INTRODUCTION develop procedure and innovation. Unstructured data

INTRODUCTION

 

Structured data vs unstructured data: structured data is involved of clearly characterized data
types whose pattern makes them effectively searchable; while unstructured data “everything
else” contains data which is not easily searchable such as social media
postings.

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Unstructured data versus structured
data does not signify any genuine clash between the two. 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 ?

The
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 oftenly
managed using Structured
Query Language (SQL) – which is a
programming language created for management and query of data

 

What
is unstructured data?

 

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

 

1.    
Unstructured
data cant be stored in a table without preprocessing

2.    
 Examples: social media (tweets, blogs, posts,
etc.), call centre data, email, surveys with open questions, etc

 

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.

 

 

 

 

 

 

 

How prevalent are
unstructured data?

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

 

1.    
More
data storage is required for pictures and videos which is also called as “Rich
Content”

2.    
The
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.

3.    
The
structured data when combined with unstructured data sources help to obtain a
more complete picture of the needs and what customers want.

4.    
Unstructured
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.

 

 

The 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

 

 

 

 

DIFFERENCE BETWEEN AND STRUCTURED AND UNSTRUCTURED DATA

 

STRUCTURED DATA
 

UNSTRUCTURED DATA

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

By
contrast, unstructured systems are those systems which  have very less or no predetermined form or
structure.
 
Unstructured
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.
 
The
rules of unstructured systems are fewer and less complex.

 

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 removed.

 

A NEW PERSPECTIVE OF DATA

Business intelligence
faces certain limitations because of it is primarily based on the numbers.The
most important way to reduce the gap between structured and unstructured data
is to combine text and numeric data, which can lead to better information and
insight which was not possible previously.

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 substances
such as communication, date of contact, with whom person contacted, nature of
the contact and many more.

 

USES 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:

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

CUSTOMER ID
 

·      
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 perspective of the
client, you should upgrade this organized 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 various types of data are coordinated together.

 

 

 

 

 

 

BUILDING 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 identification

• 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. As this approach requires
more system resources , it does reduces the required work finding a specific
email.

 

 

 

 

 

 

USES
OF UNSTRUCTURED CONTENT IN OTHER APPLICATIONS

 

The
most important use of unstructured data is in litigation support. For instance
:- if a company is sued by someone. The first thing which that company should
know is that what contact it had with that person. With whom he/she was working
with and with whom her/she contacted. In this kind of case, the ability to view
unstructured data is invaluable.

 

There is
another use of mixing structured and unstructured data to increase the business
intelligence and reports. While it is through reports and business
insight that applications pass on their discoveries to the end client, there is
an incredible impediment to reports and business insight since they essentially
depend on structured frameworks for their data. Structured applications are
great at:

1.     Summaries
creation

2.     Drill
down creation

3.     Drill
across creation

4.     Summary
of data break down into different categories.

 

 

 

How Semi-Structured Data Fits with Structured and Unstructured
Data

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

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

Email is a gigantic
utilize case, yet most semi-structured development focuses on facilitating
information transport issues. Sharing sensor data is a developing use case, as
are Web-based information sharing and transport: electronic data interchange
(EDI), numerous web-based social networking stages, report markup dialects, and
NoSQL databases.

Examples of
Semi-structured Data

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. (In spite of
the fact that  XML is comprehensible
doesn’t pack a major punch: anybody attempting to read an XML record has
better activities with their time.) 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.

 

Open standard
JSON (JavaScript Object Notation) 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 (or question, hash table, and so on.) and a
requested value list (or cluster, sequence, list). Since the structure is
exchangeable among languages, JSON exceeds expectations at transmitting
information between web applications and servers.

 

NoSQL Semi-structured
data is additionally a critical component of numerous NoSQL (“not
just SQL”) databases. NoSQL databases contrast from relational
databases since they don’t separate the organization (composition) from
the data. This settles on NoSQL a superior decision to store data that
does not effectively fit into the record and table format, for example,
content with changing lengths. It likewise takes into consideration less
demanding information trade between databases. Some more up to date NoSQL
databases like MongoDB and Couchbase additionally fuse semi-structured
data by locally putting away them in the JSON format.

In enormous data situations, NoSQL does
not require administrators to isolate operational and examination databases
into separate arrangements. NoSQL is the operational database and hosts local analytics
instruments for business insight. In Hadoop conditions, NoSQL databases ingest
and oversee approaching information and serve up analytic outcomes.

 

These databases are normal in enormous data
infrastructure and constant Web applications like LinkedIn. On LinkedIn, a huge
number of business clients openly share work titles, areas, skills, and more;
and LinkedIn catches the enormous information in a semi-structured format. At
the point when job seekers make an inquiry, LinkedIn matches the question to
its monstrous semi-structured data stores, cross-references information to
enlisting patterns, and offers the subsequent proposals with work searchers. A
similar procedure works with deals and marketing inquiries in premium LinkedIn
administrations like Salesforce. Amazon likewise constructs its reader
suggestions with respect to semi-structured databases.

Structured vs. Unstructured Data:
Next Gen Tools are Game Changers

New tools are accessible to break down
unstructured data, especially given particular utilize case parameters. The
vast majority of these tools depend on machine learning. Structured data
investigation can utilize machine learning too, yet the gigantic volume and a
wide range of different kinds of unstructured data requires it.

 

A few years prior,
experts utilizing keywords and key expressions could look unstructured data and
get a better than average thought of what the information included. eDiscovery
was (and is) a prime case of this approach. In any case, unstructured data has
developed so drastically that clients need to utilize examination that work at
figure speeds, as well as consequently gain from their action and client decisions.
Natural Language Processing (NLP), design sensing and characterization, and text
mining calculations are on the whole normal cases, as are report relevance
examination, and filter driven Web collecting. Unstructured data examination
with machine-learning insight enables associations to:

 

•Analyze digital communication for consistence. Failed
consistence can cost organizations a millions dollars in expenses and lost
business. Pattern recognition and email threading investigation programming
seeks enormous measures of email and visit information for potential noncompliance.
A current case incorporates Volkswagen’s burdens, who may have maintained a
strategic distance from a tremendous fines and reputational hits by utilizing
examination to screen correspondences for suspicious messages.

 

 

 

 

•Track high-volume client conversations in social media.
Content analytics and opinion investigation gives experts a chance to audit
positive and negative consequences of advertising efforts, or even distinguish
online dangers. This level of examination is significantly more modern
straightforward keyword search, which can just report basics like how
frequently notices said the organization name during new campaign. New
investigation likewise incorporate setting: was the say positive or negative?
Were notices responding to each other? What was the tone of responses to
official declarations? The automotive business for instance is intensely
engaged with examining online networking, since auto purchasers frequently
swing to different notices to measure their auto buying experience. Experts
utilize a mix of text mining and assessment analysis to track auto-related
client posts on Twitter and Facebook.

• Gain new advertising intelligence. Machine-learning examination instruments rapidly work
enormous measures of archives to investigate client behaviour. A noteworthy
magazine distributer connected content mining to countless articles, examining
each different production by the prevalence of major subtopics. At that point
they broadened analytics over all their substance properties to see which
general themes got the most consideration by client statistic. The analytics
kept running crosswise over a huge number of bits of substance over all
productions, and cross-referenced interesting issue comes about by segments.
The outcome was a rich instruction on which topics were most fascinating to
particular clients, and which marketing messages reverberated most firmly with
them.

 

 

 

 

 

 

 

 

 

 

 

 

 

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