This presentation briefly discusses the following topics: Classification of Data What is Structured Data? What is Unstructured Data? What is Semistructured Data? Structured vs Unstructured Data: 5 Key Differences

1. CL ASSIFICATION OF DATA
Dr. C.V. Suresh Babu
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OBJECTIVES
• To understand the various Classification of Data
• To know What is Structured Data?
• To know What is Unstructured Data?
• To know What is Semistructured Data?
• To understand the Key Differences between Structured and Unstructured
Data

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DISCUSSION TOPICS
• Classification of Data
• What is Structured Data?
• What is Unstructured Data?
• What is Semistructured Data?
• Structured vs Unstructured Data: 5 Key Differences

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CLASSIFICATION OF DATA
• Data classification is broadly defined as the process of organizing data by relevant
categories so that it may be used more efficiently. On a basic level, the classification
process makes data easier to locate and retrieve. Data classification is of particular
importance when it comes to risk management, compliance, and data security.
• Data classification involves tagging data to make it easily searchable and trackable. It
also eliminates multiple duplications of data, which can reduce storage and backup
costs while speeding up the search process.

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PYRAMID OF DATA

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WHAT IS STRUCTURED DATA?
• The term structured data refers to data that resides in a fixed field within a file or
record. Structured data is typically stored in a relational database (RDBMS). It can
consist of numbers and text, and sourcing can happen automatically or manually, as
long as it's within an RDBMS structure. It depends on the creation of a data model,
defining what types of data to include and how to store and process it.
• The programming language used for structured data is SQL (Structured Query
Language). Typical examples of structured data are names, Reg. No., Marks,
Attendence, and so on.
S.No. First Name Last Name Reg. No.
1 Priya Dharshini 18132001
2 Mawa Chouhan 18132002
3
Sai
phanindra Muvvala 18132003
4 Nandhini
Venkatesa
n 18132004

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WHAT IS UNSTRUCTURED DATA?
• Unstructured data is more or less all the data that is not structured. Even though
unstructured data may have a native, internal structure, it's not structured in a
predefined way. There is no data model; the data is stored in its native format.
• Typical examples of unstructured data are rich media, text, social media activity,
surveillance imagery, and so on.
The amount of unstructured data
is much larger than that of
structured data. Unstructured
data makes up a 80% of all
enterprise data, and the
percentage keeps growing. This
means that companies not taking
unstructured data into account
are missing out on a lot of
valuable business intelligence.

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EXAMPLES: UNSTRUCTURED DATA

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WHAT IS SEMI-STRUCTURED DATA?
• Semistructured data is a third category that falls somewhere between the other two.
It's a type of structured data that does not fit into the formal structure of a relational
database. But while not matching the description of structured data entirely, it still
employs tagging systems or other markers, separating different elements and enabling
search. Sometimes, this is referred to as data with a self-describing structure.
• A typical example of semistructured data is smartphone photos. Every photo taken
with a smartphone contains unstructured image content as well as the tagged time,
location, and other identifiable (and structured) information. Semi-structured data
formats include JSON, CSV, and XML file types.

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STRUCTURED VS UNSTRUCTURED DATA:
• Defined vs Undefined Data
• Qualitative vs Quantitative Data
• Storage in Data Houses vs Data Lakes
• Easy vs Hard to Analyze
• Predefined format vs a variety of formats

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DEFINED VS UNDEFINED DATA
Defined Undefined Data
Structured data is clearly defined
types of data in a structure
unstructured data is usually
stored in its native format
Structured data lives in rows and
columns and it can be mapped
into pre-defined fields
Unlike structured data, which
is organized and easy to access
relational databases,
data does not have a predefined
data model

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QUANTITATIVE VS QUALITATIVE DATA
Quantitative Data Qualitative Data
Structured data is often quantitative
data, meaning it usually consists of
hard numbers or things that can be
counted.
Unstructured data, on the other hand,
is often categorized as qualitative data,
and cannot be processed and analyzed
using conventional tools and methods.
Methods for analysis include regression
(to predict relationships between
variables); classification (to estimate
probability); and clustering of data
(based on different attributes).
In a business context, qualitative data
can, for example, come from customer
surveys, interviews, and social media
interactions. Extracting insights from
qualitative data requires advanced
analytics techniques like data
mining and data stacking.

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STORAGE IN DATA HOUSES VS DATA LAKES
Storage in Data Houses Storage in Data Lakes
Structured data is often stored in data
warehouses
unstructured data is stored in data
lakes
A data warehouse is the endpoint for
the data’s journey through an ETL
pipeline. Both have the potential for
cloud-use
A data lake, on the other hand, is a
of almost limitless repository where
data is stored in its original format or
after undergoing a basic “cleaning”
process.
Structured data requires less storage
space
unstructured data requires more. For
example, even a tiny image takes up
more space than many pages of text
As for databases, structured data is
usually stored in a relational
database (RDBMS),
the best fit for unstructured data
instead is so-called non-relational,
or NoSQL databases

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EASE OF ANALYSIS
One of the most significant differences between structured and unstructured data is how
well it lends itself to analysis..
Structured
data
Unstructured data
Structured data is
easy to search,
both for humans
and for
algorithms
Unstructured data, on the other hand, is intrinsically more
difficult to search and requires processing to become
understandable
It's challenging to deconstruct since it lacks a predefined
data model and hence doesn't fit in in relational databases.
there are a wide
array of
sophisticated
analytics tools for
structured data
most analytics tools for mining and arranging unstructured
data are still in the developing phase
The lack of predefined structure makes data mining tricky,
and developing best practices on how to handle data
sources like rich media, blogs, social media data, and

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PREDEFINED FORMAT VS VARIETY OF FORMATS
Predefined Format Variety of Formats
The most common
for structured data is text
and numbers
Unstructured data, on the other hand, comes in a
variety of shapes and sizes. It can consist of
everything from audio, video, and imagery to
and sensor data.
Structured data has been
defined beforehand in a
data model.
There is no data model for the unstructured data; it
is stored natively or in a data lake that doesn't
require any transformation.
Structured data requires
less storage space
unstructured data requires more. For example,
even a tiny image takes up more space than many
pages of text
As for databases,
structured data is usually
stored in a relational
the best fit for unstructured data instead is so-
non-relational, or NoSQL databases