It
is essential to understand the market trends in order to make improvements in
all the business. These trends can understand with the help of proper tools and
techniques which have been designed specifically for this purpose. In this
study the technology graph database will be analyzed in order study its use and
effects in the evaluation of the market trends. First of all, the various aspects
of the market trends and the process of determining the same will be studied.
Thereafter, the technology will be studied in details this will include a clear
concept of the technology along with the various uses of the same. The
different applications of these use cases will be studied in details. In
addition to this, the benefits of using this technology in different
organizations will also be discussed in details.
The small as well as large organization needs to do
market analysis so that they can be prepared for the future. This helps them to
keep track of the key variables and making prediction about the upcoming trends
of the industry. By proper trend
analysis, the companies are able to evaluate the impact of the new technologies
as well the opportunities associated with them. There are several other factors
which affect the market. These include the change in population and the latest
mindset of the people as well. It is essential to understand the factors related
to the acceptance of the products and the choice and discretion of buyers in
order to create the products and services as per these data and increase the
sales.
Market trends are basically the movements of a market
in a particular direction. In order to predict the market trend, the following
need to be studied:
Customer
analysis:
A proper analysis needs to done regarding the
customers, this is essential to identify the various factors regarding the
customers which have a direct or indirect impact on the growth of a business.
There are certain trends which these customers follow. It is very essential to
determine the target customers for any product before releasing it into the
market. In order to understand the trend of the market, the organization needs to
trace the demands and expectations of the customers. This helps in increasing
the focus towards providing adequate level of satisfaction to the customer.
This information can be collected from surveys and researches. However, the
recent way of obtaining this information is to track the data of their search
on the internet.
Data
analysis:
In order to study about the latest trend, the companies
make use of different data related to the various elements of a business. The
data is studied with respect to a certain time frame. These data are very
important in the trend analysis as they are able to track any unwanted results
in the organizational activities. This also helps in analyzing the areas where
the performance is positive so that the success can be enhanced by encouraging
these processes. The data can be analyzed on the weekly, monthly or yearly
basis. This is basically done by comparing the expected results to the actual
results.
The trends in the market can be analyzed with the help
of the following methods:
Chart
patterns: The
analysis of the patterns of a chart help in understanding the changes which
have taken place.
Timeframe patterns: These patterns provide a proper
definition of the interpretation of the pattern. Some patterns occur very
frequently while others may not be very frequent. It is believed that the
trends which take longer duration to establish stay for a longer time and the
ones which emerge very rapidly are likely to fade away early as well.
A graph database is basically a database which is one
of the categories of NoSQL database and makes use of the graph theory for the
purpose of storing, mapping and creating queries for relationship. A graph
database can be called as a collection of edges and nodes. Each of the nodes is
a representative of an entity. An entity can be a person, a business and
account as well as any other item which can be retrieved. The edges depict a relationship in between
two nodes. There is a unique identifier which provides a definition to the
nodes, a set of incoming or outgoing edges and a group of properties which are
known as value pairs. All the edges also have a starting and ending node,
unique identifier and some specific properties as well.
Graph databases can be best used for establishing and analyzing
interconnectivity with the help of a graphical representation. The processing
of data becomes convenient by studying the relationship in between the nodes
and doing calculations on the basis of the properties of a graph. The major
fact about these databases is that they have the capability to store the data
as individual points as well as relationships. As per, the relationships of data provide much more important
information than the data alone. These relationships are an essential part of
data management and the information which can be extracted is directly
proportional to the amount of data stored. The graph database is far quite
different from the relational database. The relational databases are databases with
a lot of tables with a SQL which is tacked upon them making the processing
slow. On the contrary, the speed of graph databases can be as high as a multiple
of thousand to the speed of a relational DBMS. The capability to explore the
connections of the data makes it highly convenient to find the solutions to
highly complicated issues in a very short period of time. The nature of
connections can also be represented in these graphs which help in the
extrication of various qualitative and quantitative data about these
connections. This database is based on the Property graph model which is
discussed hereby:
The property graph model consists of entities which are
connected and have the capability to hold any number of attributes. The nodes
can be provided with labels which shall define their roles in a specific
domain. These tables also play the role of metadata. A metadata can be explained as an index to any
stored data. The relationship provides the connections between the node which
are semantically relevant, named and properly directed. In many cases, these
nodes do have some quantitative properties like cost, weight, ratings,
distances and time intervals. The storage of the relationship is efficiently
done which allows the nodes to share multiple relationships without affecting
the performance. The most important rule of the graph databases is that there
should be no broken links. In this case, the deletion of anode must be followed
by the deletion of its relationship as well. There are several use cases for
the graph data. They are discussed as follows:
Real
time recommendations:
This is considered as an essential driver to success.
