Quantity and Quality of Data

This document discusses the approaches organizations use to manage data, data structures, and databases. It shares real-life experiences and highlights the importance of big data solutions in maintaining the growth and progress of organizations. The document also explores the benefits of database management systems and the role of big data analytics in identifying new business needs.

Running head: QUANTITY AND QUALITY OF DATA
Quantity and Quality of Data
Name of the Student
Name of the University
Author Note:

QUANTITY AND QUALITY OF DATA
Question 1
What are some of the approaches an organization might use to manage data, data
structures and databases? Share experiences from your own working environment, where
possible.
Most of the global organizations have huge volumes of structured and unstructured data
which are needed to be managed so that growth and progress of the organization is maintained.
Most of those data are managed with the help of the big data solutions (Baek et al. 2015).
Location and efficiency of the data is maintained if the big data solutions are professionally
maintained in the work places. The efficiency of different units of the business can be improved
significantly with the use of big data solutions. New business needs are also identified with the
help of the big data solutions (Storey and Song 2017). Adaptations to new changes is one of the
new findings of the big data solutions. The different complexities during the auditing time can be
solved purpose fully with the help of the big data solutions. The business opportunities can be
hugely capitalized with the help of the database management structure. The IT functionalities of
the global organizations can be hugely benefitted from the use of the DBMS which helps in
maintaining the structures of the organization (Jiang et al. 2014). The structure of the data which
are getting transmitted from one unit of the system to another is also managed with the help of
efficient business models based on DBMS. Databases are the most important business aspects of
the global organizations which can be solved purposefully with the use of the relational database
management system (Yan et al. 2016). The big data analytics can be embedded into the
advanced devices which is the main reason behind its incorporations across most of the
successful organizations.

QUANTITY AND QUALITY OF DATA
Question 2
Once you place your data in the cloud, are you really in charge of who it is shared with and
how long it is held? Do you think there are enough laws in place governing these decisions?
Hosting is defined as defined as the type of method by which organizations can store
their valuable information. There are different types hosting techniques. This unit of the paper
will be focusing on the shared hosting service, during this service multiple users can share same
data file. Dedicated hosting in the cloud services is completely different from the shared hosting
services. The validity of the users managing their data can be either defined by the users or else
by the cloud service departments (Rickscloud.com 2018). When the data is successfully stored in
the cloud we can choose about the type of package we want to store the data. There is different
type of cloud services present which helps in protecting the data which is stored by the users
such as the Clarifying Lawful Overseas Use of Data act which was passed in the February, 2018.
There are different regional laws all over the world which helps in protecting data stored in the
cloud computing services (Findlaw 2018). The sector specific laws are also very much useful in
managing the data in cloud services.
Question 3
Analysis of given scenario
The data management system which was incorporated in an organization in Japan is very
much efficient is storing and accessing data with an increased speed. Both structured and
unstructured data can be professionally managed by this platform architecture. The complexities
faced by the organizations in terms of downloading data from unreliable sources (Arasu et al.

QUANTITY AND QUALITY OF DATA
2016). The data which are generated from different types of sources are very much important to
be managed in an efficient way as there are lots of issues associated with the processing of those
data in terms of their transfer speeds. The storage capacity of the proposed system is very much
important for most of the organizations. Management of the data requires a variety of software’s
such as the Cassandra, Spark worker and log stash and also ulta fast storage based on NVM e
solid stage storage waste similar to SRAM. Redis and Cuda is very much useful for the machine
learning system, which again requires GPU assisted CPU nodes (Tang et al. 2016). In the
internal mechanism the data considered in the architecture considers process able SQL relational
database using the presto. Visualization is useful in returning the data from the platform.
Question 4
Analysis of given scenario
The value of information is getting increased day in the global organizations. Huge
volumes of data are transferred from one unit of the organization to another and there are lots of
security issues associated with the data (Baller, Dutta and Lanvin 2016). Data is considered as an
asset in most of the global organizations which is available in different types of form such as the
semi structured data, structured data and the unstructured data each of the data is dedicated for
specific units of the business as any kinds of security issues mat result in huge financial loss for
the business organizations (Laudon and Laudon 2015). The customers of the organization can
also be managed with the help of the effective data management procedures. Effective data
management plans are required to minimize the vulnerabilities associated with the data revolved
in the organization. Calculation of the vulnerabilities is much easier with the use of the data
management plans as there are lots of complexities associated with the data considering the

QUANTITY AND QUALITY OF DATA
future extensible infrastructures. Thus effective management of data is required to minimize the
risks associated with the data considering the availability, integrity and confidentiality.
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