data?
What is difference structured and unstructured?

Data is broadly categorised into three types:

a) Structured

b) Semi-structured

c) Unstructured

Semi-structured data contains characteristics of both structured and unstructured data. A great example are e-mails, these have structured components; like the sender, receiver and subject, but also an unstructured component; the body.

Structured and unstructured differ not only in their quantity and quality but also in the way they are analysed. Ever wondered, how you posted about a sandwich maker on Facebook and the next thing on your feed is a company selling sandwich makers? Yeah, thats’s the power of analysis and data being converted into information. And this data is spread in various forms throughout the digital world. For this blog, we will differentiate between structured & unstructured data to find out how they behave and differ.

Table: Diference between structured and unstructured data

Parameter	Structured	Unstructured
Storage format	Specific data stored in a predefined format	Variety of data types stored in their original format
Size in a company	20% of enterprise data	80% or more of enterprise data; Growth rate y-o-y - 55% - 65%
Stored in	Data warehouses, relational databases, CSVs	Data lakes/oceans NoSQL databases Applications, fileshares, OneDrives and SharePoint sites
Searchability	Can be easily searched as has a predefined format	Very difficult to search as do not follow a predefined format
Generated by	Can be human or machine generated: Example: Machine generated - point of sale data such as barcodes and quantity. Human generated - spreadsheets	Can be human or machine generated too: Example: Machine generated - satellite imagery, digital surveillance. Human generated - text files, mobile data
Usage	Can be used by a normal data user	Needs a data scientist to make sense out of it
Tools for analysis	Tableau Apache Hadoop Oracle BI etc.	Text Mining Natural Language Processing Pattern Sensing & Classification etc.
Flexibility	Less flexibility of usage and less number of use cases	More flexible, which means more usability of such data
Applications	Digital reservation systems, ERP CRM	Word processing Email clients Tools for viewing or editing media
Example	Example: Relational database - data stored in a specified format and is queryable.	Example: audio, video, social media posts, emails, presentations, chats, PDFs, Word documents stored in folders.
Ease of collection	Cannot be easily and quickly collected	Can be easily and quickly collected
Risk of privacy breach	Low, since data is stored in a predefined location in a predefined format can easily be processed masked	Very high, since data is stored in various locations in undefined formats

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In context of Big Data we know that it deals with large amount of data and its execution. So in nutshell we can say that Big data is something which deals with the large amount of data and as amount of data is so large then broadly there are three categories which are defined on the basis of how data is organized which are namely as Structured, Semi Structured and Unstructured Data.

Now the basis of level of organizing the data we can find out some more differences between all these three types of data which are as follow.

Following are the important differences between Structure and Union.

Sr. No.	Key	Structured Data	Semi Structured Data	Unstructured Data
1	Level of organizing	Structured Data as name suggest this type of data is well organized and hence level of organizing is highest in this type of data.	On other hand in case of Semi Structured Data the data is organized up to some extent only and rest is non organized hence the level of organizing is less than that of Structured Data and higher than that of Unstructured Data.	In last the data is fully non organized in case of Unstructured Data and hence level of organizing is lowest in case of Unstructured Data.
2	Means of Data Organization	Structured Data is get organized by the means of Relational Database.	While in case of Semi Structured Data is partially organized by the means of XML/RDF.	On other hand in case of Unstructured Data data is based on simple character and binary data.
3	Transaction Management	In Structured Data management and concurrency of data is present and hence mostly preferred in multitasking process.	In Semi Structured Data transaction is not by default but is get adapted from DBMS but data concurrency is not present.	While in Unstructured Data no transaction management and no concurrency are present.
4	Versioning	As mentioned in definition Structured Data supports in Relational Database so versioning is done over tuples, rows and table as well.	On other hand in case of Semi Structured Data versioning is done only where tuples or graph is possible as partial database is supported in case of Semi Structured Data.	Versioning in case of Unstructured Data is possible only as on whole data as no support of database at all.
5	Flexible and Scalable	As Structured Data is based on relational database so it becomes schema dependent and less flexible as well as less scalable.	While in case Semi Structured Data data is more flexible than Structured Data but less flexible and scalable as compare to Unstructured Data.	As there is no dependency on any database so Unstructured Data is more flexible and scalable as compare to Structured and Semi Structured Data.
6	Performance	In Structure Data we can perform structured query which allow complex joining and thus performance is highest as compare to that of Semi Structured and Unstructured Data.	On other hand in case of Semi Structured Data only queries over anonymous nodes are possible so its performance is lower than Structured Data but more than that of Unstructured Data	While in case of Unstructured Data only textual query are possible so performance is lower than both Structured and Semi Structured Data.

Updated on 24-Feb-2020 11:11:34

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What is the difference between semi structured and structured?

semi-structured data: Organization: Structured data is well organized; therefore, it has the highest level of organization, while semi-structured data is partially organized; hence the level of organizing is lesser than structured data but higher than that of unstructured data.

What are the differences between structured and unstructured data?

Structured data is highly specific and is stored in a predefined format, where unstructured data is a conglomeration of many varied types of data that are stored in their native formats. This means that structured data takes advantage of schema-on-write and unstructured data employs schema-on-read.

What are the examples of structured semi structured and unstructured data?

What is structured, semi structured and unstructured data?.

What is difference structured and unstructured?

Non-structural items include things like doors, cabinet sets, flooring, trim, windows and other finishing materials. In contrast, structural deconstruction requires more integral components of a building, like load-bearing walls, to be systematically dismantled.