Examples Of Data Management

Examples Of Data Management
Organizations strive to perform as well–oiled machines, with little to no mistakes in their day–to–day workflow. Unfortunately, the millions of moving
parts and the chance of human error leads to incidents that interrupt the organization's normal activity. In these cases, the first and most important step
is collecting data that explains the setting in a contextual manner (Carroll, 2009, pg 27). This includes the environment surrounding the incident, as
well as the people and equipment that may have been involved. For example, if a patient in a nursing home falls down the stairs, it is imperative to
gather as much information from the scene after tending to the patient's medical needs and safety. Some examples of types of data to collect... Show
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For example, if the organization set forth a new procedure to check on patients every 15 minutes, this new system could be tested in its effectiveness
in the new incident and may also allow analyzers to locate human errors that may further explain and corroborate a timeline. Although these analyses
differ in their methodical process, both are vital components in the last major incidence analysis: root cause analysis. The end all goal of a Root
Cause Analysis is to find the root cause of the problem. This process begins with the information that was collected during data gathering and
analysis. It focuses on learning what parties were involved, what information they knew and during what time period. The analysis dips further into
identifying who is accountable, what they are accountable for and why this method of accountability. The main focus of this evaluation is asking a
series of "why" questions to explain how certain events happened (Andersen & Fagerhaug, 2006). Continuing the previous example, the root cause
analysis asks direct questions that may be able to explain how the patient ended up slipping and falling. These questions include: why was there water
present on the floor, why wasn't the facilities crew notified of the liquid, why the facilities crew had a delayed response if this message was relayed,
and finally, why the nursing home staff took too long to
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Organizational Data And Management System Essay
Abstract
Organizational data is increasingly prevalent due to the ease of collecting data from several sources. Because so much data is now available and
gathered, organizations must set a data management system to clean then consolidate the data to give users clear insight into the organization's
behavior. To implement such a system requires the collaboration of both the business manager and the business's IT organization. The IT organization
must have a clear understanding of the data standards and data model that the manager requests to correctly implement it. Furthermore, the IT
organization must determine how the system should be designed to match the data needs of the organization. Alongside the IT team, the business
manager must address several issues concerning the system, including how the data should be organized, collected, and distributed. The business
manager is also responsible for ensuring the quality of the data gathered and how to address any technical issues that arise, as well as oversee the
support for any transitions to updated software, so that data is not lost. Keywords: data management, data model, data management design, database
implementation
Data Management and Its Technical Aspects and Managerial Issues
Introduction
With the increasing amount of data available, from social media and software, organization data has increased to an overwhelming number.
Organizations can now easily gather data from their own products by implementing analytics

Ehr Database & Data Management
EHR Database & Data Management
Gay P. Montague
Grand Canyon University: DNP805
June 24, 2015
EHR Database & Data Management
Introduction/Patient Problem
Asthma is one of the most widespread childhood chronic illnesses in the United States leading to nearly 190,000 pediatric hospitalizations yearly
(Banasiak, 2004). This chronic inflammatory condition impacting the respiratory system and characterized by an obstruction of airflow. For children
from kindergarten through high school, asthma accounts for a loss of 10 million school days annually and costs caretakers $726.1 million per year
because of work absence (Sharma, 2014). In response to the increasing number of children with asthma, the cost involved with care, the school days
lost due to exacerbations and time lost from work for parents/caregivers, the need has arisen for primary care providers (PCP) to effectively identify
this high–risk population and refer them to an asthma specialist who is able to effectively manage the condition, monitor the patient's/caretaker's
compliance and educate the patient and family on precautions, medications, treatments and emergency protocols.
Using data – structured and unstructured – to manage the identified problem
To meet these identified care needs, a well–thought–out management program should be initiated that is supported by information accessible from the
patient's electronic health record (EHR) and is accessible by follow–up practitioners via an

The Technical And Management Challenges At Big Data...
1. Abstract: Inventions in technology and excessive use of digital devices have presided over today's Age of Big Data, in Three V's of data. These
data allows the users to enhance the social security, understand the existing systems and to track improvement progress. For example, transforming
Big Data (banking transactions, call records, online user created data like Tweets and blogs, online searches, etc.) into useful data needs
computational methods to reveal structure among and inside these very big socioeconomic data. The data driven management is now familiar
and there is increasing interest for the concept of "Big Data". Currently there is a gap between its insight and its potentials of Big Data. This paper
highlights five steps in analysis of big data and discusses what has already been done. This paper also list out the technical and management
challenges in Big Data analysis. We begin by considering the five stages in the pipeline, then move on to the challenges, and end with a conclusion.
2. Introduction Today data is being flooded in all means as it is being collected in unprecedented ways. Decisions which were taken by way of
guesswork and difficult models can now be made on the base of data itself. Big data analysis can be dream on every aspect of today's society – Mobile
services, manufacturing, retail, life sciences, financial services and physical sciences. Big Data has the potential to revolutionize scientific research,
education, use of Information

Data Report On Data Management And Analysis
This section presents data process involved in data management and analysis, it discusses data entry, data cleaning and data analysis. The section starts
with data entry where the data cleaning and analysis were presented thereafter. 3.14.1 Data entry Data entry refers to the process of recording data,
regularly into a computer programmes (Rahm & Hai Do, 2010). During the evaluation, data were entered into computerised software packages to assist
in analysis process. Quantitative data from questionnaires and documentary review were entered to stata software programme whereby qualitative data
obtained from interviews were entered to Atlas.ti software. A verbatim (transcription) was performed to transform word by word audio recorded
interview data to written a document (Creswell, 2007), which by then were translated from Swahili to English language before being entered into
Atlas.ti where the local translator was consulted to review the interview transcript. 3.14.2 Data cleaning Data cleaning refers to systematic procedure to
identify and correct data errors and inconsistencies and omit them to enhance quality of collected data for analysis (Rahm & Hai Do, 2010). In this
evaluation all qualitative and quantitative data undergo cleaning process to ensure its consistency and accuracy. In this evaluation data cleaning for
qualitative data involved a series of activities that included the followings as suggested by Miles and Huberman (1994): summarizing the qualitative

