Quality Data Characteristics

Quality Data Characteristics
Discuss some characteristics of quality data.
Precisely some of the characteristics are generally based on the main levels of quality assurance such as with accuracy, accessibility, comprehensiveness,
consistency.
Data accuracy– means that it must be correct at all time, error free.
Data accessibility– means should be easy to get a hold of at all time.
Data comprehensiveness– means that all data must be updated and completed.
Data consistency– means that all information within the document must be a reliable source of data.
Discuss how gaps (or breaches) are predicted and handled.
Most organizations with company data files must predict that it is possible that someone would possibly hack into their files and cause a potential data
breach. ... Show more content on Helpwriting.net ...
This data system for HIPAA is constantly amended based on changes due to civil right laws or legal laws suits that have made ground breaking
changes in health care today. The law has allowed it to be possible to maintain the privacy rights for all patients with written consent and is continuing
to be debated each year.
Discuss Web 2.0 and its primary features.
In a nut shell the web 2.0 is the way the information is shared, stored, created, displayed, manipulated, and distributed how it effects the internet.
Review each of the following Web 2.0 tools (you will need to create a free account to use Practice Fusion). Are these tools useful? What are the
benefits or drawbacks of using tools such as these? Share your observations with the class.
Typically, the tools used from web 2.0 have brilliantly displayed how easy it is to maneuver around the Practice fusion website. Initially, the setup
process required that an access code was used to setup the account which clearly meant that there were security safe guards that needed to be passed
through first. Overall, this website is an easy part to setting up ordering prescriptions, billing insurance companies, and setting up patient electronic
medical
... Get more on HelpWriting.net ...

Evaluating The Quality Improvement Initiative And...
The Evaluation Plan Many different conceptual models exist for evaluating a process. An evaluation is a necessary step to determine how well a
process is working and if the targets are being met. The measurements in a quality improvement project are important to assess where the organization
stands with the project, and to determine success of the project (Sadeghi, Barzi, Mikhail, & Shabot, 2013). There are both financial performance
metrics and quality performance metrics that are used in healthcare to determine success. The purpose of this paper is to propose on outline for
evaluating the quality improvement initiative and financial implications, along with giving a description of specific metrics. A recommendation will be
discussed as to how the organization can represent the data related to the quality improvement issue for ongoing monitoring. Also, there will be an
explanation of how the organization can create an integrated view of performance that links finance and quality.
Methods for Evaluating
To improve the quality, safety, efficiency, and effectiveness of patient care, applying research and evidence
–based practice is necessary. In the Institute
of Medicine's report, Keeping Patient Safe: Transforming the Work Environment of Nurses, there is an emphasis on adequatenurse staffing (Hickey &
Brosnan, 2012). Therefore, the quality improvement initiative is to focus on closing the gap between the core staffing and actual staffing in a six–week
schedule.

Data Management, Data And Information Quality For Big Data?
type of data, and it has a massive amount of processing power, and can handle a boundless number of jobs or tasks. Data Management, Data ingestion,
Warehouse, and ETL provides features for effective management and data warehousing for data managing as a valuable resource. The Stream
computing features pulls streams of data and then streams it back out as a single flow and then processes that data. Analytics/ Machine Learning
features advanced analytics and machine learning. Content Management which features document management and comprehensive content lifecycle.
Integration features the integration of big data from any sources with ease. Data governance which is a compliance solution to protect the data and
comprehensive security, and ... Show more content on Helpwriting.net ...
Making it a friendlier drag–and–drop graphical interface that would automatically generate the fundamental Hadoop code. The Talend tool includes
components for leading Apache Hadoop software's like HDFS, HBase, Hive, Pig, and Sqoop.
Talend's Hadoop–leveraging big data quality functionality has made it possible for data quality management across an organization or business entire
enterprise. Talend Big Data Platform distributes data quality features that include Data profiling, Data standardization, matching and cleansing, Data
enrichment, Reporting and real–time monitoring, and Data governance and stewardship (Big Data Quality: Talend Hadoop Data Quality &
Management 2017).
The challenges of data quality and data quality assessment
High–quality data are the precondition for guaranteeing, using big data and analyzing. Big data has a quality that faces many challenges. The
characteristics of big data are the three Vs Variety, Velocity, and Volume, as explained in the what is big data section of the paper Variety of data
indicates that big data has a different kind of data types, and with this diverse division puts the data into unstructured data or structured data. These data
need a much higher data processing capability. Velocity is the data that is being formed at and unbelieve amount of speed and it must be dealt in an
organizational and timely manner. Volume is the tremendous volume

Company Analysis : Pb And The Erp Re Engineering Project
1 Introduction
This chapter aims to provide an overview of the thesis topic including: an introduction to the problem Pitney Bowes (PB) want to solve and the related
business topic; the company summary of PB and the ERP re–engineering project which they are undergoing; the objectives and scope as well as the
structure of this thesis project. The problem definition will show its importance and rationale to this topic as well as PB. The company summary will
provide contextual and valuable background. The objectives and scope will illustrate the key deliverables considering the limitation of resource and
time duration of this project as well as how they will be acquired.
1.1 Introduction to the problem
In the recent decades, Information technology (IT) has become an imperative part of business for most companies, especially international
corporations. It is quite difficult for an international corporation to operate without a mature IT system to collect and organise data and information.
With the development of IT, systems such as SAP have enabled companies to collect, store and utilise many times more data than they used to be. It
means companies now have to deal with huge amount of data, which indicates that more data quality problems are more likely to occur. With their
increasingly dependency upon the IT systems or databases to support business process and decision making, the number of errors in stored data and
organisational impact of these errors are likely to increase.

Quality And Data Management Essay
Data management, statistical analysis & quality assurance Data collection The data and the values of the sensitivity scores would be collected at
the general dental practice by the trained dentists who will report to the second investigator responsible for the overall collection of the data.
Direct patient examination would be carried out at base line, immediately, 3, 6 and 9 months post application using visual analogue scale for tactile
stimuli response and Schiff cold air sensitivity scale for standard cold air blast. Case report forms (CRF) would be given to the investigators for
better understanding and optimum care would be maintained to avoid giving any information which can lead to bias for example the treatment
carried out etc. Data storage: Research data would be documented in the papers to begin with which would be regularly updated on to the computer
by the data manager who will be responsible for managing and storage of the data .An assistant would be provided to him on his request of needed
Data will be checked at follow–ups and will be collected by the principal investigator .It will then inserted in to the software by the data manager with
the help of the assistant f necessary who will completely blinded of the procedure again by providing minimum information required to avoid bias. The
main office of the surgery will be accommodating the computer where all the data would be inserted. No one else other than the principal investigator
himself and the