This technology can be used for establishing a proper correlation in between
the products, customers, logistics, and suppliers as well the data regarding
the social sentiments. This technique requires an instant capturing of the
interests of the customers which are depicted by the searches they have made in
the context of any products. In this case, the database makes the matches in
between the session and the historical data to find the appropriate
recommendation to be provided to the customer on the basis of his search. This
technology makes use of the graph for mapping the connections in between the
search and products for providing recommendations.
Master
data management:
The businesses in today's world have become more focused
in the customers. These businesses are dealing with a large amount of data
about the customers, this data needs to be stored and managed appropriately so
that the business is able to take care of the demands and expectations of the
customers. In the case of a business having different business systems, it is
essential to ensure that the data is similar in all these systems. If the
customers access the system through a new channel, it becomes essential to
connect them to their original records also known as master records. These
customers may make changes on their data like the address or the contact number,
it is necessary that these changes are updated in all the system. Moreover, the
information of one of the customers in a specific system should be easily
available to another system as well. These can be done with the help of master
data management. This is done by
creating a common repository which carries out the collection of the data from
the various systems and storing it at a central location. Any updates made in
the data are made art this location so that the same data can be retrieved by
all the systems.
Fraud
detection:
Fraud detection is another very important use case of
the graph database. With the enhancement in the digital technology, the online
transactions have witnessed a substantial growth. The frauds have taken
advantage of this system and created several strategies for misusing the
technology. The graph database makes use of the relationship information to
detect any kind of frauds in this context. In general, an online transaction
has a number of identifiers including user ID, credit card number and a
tracking cookie. The ideal relationship mapping of these identifiers is
supposed to be one to one. There may be some cases where more than one member
of a family may use these identifiers. However, if the relationship mapping
exceeds an average number, the possibilities of fraud can be considered in
those cases. In such cases, the graph changes its shape to a large and
tight-knit structure which can be considered as an alarm for fraud.
Graph
based search:
Graph based search can be used by the organizations in
order to improve their capabilities to search the products, contents, services
and knowledge catalogues. This search
makes use of the technique of augmenting a single keyword with other keywords
which are associated with it. This is one of the latest approaches to the
management of data and digitals assets. In this case, if a single word is
search for, the graph looks for all the words which have a relationship with
this word. Thereafter, all the words which do have a relationship are extracted
and provided. Moreover, the graph
systems have the ability to understand the connections of the data which helps
in providing more relevant and precise data. In a graph database, all the
connected data can be queried and the answers received can be focused upon. In
this case, the data can be structured in the exact sequence of their occurrence
and the search can be a carried out on the basis of the inherent structure
owned by them. The concept of metadata is used for speeding up the search
process.
Identity
management:
A graph database is capable of storing the control
structures which are highly complex and dense in nature which may cover
numerous resources. It contains the structured data model which supports
hierarchical as well as non-hierarchical structures. In addition to this, the extensible property
of this database facilitates the capturing of metadata regarding all the
elements in a system. This technique is used in access management as it is able
to track the relationship in between events and entity. Whenever a person tries
to get an access to a certain system, his relationship are traces with that
system and if there is a relevant relationship, the access is granted.
Customer
view:
The graph database helps in extending the boundaries of
knowledge regarding the customers. It enables the visualization and analysis of
the constituents of a business and their
connectivity with different operations. This performs the mapping of the
customer to all the activities they have performed can all the actions they
have carried out. This provides a detailed understanding of the customer and
the trends of their interests. The graphs enable the mapping of one customer to
many relationships which help in managing the different interests of the
customer. As the data of the customers are maintained in a single storage, the
different aspects can be studied and compared with each other. This further
helps in providing a customized support to the customers.
Position:
The graph DBMS lies at the peak of inflation of
expectations, this is basically due to the reason that a large number of
organizations are inclined towards adopting this technology in order to use the
graph analytics.
Business
impact:
The overall impact of this technology on the business
can be considered as moderate. However, it has expressed a radical change in
the pattern of managing the data.
Properties:
The basic properties of the graph DBMS are stated as
follows:
●
The
operations of this graph are very fast in case of associative data sets.
