SOLUTION: UOC Wk 7 Excessive data usage Data Visualization Discussion

Subject: Analyzing and Visualizing
Initial Post 1:
There are numerous mistakes we can make when we initially get into data visualization. Data
Visualization is the way toward showing information as the visual substance or graphical figures,
bars, and outlines. Even though the diagrams and outlines are a staggeringly ground-breaking
approach to speak to our information, submitting even a minor error can cover our bits of
knowledge in disarray and mess. The five pitfalls in data visualizations and how to fix them are :
The pitfall of Color: Using too many colors in the visualization can make our presentation ugly or
complicated. (Murali Mani, 2017) It is better to go with color blind and more shapes to give the
data visualization more meaning and communication.
Pie Chart misuse: Many times, it is seen that the pie charts are used in visualization reports to
compare the data, but when in the case of comparing data of more lavish sets, it is not
recommended to use a pie chart.
Poor Design: Good visualization will have good design, and the designs will be more useful to
convey the message more efficiently. A poor design with poor visuals and a low dashboard do not
make the visualization useful.
Oversimplification: This is another pitfall in data visualization where the user makes the
visualization simpler and fails to show all the details in it.
Including too much data: Including a more significant number of information than required can be
diverting and befuddling to our crowd as they will think that it is hard to recognize the correct way
to take to comprehend the story our measurements are attempting to pass on.
Avoiding future mistakes: The data visualization mistakes can be avoided by keeping in mind the
pitfalls that make the visualization bad. Information visualization should never be tied in with
communicating the imaginative abilities of an information researcher or making extravagant
graphs. Its essential goal is to help our crowd in accomplishing its objectives. (Alun Evans, 2015).
Extraordinary representations start with incredible information. If our perception uncovers sudden
outcomes, we might be the survivor of terrible information. Try not to let our perception become
the substitute for terrible information. Utilize our diagrams to spot issues with our information and
address those issues before introducing our information. Try not to let our perception assume the
fault for terrible data. It is critical to comprehend the contrast between an unforeseen disclosure
and an information issue.
Initial Post 2:
Charts and graphs are a compelling way to present our data, but even minor mistakes can bury our
insights in clutter and confusion (Hepworth, 2017).
Data Visualization pitfalls:
Color Abuse – Color has its place, but it is essential not to overdo it in data visualizations.
The wrong color can lead to confusion or, even worse, misinterpretation.
Misuse of Pie Charts – If we try to squeeze too much information into a pie chart, the big
picture gets lost. Too much detail leaves our audience feeling unsatisfied and confused.
Visual Clutter – Making discoveries in a cluttered visualization is like finding a needle in a
haystack. Too much information defeats the purpose of clarity, and unnecessary elements crowd a
visualization, obscure meaning, and lead to inaccurate conclusions.
Poor Design – Just because visualization is beautiful to look at does not mean it is useful.
Compelling visualizations incorporate design best practices to enhance the communication of data.
Do not just create visuals and dashboards; design them. Work with designers to ensure that the
visualization is as effective as possible.
Bad Data – Great visualizations start with essential data. If our visualization reveals
unexpected results, we may be the victim of insufficient data. Do not let our visualization become
the scapegoat for insufficient data.
How to fix these pitfalls and avoid making mistakes in visualizing the data:
Analysis always comes first. So, despite what our branding department might say, brand
colors are often not the best choice for visualizations. Consider the colorblind and use shapes and
colors that are easiest for people to see. Do not rely on color alone to convey meaning (Kelleher
& Wagener, 2011).
Pie charts work best for limited dimensional values that let us easily distinguish each slice
of the pie. Use pie charts to compare parts of a whole but do not compare different data sets. Order
our slices from largest to smallest for easier comparison meaning (Kelleher & Wagener, 2011).
Limit the number of objects in a dashboard to eight or less. Too many objects are
distracting, so remember to keep the visualizations simple. The less there is to interpret, the easier
it is to understand. If our visual looks cluttered, try a different format. The cleanest format is
usually the best.
Think about how our users will navigate through our Qlik apps – what do they need to see,
how they need to see it, what additional context they require, and how they will access this? It is
worth answering all these questions before building our visualization.
Use our charts to spot issues with our data and address those issues before presenting our
data. Do not let our visualization take the blame for lousy information. It is essential to understand
the difference between an unexpected discovery and a data issue.
Subject: Cloud Computing
Initial Post 1:
The system has to maintain a life cycle of software development. The reason behind the
maintenance in the primary phase has life for a long time in software. The software program has
become the most operational until it becomes an obsolete change due to the user needs for
maintaining the cost of the software becomes to prohibit the future use of the software. It can
realize and design the new replacement of the software in various versions to continue undergoing
maintenance. It can be introduced by Microsoft office in 2010 according to window 7 in the
operating system. The new version has been established in software programming commercials
available to computer users. The company can maintain and continue the previous version to patch
the software, available through the window update in service during the specified time (S, 2017).
It can maintain the existing software to become a program in lifecycle through the window
update. Some products can be maintained, updated, and replaced several years ago. The software
has become the cost that prohibits maintaining the end for software life cycle, and the process can
be developed as a new version to existing software development. The software has become the
initial source to develop the software version. The computers have commercial updates for the