Database: Data Management and Ref
Chapter 1: Database Systems
TRUE/FALSE
1.Data and information are essentially the same thing.
ANS:FPTS:1REF:5
2.Data processing can be as simple as organizing data to reveal patterns.
ANS:TPTS:1REF:6
3.We are now said to be entering the "knowledge age."
ANS:TPTS:1REF:6
4.Information implies familiarity, awareness, and understanding knowledge as it applies to an environment.
ANS:FPTS:1REF:6
5.Data constitute the building blocks of information.
ANS:TPTS:1REF:7
6.Metadata present a more complete picture of the data in the database than the data itself.
ANS:TPTS:1REF:7

7.The only way to access the data in a database is through the DBMS.
ANS:TPTS:1REF: ... Show more content on Helpwriting.net ...
|a. |Queries |c. |Metadata |
|b. |End–user data |d. |Information |
ANS:CPTS:1REF:7
6.The ____ serve(s) as the intermediary between the user and the database.
|a. |DBMS |c. |end–user data |
|b. |metadata |d. |programming language |
ANS:APTS:1REF:7
7.The database structure in a DBMS is stored as a ____.
|a. |file |c. |set of key/value pairs |
|b. |collection of files |d. |collection of queries |
ANS:BPTS:1REF:7
8.A(n) ____ might be written by a programmer or it might be created through a DBMS utility program.
|a. |query |c. |database management system |
|b. |operating system |d. |application program |
ANS:DPTS:1REF:7
9.____ exists when different

Enterprise Data Management And Administration
U–Commerce and Data Management
Enterprise Data Management and Administration
02/2013
Abstract
This report examines the emergence of U–commerce and the implications on data management it's faced with. Through research of real cases, the
paper will examine how U–commerce has been implemented into the operations of businesses and the roles that it plays. It will also provide basic
examples of the four elements which make up U–commerce, Ubiquitous, Universal, Unique, and Unison. The paper will address the importance and
growing concern of data management of this technology. Enterprise data has never been more accessible to users and across devices than it is now.
Assuring the right data makes it to the right places and people, can be very critical to a business's operations or decision strategies. With a multitude of
devices with various interfaces, U–commerce's data management stability, and privacy is continuously at risk and monitored. The paper will provide a
sound rational for why today's businesses need to make sure that data management is a top priority, as they move into new phases of outlets for doing
business, and share business related information.
OUTLINE
I. INTRODUCTION
II. RECOGNIZING U–COMMERCE
A. ELEMENTS
i. UBIQUITOUS

ii. UNIVERSAL
iii. UNIQUE
iv. UNISON
III. U–COMMERCE DATA MANAGEMENT
B. CURRENT STATE OF DATA MANAGEMENT
C. CHALLENGES OF DATA PROTECTION
v. –ISSUES
vi. –SOLUTIONS
D. DATA ADMINISTRATION
vii. ACCESS
viii. INTERFACES
E. BACKUP

Data Management And The Library System
Data management in Libraries
Iteration 1
Snowy Osahan
Wilmington University
Table of Contents
Iteration 1: Orientation to Inspiration Space3
Plan3
Action5
Observation6
Reflection7
References9
Iteration 1: Orientation to Inspiration Space The orientation session will be conducted for the interns at Inspiration Space for a period of two days.
During this phase, the interns will be introduced to the employees of Inspiration Space and the library that are associated with different departments in
the Wilmington Public Library. This short term session will help the interns and new employees in understanding the architecture of the library system
and familiarize with the real work environment. This phase ... Show more content on Helpwriting.net ...
The company's website was also reviewed during this meeting. A formal document was handed to the interns for providing the detailed information
related to the company's manuals and policies. All the interns and new employees were requested to submit all their documents to the Human Resource
Department for verifications. The new hire forms were provided and completed by all the new people. Being an intern, I was not familiar with the
current working environment of the company. Inspiration Space Coordinator had provided full support to me in getting acquainted with the day to day
operations of the organization. The hierarchy, on–going projects and existing clients of the organization were also discussed during this time. The
company's association with the state libraries and centralized data system was also explained by the supervisor (C. Shaw, personal communication,
September 17, 2015). The internship objectives and responsibilities were discussed among the interns and supervisor to avoid ambiguity. I was
introduced to the data management team. The project's objectives and various issues in the current system were discussed. The roles of the team
members in the project were also specified. During this period, I had met various employees from different departments and communicated with them
to understand and study the existing data management system. The information was provided to the interns about the current tools, processes and high

level

Big Data Management And Management Of Huge Volumes Of All...
3. Big Data Management
Big Data Management (BDM) is the governance and management of huge volumes of all types of data. Big data management is the huge change to
technology that will help to make a better society and the industrial sector. The integration, manipulation, quality and governance are the things that big
data management has to deal with and management of Big Data including the key factors– Volume, Velocity and Variety of Big Data. Big data is all
about size of data. Big data is very large databases. So these ample amount of data needs to be managed in order to use this data at any time. This is
known as Big Data Management (BDM). Big Data management is around two things–big information and information management. Big Data
Management serves as the essential step for overseeing and administrating huge amount of information called as Big Data in the organizations.
The security of data is another huge concern of Big Data Management which is expanding with the increase in Big Data. Big Data Management is
not only restricted to storage of ample data. It also offers data security and integrity. This gets to be the greatest preference of Big Data Management
which makes the associations to adopt BDM and also to create new technique for managing Big Data. Precision of data is guaranteed in this way.
Otherwise it would be troublesome for the enterprises to tackle with the beast of Big Data.
Management of Big Data does not only cover the area of managing Big Data. It also

