INTRODUCTION Structured data vs unstructured data: Structured data, for



Structured data vs unstructured data: 

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Structured data, for the
most part message documents, showed in titled segments and columns which can
without much of a stretch be requested and handled by information mining
apparatuses. This could be imagined as a flawlessly composed file organizer
where everything is recognized, named and simple to get to. Most associations
are probably going to be comfortable with this type of information and as of
now utilizing it viably.

Unstructured information,
generally binary information that is exclusive, is what has no identifiable
inside structure. It can be imagined as a level 5 hoarder’s lounge room; it’s a
gigantic chaotic aggregate of different articles that are useless until
distinguished and put away in a composed manner. When this association
procedure has occurred (using particular programming), the things would then be
able to be looked through and sorted (to a degree) for getting experiences.
While information mining instruments won’t not be prepared to parse data in
email messages (however sorted out it might be), you may have justifiable
reason motivation to gather and arrange information from this source. This
outlines the significance and conceivable broadness of unstructured


What is structured data ?

structured data depends upon the creation of data model :- which tells the type
of business data which will be recorded and how it will be stored and
processed. It also includes which field of data is stored and how the data will
be stored which is called data type and it includes Numeric, textual, name,
address, etc and also the restrictions on the data input. Structured data has a
benefit that it can be easily stored, processed and analysed. Structured data is often
managed using Structured Query Language (SQL) – which is a programming language
created for management and query of data


is unstructured data?


Unstructured data is not arranged
in fixed pre defined way and it’s the data which have no fixed data model


data cant be stored in a table without preprocessing

 Examples: social media sites(tweets, blogs,
posts, etc.), call centre data, email, surveys with open questions.


Unstructured data has strong
influence of three V’s:-

Volume:- Unstructured data usually
requires more storage than structured data.

Variety:-Unstructured data
previously was generated by untapped data sources, which can reveal personal
information of customers.

Velocity:-The unstructured data is
increasing at more pace than the structured data.

Figure representing 3V’s is below:-




Figure 1                                         Source



How prevalent are unstructured data?

 Most of the
business data is unstructured data. It grows much more faster than the
structured data.


data storage is required for pictures and videos which is also called as “Rich

data which is produced by objects that are formerly not connected, like
watches, cars, robots, etc are very important for the growth of data.
Unstructured data sources become transcendent reason for customer insights.

structured data when combined with unstructured data sources help to obtain a
more complete picture of the needs and what customers want.

data is more subjective, while the structured data tends to provide answers to
“what” questions while unstructured data usually provides the answer to “why” questions.



universe of computing has developed from a little, moderately unsophisticated
world in the mid 1960’s to an environment of enormous size and modernity.
Everything from the day by day life of people to our national financial
profitability has been significantly and emphatically influenced by the
development of the utilization of the computer. Furthermore, this development
can be measured in two ways :- structured systems and
unstructured systems









systems are those systems where the activity of processing data and output is
predetermined and highly composed.
systems are designed, built and operated by the IT department.
transactions, manufacturing inventory control systems are all forms of
structured systems.
rules in structured system are little complex.

contrast, unstructured systems are those systems which  have very less form or structure.
systems include email, reports, contracts, and other communications.
 A person who performs a communications
activity in an unstructured system has wide latitude to structure the message
in whatever form is desired.
rules of unstructured systems are fewer and less complex.


Figure2:- Great benefits can be achieved from
bridging the gap between structured and unstructured systems


The structured and
unstructured data system has grown in parallel but separately. So, both has
separate environment and different from each other in ways such as:-

1.     Structural

2.     Organisational

3.     Functional
and technical


There could be
huge number of possibilities if both of the systems are connected in an
effective way. The new type of systems can be built with the enhancement to
existing systems. There could be more amazing benefits which could be achieved
if all the technical, structural, functional and organisational barriers can be



Business intelligence
faces certain limitations because  it is totally
based on the numbers. The most distinctive and necessary way to reduce the gap
between structured and unstructured data is to merge the text and numeric data,
which can lead to better and higher information and insight which was not attainable

There are numerous ways
with which the merger of numeric and textual data can be used to make more
innovative results. An example is to create an unstructured contact file, which
has access to every communication which the customer had previously with the
organisation including letters and emails. So, this file will have all useful
sources such as communication, date of contact, with whom person contacted,
nature of the contact and many more.



The most powerful use of
contact file of customer in terms of increasing a CRM system to create a
broader view of a customer, enables us to attain these important objectives :-

One of the most powerful
uses of the customer contact file is in terms of supplementing a CRM system to
create the broad view of the customer, enabling 
to accomplish these important objectives:

1.     Cross
Selling:- If one understands a lot about the customer in one arena, the chances
to sell to the same customer in another arena will materialize.

2.     Prospecting:-
Better one knows or understands a customer, the better one can qualify sales
prospect list.

3.     Anticipation:-
By understanding more about the customer, we can meet the future needs.

One of the essential
fundamentals of CRM is that it is substantially simpler to offer into a established
client than get another client. This long haul relationship is set up in view
of coordinated learning about the client, including:

·       Age

·       Occupation

·       Net worth

·       Marital status

·       Education

·       Children

·       Income

·       Address

The idea behind making
the 360 degree perspective of the client is to unite information from a wide
range of places in request to coordinate the information and accomplish a
genuinely strong and far reaching perspective of the client.






