Dashboards, Data Quality and Risk
In this paper
will discuss dashboard design and quality, and the effectiveness of its
usability. Also there is a prototype design by Excel, and the reason of
particular visibility of data within the dashboard. There will also be a brief summary
of data quality and risk factor; along with possible risk factors of the passing
of data via online for Internet Shopping.
Literature Review
Business
Intelligence Systems that are interactive computer-based structures and
subsystems to help decisions makers use as communication technologies such as
data and documents for knowledge.
Because
dashboards are a view of Business intelligence systems (BIS) of interactive
computer-based structures and subsystems that will assist decision makers in
using technologies, data, documents, knowledge and analytical models in
identifying and solving problems; they are used to improve operational
performance. On a dashboard; they should be categorized based by two types:
first, Model-driven, which are to utilize analytical construction for
forecasting, optimization algorithms, and simulations, decision trees, and
rules engines. Secondly, Data-driven; deal with data warehouses, and databases,
and online analytical processing (OLAP) technology (Hall, PhD., 2003).
This would also
depend on the mission of the organization and their goals. Based by the outputs
will showing decision making to organization performance such as: displaying
metrics, graphical trend analysis, capacity gauges, graphical maps, percentage
shares, stoplights, and variance comparison. The dashboards normally shows a
user interface design allowing presentations of complex relationships and
performance measurements in formatting easy understandable time pressured to
managers and CEOs of the organization.
Dashboard
Requirements
Recommending
the requirements to the school’s CEO which is really about competition and
decisions making. The organization would need to use dashboards for accurate
decision making to evaluate current trends, historical performance metrics, and
forecasting planning.
BIS
developments offer many systems in providing links and end user interface, such
as the CEO, to access and receive selective information like the competitor
behavior, industry trends and current decision options. Using dashboards are to
increase the school’s acceptance and use new systems to allocate the decision
making and assisting in balance organizational visibility.
Specific features that should be standard to the end user
are:
·
Filter, sort, and
analyze data
·
Formulate ad hoc,
predefined reports and templates
·
Provide drag and
drop capabilities
·
Produce drillable
charts and graphs
·
Support
multi-languages
·
Generate alternative
scenarios.
Dashboards
have many varieties of linking performance metrics of decision making to the
company. The school can view near to real time data of the new flavored
peanut being sold in the city’s locations, the amount of students buying, and
the peak times they are being sold. The usefulness of
using dashboards for the standpoint of decision making is BIS visualization
tools providing an exceptional way to view data and information in detailed
outputs. Such outputs results in performance
such as: displaying metrics, graphical trend analysis, capacity gauges,
graphical maps, percentage shares, stoplights, and variance comparison (Hall,
PhD., 2003).
A basic dashboard illustration
structure on the decision making process:
In the above illustration, demonstrates
the basic structure of a dashboard in the process of a decision making process. Basically, the dashboard integrates the data warehouse
and analytical models directly into the decision making process. Continuously,
the process is formatted of an ongoing environmental scanning and feedback from
current performance metrics such as inventory turns. Behind the scenes, is
really a graphical interface with supportive analytical systems of statistical
analysis data validation, combination forecasting algorithms, and expert
systems for expert systems for decision making options analysis and
recommendations (Hall, PhD., 2003).
Significance of Training
The significance of training for a
successful BIS application, is at time at the last minute based by “how to use
the system”. The seriousness of training before, during and after the
implementation of the system assisting the culture change needed to maximize
accepting the organization such as Training simulators representing one
approach to improving system utilizations and increasing the schools return on
investment.
Some basic challenges the CEO
should know:
·
Integrating optimization based models with
enterprise resources planning systems.
·
Developing observational oriented approach to
data modeling including manual and automated processing.
·
The design of intelligent agents used for the
process of support decision making.
·
To formulate the adaptive and cooperating
systems used for evaluation and feedback in improving the decision making
process.
·
Also the use of speech recognitions in the
development of improving human/computer interface, allowing managers to
increase decision making supporting flow volumes and the exploration in a wide
range of unstructured decision applications (Hall,
PhD., 2003).