Information Management, The Data, And Infrastructure
Harnessing Information Management, the Data, and Infrastructure Today's competitive consumer market warrants retailers, such as Macy's, and other
big business owners to find solutions for managing enormous amounts of collected consumer data. This writer ascertains information management to
be an imperative operational part of an organization because the compiled information is used to assess the past performance of a company as well as
predict future events and happenings. (Accelops, 2015). Well–organized information management systems afford business owners the ability to collect,
process and interpret data. Collected data sets can include nearly all aspects of business operations, including sales revenues, production costs and
employee output. This can also include data analytics compiled from consumer purchasing and spending habits, as well as consumer internet browsing
patterns. Retailers assess the collected data, compare it to previous time frames and then use it in their projections for seasonal or holiday production
strategies. In this paper this writer will continue to determine the importance of information management related to Macy's. This writer will analyze the
impact of IT architecture on information management, and determine if it influences the effectiveness or efficiency of information management.
Additionally, this writer will suggest two data storage methods and conclude by determining the optimal data storage method for Macy's. Impact of IT

Multimedia Big Data Management Processing And Analysis
VII.MULTIMEDIA BIG DATA MANAGEMENT PROCESSING AND ANALYSIS After categorizing multimedia big data, the next important
phase in the data management cycle is its processing and analysis. So far, the possible types, sources and perspectives of multimedia big data have
been highlighted; but this is only the first of the necessary stages in big data management. Generally, the stages involved in big data processing and
analysis include data acquisition, data extraction, data representation, modeling, analysis and interpretation [21]. These stages are illustrated in
Figure 5 and are explained briefly also. Fig. 5.Steps in Big Data Processing (Source: [22]) A.Acquisition and Recording This is the first step in the
data processing cycle. It is mostly concerned with the sources of big data and techniques required to capture the data. As it has been discussed in prior
parts of this paper, big data can originate from multiple sources and therefore requires an intelligent process to acquire and store this raw data. Another
relevant aspect of this phase is metadata generation and acquisition. This acquisition of the right metadata enables for a description of the recorded data
and how exactly it is being measured. B.Information Extraction and Cleaning In some cases, the information gotten from various sources may not be
ready for analysis. Such data usually contains images, audio, or in some cases they are gotten from environmental sensors such as surveillance cameras.

7000 Data Management
Create a memo describing your initial analysis of the situation at FAME as it relates to the design of the data base application. Write this as though you
are writing a memo to Martin Forondo. Ensure that your memo addresses the following points:
a. Your approach to addressing the problem at hand (for example, specify thesystems development life cycle or whatever approach you plan on taking).
Mr. Forondo, FAME (Forndo ArtistManagement Excellence) needs technology support to track the contracts of artists, a proposal for contracts to new
artists, calendars of artist schedules and the performance shows, etc. In any event, music manager today have to be informed about a lot more things
than they used to in the past including music ... Show more content on Helpwriting.net ...
b. What will the new system accomplish? What functions will it perform? Which organization goals will it support?
This is dangerous because management is the one field in the music business where you are required to know at least something about every aspect of
the music business. Gathering content from various sources leaves you not only with an incomplete picture; but also is a very inefficient use of your
valuable time and energy. In light of that we have put together the perfect knowledge resource for theArtist Management; saving you valuable time,
effort and money so that you can leverage the information into actionable intelligence and generate income for yourself and your artists or clients. The
logical sequence in a "how–to" manner that you can use much like a to a to–do list; making sure all the important aspects are taken care of before you
move on to the next task.
The information of the artists are stored in the database, FAME can access the database where they can achieve the business goals of the customers and
to act on the artists within the budget. The application helps managers to set the new goals for the artists to assign new assignments by creating
separate tables ARTIST table, ASSIGNMENT table where the application can directly read the artist information and show up on the application.
c.

Developing Highly Scalable And Autonomic Data Management...
III. RELATED WORK
Provide an approach for research efforts towards developing highly scalable and autonomic data management systems associated with programming
models for processing Big Data. Aspects of such systems should address challenges related to data analysis algorithms, real–time processing and
visualisation, context awareness, data management and performance and scalability, correlation and causality and to some extent, distributed storage
[1]. Provide an approach for framework for evaluating big data initiatives [2]. Provide an approach for summarize opportunities and challenges with big
data. Recent technological advances and novel applications, such as sensors, cyber–physical systems, smart mobile devices ,cloud systems, data
analytics, and social networks, are making possible to capture, process, and share huge amounts of data – referred to as big data – and to extract useful
knowledge, such as patterns, from this data and predict trends and events. Big data is making possible tasks that before were impossible, like
preventing disease spreading and crime, personalizing healthcare, quickly identifying business opportunities, managing emergencies, protecting the
homeland, and so on [3]. Provide an approach for sources of structured and unstructured big data. Unstructured data is everywhere. In fact, most
individuals and organizations conduct their lives around unstructured data [4]. Successful decision–making will increasingly be driven by
analytics–generated

Hci / 520 Data Management
TaSharon Collins
HCI/520 Data Management & Design
November 3, 2014
Professor Carl Moore
What Are Systems? A system is an organized structure that has inputs and outputs that carry out a specific activity. A system is a group of components
that makes up a complex functioning unit. When an element changes, the system will stop functioning right. Once the system has been defined by
jurisdiction, budget, coverage requirements and user needs, the next step is to design the system using components and systems that are obtainable and
have the desired features that the customer and the design engineer have agreed upon. If the design engineer is not careful, then there will be coverage,
operational, maintenance and reliability issues that will plague the system forever. The equipment–engineering phase will specify each and every
component in the system, (Wiesenfeld, 2010). Due to a problem or situation, there is a beginning and an end to a system that is tasked to solve that
problem.
Importance of Integrating A Life Cycle Into A Plan For Development of aDatabase According to Bernard (2009), the purpose of IT systems life cycle
planning is to optimize technology deployments for performance, efficiency and cost containment, including costs of maintaining the networks and
systems and even training. The system or network tends to evolve over time as it is continuously modified, improved, enlarged; as various components
and subsystems are re–built, decommissioned or adapted to other

