1. Trends
of Data Analytics in Different Sectors
The data analytics market is replete with techniques and tools that are
rapidly expanding to keep up with the growing volumes of data gathered and used
by businesses.
The first trend is growing interest in self-service data analytics. Data
analytics informs both internal operations and client-facing decisions on
everything from revenue targets to marketing touchpoint performance and staff
turnover data. Every type of data that businesses generate can and should be
put into data analytics software to better understand what's going on in the
business and how that knowledge affects the organization's plans.
The second trend is stronger data integration through embedded
analytics. Many users lack not only the necessary skills or access to data
analytics tools but also the time or user interfaces to make data analytics a
part of their daily work. As a result, the data analytics market is moving
toward more robust data integration via embedded analytics or analytical
insights that appear immediately in the applications and interfaces where
workers already operate.
Besides, the trend of data analytics is finding tools that support data
integrity. If data quality isn't guaranteed and data compliance rules aren't
fulfilled, data analytics processes or technologies are useless. Data
integrity, or ensuring that data is correct and ethically obtained, is one of
the most important developments in data analytics right now.
The last trend of data analytics is the emergence of analytics on the
blockchain. Performing data analytics on the blockchain is maybe the most
intriguing and underappreciated data analytics trend. Most people think of
blockchain as a technology for cryptocurrencies or safe transactions, but it's
rapidly being utilized for accurate data analytics calculations due to its high
levels of documentation and security.
Reflection
Data analytics drives both internal operations and client-facing
decisions on everything from revenue targets to marketing touchpoint
performance and staff turnover data, thanks to the increased interest in
self-service data analytics. The advantages of embedded analytics will vary
depending on the business application to which they've been introduced, but
there are a few overarching themes in the areas of the user interface, user
experience, data blending, mashups, and process optimization. Data integrity is
critical because data integration is impossible without it. When data integrity
is preserved, it indicates that the data values saved in the database are
consistent with the data model and/or data type. As a result, the data model
can provide trustworthy insights, allowing users to make well-informed business
decisions with the finding tools that support data integrity. With the
emergence of analytics on the blockchain, participants in the network verify or
validate the blocks, removing the requirement for a trusted authority to
authenticate the information in them, such as a regulator or an accounting
firm.
2. Industrial
Talk 7: Introduction to Data Visualization (iCEP)
In the
discussion of the talk, we have learnt several things related to data
analytics. We have learnt types of data that exists, types of data relations,
and types of charts that can be used to visualize our data and findings. As per
the talk as well, we were briefly introduced to Power Bi as one of the
interfaces that can be used to visualize our data.
To
simply put, Power Bi is an interface that can be used to present and visualize
data. Multiple tools can be used to visualize our data and make the insights to
be understood in simpler ways. Power Bi also is a beginner-friendly
interface, as we are already briefed and thought in the talk itself. With a
mere click of a button, we will be able to have our data into proper and
understandable information and insights.
We can
use Power BI multi-dimensional categories. Power Bi is different from excel in
terms of visualization of the data itself, Power Bi transforms data from excel
into a graphical form that is easier to be understood. It also have an automation feature that
allows easier job for companies, you don't have to create the visualization
every month.
You can
use Power Bi for various sectors and reasons depending on how you would like
the date to be visualized. For example, Power Bi can be used to study the
number of diseases at certain locations of the country with proper insight
which will enable the user to know the reasons the disease spreads and how to
maintain them and come out with solutions.
A
narrative of the analytics was conducted.
In the
talk, we were walked through certain analytics that was prepared by the
speaker. The speaker explained briefly the sections
and tools that are available for us to be able to use Power Bi. The speaker
used data from an excel template that was prepared earlier before the talk on
product orders with the customer details. The modelling was prepared before the
talk. The speaker used a clustered column chart to visualize the category of
the products and sales of the products which enable us to get insight on the
number of sales for each category of products available. We were also thought
about how to design the data and make it more look-able and understandable. The
speaker also used different types of data visualization tools to represent the
same data in different ways, such as aquarium data visualization. We were also
shown a proper data visualization that can be presented in a professional
platform and how different data visualization can interact with one another.
Reflection
Information
can be visualized in several ways that can provide specific insights. It is
important to identify and understand the story you are going to tell. Proper
visualization is very important when it comes to delivering your message or
insight on the data you have collected. With good insights and a storyline from
the data, companies will be able to improve themselves to a whole new level.
The reason behind this is because they can identify what is the problem the
company is facing and what needs to be improved so that the companies’ sales or
production can be generated better. As discussed in the talk, Power BI is one
of the user interfaces which is easy and functional that can be used to create
your data visualization and interpretation. You can manage your data and keep
an insight on the changes of the data up to date just by clicking some simple
buttons. It is believed that usage of interfaces such as Power Bi on data
analytics will be growing and much enhanced in coming years together with the
technological growth and how much important data and information has become.