end-users to develop the never-ending development for the life cycle. The software has no cost to
be predetermined in the time limit. It is no longer free to use the programming of many methods
to describe the trial of the software. It is necessary to involve exceptional software cases to initiate
the development of the software more effectively. To restrict the entire to develop the teams to
generate possible results. It defines the task to perform each step to be involved and helps the core
team get an idea (Elamin & Daleel, 2016).
Initial Post 2:
A software product may run to several hundred thousand lines of code. Software engineers can do
very creative things to optimize their own and their team’s work (Why Is Software Maintenance
the Most Expensive Part of the Software Development Life Cycle? – Quora, 2018). Various
conceptual approaches have emerged to support developers in their work. These can go under
slightly obtuse sounding titles such as (depending on your programming affiliations) convention
over configuration, Model-View-Controller (MVC), relational, NoSQL, graph databases – and
many others.
These approaches -which in sum can impact the entire software stack- may address how
the system is ‘parameterized’ (constrained), how the underlying file-system layout is structured,
how work is packaged into dedicated and decoupled modules, or even how data is stored (Why Is
Software Maintenance the Most Expensive Part of the Software Development Life Cycle? – Quora,
2018).
Most are primarily concerned with making life easier for the programmer and may have
little to do with the real-world needs being addressed. Model-View-Controller (MVC), for
example, primarily encapsulates areas of programmer responsibility. The central mechanism is
conceptual mapping. Applied dogmatically, they obscure an application domain ecosystem’s
inherent evolutionary paths and structures. They are an additional cognitive load (Why Is Software
Maintenance the Most Expensive Part of the Software Development Life Cycle? – Quora, 2018).
In the past, programmers tried to model systems using, for example, use-case-driven
object-oriented techniques. An artificially limited set of use-case-specific properties were
abstracted and mapped without reference to existing classification trees – and hence the system’s
natural evolutionary paths and order.
One of the more recent complexity manifestations is graph databases, where what should
be decoupled and easily maintained, classification and storage trees are merged. Suddenly (“cannot
see the wood for the trees”), you have everything in one pot. The graph database engine can find
stuff – until something goes wrong (Why Is Software Maintenance the Most Expensive Part of the
Software Development Life Cycle? – Quora, 2018).
Additional complexity may arise through attempts to overcome system limitations or
ballast (virtual DOM to get around DOM processing overheads, for example). Worse still, the
concepts themselves tend to evolve (or are replaced) over time. The result is a veritable stack of
mappings (hence the emergence of non-semantic URLs and the noble art of routing). Sweep it
under the carpet and hope no-one notices (Why Is Software Maintenance the Most Expensive Part
of the Software Development Life Cycle? – Quora, 2018). Fair enough for the original team – but
someone must maintain all this.
Documentation: As a maintenance drone, to understand a system quickly, we are best served by
diagrams and tools at different granularities, supporting ruthlessly abstract top-level views and
monitoring down to medium-level-detailed interworking overviews. Whether documentation or
troubleshooting tools, we need a cascade of insight. In a complex system, even the tools need to
be documented. While there are standard strategies for troubleshooting, for an extensive system,
conceptual diagrams allow us to work out a dedicated troubleshooting strategy -including which
tools to use- in a matter of minutes. With (as is often the case) neither good documentation, nor
adequate tools, nor necessarily an intimate system knowledge, it can take hours to isolate likely
problem locations. It can translate into millions of lost revenues (Why Is Software Maintenance
the Most Expensive Part of the Software Development Life Cycle? – Quora, 2018).
Initial Post 3:
Every software in the markets must undergo the maintenance phase. It is the most extended phase,
and it is the most expensive. The reasons for being the most costly phase include the duration it
lasts since maintenance cost must be met until the software is completely phased out of the market
or becomes outdated. The software undergoes the maintenance phase until it becomes obsolete.
Another reason is that the maintenance cost is obtained once there are changes that have
been made to the software. There can be a need to readvertise, and also meeting the costs of
repairing and improving the software performance is the face in which defects are analyzed and
dealt with hence adding up to the operational cost (Chen et al., 2016). Other commodities are
known to wear and tear, but for software, they only change and may become less essential, and
after that, it is gotten rid of. The loosing of the software out of the market also makes the
maintenance cost group.
Another important reason for the maintenance cost to remain high is the complexities
involved. It is a complicated process, and the organization that deals with software have to meet
the requirements of the maintenance phase (Galar et al., 2017). It involves mapping; mapping is
usually an inherent process in software development, especially in the maintenance phase, and
adds to the cost, making it essential.
The process also involves documentation. Documentation is a process in which the
individuals have to report about the happenings and the best way to deal with some notable defects
(Galar et al., 2017). This act itself adds up to the cost making the process and the phase expensive
or costly.
Instructions:

Total of 5 Responses

Minimum of 200 words for each response

Minimum of 2 APA References for each response

Responses should cover:
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Ask a captivating, thoughtful question about the topic.
o
Provide extensive additional information on the topic
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Explain, define, or analyze the topic in detail
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Make an argument concerning the topic.

Please follow the format below:

Response 1, References

Response 2, References

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Response 5, References

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