Dashboard Prototype
The dashboard
that was created by a prototype based by the idea to promote to the CEO on
different sales based by: students who bought peanuts, sales by flavored
peanuts, sales by school regions, and lastly sales by flavors and dates. Each
dimension and metrics shows a pie graph, different angles of bars and a
timeline of sales by flavors. The dashboard showing data based by the different
metrics and dimensions is imperative for a company’s business to understand
what flavors have been sold, the dates when the flavors were sold, for
forecasting, and the school region sales, and to know which school is selling
the most peanuts. The CEO can make a better decision based by the above
prototype to make the decision on the up-coming new flavored peanuts.
Risk Factors of Data Analysis, Issues and
Quality
The challenges of Big Data quality are the characteristics
of: volume, velocity, variety, and value (Cai and Zhu, 2015).
·
Volume of data means the measurement of data of
terabits or above magnitude
·
Velocity means the speed of data that is formed
in an exceptional pace
·
Variety is the big data that has many types,
diversities dividing into structures and unstructured.
·
Value represents the low-value density that is
inversely proportional to the total size data that is greater in scale and
relatively valuable data.
Based by these four characteristics in quality of data;
the challenges happen when enterprises are used and process big data,
extracting high-quality and real data from massive, variable, and complicated
data sets becomes a crucial issue.
These concerning issues are:
·
Diversity in data sources that brings many data
types and complex structures and increases the difficulty of data integration.
·
The data volume is huge and difficult to judge
data quality within a reasonable length of time.
·
Data changes very fast and the timelines of the
data is short, in which the necessities are higher in the requirements for
processing technology.
Data quality really is dependent on the business
environment that is using the data, including process and business users.
Ideally, data is only conformed to the relevant uses and meets requirements are
considered as “qualified or good quality data”. The quality standards of data
are based by the perspectives of data creators. Also, consumers are either
direct or indirect creators, which is also ensuring the data quality. The start
of Big Data along with the diversity of resources, and data users are not
really producers, which is difficult to measure data quality (Cai and Zhu,
2015).
Other recommendation of risk based by Internet shopping,
include demographics of the consumer and credit card fraud via Internet. Here
are a few risk that could be involved:
·
Some consumers do not want to shop online because
of the risk of credit card number securities that could cause financial risk
·
Difficult of judging quality of product/service
of (perceived product performance risk).
·
Do not trust of personal information will be
kept private (perceived as a psychological risk)
·
Faster/easier to purchase locally (perceived time/
convenience loss risk)( Forthsythe, and Shi, 2003).
The imperative risk factor is credit card fraud via
internet is mostly with security protection and confidentiality, this is very
important to the consumer of breach of data. In recommending the school
insuring data is not breached, the CEO will need to insure proper Internet Protocols
are placed and securities of network layers, which is responsible for data
transmission across networks between layers (Rhee., 2003) .
Conclusions
Dashboard help with the
visualization of understanding the data to make companies make decision for the
present and forecasting marketing plans. Without painting a perfect picture
there is no view to see the beauty art. The dashboard gives clear colorful view
for the CEO to make decision to drive the schools profit in selling flavored
peanuts. There are many risk factors to consider in Internet Shopping, but carefully
placing the right protocols to secure data will be the corrective action to
keep consumer data safe.
References
Beasley, M. (2013).
Practical web analytics for user experience: How analytics can help you
understand your users. Boston: Morgan
Kaufmann imprint of Elsevier.
Cai, L., and Zhu,
Y., (2015) The Challenges of Data Quality and data Quality Assessment in the
Big Data Era., Data Science Journal. Retrieved from https://datascience.codata.org/articles/10.5334/dsj-2015-002/
Forthsythe, S.,M.,
and Shi, B., (2003) Consumer Patronage and Risk Perceptions in Internet Shopping.
Retrieved from http://www.drronmartinez.com/uploads/4/4/8/2/44820161/consumer_patronage_and_risk_perceptions.pdf
Hall, O., P.,
PhD., (2003) Using Dashboards Based Business Intelligence Systems. Retrieved
from https://gbr.pepperdine.edu/2010/08/using-dashboard-based-business-intelligence-systems/
HowtoExcell.net
(2018) How To Create Dashboards in Excel YouTube Video link https://www.youtube.com/watch?v=JcdORXZjbbg&feature=youtu.be
Rhee., M. Y. (2003)
Internet Security. Cryptographic principles,
algorithms. Retrieved from and protocols
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.182.9664&rep=rep1&type=pdf
USCDornsife.com,
University of Southern California, (2020) Data Sample https://dornsife.usc.edu/assets/sites/298/docs/ir211wk12sample.xls
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