Thus, everyone should be able to adapt to the usage of data analytics in daily
life as it will be a trend in future for the humans to be able to do certain
actions in their daily lives.
3. Microsoft Power
BI Data Visualization & Analysis
Millions of people suffer from pain and are often prescribed drugs to
treat their conditions. However, the dangers of prescription misuse, drug use
disorder, and overdose have been a growing problem throughout the world. Since
the 1990s, when the number of drugs prescribed to patients began to grow, the
number of overdoses and deaths from prescription drugs has also increased. The
rate of overdose deaths in Connecticut increased from 9.9 per 100,000 residents
in 2012 to 28.5 per 100,000 residents in 2018; a 221 % increase with the
majority occurring among persons aged 35-64 (65.3 %), men (73.9 %), and
non-Hispanic whites (78.5 %). Among deaths involving fentanyl, the overall
deaths escalated from 5.2 deaths per 100,000 residents in 2015 to 21.3 deaths
per 100,000 residents in 2018 and more than 50% of these fentanyl-related
deaths involved polysubstance use.
Our main objective is to use data visualization using Microsoft Power
BI. The beauty of Power BI is that we can look at each element as a whole to make better decisions. The dashboard’s visual
representation allows us to quickly see the data that’s relevant for the
choices we have to make.
The advantages of Power BI are numerous, and it aids management teams in making
fast decisions. Data visualization is made simple with Power BI. It offers a
full summary of our data in visual form, with display choices such as tables,
charts, gauges, and maps, making it easier for us to use it. Other than that,
Power BI is also a powerful decision support tool that makes the difference
between typical data and an efficient, agile, and flexible organization, thanks
to its simplicity and visual representation.
We also came up with several questions for our research data. Our first
question is what is
the trend of death due to drug overdose by year in
the state of Connecticut? The second question that we are trying to figure out
its answer by applying data visualization would be what is the gender trend of
drug overdose death by the year from 2012 to 2018? The third question that we
are trying to figure out is the answer by applying data visualization would be
which race suffers from most death due to drug overdose by the year from 2012
to 2018? The last question that we are trying to figure out its answer by
applying data visualization would be which city has the highest count of
mortality due to drug overdose?
|
Figure 1 Figure 1 illustrates the
number of dead people by the city in Connecticut state from the year 2012 to
the year 2018. According to the bar chart, Hartford city has the highest
number of dead people which is 555 people due to drug overdose. The
second-highest number of dead people is New Haven city whereas the
third-highest number of dead people is Waterbury city. The New Haven city and
Waterbury city have almost the same number of dead people which are 366
people and 363 people. Furthermore, Bridgeport city has 336 dead people while
New Britain city has 223 dead people due to drug overdose. The rest of the
city has a number of
dead people less than 200 and below. From this chart, we can conclude that
Hartford has the highest number of death due to drug overdose. |
|
Figure
2 The
line chart depicts the number of dead people due to drug overdose from 2012
to 2018 and different races. Based on the line chart, white people have the
highest number of dead people which is 176 people in 2012. All other races have
less than 100 dead people due to drug abuse in that year. The number of dead
people for white people races increased steadily which is 176 people
increased to 760 people from the year 2012 to the year 2017. For black people
and white Hispanic people, the number of dead people rose gradually which are
33 to 103 for black people and 32 to 128 for white Hispanic people in this
period. In contrast, the number of dead people due to drug overdose for other
races were nearly zero in this entire period. In essence, it can be said that
most of the drug overdose case happens to white people with green line
representation. |
|
Figure
3 This area chart in Figure 3 represented the number of dead people in
Connecticut state by year. In 2012, there had 242 people dead due to drug
abuse and increased dramatically to 464 people in 2013. After that, the
number of dead people in 2014 which is 528 people grew strikingly to 971
people in 2017 and decline steadily to 955 people in 2018. This
shows the trend of people dying due to drug overdose in Connecticut is
increasing by the year. |
Figure 4
This clustered column bar
chart shows the number of dead people involved in drugs abuse by year and sex.
According to the chart, gender male has a higher number of dead people due to
drugs overdose than females from the year 2012 to the year 2018.
For males, the number of dead people increased slowly from 177 people
in 2012 to 366 people in 2014 and then spiked to the highest which is 727
people in 2017. The number of dead people in the year 2017 is almost the same
in the year 2018 which is 727 people and 729 people. For females, the number of
dead people rose steadily from 65 people in 2012 to 244 people in 2017 and then
have a little decrease in 2018 which is 226 people. In conclusion, it can be
said that both men and females that are associated with drug overdose death
increase over time, even though women are more than 50% lower compared to men.
Feel free to visit our Microsoft Power BI
Dashboard;
https://app.powerbi.com/groups/me/dashboards/f74fe1ab-88c6-46ad-9fae-31fc0c6904f1?ctid=0e0db2ad-c416-47c7-88ec-ceac4ee76767&pbi_source=linkShare