Data Governance Importance and Effectiveness Dissertation DATA GOVERNANCE IMPORTANCE AND EFFECTIVENESS: HEALTH SYSTEM EMPLOYEE PERCEPTION You are to review

Data Governance Importance and Effectiveness Dissertation DATA GOVERNANCE IMPORTANCE AND EFFECTIVENESS: HEALTH SYSTEM
EMPLOYEE PERCEPTION You are to review the Dissertation planning and discuss
the methodologies used in your own words. You will expand on the issue and
how researcher has attempted to examine that issue by collecting data – you are
NOT collecting data, just reporting on how researchers did their collection. APA format and without plagiarism.Just discuss on the methodologies used in the paper in our own words. DATA GOVERNANCE IMPORTANCE AND EFFECTIVENESS: HEALTH SYSTEM EMPLOYEE
PERCEPTION
CHAPTER III METHODOLOGY
This chapter, and this study’s results, findings, and discussions found in Chapters IV and V are
based on a mixed methods approach. The data were collected using open ended, Likert scale, and
multiple-choice questions. Analysis of the data includes a qualitative study and multiple linear
regression statistical tests to evaluate whether the research questions and their related hypotheses
were supported. The dataset used for this study was provided by a large midwest health system’s
Enterprise Data Governance Program, which conducted an internal survey available to all
employees in support of the organization’s Data Governance initiatives. Research Design Cooper
& Schindler (2006) posited that research should be guided by appropriate theoretical constructs
to ensure meaningful results are derived. A mixed methods approach was taken for this research
for analyzing the collected sample data to understand how health system employees define data
governance, then to understand their perceptions of the importance and effectiveness of data
governance to their role, and its impact on strategic outcomes of their health system. The data
was collected through an electronic survey containing one open-ended question, and multiple
Likert scale and multiple-choice questions. According to Fowler (2002), survey instruments
provide researchers with the ability to capture and explore patterns of participant’s behavior.
Examining the results of the dataset may provide significant insights to researchers interested in
understanding how employee perceptions are barriers or enablers of change brought about by
Data Governance. 54 The resulting data set will be used to answer the following research
questions: RQ1. Do health system employees differ in their understanding of Data Governance?
RQ2. Which factors influence a health system employee’s perception of the importance of data
governance to their role? RQ3. Which factors influence a health system employee’s perception
of the current effectiveness of data governance to their role? RQ4. Which factors influence a
health system employee’s perception of the impact of data governance to strategic outcomes of
their organization? The following hypotheses were derived from the above research questions
and were tested to determine whether they were supported. The first research question will be
answered through a qualitative analysis. H01: Employee demographics, such as age, role, or job
level, do not have a significant impact on how employees perceive the importance of data
governance to their role. Ha1: Employee demographics, such as age, role, or job level, do have a
significant impact on how employees perceive the importance of data governance to their role.
H02: Employee demographics, such as age, role, or job level, do not have a significant impact on
how employees perceive the current effectiveness of data governance to their role. Ha2:
Employee demographics, such as age, role, or job level, do have a significant impact on how
employees perceive the current effectiveness of data governance to their role. H03: Employee
demographics, such as age, role, or job level, do not have a significant impact on how employees
perceive which disciplines of data governance most impacts the quality of care the organization
provides. 55 Ha3: Employee demographics, such as age, role, or job level, do have a significant
impact on how employees perceive which discipline of data governance most impacts the quality
of care the organization provides. H04: Employee demographics, such as age, role, or job level,
do not have a significant impact on how employees perceive which disciplines of data
governance most impacts the cost of care the organization provides. Ha4: Employee
demographics, such as age, role, or job level, do have a significant impact on how employees
perceive which discipline of data governance most impacts the cost of care the organization
provides. H05: Employee demographics, such as age, role, or job level, do not have a significant
impact on how employees perceive which disciplines of data governance most impacts the
patient experience the organization provides. Ha5: Employee demographics, such as age, role, or
job level, do have a significant impact on how employees perceive which discipline of data
governance most impacts the patient experience the organization provides. H06: Employee
demographics, such as age, role, or job level, do not have a significant impact on how employees
perceive which disciplines of data governance most impacts the quality of analytics within the
organization. Ha6: Employee demographics, such as age, role, or job level, do have a significant
impact on how employees perceive which discipline of data governance most impacts the quality
of analytics within the organization. 56 Sample Participants The Enterprise Data Governance
Program (EDGP) at the health system, “an integrated health system, with an award-winning
health plan, teams of nationally recognized doctors and providers and a network of hospitals and
care facilities spanning 13 counties” wanted to understand how role, age, gender, and job-level
impact employee perceptions of data governance (“About Us”, 2017, para. 2). A survey was
created including both qualitative and quantitative questions and was made available and
marketed to employees across the health system. The target population for this study included all
employees and contractors; however, due to company policy e-mail distribution to all employees
was prohibited. The EDGP distributed the survey via their intranet social-media sites, word of
mouth, and through regularly schedule data governance meetings. Participation was voluntary
and there were no incentives provided to those who completed the survey. Additionally,
participants, likely, either had an interest in Data Governance prior to taking the survey or had a
first or second degree of separation with members of the existing Data Governance Program who
suggested that they participate. A degree of separation is the social distance between two people.