Itkm 548 Master Data Management Paper
Master Data Management
University of Bridgeport
ITKM 548– Research Paper
Introduction
Data is very important thing in every business, especially in today's dynamic world where optimal use of data leads to success in shorter span of
time as lots of companies are struggling for truthful and accurate data. These data must be analyzed in exact time and in a proper way so that the
decision is more effective, but the data we receive are very redundant and carry lot of space in our system. This creates a challenge for the Analytics
people to remove the redundancy and bring out only those relevant data that aids in decision making process. Master Data Management is a solution for
such Analyst who wants to eliminate the redundant and inconsistent data of the organization (Vinculum, 2016).
Findings
Master Data Management (MDM) is one of the method used to make it easy to use for the users by establishing a new platform for the organization to
link all its data, which are critical in nature, to one data ... Show more content on Helpwriting.net ...
MDM trim the data redundancy and improves the accuracy in the decision–making process as it can handle different types of data and makes it easy to
use for the users.
MDM is very expensive to install so small companies try to skip it.
List of References
Rouse, M. (2010, 11). Master Data Management. Retrieved 11 07, 2017, from Techtarget: http://searchdatamanagement.techtarget.com/definition
/master–data–management
Vinculum. (2016, 09). What is master data management. Retrieved 11 07, 2017, from https://www.vinculumgroup.com
/all–about–master–data–management/
Wailgum, T. (2008, 05 28). Master data management : companies struggle to find the truth in massive data flows. Retrieved from

Computer Science And The Big Data Management Essay
Deepak Singh Latwal
Department of Computer Science
University of Technology and Management
Shillong, India
Deepak.latwal@stu.utm.ac.in
Jayanta Chaudhary
Department of Computer Science
University of Technology and Management
Shillong, India jayanta.chaudhary@stu.utm.ac.in Abstract– The Data which is structured and unstructured and is so large with massive volume that it
is not possible by traditional database system to process this data is termed as Big Data. The governance, organization and administration of the big
data is known as Big Data Management. For reporting and analysis purposes we use data warehouse techniques to process data. These are the central
repositories from disparate data sources. Now Big Data Management also requires the data warehousing techniques for future predictions and
reporting. So in this paper we touched certain issues of data warehousing usage in Big Data management, its applications as well as limitations also
and tried to give the ways data warehousing is useful in Big Data Management.
I.INTRODUCTION
We are living in data age, around twenty one zetabytes of data is predicted to be there till 2020. Recent years have witnessed a dramatic increase in
our ability to collect data from various sensors, devices, in different formats, from independent or connected applications. This data flood has outpaced
our capability to process, analyze, store and understand these datasets. Today people are totally into social networking sites

Enterprise Data Management Architecture And Implementation...
Final Project:
Enterprise Data Management Architecture and Implementation Plan
Matthew Brantner
Southern New Hampshire University
Final Project:
Enterprise Data Management Architecture and Implementation Plan Up until this point, Third Star Financial Services has operated via a succession of
mergers and acquisitions where systems were inherited but never integrated into the network. Its data management has been virtually non–existent and
entirely ineffective. Evidence of this can be found in the absence of an enterprise–wide data management solution and the presence of several disparate
systems operating independently with no measurable benefit to the company. Due to a lack of actionable data, management makes decisions based on
instinct rather than through analysis. A direct consequence of this is a steadily declining market share and loss of high–level employees to competing
companies. Fortunately, this discrepancy has been identified and Third Star executives have established the new goal of modernizing and streamlining
operations. Using concepts outlined by the Data Management Association (DAMA), this proposed enterprise architecture will allow Third Star to
transform their data from a liability to an asset.
According to Berson and Dubov (2011), there are four typical categories of drivers that explain the need for data management: Business Development,
Sales and Marketing; Customer Service; Risk, Privacy, Compliance and Control; and Operational

Use Of Master Data Management Techniques
Article Summary: In the white paper "Challenges in the Effective Use of Master Data Management Techniques", author David Loshin addresses the
most critical challenges that organizations may face in their quest to develop an MDM strategy and suggests that phased implementation is an ideal
approach. Threats to a successful implementation of a Master Data Management methodology could be encountered throughout the entire project. The
planning phase of the initiative is the most frequently realized as organizations fail to align goals and disagree on the definitions and attributes for the
master records that will reside in the central repository. During the execution phase, data loss or corruption could become imminent if IT is required to
... Show more content on Helpwriting.net ...
Additionally, it is highly recommended that organizations scope out a smaller initiative to prove the value of the program and promote acceptance
within other business units. Furthermore, the program needs the appropriate sponsor who is able to impress the importance of data governance onto
the rest of the organization. These first steps towards successful implementation of data governance are the most crucial. Once the initial planning and
design phases are completed, and the culture is accepting of the initiative, the organization must ensure that the proper resources, both human and
technology, are available to execute the strategy. Finally, it is vital to continually stress the importance of adherence to the established data governance.
Data governance should be considered a continuous effort that will support the goals of the organization.
Critical Analysis: The concepts presented in Fisher's "The Data Asset: How Smart Companies Govern Their Data for Business Success" are reflective
of the fundamental points that were emphasized in the referenced white papers. Additionally, Fisher expands upon these theoretical concepts and
illustrates the benefits of their application through real–world examples of organizational pursuits of master data management and data governance. In
"Challenges in the Effective Use of Master Data Management Techniques", David Loshin

Using Data Warehouse Systems And Human Resource Management
Due to significant growth organically and through acquisitions in recent years, Global Payments facing many challenges connecting various data
warehouse systems and applications throughout the organization. Data sharing become a major issue. It is sometimes impossible to access certain
systems within the organization due to different technology and security. Dependency upon each entity or individual to send their data or report can
lead to greater risk of getting incorrect data interpretation or errors. As we continue to support more internal and external customers in more locations,
the administration of our workforce has become more difficult. Many of our internal requirements such as reporting, payroll activities, and human
resource management have been done via legacy data warehouse systems. As more demand on data collections and analytics, these traditional data
warehouse systems have become inadequate to effectively manage these administrative activities. This inadequacy is manifested in higher costs and
increased merchant attritions. In order to more effectively manage our administration, reduce costs, and improve customer retention, Global Payments
must move to a web–based application as outlined in this business case. By doing so, enabling the company to manage its administration from one
central and common platform.
1.2.Anticipated Outcomes
Moving to a centralized web–based database platform will enable Global Payments to performed several activities that