Data Quality Management : The Business Processes That...
Data Quality Management: The business processes that ensure the integrity of an organization 's data during collection, application (including
aggregation), warehousing, and analysis. While the healthcare industry still has quite a journey ahead in order to reach the robust goal of national
healthcare data standards, the following initiatives are a step in the right direction for data exchange and interoperability: Continuity of Care Document
(CCD), Clinical Documentation Architecture (CDA) Data Elements for Emergency Department Systems (DEEDS) Uniform Hospital Discharge Data
Set (UHDDS) Minimum Data Set (MDS) for long–term care ICD–10–CM/PCS, Systemized Nomenclature of Medicine–Clinical Terms (SNOMED
CT), Logical Observation Identifiers Names and Codes (LOINC). Data Quality Measurement: A quality measure is a mechanism to assign a quantity to
quality of care by comparison to a criterion. Quality measurements typically focus on structures or processes of care that have a demonstrated
relationship to positive health outcomes and are under the control of the healthcare system. This is evidenced by the many initiatives to capture quality
/performance measurement data, including: The Joint Commission Core Measures Outcomes and Assessment Information Set (OASIS) for home
health care National Committee for Quality Assurance 's (NCQA) Health Plan Employer Data and Information Set (HEDIS) Meaningful Use–defined
core and menu sets These data sets will be used within

Statistics And Its Impact On The Quality Of Data
Statistics is defined as a branch of mathematics used to analyze, explain summarize as well as interpret what we observe– in order to make sense or
meaning of our observations. Every day in life we encounter information that originates from diverse forms and ways. This means that to make this
information to have sense, there is the need to use statistics. However, due to its focus and empirical on applications, statistics is classically considered
a distinctive mathematical science rather than a branch of mathematics (Chance et al, 2005). Thus, some tasks a statistician use are less mathematical;
for instance, making sure data collection is carried out in a manner that yields valid conclusions, reporting results, or coding data in ways
understandable to the users. Statistics is known to improve the quality of data by fashioning specific survey samples and experiment designs. It also
offers tools used to predict and forecasting the use of data as well as statistical models. It is applicable in many academic fields that include business,
government, social and natural sciences.
Descriptive statistics are entirely used to describe the sample under study. They are used basically to describe the fundamental characteristics of a given
data. They offer simple summaries concerning the measures and the samples. When utilized together with simple graphics study, they form the heart of
practically each quantitative study of data. This means that descriptive statistics utilized both

Pharmaceutical Validation Tool For Quality Management
Pharmaceutical Validation tool for Quality Management
Abstract
Pharmaceutical validations is part of CGMP regulations by which one can build quality attributes of pharmaceutical specification, i.e. safety, efficacy,
purity, in pharmaceutical products. It assures that the process follow for the manufacturing of pharmaceutical products is well controlled and
monitored at its critical parameters for consistently producing the quality products.
The present review describes the importance of validation in pharmaceutical industry, its requirement for approval of new drug application by the
various regulatory agencies. Furthermore it highlights the current guidance on process validation by USFDA, EMA.
Key Words: Validation, Quality assurance, critical parameters, NDA, USFDA, EMA, Validation Guidelines
Contents
Introduction2
History of Pharmaceutical validation2
Approaches to Process Validation4
Stages of process validation according to life cycle approach4
Stage I: Process Design4
Stage II: Process Qualification4
Stage III: Continued Process Verification5
Features of USFDA Process validation guidance 20115
Conclusion6
References6
Introduction
Quality is concept applicable at everywhere from business to have successful life. Everyone wants to buy the quality products and want to live the
quality life as per the standards determined by them for it. As concern with pharmaceutical industries, they are built for to bring the quality in health of
human and animal beings.

Collect Quality Data
Emergence of Tools and Methods to Collect Quality Data While it is easy to decide that quality needs attention in emergency medicine the first
question when reviewing something is of course, how? Tools and methods must be developed when investigating anything in order to collect
information. The Centers for Medicare and Medicaid Services (CMS) describes quality measures as tools that help us measure or quantify healthcare
processes, outcomes, patient perceptions, and organizational structure and/or systems associated with the ability to provide high–quality health care
and/or that relate to one or more quality goals for health care (CMS, 2015). In order for quality measures to be used, however, how is that data
collected? According to CMS... Show more content on Helpwriting.net ...
The AHRQ has developed many tools in order to measure quality in healthcare. To truly grasp the history and evolution of research and findings of
quality measurement and healthcare, their databases should be explored. On their website, www.ahrq.gov, you can find reports, research, and fact
sheets to update you on the most up to date quality requirements and measurement tools and outcomes of their use in healthcare. The AHRQ uses
evidence based reports that they collect with their work done through Evidence–based Practice Centers (EPC's). Their basis always going back to their
key quality indicators, used by health care organizations everywhere. Their mission statement reads, "The Agency for Healthcare Research and
Quality's (AHRQ) mission is to improve the quality, safety, efficiency, and effectiveness of health care for all Americans" (U.S. Department of Health
and Human Services, 2015). The work of the AHRQ helps set the standards in quality measurement tools and their

Monitoring Quality Data
How a Facility Evaluates and Monitors Quality Data
Yadira Garcia
University of the Incarnate Word Online
How a Facility Evaluates and Monitors Quality Data
The purpose of this paper is to discuss the methods used by a local health care facility, Southwest General Hospital, to evaluate and monitor healthcare
quality data. Quality measurement in health care is the process of using data to evaluate the performance of health care providers against recognized
quality standards (FamiliesUSA, 2014). The measuring of quality plays a vital role in the creating, maintaining, and managing of the data that this
healthcare facility aims in focusing on quality of health care.
Quality measures are set in place by Southwest General Hospital (SGH) to assess the delivery of its health care. These measurement systems allow the
hospital access to tools that enhance the efficiency and delivery of patient care. One system that SGH uses to promote quality is the use of
computerized physician order entry (CPOE) system which reduces medication errors. Another way Southwest General promotes the measurement of
quality data is by becoming the first local hospital to use a high–tech system to track staff in promoting hygiene. Karen Barnhart RN, Director of
Quality states, "Our staff members clip a small device onto their badges which then interfaces ... Show more content on Helpwriting.net ...
(DNV). Creating, maintaining, and managing quality health information plays a vital role in achieving DNV accreditation for this facility. Measuring
quality of this facility requires that an entire hospital is dedicated to meeting the needs its patients and achieving the goals required to initiate and
maintain accreditations. As a result of its hard work and dedication, Southwest General Hospital has earned numerous distinctions for quality, including
accreditations which include Chest Pain, Stroke, Bariatric Surgery, and Wound

