Data Analysis: Organizational Culture, Decision Making & Modeling

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Added on 2024/07/12

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This report explores the influence of organizational culture on data-driven decision-making (DDDM) and demonstrates exploratory data analysis (EDA) and linear regression modeling using RapidMiner and Tableau. It begins by defining organizational culture and its impact on DDDM, outlining key steps for effective data utilization within an organization. The report then delves into EDA, showcasing attribute selection and analysis using RapidMiner on the housing.csv dataset. Furthermore, it explains the implementation of a linear regression model, detailing the process flow and accuracy results. Finally, it shows data visualization with Tableau, presenting graphical analysis and geo-mapping of selected attributes. The report uses figures and tables to support the analysis and provide a clear understanding of the concepts and methodologies employed.

CIS 8008 ASSESSMENT-2
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Table of Contents
Task1.1: Organizational culture and data-driven decision making.................................................3
Task1.2: How organizational culture could impact on the use of data-driven decision making.....4
Task2.1 Exploratory data analysis................................................................................................... 6
Task 2.2 Linear Regression model................................................................................................ 12
Task 3.1 Graphical view................................................................................................................ 17
References......................................................................................................................................20
Table of figure
Figure 1: EDA process of attributes selection.................................................................................7
Figure 2: Analysis1-Selected Attributes.......................................................................................... 8
Figure 3: Analysis2-latitude and median house value..................................................................... 9
Figure 4: Analysis3-Households and total bedrooms.................................................................... 10
Figure 5: Analysis4: household and median house value..............................................................11
Figure 6: Statistics.........................................................................................................................12
Figure 7: process flow diagram..................................................................................................... 13
Figure 8: accuracy in tabular view.................................................................................................14
Figure 9: Accuracy result in plot view..........................................................................................15
Figure 10: Description of accurate result.......................................................................................15
Figure 11: Relative error................................................................................................................16
Figure 12: DESCRIPTION OF Relative error...............................................................................16
Figure 13: Absolute error...............................................................................................................17
Figure 14: Description of absolute error........................................................................................17
Figure 15: analysis 1- graph view..................................................................................................19
Figure 16: Analysis 2- Geo Map................................................................................................... 20
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Task1.1: Organizational culture and data-driven decision making
Organizational culture means the fundamental belief, assumptions, values and the process of
interaction that contribute to the distinctive psychological and social culture of an organization. It
includes the expectations of an organization, philosophies, their experiences, behaviour of their
member, their interactions with others group, and future expectations. Organization culture can
be generated and communicated when the leaders appreciate their jobs to maintain the culture of
their company. This deeply embedded culture defines the way that people behaves and this can
lead the employees to achieve their objectives Cameron, (2008). But when the organizational
culture changes, it is the duty of the leaders to ensure their employees about the benefits arise
from these changes and convince them that this change can bring more success. These changes
can be bringing through many ways like formulating the clear vision, change at the higher level,
select the newcomers and socialize them etc. The decision of these organizational changes can be
managed by DDDM (Data-Driven Decision Management) which is the approach that values the
changes and decisions with the verifiable data. It is generally undertaken as the method to
achieve the more profit as a competitive benefit. The main aim of using the data-driven decision
making is to extrapolate from the key data sets that represent the projected efficacy of the
organization and the process of how the organization can work out in order to achieve more
success Rouse (2014).
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Task1.2: How organizational culture could impact on the use of data-driven
decision making
The organization can make a change in their organizational culture by using the data-driven
decision making and it has a great impact on the success of the organization. There are some