Big Data Analytics Driven Enterprise Asset Management For...
В¬Big Data Analytics Driven Enterprise Asset Management for Asset Intensive Industries. Abstract "Information is the oil of the 21st century, and
analytics is the combustion engine." was introduced by Peter Sondergaard during Gartner Symposium/ITxpo 2011. In fact, data is like oil! It has
value, but it needs to be extracted and refined to get the true value from it. In today's business climate, organisations across various sectors are
realising the importance of collecting data from different business processes across their enterprise. This increase in data gathering and integration is
fuelled and driven by advanced technologies for collecting data from various data sources, storing the data using standardised approaches and most
importantly advances in Artificial intelligence (AI) and Big Data analytics to extract value from data. Enterprise Asset Management (EAM) is a
strategic approach for organisations that heavily rely on physical assets to generate revenue, it's a data driven process that collects and uses data about
assets to achieve optimal allocation of resources for the management, operation, maintenance, and preservation of asset infrastructure. Oil and Gas
industry is an asset intensive industry where asset management is one of the main business drivers, asset manager's needs to make critical decisions in
real time in order to make sure their assets are running in the most optimal way. The focus of this research is a marriage between Big Data analytics
and EAM in

Data Warehousing : Big Data Management Essay
Abstract– The Data which is structured and unstructured and is so large with massive volume that it is not possible by traditional database system to
process this data is termed as Big Data. The governance, organization and administration of the big data is known as Big Data Management. For
reporting and analysis purposes we use data warehouse techniques to process data. These are the central repositories from disparate data sources. Now
Big Data Management also requires the data warehousing techniques for future predictions and reporting. So in this paper we touched certain issues of
data warehousing usage in Big Data management, its applications as well as limitations also and tried to give the ways data warehousing is useful in
Big Data Management.
I.INTRODUCTION
We are living in data age, around twenty one zetabytes of data is predicted to be there till 2020. Recent years have witnessed a dramatic increase in
our ability to collect data from various sensors, devices, in different formats, from independent or connected applications. This data flood has outpaced
our capability to process, analyze, store and understand these datasets. Today people are totally into social networking sites such as Facebook, Orkut
etc. Each user stores their data like photos, statuses etc into these that contributes to the ever increasing size and speed of datasets. Now if we look into
the upcoming boom topic in the industry i.e. IOT, the internet of things, it will connect people

Multimedia Big Data Analysis Framework For Semantic...
%chapter{Conclusions and Future Work} chapter{CONCLUSIONS AND FUTURE WORK} label{chapter:Conclusions} section{Conclusions} In this
dissertation a multimedia big data analysis framework for semantic information management and retrieval is presented. It contains three coherent
components, namely multimedia semantic representation, multimedia concept classification and summarization, and multimedia temporal semantics
analysis and ensemble learning. These three components are seamlessly integrated and act as a coherent entity to provide essential functionalities in the
proposed information management and retrieval framework. More specifically: egin{itemize} item A novel correlation–based feature analysis method
is presented to derive HCFGs for multimedia semantic retrieval on mobile devices. The proposed framework explores the mutual information from
multiple modalities by performing correlation analysis for each feature pair and separating the original feature set into different HCFGs by using the
affinity propagation algorithm at the feature level. Then, a novel fusion scheme is proposed to fuse the testing scores from selected HCFGs to obtain
optimal performance. Finally, an iPad application is developed based on our proposed system with a user–feedback processing system to refine the
retrieval results. item A hierarchical disaster image classification scheme based on textual and visual information fusion is proposed for enhancing
disaster situation reports with

Data Management, Data, Warehousing, And Warehousing Essay
There are many different areas in information systems to study. Data management, data mining, data warehousing, information management,
information security, information assurance, healthcare informatics and bioinformatics are just a small sample of some of the different areas of study
that will be examined in this paper. Also included in this paper are answers to questions posed by the rubric for this assignment.
Data management, mining, and warehousing all deal with data in different ways. Data management establishes the groundwork for an organization to
structure, regulate, process, and store data that they acquire (Rouse, 2016). Data management also encompasses the creation of definitions and
standards for the acquired data which will be adhered to throughout the organization (Definition of: Data management, 2016).
Data mining is "[t]he process of finding significant, previously unknown, and potentially valuable knowledge hidden in data" (Gordon, 2007).
Organizations use data mining to sift through massive quantities of raw data in order to find patterns and relationships that will ultimately be used for
business purposes (Definition of: Data mining, 2016). Organizations mainly use data mining to get a better idea of their customer's purchasing habits,
product preferences, etc. in order to create sales tactics targeted at a certain customer demographic (Definition of: Data management, 2016).
Data warehouses are huge repositories where data from various sources all

Disadvantages Of Minitrex Data Management
ABSTRACT This report illustrates a CRM theory based approach towards the discovery of the strategic plan on Minitrex CRM problem areas. The
strategic plan comprises of different options the companies VP of Marketing "Jon Bettman" and Director of sales "Georges Degas" could have
adopted for having a smooth customer–relationship. The two of the most benefit theory we will be adopting to solve the data management in the
Minitrex will be – integration of CRM and Utilization of CRM. The result as suggested itself is a holistic view on CRM. Leadership and an integrated
approach are found to be critical, but not software. Software centered approaches fail to deliver long–term results because of their exclusion of 'soft'
issues, in particular organizational culture.
INTRODUCTION Extensive research has been conducted on Customer Relationship Management to link business performance to CRM competence.
CRM implementations have that capacity of improving the overall organizational performance especially in the important areas of customer
acquisition, retention and development. The availability of empirical evidence has established a different ... Show more content on Helpwriting.net ...
Regards of advantages there are many disadvantages associated with CRM practice. CRM is difficult to manage in terms of technology, people, initial
money investment, safety of information that companies need to keep about their customers, sharing information with third party, and its overall
maintaining or protection. CRM success is based upon the organizations ability to detect and respond to current customer's needs and their preferences.
The CRM software (Mark Rittman, 2008)process success requires management team of the organization to provide continuous asses and prioritizes
customer relationship based on their lifetime profitability. The organization need to be customer–centric and should be carefully driven by
understanding the changing need of the

