Encrypted Data Management With Deduplication In Cloud...

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.
1.2 PROPOSED 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.
1.2.1 ADVANTAGES 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
... Get more on HelpWriting.net ...

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

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

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

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

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

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

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

Data Management And The Library System
Data management in Libraries
Iteration 1
Snowy Osahan
Wilmington University
Table of Contents
Iteration 1: Orientation to Inspiration Space 3
Plan 3
Action 5
Observation 6
Reflection 7
References 9
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

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

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

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

Big Data Analytics Driven Enterprise Asset Management For...
¬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

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

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

Database: Data Management and Ref
Chapter 1: Database Systems
TRUE/FALSE
1. Data and information are essentially the same thing.
ANS: F PTS: 1 REF: 5
2. Data processing can be as simple as organizing data to reveal patterns.
ANS: T PTS: 1 REF: 6
3. We are now said to be entering the knowledge age.
4. Information implies familiarity, awareness, and understanding knowledge as it applies to an
environment.
ANS: F PTS: 1 REF: 6
5. Data constitute the building blocks of information.
6. Metadata present a more complete picture of the data in the database than the data itself.
7. The only way to access the data in a database is through the DBMS.
ANS: T PTS: 1 REF: ... Show more content on Helpwriting.net ...
|a. |Queries |c. |Metadata |
|b. |End–user data |d. |Information |

ANS: C PTS: 1 REF: 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: A PTS: 1 REF: 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: B PTS: 1 REF: 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: D PTS: 1 REF: 7
9. ____ exists when different

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

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

Data Base Management System
Fie lFile Organization Terms amp; 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

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

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 a Database 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

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

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

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

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 flow 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 shuffle phase) and merged to obtain the final result (the reduce phase).
The network footprint of a MapReduce job consists pre dominantly of traffic sent during the shuffle

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. Introduction 4 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.
Conclusion 15 9. References 16 10. Appendices 20
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

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 more content on Helpwriting.net ...
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

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

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 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.

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

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

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

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 the systems development
life cycle or whatever approach you plan on taking).
Mr. Forondo, FAME (Forndo Artist Management 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.

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. DATAADMINISTRATION
vii. ACCESS
viii. INTERFACES
E. BACKUP

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

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

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

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

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