steps for data-driven decision making that an organization can implement which are:
 Plan a strategy: The first thing that the organization should do is to plan the strategies as this
can help to know the complete overview of the requirement and changes. Rather than starting
with the data that an organization can access, they should first work on the requirements that
they are looking to achieve.
 Hone in on the organizational area: After making the strategic plans, an organization
should identify which specific areas are more important to achieve the complete strategy.
This can reduce the time which is wasted in looking for the whole areas.
 Determine the unanswered questions of the organization: The next step is to find the
questions which arise during the strategic plans to achieve those goals. By aiming at what an
organization needs to know, they can focus only on those particular data that is actually
required and this can reduce stress level as well as cost.
 Identify the data to provide answers to the questions: As the organization determines the
questions, the next step is to identify the answers to those questions and focus only on that
solutions.
 Check which data from the selected one already have in the organization: Now as the
organization finds the answers and select the data, check which data from the selected
information is already present in the organization or not. If it is not present, then identifies
the ways to gather the data by accessing or acquiring from the external source.
 Check the cost spent on the data is justified or not: Once the organization knows the cost,
they can work out to know if the touchable benefits balance those costs. Study the clear event
of the investment that plans the data at long-term in the organizational strategy. The amount
of the data may be fall at some point, but it can be still adding up if the organization gets
supported away incorrect way.
 Gather and analyses all data: The next thing to do is to collect some amount of data by
using some resources and then analyse these data to extract the useful and meaningful
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organization insights. The common analytics that can be used are speech analytics, text
analytics, and image/video analytics.
 Present and allocate the data: Now distribute and present the data to the members of the
organization at the correct time because the use of analytical tools gets wasted if these data
are not presented to the correct members at the right time.
 Incorporate this data into the organization: The final step is to apply the perceptions taken
firm the data with the use of analytical tools to the decision making that will implement this
data to transform the organization in the better way and attained the desired result to achieve
success Marr (2016).
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Task2.1 Exploratory data analysis
Introduction
In this task, we are using the tool RapidMiner for the exploratory analysis of data by performing
the EDA (Exploratory data analysis). We are using the RapidMiner for the data analysis of
housing.csv. it has provided numerous values of the data which we will use and implement.
Among many data values, we are selecting some subsets on which the comparison is done and
present through the chart In the housing.csv, it is used that will obtain different values and this
will be helpful in better understanding the process that has an effect on the pricing values of the
system.
Attributes selected
FIGURE 1: EDA PROCESS OF ATTRIBUTES SELECTION
Among the given attributes of the housing.csv, we have selected some attributes on which
analysis is performed. These selected attributes are:
 Households
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 Total Bedrooms
 Latitude
 Median house values
 Ocean proximity
We have used these attributed and analyse the amount of the house. Below figures represent the
analysis performed on these attributes:
FIGURE 2: ANALYSIS1-SELECTED ATTRIBUTES
Above figure represents the attributes that are selected among the various attributes provided by
the housing.csv
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FIGURE 3: ANALYSIS2-LATITUDE AND MEDIAN HOUSE VALUE
Result:
The above figure represents the output produced from the analysis of values of latitude and
median house value attributes. This scatter chart shows the increment of values of both attributes.
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FIGURE 4: ANALYSIS3-HOUSEHOLDS AND TOTAL BEDROOMS
Result:
The above figure represents the result obtained from the analysis done between the cost values of
attributes total bedrooms and households.
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FIGURE 5: ANALYSIS4: HOUSEHOLD AND MEDIAN HOUSE VALUE
Result:
This is the fourth analysis done between the values of households and median house value but it
does not show any clear vision about the outcomes produced.
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FIGURE 6: STATISTICS
This above figure represents the statistics of the selected attribute obtained after the analysis
performed.
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Task 2.2 Linear Regression model
Introduction
The linear regression model is the method to perform the analysis among the different values and
variable of the attributes in the form of a graph on x-axis and y-axis. In this task, we are
implementing the process of linear regression on the attribute in the tool RapidMiner. The
process of performing the linear regression in the RapidMiner is done in many steps.
First of all, import the file of housing.csv and extract the data in the design view of the sheet. As
the data is extracted, connect the one port to the other to obtain the result and this can be done
through steps.
Attributes selected
We have selected some attributes in which the linear regression is performed. These attributes
are:
 Household
 Floors
 Proximity
 Grade
 View
Process flow
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