Data Modeling For A Relational Database Management System
The need to store and evaluate data is a perpetually growing field in the world of information systems. From the days of using flat files to very large
database management systems that store petabytes of data in real time, the practice of building information from data continues to evolve. Today, the
relational data model is quite ubiquitous and is used in a plethora of information systems ranging from accounting systems, banks, retail business, and
scientific usage. It is important to understand the concepts involved in data modeling for a relational database management system in order to build an
effective and efficient system.
Data models weren't as sophisticated in the early days as they are today. In the 1960's and 70's the first generation data models were comprised of
an ad hoc file system with no concept of relationships between the files (Coronel & Morris, 2015). For instance, one file might contain rows of
customer records while another would house invoices. For simple data, file systems worked, but for large sets of interconnecting data, a data
processing specialist was needed to create a program that fetched the proper data, analyze it, formatted it, and presented it in a report that made sense
to the end user. For every new query, the data processing specialist needed to create a separate application. Files became increasingly cumbersome the
more that were added and they duplicated quite a bit of data since there were no relationships between files. The time

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

Benefits of Fleet Management Data Integration
NAME
DBM 502
University of Phoenix
Abstract
Huffman Trucking maintains extensive vehicle fleet maintenance logs, with data on vehicles, parts, tires, maintenance, warranty, costs and dates of
service. Management wants to know whether it would be strategically advisable to integrate this information into their current data warehouse and how
to leverage it.
Investigation shows that there could be significant benefits in efficiency and cost reduction by this consolidation and the appropriate analysis techniques.
Regression analysis is recommended for this data mining, to understand the relationships among independent and dependent variables ... Show more
content on Helpwriting.net ...
Are defective parts returns being handled properly? Too frequent scheduled inspections and maintenance increase labor costs. Poor scheduling can
result in fleet support staff being over or under–utilized. Due to the highly skilled personnel involved, there could be significant costs involved in not
understanding the best use of their time. The vehicle fleet and its operational support is the major cost driver in this industry. Optimal management is
critical.
There has been much written on the different approaches to such data mining as described above. Much is available from the vendors of data
management and analysis software, including some customized for fleet management. The company could decide to use one of these packages or
utilize in–house personnel to report against the new data in the expanded database. (Oracle, Collective Data)
The United States Navy Submarine Maintenance Engineering, Planning and Procurement (SUBMEPP) completed a study in 1962 in which the
majority of components analyzed by SUBMEPP did not demonstrate an age and reliability relationship and consequently, many existing time directed
component overhauls have been deleted from class maintenance plans, eliminating significant costs and downtime (Allen, 1997). Regression analysis
against Huffman's fleet maintenance data could discover any relationships in scheduled maintenance and failure rates.
Because all data manipulation and analysis have

Can Big Data Analytics Be Used By Management Accountants?
Can big data analytics be used by management accountants to provide a better understanding of a business's position and outlook? Abstract This paper
discuses traditional accounting methods using structured data. It introduces unstructured data that makes up the majority of big data and looks at
various types of unstructured data. It looks at traditional storage of data and how data lakes are used for storing unstructured big data. It moves on to
show how analysing big data can be used to highlight trends rather than causes. It also highlights some of the pitfalls of not being aware of the
relevance of the data being analysed. Various examples of how big data analytics can be of use in the compiling of management accounts are
discussed. The... Show more content on Helpwriting.net ...
These facts can be used to give a snapshot of a company's situation at a particular point in time but can only offer a limited view to what lies in the
future. Samantha Searle, a research analyst at Gartner says, "Using historical measures to gauge business and process performance is a thing of the
past, to prevail in challenging market conditions, businesses need predictive metrics rather than just historical metrics." (Gartner Inc, 2014) The
increase of both structured and unstructured data, along with more sophisticated analytical tools, has resulted in the development of better predictive
metrics. (Institute of Management Accountants, 2015) Traditionally data used for accounting purposes is structured in nature. Big data on the other
hand can be both structured and unstructured. Up until 2000 only 25% of the world's total data was stored in digital form, today less than 2% of
stored data is not in digital form. It is estimated that digital data is doubling every three years. (Cukier, 2013) According to (Gartner Inc, 2013) Big
Data is high–volume: the amount of data is extremely large, high–velocity: the data can be gathered quickly in real or next to real time and/or
high–variety: the data can take many forms both structured and unstructured. This data then requires suitable processing to give added advantages to

Encrypted Data Management With Deduplication In Cloud...
Encrypted Data Management with Deduplication in Cloud Computing
Hariharan.R1, Pourselvan.M1 and Hema Sundari.S1 guided by prof. Sudheer.K 1
1 vit university, site school, mca department, vellore , Tamil nadu , india– 632014
Abstract: Cloud computing plays an important role in supporting data storage, processing, and management in the Internet of Things (IoT). To
preserve cloud data confidentiality and user privacy, cloud data are often stored in an encrypted form. However, duplicated data that are encrypted
under different encryption schemes could be stored in the cloud, which greatly decreases the utilization rate of storage resources, especially for big
data. Several data deduplication schemes have recently been proposed. However, ... Show more content on Helpwriting.net ...
However, the same or different users could save duplicated data under different encryption schemes at the cloud. Existing solutions for deduplication
are vulnerable to brute–force attacks2 and can't flexibly support data access control and revocation. Existing industrial solutions fail in encrypted data
deduplication. Deduplication technology has become quite the staple in many data storage environments. But what makes it a good fit in one data
center, may not be the case in another.
1.2PROPOSED SYSTEM:
We proposes a scheme based on attribute based encryption (ABE) to deduplicate encrypted data stored in the cloud while at the same time supporting
secure data access control. proposes to outsource only encrypted data to CSPs. However, the same or different users could save duplicated data under
different encryption schemes at the cloud. Although cloud storage space is huge, this kind of duplication wastes networking resources, consumes
excess power, and complicates data management. intra–user deduplication and inter deduplication. In their scheme, the ciphertext C of convergent
encryption is further encrypted with a user key and transferred to the servers. However, it doesn't deal with data sharing after deduplication among
different users.
1.2.1ADVANTAGES IN PROPOSED SYSTEM:
The scheme can easily realize data access control by introducing control policies into AP when calling EncryptKey(DEKu, AP, PKIDu) by updating AP
to support