Reaction, Recommendation, Conclusion Paper
Corrective and Preventive Actions (CAPA) * Inspectional Objectives * Decision Flow Chart * Narrative * Medical Device Reporting * Inspectional
Objectives * Decision Flow Chart * Narrative * Corrections & Removals * Inspectional Objectives * Decision Flow Chart * Narrative * Medical
Device Tracking * Inspectional Objectives * Decision Flow Chart * Narrative
Corrective and Preventive Actions (CAPA)
Inspectional Objectives
1. Verify that CAPA system procedure(s) that address the requirements of thequality system regulation have been defined and documented.
2. Determine if appropriate sources of product and quality ... Show more content on Helpwriting.net ...
Once you have gained a knowledge of the firm's corrective and preventive action procedure, begin with determining if the firm has a system for the
identification and input of quality data into the CAPA subsystem. Such data includes information regarding product and quality problems (and potential
problems) that may require corrective and/or preventive action.
2. Determine if appropriate sources of product and quality problems have been identified. Confirm that data from these sources are analyzed to identify
existing product and quality problems that may require corrective action.
The firm should have methods and procedures to input product or quality problems into the CAPA subsystem. Product and quality problems should be
analyzed to identify product and quality problems that may require corrective action.
The firm should routinely analyze quality data regarding product and quality problems. This analysis should include data and information from all
acceptance activities, complaints, service, and returned product records. Determine if the firm is capturing and analyzing data from acceptance
activities relating to component, in–process and finished device testing. Information obtained subsequent to distribution, which includes complaints,
service activities and returned products, as well as information relating to concessions (quality and nonconforming products), quality records, and other
sources of quality data should also be captured

Measuring Data Quality Business Value Essay
3.5 Data Quality Business Value:
According to Umar at al. (1999), (as cited in Haug., & Arjborn., 2011 ) there are many big organizations losing millions of dollars annually because of
Data Quality related problems. They lose this amount in terms of revenue opportunities and failure to address customer issues in a timely manner. In
addition; poor Data Quality is one of the biggest reasons for failure of critical information projects. So, here there are some major benefits of using
proper Data qualities which are below:
Deliver high–quality data
Deliver high quality data for a range of enterprise initiatives including business intelligence, applications consolidation and retirement, and master data
management.
Reduce time and cost
Data Quality uses to implement Client relationship management (CRM), data warehouse, data governance, and other strategic IT initiatives and
maximize the return on investments.
Help improve customer service Data Quality helps to improve customer service and to identify a company 's most profitable customers.
Provide business intelligence for research, fraud detection, and planning
Data Quality provides business intelligence for research, fraud detection, and planning. Better Data Quality leads to better analysis in research, fraud
detection and in the part of planning.
For example, HSBC Bank used good Data Quality to manage relationship between organization and customers. After using new technology, this
organization manages data three

Quality Improvement Is Defined вЂњAs Systematic, Data-Guided
Quality improvement is defined "as systematic, data–guided activities designed to bring about immediate improvement in health care delivery in
particular settings"(Lynn et al., 2007, p 667) by utilising the Model for Improvement, which is not a replacement for one already used by the
organisation. Instead, the model of improvement accelerates improvement of health care process and proved to be successful. It consists of two parts
such as the three questions and the Plan–Do–Study–Act (PDSA) cycle of rapid change tool to determine if the change is an improvement (Institute for
Healthcare Improvement, 2017).
Jane (pseudonym) is a student nurse in the first week of practicum placement in an aged residential care facility. Jane has ... Show more content on
Helpwriting.net ...
All of these are not only unavoidable but also impact a person's ability to interact with family, health professionals and health care providers. This also,
impacts their ability to take an active part in the decision–making of their care limiting participation in therapy, counselling and education. There was
no data or resources available at the residential care facility on communication aids to support those with communication problems. Jane added activity
facilitators, nurses, student nurses and health care assistants to the team to represent the interdisciplinary aspects of the communication issue. She also
recruited a family member of one of the residents on a hospital wing to provide their perspective.
Jane then applied the Plan–Do–Study– Act (PDSA) cycle of rapid change tool or process to her project on q–cards aid tool. In the "Plan" part of the
cycle, Jane talked to the activity facilitator regarding the communication aids available in the rest home facility for those who have aphasia, dysphasia
or dysarthria. She found that even though the activity facilitator uses cards for people with dementia in the dementia unit; it is more a picture
recognition rather than supporting communication. Furthermore, Jane spoke to the clinical manager regarding an idea to use the q–cards as a means
to support

Data Preparation And Quality Of Data Essay
Introduction Data gathering methods are often loosely controlled, resulting in out–of–range values (e.g., Income: –100), impossible data combinations
(e.g., Gender: Male, Pregnant: Yes), missing values, etc. Analyzing data that has not been carefully screened for such problems can produce misleading
results. Thus, the representation and quality of data is first and foremost before running an analysis. If there is much irrelevant and redundant
information present or noisy and unreliable data, then knowledge discovery during the training phase is more difficult. Data preparation and filtering
steps can take considerable amount of processing time. Data pre–processing includes cleaning, normalization, transformation, feature extraction and
selection, etc. The product of data pre–processing is the final training set. Data Pre–processing Methods Raw data is highly susceptible to noise,
missing values, and inconsistency. In order to help improve the quality of the data and, consequently of the results, raw data is pre–processed. Data
preprocessing is one of the most critical steps in data analysis which deals with the preparation and transformation of the initial dataset. Data
preprocessing methods are divided into following categories: пѓ Data Cleaning пѓ Data Integration пѓ Data Transformation пѓ Data Reduction
Data Cleaning Data that is to be analyzed can be incomplete (lacking attribute values or certain attributes of interest, or containing only aggregate data),
noisy

SWOT Analysis: Identifying Quality and Shortcomings Essay
SWOT investigation is a fundamental, clear model that gives guidance and serves as a premise for the advancement of promoting arrangements. It
finishes this by surveying an associations qualities (what an association can do) and shortcomings (what an association can't do) notwithstanding open
doors (potential ideal conditions for an association) and dangers (potential unfavorable conditions for an association). SWOT investigation is a
paramount venture in arranging and its esteem is regularly disparaged in spite of the effortlessness in creation. The part of SWOT dissection is to take
the data from the natural investigation and separate it into inward issues (qualities and shortcomings) and outer issues (open doors and dangers). When
this ... Show more content on Helpwriting.net ...
Furthermore, a concentrate on a company's qualities in promoting is advancement is vital to expand mindfulness in ranges that a firm exceeds
expectations in. This technique not just inspires a positive reaction inside the personalities of the shopper, however pushes the shortcomings further
from the choice making methodology (Promoting Procedure, 1998).
Shortcomings ought to likewise be considered from an interior and outside perspective. It is essential that posting of a company's shortcomings is
truthful so they may be overcome as fast as would be prudent. Deferring the disclosure of shortcomings that as of now exist inside an organization
will just further damage the firm. A decently created posting of shortcomings ought to have the capacity to answer a couple of inquiries. What could
be moved forward? What is carried out crudely? What ought to be evaded (PMI, 1999)?
The part of the inward parcel of SWOT is to figure out where assets are accessible or needing so that qualities and shortcomings could be distinguished.
From this, the advertising chief can then create showcasing systems that match these qualities with open doors and accordingly make new capacities,
which will then be a piece of ensuing SWOT investigation. In the meantime, the administrator can create techniques to defeat the association's
shortcomings, or discover approaches to minimize the negative impacts of these shortcomings (Showcasing Method,