Associative data sets are basically the data sets where the metadata are also
stored along with the data itself.
●
It
provides a direct mapping to the applications which run on the object oriented
concept.
●
The
scaling to large data sets in much easier and natural in this case as there is
no need to make use of the joint operations
like the other DBMSs. This makes it cost effective by eliminating the costly
join operations.
●
The management of data as well as changing the
same is much easier as they are less dependent on a rigid schema.
The large organizations which are already involved in
making predictions of the market trends can improve their approach with the use
of the graph data. This can be done in the following ways:
Data
mining:
The companies which are involved in the process of online sales, can make use
of data mining which is a use case of the graph database. The customers who are
involved in purchasing items from these companies often search for some items.
The data of this search can be trapped and stored. This data is then mapped to
the other searches of the same customer.
The organization can study this pattern to understand the pattern of the
customer's choice. As the graph database can store highly complex data, there will
be records for a long duration of the search. This will easily display the
trend of the customer expectations.
Improved
data quality by Master data management:
The regular customers of the company provide their
personal data at the time of purchasing the stuffs. This data is stored in
their database. In case of any changes made in this data, there is an update of
this data in the central location. This improves the quality of data. The
accuracy of this data leads to a better understanding of the trends. In
addition to this, there are several customers who buy the similar products. The
graph database provides a mapping for this relationship. This can be used to
identify the number of customers opting for a specific product. This can be
really helpful in understanding the choice of the customers as well as the
response provided to that particular product. This data can be used by the
company to produce more products of that category so that there can be a higher
amount of sales and revenue and the company can grow further.
Access
management:
The large organizations have a large number of
employees and a large number of systems. They deal with large amount of data
which needs to be stored and managed at an optimum level. In these
organizations, it is a major issue to handle the safety of these data. In such
a case, if the access to these data is granted to the unauthorized personnel,
they may misuse or tamper this data. This problem can be removed with the help
of access management which follows the technique of graph database. This
technology helps in mapping the relationship of the data with the personnel and
the access is granted accordingly. In
this way, the large companies can manage the access to their data in an
efficient manner.
The small organizations which have a less a number of
employees and operate at a small scale, need to make use of this technology for
improving the performance of their business and moving ahead towards growth and
development. The main use case which can be used by these firms is the graph
based search. There are several benefits of this technique to such
organizations.
Customer
support:
As these companies have a smaller number of customers, the main focus of these firms
is to increase the satisfaction level of the customers in order to retain these
customers. This can be done by providing them proper support. As the graph
based search is able to establish a relationship in between the customers and
their requirements, it becomes easy to find solutions to the issues of the
customer. Thus helps in providing them with better alternative. In this way,
the customers feel that they are being provided with special care and their
loyalty towards the company increases. A customer support portal can be created
by these companies where the customers will be able to place their queries and
suggestions regarding the products and the company will be able to connect with
the customers in a better manner.
Social
network:
Another need of these small scale companies is to create an awareness about
their company as well as the products produced by them. For this purpose, these
firms can make use of the social network which makes use of the graph based
search. With the use of social media, these companies can easily interact with
the people and conduct surveys about the choices and preferences of the users.
Moreover, there are some pages on these media which are joined by the users.
This data can also be used for understanding their need. These people can be
recommended to buy these products of this particular company which is similar
to the product they are searching for. This will help the company in creating a
name and position in the market.
Improved
access to information:
As the capital of these companies is low in comparison
to the large companies, it is essential to invest this capital judiciously so
that maximum can be gained out of that. As the graph based search provides a
better access to the information related to the customers, these companies can
make use of the same to optimize their services. The data can be collected
about the latest trend in the buying behavior of the customers and the product
and services of such firms can be designed on this basis. In this way, the
capital will be used only for the production of the required items and there
will be no wastage of resources. This will help the company earn maximum
profits and grow in terms of revenue as well as reputation.
In this particular study, it has been established that
the graph database is a technology which can be used for the prediction of the
market trends. This technology basically deals with analyzing the relationship
between different entities which can be used to study about them. There are several
use cases of this technology which can be used for serving this purpose. These
include data management, real time recommendations, fraud detection and graph
based search. All these use cases have their own applications and can be used
for various purposes. These include identification of the market trends, analyzing
the customers, managing their data and working on the basis of these data.
Hence it can be concluded that this technology can be used by the small as well
as large organizations for maximizing their profit.