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

Essay on Social Media's Role in Network Management in Big...
Network Management in Big Data
In day today world social media and social networking has received much attention from every people, like almost everyone has a Facebook
account. This is where huge amount of data is being processed every day, in fact every second where Social networks accounts for large amount of
consumer "big data". The average global Internet user spends two and a half hours daily on social media, in this scenario just consider how much
data is being generated every minute by every user. The leading social networking sites are handling this big data in efficient way, when it reaches a
comparison stage there's no beating Facebook in driving traffic to publishers. According to the data form US news the world's largest social ... Show
more content on Helpwriting.net ...
Schedule computation or schedule communication helps to optimize utilization and keep running time low. Several works propose to improve job
scheduling by preserving data locality maintaining fair allocation among multiple resource types or discarding time–consuming tasks. Even with
optimal computation scheduling, the cluster network can still become a blockage. The optimization of network transfers can be done by improving the
п¬‚ow bandwidth allocation or by dynamically changing paths in response to demand. These approaches need accurate and timely application demand
information, obtained either from the application itself through instrumentation, which is quick and accurate but intrusive, or from the network through
monitoring , which does not require application involvement, but can be expensive, slow, and detects changes in demand only after they have occurred.
FlowComb also uses MapReduce framework to influence the design of the system. MapReduce provides a divide and conquer data processing
model, where large workloads are split into smaller tasks, each processed by a single server in a cluster (the map phase). The results of each task are
sent over the cluster network (the shufп¬‚e phase) and merged to obtain the п¬Ѓnal result (the reduce phase). The network footprint of a MapReduce
job consists pre dominantly of trafп¬Ѓc sent during the shufп¬‚e

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

Impact Of Big Data Technology On The Field Of Accounting...
Abstract: Balanced Scorecard (BSC) is considered as an important system to measure the performance, however, it suffers from some difficulties,
particularly in the implementation phase, which may reduce its benefits. This paper aims to review the literature to have evidence about BSC model
implementation challenges also review literature related to Big Data technology applications in the field of accounting and management so I conclude
that Big Data technology can strongly contribute to the BSC model improvement thus I discuss the influence of big data analytics as a proposed
solution to improve and develop BSC model, and then investigate BSC model in a big data environment.
1.Introduction The increase of performance measurement systems (like Balanced Scorecard (BSC)) complexity has led to an increase in the amount of
data (created by smart devices, RFID technologies, sensors, social media, video surveillance and more) to be acquired, processed, and analyzed to
provide meaningful information to support decision–making in companies. Moreover, organizations, and customers have also been increasingly
producing large amounts of structured and unstructured data that should have veracity to create value. Big data analytics methods and techniques
(which is the application of predictive methods, pattern recognition techniques, cluster analysis, and other quantitative and qualitative methods in big
data sets) can

Applying Data Mining Procedures On A Customer Relationship...
The purpose of this document is to present a proposal for applying data mining procedures on a Customer Relationship Management System of a
company to reduce Churn Rate and identify valuable customer termed in this document as optimal customers. The constantly updated database of the
company will be used as the source of the data for the analysis purpose.
Exploiting the customer information hidden in large database can help identify valuable customers and predict future behavior, enable the company to
become proactive with their campaign, implement knowledge–driven decisions and make it possible for the organization to limit the defect rate.
The aims and objectives will be discussed in this document in regards to the various data mining procedures that will be applied. Next the background
section will shed light into the existing technologies being used in the CRM domain. Continuing on this document will present the CRISP–DM
methodology based process that will involve Business understanding, Data understanding, Data preparation, Modeling, Evaluation
Towards the end of the document, how the results will be evaluated and deployed will be discussed followed by a brief project deployment plan and
conclusion.
2. Aims, Objectives and Possible Outcomes
2.1 Aims
The key aim of implementing data analytics techniques on a Customer Relationship Management system is to increase profitability of an organization
by reducing the churn rate and identify key customers.
Accomplishing

Technical Review Task Force : Master Data Management...
Following the first two deployments of Umoja, the Umoja Post Implementation Review Task Force – UPIRTF was created to identify issues
surrounding the implementation of Umoja in field missions (PK and SPMs) and recommend corrective actions to adjust organizational aspects,
policies, procedures and control mechanisms to effectively complement the operation under the Umoja model.
Master Data Management was identified by the UPIRTF as one of the top issues and proposed that an Integrated Master Data Management Service
(IMDMS) along with any other required governance structures be established.
To this end, the IMDMS is being established by the IMDMS Working Group and led by a dedicated resource both appointed by the UPIRTF. The
IMDMS, in ... Show more content on Helpwriting.net ...
A service catalogue for the IMDMS has been also developed which outline the services to be provided as part of the IMDMS organization. Based on
the service catalogue, the IMDMS organizational structure is hereby proposed.
PURPOSE
This document outlines a proposal for the Integrated Master Data Management Service (IMDMS) organization which includes the organizational chart
and the roles and responsibilities required to implement the Integrated Master Data Management Service as a program in the United Nations.
The target audiences for this document include the program sponsor, members of the IMDMS working group and other stakeholders like the process
owners in OAHs and Regional Commissions.
This document will be periodically reviewed to reflect changes in the MDM strategy.
DOCUMENT OVERVIEW
The remainder of this document is organized into the following sections:
Organizational structures
This section provides an organizational chart of the global IMDMS
Roles and responsibilities
Resource requirements
Annexes
This section provides a Glossary of Terms, job descriptions
ORGANIZATIONAL STRUCTURES
The following principles have been followed in the development of the organizational structures of the Integrated Master Data Management Service:
a) The Integrated Master Data Management Service is a global initiative led by the Department of Management

b) The Integrated Master Data Management Service is a cross functional initiative composed by business process