Wireless Sensor Networks : Data Quality For Better...
Abstract– An outlier is a data value which is significantly deviated from the remaining data. In Wireless sensor networks (WSNs) outliers are the
major issues that affect the inherent characteristics, such as flexibility, maintenance costs and scalability. The sources of an outliers include noise,
errors, events and malicious attacks on the network. In this paper, we proposed a Compressive sensing algorithm (also known as compressive
sensing, compressive sampling, or sparse sampling) to detect outliers images obtain from wireless sensors. The objective of this proposed method is to
obtain an outlier degree in images through wireless sensors which provides the data quality for better selection process. CS theory... Show more content
on Helpwriting.net ...
The ideal wireless sensor is networked and scalable, consumes very little power, is smart and software programmable, efficient for fast data acquisition,
reliable and accurate over the long term, costs little to purchase and install, and requires no real maintenance.
i.Architecture of Wireless Sensor Network
1.The tiny sensors are deployed all over the implemented background in WSNs.
2.The Networks are usually comprised of few sinks and large quantity of sensor nodes.
3.Sensor nodes are ordered into clusters.
4.Each node has a corresponding cluster header.
5.Each sensor node can sense different parameters such as temperature, smoke and relative humidity.
6.Nodes location details can be obtained by equipment such as Global Positioning System (GPS) or Bluetooth.
Figure 1: Block diagram of a Wireless Sensor Network
Sensor nodes can be used for continuous sensing, event detection, event ID, location sensing, and local control of sensors. We classify the applications
in military, environment, health, home and many commercial areas. It is possible to expand this classification with more categories such as space
exploration, chemical process industries and disaster relief. Most of the sensor network routing techniques and sensing tasks require the knowledge of
location with high accuracy without losing information. Thus, it is important that a sensor node has a location finding system. A mobilizer may
sometimes be

The High Quality Data Gathering System Essay
Measured and Monitored In China, as well as other countries, including the United States, controlling the rising cost medical care while providing
high–quality services (value for money spent) is an arduous task in advancing the cause of imparting universal healthcare coverage to a country's
population (Tang, Tao, & Bekedam, 2012). This quest is particularly problematic in China with respect to quality, due to a lack of appropriate
systematic monitoring which may be considered essential for any type of improvement (Ma, Lu, & Quan, 2008). As China has no reliable clinical
quality data gathering system, the current extent to which the quality of medical services is measured and monitored consists of graded public
hospitals divided into levels based on the inherent resources available to patients (Xu, Liu, Shu, Yang, & Liang, (2015). In terms of measuring and
monitoring cost control, China has begun to monitor and evaluate pharmaceutical drugs brought into all of the public hospitals, as this is a significant
source of the country's health care expenditure, and reportedly obtains some measurement data through the collection of information from national
health services surveys (Tang, Tao, & Bekedam, 2012), which are only instituted on an infrequent five–year basis (The Commonwealth Fund, 2016,
p.35). In the United States, measuring and monitoring the quality of care has been reported to be lacking, given the immensity of this country's health
care sector (Schuster, McGlynn, &

Ops 571 Statistical Process Control
Chase, Jacobs and Aquilano pose questions such as, "How many paint defects are there in the finish of a car? [and] Have we improved our painting
process by installing a new sprayer?" These questions are meant to investigate and apply different techniques that we can use to improve the quality of
life. Quality control not only applies to manufacturing techniques, it can also be applied to everyday life. This discussion will focus on a specific
method of quality control called statistical process control that will ensure my morning process is effective.
One method of quality control can be pursued through process control procedures like statistical process control or SPC. SPC "involves testing a
random sample of output from a process to ... Show more content on Helpwriting.net ...
The more data that is available the stronger your confidence intervals are.
UCL = p + z Sp
UCL = p + 3Sp
UCL = .08333 + 3(.05050) = .23483
LCL = p – z Sp
LCL = p – 3Sp
LCL = .08333 в€’ 3(.05050) =–.06817
In the control chart, the data from the sample stays in between the controls. This means that my process in the morning is working properly and is
effective. Now, it is important to look to the future trends in order to predict seasonal factors. "A seasonal factor is the amount of correction needed
in a time series to adjust for the season of the year." (Chase, Jacobs & Anquilano, 533) Seasonal factors may affect the samples by taking into
consideration factor based on seasons or time periods. The alarm clock that is used to wake me up in the morning is not dependent on any factors of
time or season.
Statistical process control is one way to control quality and make sure goals are attained. Statistical methods show that the samples taken can create
visual representations that conclude my alarm clock is an effective method to starting my morning process. This ensures that it is operating at its fullest
potential.
REFERENCES

Chase, R. B., Jacobs, F. R., Aquilano, N.J. Operations management for competitive advantage (11th ed). New York: McGraw Hill/Irwin.
Green Jr. K, Toms L, Stinson T. STATISTICAL PROCESS CONTROL APPLIED WITHIN AN EDUCATION SERVICES ENVIRONMENT.
Academy Of Educational Leadership Journal [serial online]. June 2012;16

Manual vs. Automated Statistical Process Control: Food...
Israel Ortega–Ramos
The Prime Example
Our recent visit to a food packaging plant in New Jersey highlighted the inconsistent results of statistical process control routinely faced by Quality
Control Managers. Product weight readings were taken from the manufacturing floor, entered into an Excel spreadsheet and analyzed. The results
produced no predictable under or over filling trend despite the fact that the same people used the same scales at the same time of day. The problem is
simple and fundamental. Human error is an inevitable part of the process of collecting statistical data. This is consistently overlooked in companies that
utilize manual SPC[1] (statistical process control) for their manufactured goods. To ensure the... Show more content on Helpwriting.net ...
The scale will then calculate the statistical data after the last product is placed on the scale and store this data in a password–protected memory for
collection by the Quality Manager. This statistical data can then be sent wirelessly to a spreadsheet, printed on a label to accompany the sampled
product, or simply viewed on the scale interface. The flow diagram below shows the improved SPC process.
Companies can also utilize various connectivity and software options that can integrate filling machines to automated SPC scale systems. This means
that fill volumes based on trends calculated by the scale can be adjusted via an automated system. Quality Control Managers and Plant Managers can
also connect all the SPC scale systems in a factory via a central control computer that will provide easy access to "real–time" data. Integrating an
automated SPC Scale System into a manufacturing environment will have the following advantages over the older manual SPC systems: Upgrading
outdated manual SPC processes is the first step to improve overall quality, efficiency, and trace ability. This can be accomplished with as little as
$5,000 in capital investment. Quality Control Managers and Plant managers have to take a hard look at how their product samples are being weighed
and how these measurements are turned into results that can improve production line efficiency. It is now time for

How Statistics Is Important For The Quality Of Data
Statistics is well–defined as a division of mathematics and is most often used to explain, analyze, and summarize as well as interpret what we
perceive– in order to make sense or meaning of our interpretations. Every day we encounter statistics that originate from various forms and behaviors.
Which means that to in order for all of the information gathered to make sense, there would realistically be no need for the use of statistics. Conversely,
due to its focus and pragmatic on applications, statistics is characteristically considered a distinctive mathematical science rather than a division of
mathematics (Chance et al, 2005). Therefore, some measures a statistician utilizes are less mathematical; for example, ensuring data collection is
conceded in a manner that yields valid conclusions, recording results, or coding data in behaviors understandable to the users. Statistics has been
recognized to improve the quality of data by forming specific survey samples and experiment strategies. It also offers tools used to forecast the use of
data as well as statistical models. It is appropriate in many academic arenas that consist of business, social and natural sciences, and government, to list
a few.
Descriptive statistics are wholly used to define the sample under study; they are used essentially to describe the fundamental characteristics of a
prearranged data. Descriptive statistics offer modest summaries concerning the measures as well as the samples. When applied in

