QAC020N255: Big Data Analytics - Drug Misuse Data Report

This report critically evaluates the application of big data analytics to drug misuse data, focusing on the use of Tableau as a business intelligence tool. It explores descriptive, predictive, and prescriptive analytics, highlighting how Tableau facilitates data visualization through techniques like heat ...Read More

Running head: CRITICAL EVALUATION OF THE BIG DATA APPROACH TO DRUG MISUSE DATA ANALYTICS
A Critical Evaluation of the Big Data Approach to Drug Misuse Data Analytics
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Introduction
In this age of internet and data, the analytics has become increasingly popular for
different business organizations that are helpful in exploring different hidden patterns,
correlations as well as other insights through the analysis of the enormous amount of data. In
order to explore this kind of trends or insights different types of software tools. Tableau is one of
those Business Intelligence and analytics tool which helps the data scientists and analysts to dig
deep inside the data.
Background
Tableau is one of the leading a Business Intelligence tool which makes the process of
data analysis quick, easy while making the output as useful. Tableau helps the users to connect to
numerous data sources even the cloud-based sources. The big data sources include Google
Analytics, Salesforce SQL and even the basic spreadsheet. Using the tableau tools and data
sources it is possible to make calculations, create reference to different data sheets, make
forecasts, trend analyses, as well as basic statistical summaries. Compared to different other tools
tableau e does not require any coding skill of the user and enables the users to create pivot,
manage metadata, split data as well as to optimize data sources.
More than 40 optimized data connectors are available in tables that helps in puling data
from the sources like Google BigQuery, Amazon Redshift, Salesforce.com, Vertica, Microsoft
Excel, SQL Server, Oracle, SAP HANA, Teradata, Hadoop and so on. Furthermore, newer data
connectors are integrated regular basis with the newer version of the tool. For the traditional
data there is a generic ODBC connector for any systems without a native connector. Tableau also
provides two different modes in order to interact with the data which are live connection and in-
memory connection. Users are free to switch or choose live and in-memory connections.
One of the best features in Tableau is Dragging and dropping different fields onto either
columns or on the Rows that will automatically generate a suitable chart, helping the analysts
in order to define structure of the dashboard.

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Critical Analysis and justification
The business analytics is often classified in three different categories. Which are
Descriptive, Prescriptive, Predictive analytics. The descriptive analytics is takes available data
and describes the descriptive details and what is happening inside the dataset. As an example,
visualizing the number of deaths in the different areas using the tableau. Predictive analytics is
about depending on the past data in order to forecast the future trends. This is mainly used in the
throughout the different areas of business. Finally, the Prescriptive analytics is about including
the past data in order to develop a decision model. This decision model is can help the
organizations in order to reach some actionable recommendation. Furthermore, the tool can be
integrated with the different other big data platforms such as Hadoop.
One of the important stages in data analytics is the initial data processing which takes a
lot of time that can be easily done by utilizing the available tools in Tableau. According to the
different researchers it is argued that Tableau does not support different statistical analysis
compared to the other BI tools. Even the financial reporting is also not available using the
tableau.
For the Drug Misuse Data, mainly the descriptive data analytics approach is considered
which was helpful in the answering the different queries from the dataset. There are multiple
types of data visualizations are used.
In our analysis Heat maps are used in the analysis process. This is one of very easy to
understand optical analysis tools for exploring trends or patterns in the dataset. Through the use
of the compositions of data by using the partial/total relationships detailed analysis can be
available.
Another visualization tool is the Bar charts. The bar charts ate most commonly used
visualization techniques in order to explore data. Through the use of the bar charts the analysts
can quickly compare different information depending upon their value while revealing highs and
lows simultaneously. As the drug misuse data is numerical data can be sliced easily in different
categories to quickly visualize the trends within the drug misuse data as well as compare data
across different categories.

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Recommendation
While designing any dashboard from a dataset it is very important to structure the
visualizations in such a way that can be easily understood b the intended audience. In addition to
that, It is encouraged to use the filters while creating worksheets of dashboards. Filters in the
available data visualization lets the user or the analyst in order to slice different data segments
from different perspectives. Furthermore, it can also help in drilling down to the visualization
level for more details. The filters are very useful for enabling the users to dig down at multi-
level data exploration. As the Tableau provides numerous options in order to build powerful
filters in the created worksheet or dash boards.
Customized Visualization in Tableau: One very valuable component which is disregarded
most of the times is representation of data in different customized visualization. Given similar
information and visualization methods, distinctive parameter settings may prompt entirely
unexpected visual portrayals of the data and give individuals diverse visual impressions.
Planning adjustable representation capacities leaves the client the opportunity of changing visual
parameter setting and greater chance to pick up knowledge from the changed visualization of the
query executed on the data.
Conclusion
In case designing dashboard with the different analysis on the selected data set the most
important feature is Interactivity on the dashboard that can make huge difference among a
confusing and complete story telling visualization boards. In order to achieve this, it is
suggested to make use of filters. However, on the other hand, unnecessary interactivity on a
dash board can lead to unnecessary confusion for the audience. With the help of the Tableau
the analysis process becomes less time consuming as well as interesting for the analysts and data
scientist. From the analysis it can be concluded that Tableau is more efficient and easy to use
tool compared to other big data analytics tools.

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Bibliography
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University Library System (ULS), University of Pittsburgh.

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