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

The Characteristics Of Master Data Management
Background After discussing with Professor Bechor, my research problem is now better defined and aligned to move forward. My focus will be on
mapping cyber repositories and creating metadata from these repositories such that the characteristics of master data management (MDM) can be
leveraged to collect, aggregate, match, consolidate, and validate the diversified quantity of cyber sources. Currently, there doesn't seem to be a good
method for collecting, maintaining, and correlating cyber vulnerabilities, incidents, crimes, breaches, and events. As US–CERT provides one of the
standard vulnerability databases, does other entities offer similar databases for incidents, crimes, and breaches? As Mr. Scott Eigenhuis stated in his
feedback... Show more content on Helpwriting.net ...
Therefore, my main research opportunity will be to investigate and map cyber repositories from the Internet. In addition, develop the metadata
necessary to improve the MDM of the cyber landscape. Moreover, as time permit, construct a centralized database from the diverse amount of cyber
data, provide 360 viewpoints of such data, and visualize the results per location, date, severity, or other criteria? What is the problem being addressed?
Exploring an opportunity to map cyber repositories, create metadata of such information, and provide an ongoing centralize database to maintain
updated cyber data where the academic communities can trend, analyze, and research cyber vulnerabilities, incidents, crimes, breaches, and events. In
turn, governments and companies can leverage such information to strengthen their environments. As Mr. Scott Eigenhuis stated, "There are some
entities that are trying to pull all this data together to provide a comprehensive view." What are the potential threats? By having a collection of cyber
repositories, understanding the metadata, and being notified of cyber–activities, the academic communities along with the private and public sectors
could collaborate on similar threats and attacks. Likewise, corrected steps could be taken to detect, alert, and prevent repeated events from spreading
throughout different organizations. Thus, safeguarding

Data Analysis For Hospitality Management
Data Analysis for Hospitality Management
Assignment 1: Balance Scorecard of Hilton Hotels and Resorts (Front Office)
Total Word Count– 2135 words (excluding figures and matrix)
Table of Contents
1.Introduction4 2.Strategic Map (Group Work)4 3.Financial Perspective (Fanny Dewi)5 4.Customer Perspective (Karn Kapur)9 5.Internal Business
Perspective (Kyounghee Joo)10 6.Innovation Perspectives (Avisek Biswas)12 7.Balanced Scorecard (Summary) (Group Work)14 8.Conclusion15
9.References16 10. Appendices20
1. Introduction
Measurement of performance has always been a very important part in the success of an organisation. Balance scorecard is a tool that helps in
measuring ... Show more content on Helpwriting.net ...
Hence, leading indicators are often captured at the level of individual processes, whereas lagging indicators may be the result of changes in a number
of leading indicators (Lawson, Hatch and Desroches 2008, p. 168). The goal of the CEO is revenue growth. Focusing on the financial perspective, the
following steps will be undertaken to obtain success for balanced scorecard implementation. As the first step, author will show the need of
implementing revenue management, parallel with Front Office operations, particularly in the roadmap (Woods et al. 2007) of revenue management
(exhibit 1.1). It will then be established with financial perspective's Key Performance Indicators (KPIs), which will determine the necessary
measurements, in order to achieve the objectives in the balanced scorecard.
Exhibit 1.1: Roadmap of Financial Dimension 1.1 (Woods et al. 2007)
In this writing, the role of revenue manager and tools used by revenue managers will not be explored further. Concentration on the revenue
management essentials related to the methods used in the yield management would provide understanding towards each benchmark in balanced

scorecard. Actions for obtaining objectives are demonstrated in exhibit 1.2 in this page. The benchmark as well as target within specific time frame for
each action of objective could be both, percentage or dollar and cent measurements.
Exhibit 1.2: Objectives and Actions
For a better understanding on measurement tools used, adopted

Data Base Management System
Fie lFile Organization Terms & Conceptscomprises a record; A computer system organizes data in a hierarchy t
A computer system organizes data in a hierarchy that starts with bits and bytes and progresses to fields, records, files, and databases. * A bit represents
the smallest unit of data a computer can handle.
* A group of bits, called a byte, represents a single character, which can be a letter, a number, or another symbol.
* A grouping of characters into a word, a group of words, or a complete number (such as a person's name or age) is called a field.
* A group of related fields, such as the student's name, the course taken, the date, and the grade, comprises a record.
* A group of ... Show more content on Helpwriting.net ...
Moreover, the names are not arranged in any order. Thus it would be very difficult to locate the names, address, and phone numbers of our friends.
Thus it is essential to arrange the names in some order, say alphabetically, to make the search easy. If the number of friends gets larger, managing the
database manually becomes difficult. A database management software package is a helpful tool in such a situation. Any organization, bank,
manufacturing company, hospital, university, requires huge amount of data in some or the other form. All such organizations needs to collect data,
manipulate them and store them for future use. All such type of organizations requires data for number of purposes like; 1. Preparing sales report , 2.
Forecasting sales, 3. Preparing accounts reports, 4. Getting medical histories of patients.
Thus we can say data are very vital corporate resources. The amount of data used these days in organizations can be measured in the range of some
billions of bytes or characters. The financial statement involved is also very high. Many organizations have become critically dependent on the
continued and successful operations of database. THE DATABASE APPROACH TO DATA MANAGEMENT
Database technology cuts through many of the problems of traditional file organization. A more rigorous definition of a database is a collection of data
organized to serve many applications

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