Case Study: What Can Be Done About Data Quality?
What was the impact of data quality problems on the companies described in this case study? What management, organization, and technology factors
caused these problems? BT Group had data quality issues within the product inventory and customer billing databases, resulting in poor efficiency of
the system. The case doesn't really give the reason that these databases contained inaccurate data. However, one can assume that the errors were caused
by lack of structure within the organization at data collection points. Emerson Process Management, had a data warehouse that was collecting data from
transaction processing systems across the world. The inaccurate data was caused by assuming that all members of the global sales team would be ...
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Discuss how this statement applies to the companies described in this case study. I think each of the companies in the case study came to the
realization that they data quality was not simply a technical problem. Each company had multiple business lines with inconsistent processes of
collecting data. BT Group came to the realization that too much time and effort was being spent correcting data on a regular basis. To institute a
change in the quality of data collected, the company set measurable goals that would show the success of those changes. These goals were set within
each business group to emphasize the necessity of contribution. The end result was a substantial savings in time and money, as well as an improved
data collection process. Emerson realized that the data being collected was inconsistent from across the world. The designers of the data warehouse
made the assumption that all data collection processes would be the same. However, the absorption of acquired companies as well as cultural
differences from locations around the world, created multiple systems for data collection and entry. Once the realization was made that the data
warehouse was full of inaccurate information, it was obvious that a new system would need to be put in place. Cintas had a similar experience with the
multiple business units that operate under the same name. A customer could appear in multiple databases, yet not be

Securing the Quality of Data
2.3 Securing the quality of data
Adopting explicit evaluation criteria increases the transparency of the research and provides the researcher with the means to highlight the strengths
and limitations of that particular research (Eriksson, P., & Kovalainen, A., 2008). According to Eriksson, P., & Kovalainen, A. (2008), one of the
reasons that lead to a poor–quality research is when a qualitative research is assessed with the help of evaluation criteria adopted from quantitative
research and vice–versa. Conducting a quantitative research requires gathering data from a large number of samples whereas in qualitative research,
generally, data is collected from a relatively smaller number of participants and the focus is on understanding the participants social world in depth
through probing, asking questions and case studies. When conducting a qualitative research, the question shouldn't be "How many interviews do I need
to do to get my theory accepted"? According to Eriksson, P., & Kovalainen, A. (2008), by asking this question, the logic of quantitative acceptability
enters into qualitative research. As mentioned earlier, assessing a qualitative research with the evaluation criteria adopted from quantitative research
leads to a poor quality research and vice–versa. Instead, when evaluating qualitative research as a good–quality research, the focus should be on the
materials obtained from the interviews, quality of the interviews and the logic through which a researcher makes

The Role of the Leader in Evaluating Data to Improve...
The Role of the Leader in Evaluating Data to Improve Quality and Safety
Mary Slaton
Walden University
Leadership Competencies in Nursing and Health Care
NURS 4021–9
Dr. Merilyn Long
May 17, 2013
The Role of the Leader in Evaluating Data to Improve Quality and Safety
Quality and Safety has been recognized as important issues in creating the delivery of effective and responsive health care. To improve Quality and
Safety the leader must analyze data and interpret the information to develop a system for clinical performance by motivating, supervising, and
develop a problem solving approach to deal with system of medical errors. The purpose of this paper is to inform the reader of the role of the leader in
evaluating data to improve ... Show more content on Helpwriting.net ...
The data shows that ninety four percent of the falls reported were patients prescribed diuretics. Diuretics have the capacity to cause dehydration by fluid
volume depletion, increase the urge to void, potentially causes dizziness and cause postural hypotension therefore increasing the risk of falls in all
patients but is increased in older women (Lim, 2009). Telemetry unit have more patients with arrhythmias and other cardiac related problems and are
often prescribed diuretic, anticoagulants, antihypertensive and other cardiac medications, which can increase the risk of falls (Carey, 2001).
Quality Improvement Plan The quality management process involves review of the data that tracks activities and outcomes. Six sigma is a quality
management program that focuses on the patient, the data collected provides evidence of the results and the emphasis is on the processes used within
the system (Sullivan, 2013). Arisk management plan would identify risk for injuries, accidents and financial losses, it would review the monitoring
system, analyze the data and identify ways to eliminate or reduce the risks. A continuous quality improvement plan for fall prevention needs to be
established because falls are the leading cause of fatal and nonfatal

The High Quality Data Gathering System Essay
In China, as well as other countries, including the United States, controlling the rising cost medical care while providing high–quality services (value
for money spent) is an arduous task in advancing the cause of imparting universal healthcare coverage to a country's population (Tang, Tao, &
Bekedam, 2012). This quest is particularly problematic in China with respect to quality, due to a lack of appropriate systematic monitoring which may
be considered essential for any type of improvement (Ma, Lu, & Quan, 2008). As China has no reliable clinical quality data gathering system, the
current extent to which the quality of medical services is measured and monitored consists of graded public hospitals divided into levels based on the
inherent resources available to patients (Xu, Liu, Shu, Yang, & Liang, (2015). In terms of measuring and monitoring cost control, China has begun to
monitor and evaluate pharmaceutical drugs brought into all of the public hospitals, as this is a significant source of the country's health care
expenditure, and reportedly obtains some measurement data through the collection of information from national health services surveys (Tang, Tao, &
Bekedam, 2012), which are only instituted on an infrequent five–year basis (The Commonwealth Fund, 2016, p.35). In the United States, measuring
and monitoring the quality of care has been reported to be lacking, given the immensity of this country's health care sector (Schuster, McGlynn, &
Brook, 2005). Despite

Recommend Elements In The Design Of Audit Trails And Data...
Part 1: Recommend elements included in the design of audit trails and data quality monitoring programs With today's advancement in technology,
most hospitals have developed a data security plan to ensure that patient data is being handled correctly and is only viewed by authorized personnel.
Hospitals can keep unauthorized personnel from viewing patient information by setting up individual passwords (Wager, Lee, & Glaser, 2013) only
allowing those employees to view the patient's information for them to complete their job task. When an employee is entering information into the
system, it needs to be in real time as much as possible to keep human errors from occurring and for a correction to be made there will need to be a
note attached to... Show more content on Helpwriting.net ...
We also need a more secure software to keep those who are not authorized from accessing the system. One of the ways we can achieve this is by
making sure the software that we have is only accessible by the staff from audit department and not our normal hospital employees (Loshin, 2011). The
software also needs to be designed that once the data has been retrieved for an audit, it can no longer be changed unless it is approved through the
chain of command.
Part 3: Recommend device selection based on workflow, ergonomic and human factors The current system that we have is not effective for the
staff and needs to be upgraded. When choosing a system for the hospital, we need to make sure it meets the privacy for our patients and is effective
for our staff. When our staff is treating our patients, we need to be mindful of the placement of computers in patient rooms so staff can easily access
them and bring them to a comfortable level for them to work. These computers need to have the security screen protection covers placed over them
so visitors cannot see the screen as the staff is documenting. I also think we should install the fingerprint identification to allow fast and easy access to
the system, so the staff is able to complete real time charting and in the event of an emergency they are not trying to type out their password and
forgetting, having this device installed will also ensure that patients are not able to can access to the computer (Maksimov & Kalkis, 2016).