One degree of separation is the distance between one person and his friends, or the people he
knows. Two degrees of separation is the distance between one person and his friend’s friends
(Degrees of Separation, 2017). Although the number of employees was approximately 25,000,
only 178 participated, or 0.7% of the population. The resulting data set included participants
across multiple roles and included various levels of leadership within the organization. 57 Survey
Instrument The survey contained 30 questions broken down into the following 5 sections:
Participant Demographics, Definition of Data Governance, Importance of Data Governance,
Effectiveness of Data Governance, and Impact of Data Governance on Strategic Outcomes. A
summary of each section follows. The Participant Demographics section captured information
about the participant including age, gender, role, and length of experience captured by
dichotomous and Likert-scale questions. The Definition of Data Governance section captured the
participant’s confidence in their understanding of the definitions of Master Data Management,
Data Quality, and Meta-data Management, and provided a free-text question to capture their
definition of data governance. Section 3 and 4, Importance and Effectiveness, captures the
participants’ perceptions of the importance of data governance disciplines to their role, and how
effective they perceive the existing data governance processes. The last section, Impact of Data
Governance and Strategic Outcomes, allowed participants to select from the various disciplines,
which had the most positive impact, and which posed the most significant risk, to strategic
outcomes of the organization, such as Quality of Care and Patient Experience. Appendix A
contains the full survey, which took employees about 10 minutes to complete. Data Collection
The data set was provided by the health system’s Data Governance Office, and was collected in
the fall of 2017. The survey was developed by members of an internal Data Governance Office
and distributed electronically through SurveyGizmo. There are several 58 advantages to using an
electronic survey including low distribution costs, anonymous access by participants, simplified
collection of the data, and availability of a well-structured data set. The survey was made
available within the organization through local intranet and internal social media platforms
between August 2017 and November 2017. The survey instrument was intended to be short, able
to be completed within 5-15 minutes and was written so that it could be understood and
completed by participants in any role or department within the health system. Minimal guidance
was given to participants, and definitions for Master Data Management, Data Quality, and Metadata Management were provided within the survey to help participants understand the context of
related questions. The Data Governance Office was not allowed to distribute the survey to
location-based or employee wide e-mail distribution lists. However, it was distributed via e-mail
to leadership involved in data governance activities including the Data Governance Council and
the Enterprise Analytics Council. It was also distributed to analytic team managers, and to
individuals directly associated or known by members of the Data Governance Office.
