Structured data vs unstructured data:
The crystal clear data types in structured data
whose pattern makes them effectively searchable. But The unstructured data is
involved of data which is not searchable such as social media postings.
Unstructured data versus
structured data does not represent any genuine clash among the both. Clients
select either not founded on their information structure, but rather on the
applications that utilization them: social databases for organized, and most
some other sort of use for unstructured data.
However, there is a
growing strain between the simplicity of investigation on structured data
versus additionally difficult examination on unstructured data. Structured data
examination is a develop procedure and innovation. Unstructured data analytics
is a beginning industry with a great deal of new speculation into R&D,
however isn’t a develop innovation. The structured data versus unstructured
data issue inside companies is choosing in the event that they ought to put
resources into investigation for unstructured data, and on the off chance that
it is conceivable to total the two into better business knowledge.
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.
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
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
DIFFERENCE BETWEEN AND STRUCTURED AND UNSTRUCTURED
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
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:-
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
A NEW PERSPECTIVE OF DATA
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.
FOR THE UNSTRUCTUED CONTACT FILE
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:
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.
Better one knows or understands a customer, the better one can qualify sales
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:
· Net worth
· Marital status
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:
finding in first place.
maintainence using different technologies
the gathered data
customer’s profile up to date
of volume of collected data
Unstructured contact file
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.
THE UNSTRUCTURED CONTACT FILE
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
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
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.
Markup language XML
It is a semi structured
language. XML is an arrangement of report encoding rules that characterizes a
human-and machine-decipherable format. Its value is that its tag-driven
structure is profoundly flexible, and coders can adjust it to universalize
information structure, storage, and transport on the Web.
JSON is another semi-structured data trade
arrange. Java is understood in the name yet other C-like programming languages
recognize it. Its structure comprises of name/value matches (ex question), and
a requested value list (ex cluster). Since the structure is exchangeable among languages,
JSON exceeds expectations at transmitting information between web applications
Semi-structured information is a vital part of various NoSQL databases. NoSQL databases distinction from relative databases since they do not separate the
organization from the info.
This settles on NoSQL a superior call to
store information that doesn’t effectively match into the record and table
format, as an example,
content with dynamical lengths.
It likewise takes into thought less hard data trade between databases. Some a lot of up to this point NoSQL information bases like Couchbase &
MongoDB to boot fuse
semi-structured data by regionally put away them within the JSON format.
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.