Principles Of Data Quality Management
Principles of Data Quality
There are many principles for the data quality that ensure the data quality for the data entered to a database. The most significant principles for the data
quality include:
1–The Vision
2–The Policy
3–The Strategy
4–The collector has primary responsibility
5–User responsibility
6–Consistency
7–Transparency
8–Outliers
The Vision
It is very important for the big organization to get a high vision for their data and its quality especially when the same data will be shared with other
organization, companies or users. In the vision the managers should focus on the resources that will use to build the data like the software, like the
database software and its capabilities, and the hardware like the computers and the routers and other hardware equipment.
The Policy
As well as the vision, the organization should have a policy to implement its vision for the database, which make the organization think to improve
their database to reach its vision. Policy help the organization to be more obvious about its goals with focusing on reducing costs, improving data quality
, improving customer service and relations, and improving the decision–making process.
The Strategy
The organizations should have a good strategy to manage their database and data entry process. Therefore, the organizations need to improve a strong
strategy for capturing and checking data. The good strategy must include some clear goals for the short, intermediate, and long terms, which

Documentation For Quality Data Preparation ( Federal...
Introduction
This assignment utilizes one of the file sets in the FAA database, Aircraft Series, to demonstrate a proposed process for quality data preparation (Federal
Aviation Administration– Data Downloads, n.d.). This documentation includes a process overview, a description of the data files, two attempts at
cleaning the data, and validation and standardization of the resulting content. A short discussion of data integrity, data validation, data governance, and
documentation follows including recommendations to overcome the challenges encountered.
Process Overview
Per the advice of Robin Hunt's video tutorial, the exercise began with a notional workflow diagram (2015). After two attempts, the succeeding process
consolidated into a series of four phases, each described in the following sections. The complete diagram appears below. Figure 1. Assignment
Workflow Diagram Select Data
The FAA database provided thesource data. The data cleaning process required downloading two text formatted files in the subject area of Accident
/Incident data: the source data file and a document describing the layout of the source data file. The website download process appeared similar to the
figure below:
Figure 2. FAA Data Download Web Page
On first inspection, the downloaded source data file appeared to only contain text characters similar to as shown below. Figure 3. Aircraft Series
Source Data File
The data contained 4959 rows (as measured by Notepad ++) and

High Quality Data Quality Analysis
3.1 Introduction
High–quality data facilitate a precise analysis and the resulting statistics. Hence, high–quality data assist the organization to increase its business value.
This chapter demonstrates the concept of data quality, the effects of inaccurate data, and the factors that cause low–quality data.
3.2 Data Quality
The executive and the top management in organizations seek comprehensive reports and dashboards to enable them to understand on going processes
and facilitate the decision– making that improves their business. However, the decision–making process may be influenced by various factors. Data
quality is a critical factor because when the quality of the data is inferior, poor decisions could be made [39]. In addition, data ... Show more content on
Helpwriting.net ...
Hence, several parties share the responsibility for the quality of data. In addition, J. E. Olson (2003) observed that the poor data quality resulted from
the rapid growth of information system technology and the prompt evolution in system implementation and frequent changes that complicate and
hamper the quality control process [44]. According to C. Boulton (2016), 57.5% of poor data are caused by users, followed by 47% caused by data
migration and integration, which usually lead to gaps or duplicate information, and 43.5% caused by changes to source systems (Figure 5) [45].
Figure 5: Causes of poor data quality [45]
Jack E. Olson (2003) defined data quality as data that are linked to their fit for use. In other words, high data quality is obtained when the data fulfill
the requirements and the criteria of its intended usage. In contrast, poor quality results when the data do not fulfil their requirements [44].
3.3 Dimensions of Data Quality
In previous research, data quality was divided into two main categories: intrinsic and contextual. In the intrinsic category, value resides within the data,
which refers to objective attributes. In the contextual category, the attributes of the data mainly depend on the context in which the data are present,
used, as well as the situation or problem. Data in the contextual category include the dimensions of relevance and believability [39],

Design Of Audit Trails And Data Quality Monitoring Programs
Recommend elements included in the design of audit trails and data quality monitoring programs Audit trails are a set of guidelines that are
developed for purposes of maintaining a record of all the activities of the system and the application that are done by the system users. Importantly,
audit trails are highly used in the process of detecting any form of security violations in the system, performance issues, and any flaws in the
applications. Some of the key elements of audit trails include original source documents, transaction history database, and safe storage capabilities.
For purposes of making sure that the healthcare data is safe, there are a number of policies that have been developed to make audit trials more
efficient and effective. In this, some of the policies that have been developed include the network access for third parties, records management and
security–networked devices. The network access for third parties policy tries to make an explanation of the conditions under which the third parties
accessing the healthcare facilities are allowed to access the information contained in the database. The records management policy on the other hand
tries to offer an explanation of the records management requirements that may include the procedures of records retention and disposal. Additionally,
he policy of security–networked devices tries to offer an explanation of all the responsibilities that are given to the different data users in making sure
that all

Is Data Mining A Valuable Asset? Essay
Chapter 1
INTRODUCTION
1.1Background
It is a reputable fact that we are in an information technology motivated society, where knowledge is a priceless asset to any individual, organization
or government. Companies are provided with massive amount of information in daily basis, and there is the desire for them to concentrate on
improving these data so as to get the most essential and useful information in their data warehouses. The urge for a technology to help solve this task
for information has been on the study and development front for quite a few years now. Data in the real world is dirty such as incomplete lacking of
attributes value, lacking certain attributes of interest, or containing only aggregate data also dirty data is also called noisy data that containing errors
and outliers and the data also is inconsistent that containing discrepancy in codes or names.
Data mining is a new technology which could be used in extracting valuable information from data warehouses and databases of companies and
governments. It involves the extraction of hidden information from some raw data. It helps in detecting inconsistency in data and predicting future
patterns and attitude in a highly proficient way. Data mining is implemented using various algorithm and framework, and the automated analysis
provided by this algorithm and framework go ahead of evaluation in dataset to providing solid evidences that human experts would not have been able
to detect due to the fact that they