However, there are
challenges to integrating all this data, such as:

1.     Data
finding in first place.

2.     Data
maintainence using different technologies

3.     Merging
the gathered data

4.     Maintaining
customer’s profile up to date

5.     Management
of volume of collected data


Unstructured contact file


·       name

·       age

·       gender

·       address

·       phone

·       occupation

·       Income


Independent from anyone
else the information accumulated as a major aspect of this procedure is
profitable. In any case, to make a genuine 360 degree view of the client, one
should upgrade this information with the rich vein of unstructured client
correspondences data. At exactly that point will you have the complete
viewpoint. Rather than simply knowing odd actualities about the client, the
organization can recognize what the client has been stating what communication
have happened. So as to accomplish the 360 degree perspective of the client,
bunches of different kinds of data are coordinated together.











There are various
methods to accomplish build of an unstructured file. Using an example of email,
the easiest and common way is to index the un-structured the contact file and
leave email from where they are located originally. With the use of this
technique , an index is created for every communication, which contains few items
such as :-


• Communication date

• With whom the
communication is directed

• Customer’s name and

• Email’s location


Whenever any
corporation wants to figure out if there is any communication, the index is
used. If it seems that the communication is relevant, the corporation can see
the storage location of the email and also can read the email. Alternately, the
actual email sent with the index and there is no requirement of further search.
This approach requires more system resources , it does reduces the required
work finding a specific email.




How Semi-Structured Data Fits with Structured
and Unstructured Data

Semi-structured data
keeps internal markings that acknowledge separate data elements, that empowers
information grouping and chain of commands. The two reports and databases will
be semi-structured. This information just represents  around 5-10% of the semi-structured/structured/unstructured
data pie, but also has basic business use cases.

Email is an very basic
case of a semi-structured data type. Although further developed examination tools
are important for string chase, close dedupe, and idea seeking; email’s local
metadata empowers grouping and catchphrase looking with no extra tools.


Semi-structured Data
examples :-

Markup language XML

It is a semi organized language.
XML is a course of action of report encoding decides that describes a human-and
machine-understandable organization. Its esteem is that its tag-driven
structure is significantly adaptable, and coders can change it to universalize
data structure, stockpiling, and transport on the Web.


Open standard

JSON is another semi-organized information exchange arrange. Java is
comprehended in the name yet other C-like programming dialects remember it. Its
structure includes name/esteem matches (ex question), and an asked for esteem
list (ex group). Since the structure is replaceable among dialects, JSON surpasses
desires at transmitting data between web applications and servers.



Semi-structured info is a crucial piece of
different NoSQL databases. NoSQL databases qualification from relative
databases since they don’t separate the association from the information. This
settles on NoSQL a better call than store data that doesn’t adequately
coordinate into the record and table configuration, for instance, content with
dynamical lengths. It moreover takes into thought less hard information
exchange between databases. Exactly a considerable measure of as yet NoSQL data
bases like Couchbase and MongoDB to boot intertwine semi-organized information
by locally set away them inside the JSON arrange.




Structured vs Unstructured Data: Next Generation
Tools are Game Changers

There are new tools which
are accessible to interrupt unstructured data. Most of these tools rely on
machine learning. Structured data examination may also use machine learning,
the huge volume and a huge range of various kind of unstructured data needs it.
Unstructured information examination with machine-learning insight enables
associations to :-


Examine advanced interchanges for consistence.

Fizzled consistence can
cost organizations a huge number of dollars in expenses and lost business.
Example acknowledgment and email threading investigation programming seeks
enormous measures of email and talk information for potential resistance. A
current illustration incorporates Volkswagen’s misfortunes, who may have stayed
away from a gigantic fines and reputational hits by utilizing investigation to
screen interchanges for suspicious messages.


Track high-volume client discussions in online networking.

Content examination and opinion
investigation gives investigators a chance to survey positive and negative
aftereffects of showcasing efforts, or even recognize online dangers. This
level of examination is significantly more complex straightforward keyword hunt,
which can just report fundamentals like how regularly publications specified
the organization name amid another campaign. New investigation likewise
incorporate setting: was the specify positive or negative? Were blurbs
responding to each other? What was the tone of responses to official
declarations? The car business for instance is intensely associated with
investigating online networking, since auto purchasers frequently swing to
different notices to measure their auto purchasing background. Investigators
utilize a blend of content mining and assessment examination to track
auto-related client posts on Twitter and Facebook.


Increase new advertising insight.

investigation devices rapidly chip away at gigantic measures of archives to
dissect client conduct. A noteworthy magazine distributer connected content
mining to countless articles, breaking down each different production by the
prevalence of major subtopics. At that point they expanded examination over all
their substance properties to see which general themes got the most
consideration by client statistic. The investigation kept running crosswise
over countless bits of substance over all productions, and cross-referenced
hotly debated issue comes about by sections. The outcome was a rich instruction
on which points were most intriguing to particular clients, and which
advertising messages resounded most unequivocally with them.