SurveyGizmo stored the responses on their online website until the Data Governance Office
closed the survey on November 1, 2017. The dataset was downloaded from SurveyGizmo, preformatted as a comma separated value (CSV) file, and was imported into the Statistical Package
for the Social Sciences (SPSS) to facilitate the analysis of data. Study Variables The dataset
available for analysis included variables of different types. While participant’s age was logically
measured on a continuous scale, most of the variables were categorical. Table 1 below describes
the variables used in this research. 59 Table 1. Study Variables Used in this Research Variable
Definition (response to survey question) Type Range / Coded Age What is your age? Continuous
0-99 Age Group Age grouped into decades. Categorical 0: 20-29; 1: 30-39; 2: 40-49; 3: 50+
Gender What is your gender? Binary 0-Female, 1-male, 2 – Unknown/Other Level Which of the
following best describes your job level? Categorical Individual Contributor, Supervisor,
Manager, Director, Vice President, Chief Management Role Level grouped into
nonManagement, and Management. Binary 0-Individual Contributor; 1- Management (including
Supervisor, Manager, Director, Vice President, and Chief) Role Which of the following best
describes your job classification Categorical Clinical, Facilities, Financial, Information Services
– NONAnalytics/BI/Reporting, Information Services – Analytics/BI/Reporting, Non Information
Services – Analytics/BI/Reporting, Legal, Operations, Research, Sales & Marketing, Other IT
Role Role grouped into IT and non-IT roles. Binary 0 – non IT Role (included Clinical,
Facilities, Non Information Services – Analytics/BI/Reporting, Legal, Operations, Research,
Sales & Marketing, Other) 1 – IT Role (included Information Services – Non
Analytics/BI/Reporting, Information Services – Analytics/BI/Reporting) Analytic Role Role
grouped into analytic and non-analytic roles. Binary 0 – non Analytic Role (included Information
Services – Non Analytics/BI/Reporting, Clinical, Facilities, Legal, Operations, Research, Sales
& Marketing, Other) 1 – Analytic Role (included Information Services – Analytics/BI/Reporting,
Non Information Services – Analytics/BI/Reporting) Entity Which Entity is your role a part of?
Categorical Hospital Group, Medical Group, Corporate, Health Plan, Unknown Entity Group
Entity grouped into Health Plan and Health System. Binary 0: Health Plan; 1: Hospital System
(Hospital Group, Medical Group, Corporate, Unknown) 60 Table 1. Study Variables Used in this
Research (continued) Variable Definition (response to survey question) Type Range / Coded
Role Tenure How many years have you been in your current role? Please round to the nearest
year. Continuous 0-99 Role Tenure Group Role Tenure grouped into categories Categorical 0: 01 Years; 1: 2-3 Years; 2: 4+ Years Industry Tenure How many years have you worked within the
Healthcare Industry? Please round to the nearest year. Continuous 0-99 Industry Tenure Group
Industry Tenure grouped into categories Categorical 0: 0-2 Years; 1: 3-4 Years; 2: 5-10 Years; 3:
11+ Years DGDefinition In the space provided below, please explain what “Data Governance”
means to you. Qualitative Short answer DQImportance How important is it for the data that you
use to be of high quality Continuous Likert-scale 1-10 (low-high) MetaImportance1 How
important is it for the data that you use to be well defined? Continuous Likert-scale 1-10 (lowhigh) MetaImportance2 How important is it for the data that you need to be easy to find?
Continuous Likert-scale 1-10 (low-high) MDMImportance How important is it for the data that
you use to be from a trusted source and consistent across other systems? Continuous Likert-scale
1-10 (low-high) AggImportance The sum of DQImportance + MetaImportance1 +
MetaImportance2 + MDMImportance Continuous 4-40 DQEffectiveness How effective is our
organization at ensuring the data that you use is of high quality? Continuous Likert-scale 1-10
(low-high) MetaEffectiveness1 How effective is our organization at ensuring the data that you
use is well defined? Continuous Likert-scale 1-10 (low-high) MetaEffectiveness2 How effective
is our organization at ensuring the data that you use is easy to find? Continuous Likert-scale 1-10
(low-high) MDMEffectiveness How effective is our organization at ensuring the data that you
use is from a trusted source and consistent across other systems? Continuous Likert-scale 1-10
(low-high) DGEffectiveness The sum of DQEffectiveness + MetaEffectiveness1 +
MetaEffectiveness2 + MDMEffectiveness Continuous 4-40 QCImpact For your role, which item
in the list below do you think has the most positive impact on Quality of Care at our
organization? Categorical High Quality Data, Consistent Data Across Systems, Well Defined
Data, Easy to Find Data 61 Table 1. Study Variables Used in this Research (continued) Variable
Definition (response to survey question) Type Range / Coded CostImpact For your role, which
item in the list below do you think has the most positive impact on Cost of Care at our
organization? Categorical High Quality Data, Consistent Data Across Systems, Well Defined
Data, Easy to Find Data PatientImpact For your role, which item in the list below do you think
has the most positive impact on Patient Experience at our organization? Categorical High
Quality Data, Consistent Data Across Systems, Well Defined Data, Easy to Find Data
AnalyticImpact For your role, which item in the list below do you think has the most positive
impact on Quality of Analytics and Reporting at our organization? Categorical High Quality
Data, Consistent Data Across Systems, Well Defined Data,

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