Data Quality As Crime And Crime
In our continuing state of shrinking government operating budgets, crime scientists and crime analysts need to consider the interrelatedness of spatial
and temporal shifts in crime patterns when creating, tracking, and handling crime hot spots. Many studies indicate that crimes are clustered at the
neighborhood level, but the entire neighborhood is rarely (if ever) criminogenic and only specific parts of neighborhoods contain high concentrations of
crime
CHAPTER 2: LITERATURE REVIEW
2.1Introduction
The study of crime traditionally involved disciples such as psychology and sociology (George, 1978) but crime has always had an inherent
geographic quality as crimes will always be linked to a geographical location (Chainey and Radcliff, ... Show more content on Helpwriting.net ...
Then finally looking at the study area of the District of Columbia (Washington D.C.), identifying why this area is one of Americas problem crime areas.
2.2History of GIS and Crime The use of GIS in crime has been around for centuries with Dent (2000) tracing the mapping of crime back to 1829,
when Adriano Balbi and AndrГ© Michel Guerry created choropleth maps showing the relationship between violent and property crimes with
education levels. As time went on sociologists from the Chicago School, Shaw and McKay (1931) began to map crimes by their XY coordinates to
show the geographic location to understand the importance of the crime location. In the 1980's the reduction in price of computers meant it became
more cost effective for GIS Applications (Longley et al, 2011). With the introduction of the new GIS technologies, the ability to use police records
within the GIS applications allowed for crime and intelligence analysis (Radcliff, 2004). These days the advancements in technology and the reductions
in cost has resulted in GIS applications moving from the backroom computer analysis tool to be used by almost every discipline, from criminology to
healthcare, natural resources to economics. These advancements have not just been in the applications but also in the science behind them. This has
allowed for more advanced analysis, which use well known mathematical models within their calculations, such as spatial statistics and the use of the
Getis–Ordi

Data Quality Analysis : Analysis Of A Data Stored Data
Stored data may be retrieved as part of a patient record for review and update, or to undertake analysis across a broader dataset – particularly in the case
of research datasets or as part of ongoing data quality reviews. The processing requirements for each of these needs are very different. For an
individual patient record the volume of data to be transferred is relatively small. Where large volumes of data are to be analysed, it may be possible
to copy all the data from the storage location to a local PC or server for analysis, however it is much more efficient to leave the data in situ and have
servers undertake the analysis in the data centre. This means that alongside storage hosting considerations there also needs to be... Show more content
on Helpwriting.net ...
Having better, more accurate data opens the way for improved decision support based upon machine learning – as we will see later. Storage
Management and Compliance Management of very large datasets requires specialist expertise – each different area briefly described in this section is a
technical specialisation and whilst there are a limited number of people with expertise in more than one of these areas, there are not deep technical
specialists with capabilities in all areas discussed. This means expert management of very large data stores requires a team of specialists and in
large organisations teams of specialists in each area. Such expertise is expensive to acquire, retain, develop and manage on an ongoing basis, so
there is sense in sharing that expertise across as many datasets and users as possible. Alternatively, compromises are made and lesser levels of
expertise are utilised with smaller datasets or compromises made with regards to the robustness and rigour of the data storage. There is an analogy
with libraries – very large national libraries have more specialists than a local library with a smaller collection of books. Large datasets in a live
environment require ongoing management with daily maintenance tasks and oversight. This management will replace failed components,

Quality Improvement Data Analysis
Use of Quality Improvement Data Quality improvement data is followed in all health care setting. The use of Cerner EHRs allows data to be obtained
from patient charts to analyze core measures. According to the joint commission influenza and pneumococcal vaccinations measures should be
addressed in all hospital in–patients (The Joint Commission, 2015). Data can be retrieved on those patient who were diagnosed with pneumonia to
determine if they received the vaccinations for pneumonia and influenza. As well as tracking the time frame between diagnosis and treatment, and
additionally the patient outcome. This is captured through the documentation of the clinical staff. The information can be analyzed to determine the
quality improvement changes that need to be implemented to improve patient outcomes. Another core measure that is tracked utilizing Cerner EHRs
system is the collection of data related to tobacco use (The Joint Commission, 2015). The system will prompt... Show more content on Helpwriting.net
...
Cerner offers Skybox storage for the storage of patient information. It has an unlimited storage capacity and the data is uploaded once and then
available in the Cloud at anytime and location. Data is located at the hospital site and at Cerner data center locations. This allows for file replication
in the event of data loss or corruption. Military grade encryption is utilized with continuous intrusion monitoring (Cerner, 2015). Security standards
are also built into the system to meet HIPAA standard. HIPAA training must be completed by each new employee and a signature must be obtain that
the employee will follow HIPAA guidelines. Access to patient information is only given if it is pertains to their hired position. The hospital must
develop HIPAA policies that are updated annually. User specific logins and passwords are utilized to sign into the system and they need to be changed
at set

Data Mining of Chemical Analysis for White Wine Quality
Background Wine was once viewed as a luxury good, but now it is increasingly enjoyed by a wider range of consumers. According to the different
qualities, the prices of wines are quite different. So when the wine sellers buy wines from wine makers, it's important for them to understand the
wine quality, which is in some degrees affected by some chemical attributes. When wine sellers get the wine samples, it makes difference for them to
accurately classify or predict the wine quality and this will differentiate their profits. So our goal is to model the wine quality based on
physicochemical tests and give the reference for wine sellers to select high, moderate and low qualities of wines. We download wine quality data set
that is the white... Show more content on Helpwriting.net ...
Since our goal is to make model to give reference to the 3 categories, we can re–defined the categories into 3 other than 7 and in this way, we expected
to gain more reasonable results and give wine sellers more accurate models to support their decisions to purchase wines from the wine makers. 3.1
Clustering and redefine data set Considering that clustering's goal is to put objects that are "similar" together in a cluster, this matches our goal to
make three quality categories. So we decided to use clustering first to explore if the categories can be parted. Since the XLMiner just can run no
more than 4000 records for clustering, we need to reduce our data set size to 4000. First, we eliminated the data outside of 3 stand deviation range;
then we found that the quality 5 has about 1800 records which took nearly a half of all records which spread 7 qualities, so we randomly selected
80% of the quality 5 records and quality 5's dominant effect would be reduced. In this way the new data set was determined. After the new data set
was decided, we created a new output variable–new quality–and the values are 1, 2, and 3, each representing low quality, mid–range quality and high
quality. The details are showed as the tables below: Wine with quality 3, 4, 5| Wine with quality 6| Wine with quality 7, 8, 9| Low quality wine|
Mid–range quality wine| High quality wine| 1271 observations|

Classification-Based Data Mining Approach for Quality...
Classification–Based Data Mining Approach For Quality Control In Wine Production
GUIDED BY: | | SUBMITTED BY:| Jayshri Patel| | Hardik Barfiwala|
INDEX
Sr No| Title| Page No.| 1| Introduction Wine Production| | 2| Objectives| | 3| Introduction To Dataset| | 4| Pre
–Processing| | 5| Statistics Used In
Algorithms| | 6| Algorithms Applied On Dataset| | 7| Comparison Of Applied Algorithm | | 8| Applying Testing Dataset| | 9| Achievements| |
1. INTRODUCTION TO WINE PRODUCTION
* Wine industry is currently growing well in the market since the last decade. However, the quality factor in wine has become the main issue... Show
more content on Helpwriting.net ...
* Free Sulfur Dioxide: Amount of Free Sulfur Dioxide present in wine. (In mg per liter)
* Total Sulfur Dioxide: Amount of free and combined sulfur dioxide present in wine. (In mg per liter) Used mainly as preservative in wine process.
* Density: The density of wine is close to that of water, dry wine is less and sweet wine is higher. (In kg per liter)
* PH: Measures the quantity of acids present, the strength of the acids, and the effects of minerals and other ingredients in the wine. (In values)
* Sulphates: Amount of sodium metabisulphite or potassium metabisulphite present in wine. (In mg per liter)
* Alcohol: Amount of Alcohol present in wine. (In percentage)
* Output variable (based on sensory data)

* Quality (score between 0 and 10) : White Wine : 3 to 9Red Wine : 3 to 8
4. PRE–PROCESSING
* Pre–processing Of Data Preprocessing of the dataset is carried out before mining the data to remove the different lacks of the information in the
data source. Following different process are carried out in the preprocessing reasons to make the dataset ready to perform classification process. *
Data in the real world is dirty because of the following reason.
* Incomplete: Lacking attribute values, lacking certain attributes of interest, or containing

Quality Improvement Data
Quality improvement data: Using QI instruments to address high readmission rates and medication errors Keeping track of quality improvement data
is essential for all organizations: not simply for for–profit entities, but also for not–for–profit organizations such as hospitals. Patient readmission rates
and medication errors are serious issues at many healthcare organizations. Using quality improvement instruments such as a fishbone (Ishikawa)
diagram: which can "identify many possible causes for an effect or problem and sorts ideas into useful categories" and a Pareto chart, which "shows
on a bar graph which factors are more significant" can help pinpoint the root causes of chronic problems that seem to arise from a multitude of
factors (Cause analysis tools, 2013, ASQ). "The standard benchmark used by the Centers for Medicare & Medicaid Services (CMS) is the 30–day
readmission rate. Rates at the 80th percentile or lower are considered optimal by CMS"В¦A hospital's readmission rate is calculated by dividing the
total number of patients readmitted within seven days of discharge by the total number of hospital discharges." (Readmission rates, 2013, Mayo
Clinic). Keeping readmission rates low is essential for the hospital to demonstrate a commitment to providing quality care at low cost and "a high
readmission rate also can result in reduced Medicare reimbursements" as well as reflect negatively upon the hospital's PR when such reports are
released (Can hospital

Water Quality Data ( Fixed Interval Sampling )
Water quality data (fixed–interval sample) collected bi–monthly from 1999–2008 and monthly from 2009–2013 for all 18 monitoring sites within the
Reedy Fork and Buffalo Creek basins over a 15–year period was obtained from The City of Greensboro Stormwater Division, North Carolina. The
sampled data were grouped in ranges of years from 1999–2002, 2003–2008, 2009–2010 and 2011–2013 so as to obtain a detailed analysis on the data.
The sampling sites in the study area were numbered for simplicity of result presentation. Sites 1 to 6 were located at the highly sub–urban and
agricultural area and sites 7 to 18 were located in the highly urbanized area of Greensboro. This sites include; Bluff Run (1), Fleming (2),
Friendship Church Rd (3), Old Oak Ridge Rd. (4), Pleasant Ridge (5) and Battleground Ave. (6), are located in the Reedy Fork Creek basin (Figure
1). Whereas Aycock (7), North Church St. (8), Fieldcrest Dr. (9), McConnell (10), Merritt Dr. (11), 16th St. (12), Randleman Rd (13), Rankin Mills
Rd. (14) West JJ (15), White St. (16), Mcleansville (17), and Summit Ave. (18) sites are located in the Buffalo Creek basin. Twelve water quality
parameters were selected for statistical analysis (Total suspended solids (TSS, mg/L), total Kjeldahl nitrogen (TKN, m/L), chemical oxygen demand
(COD, mg/L), biochemical oxygen demand (BOD5, mg/L), total dissolved solids (TDS, mg/L), total Phosphorus (TPhos, mg/L),Turbidity (TURB,
NTU) nitrite nitrogen (NO2–N, mg/L), nitrate nitrogen (NO3–N,

Data Management And Quality Control Essay
Data management and quality control
To ensure internal validity of the study, we employed trained research assistants who at the time of the study were registrars in psychiatry as research
assistants. They had also received 2 weeks training on the research proctocol. The interviewers had equal level of training and have been involved in
similar studies. A 3 days of debriefing and review of all protocols was carried out prior to the commencement of the study.
All questionnaires were fully anonymized and reviewed for completeness and all data went through extensive data–cleaning process. All data were
electronically stored and had regular backups. Regular meetings were held with all members of the research team, during which conflicting and unclear
issues were discussed and rectified Data Analysis
The data obtained was cleaned and entered into statistical package for social sciences, version 16.0 Software (SPSS 16), which was also used for data
analyses. The sociodemographic characteristics of participants in the two at baseline were compared using Chi2 statistic. The association between
sociodemographic characteristics and ASSIST score between the two groups was also determined using independent t test.
Also, associations between an additional DSM IV axis I diagnosis (dual diagnosis), chronic general medical conditions and tobacco abstinence at 6
months were explored using Chi2 statistics and binary regression analysis.
Treatment Effects
In order to determine the

Case Study : Finding Quality Data
Finding Quality Data
To stay competitive in today's market, "companies are using big data analytics to understand and engage customers in a way that inspires greater
loyalty" (Rackey, 2015). Better understanding our customers will result in opportunities for increased sales through up–selling and cross–selling while
also improving customer satisfaction by catering to customer's needs more efficiently. Developing a BI program using data, technology, analytics, and
human knowledge allows us to transform data into useful BI solutions.
The first step in gaining this business intelligence is to locate appropriate customer data sources within the organization. The quality of the data must be
confirmed using data profiling and data quality ... Show more content on Helpwriting.net ...
Completeness is characterized by the presence, absence, and meaning of null values in the data tables (Batini & Scannapieca, 2006). Uniqueness refers
to the data item recorded without duplication. The dimension of timeliness measures how data represent reality from a particular point in time. Data
consistency shows that a data item is the same in multiple data sets or databases. Validity refers to data that conforms to the correct syntax for its
definition (DAMA UK, 2013). Once the data quality assessment is completed, proof of concept can be developed.
Proof of concept
A Proof of Concept (POC) is used to demonstrate the design idea using only a small part of a complete system. This system will be used to discover
the factors that help influence a customer's purchases using existing data such as customer sales history, initial purchases, discounts and other data.
Simple queries can be effective in showing the customer's paths to their purchases. The results of these queries will help predict future purchases of
existing customers, uncover up–sell and cross–sell opportunities, and possibly target new customers. The first step of the POC is to build a
scaled–down environment similar to the actual environment for testing the program using separate, reserved resources for a predetermined number of
days. The POC should be carefully documented showing configuration, installation, and test results. This documentation can then

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