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Free
Technology Term
Paper - Enterprise Data Quality Management
Data
collection is playing a vital role in determining competitive
benefit for trade and other ventures and has been well
recognized as a business dictum. Whoever holds significant
information today can influence that data for success both in
terms of production and profitability. With the increase of
data, the difficulties arose which are directly linked with
handling the mass of data formed during the businesses. This
stockpile of information has forced to develop some concepts of
information designing. Resultantly projects for instance
Operational Data Stores, Data Warehousing and Data Marts have
been developed. Together with these related corresponding
expertise had to be formulated to facilitate companies collect,
manipulate, process, analyze and deliver useful information out
of this mass of unprocessed and independent data. After years of
effort to manage the problem of data quality, a novel frontier
in the field of data management has emerged within information
growth to deal with the need for fittingly administering data
quality. This is known as the Enterprise Data Quality
Management. This is anticipated to ensure the precision,
appropriateness, relevance and steadiness of data right through
an organization, or manifold business units within an
organization, and consequently to ensure that decisions are made
on dependable and perfect information. In the field of quality
data management it is important to distinguish between the
advantages of the stewardship of information and the ownership
of it. (Leonard Dubois, 2002)
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The leading and distinctive character of thriving data warehouse
accomplishments is the participation of the business community.
It is imperative to keep the business elements associated during
the process to evade venomous consequences taking place in the
business. These result in losing contact with the project or
being unawares or reluctant to admit the part of the
responsibility for the data warehouse. There is a need to
organize the role of business community into a data stewardship
and find different ways to get the business community involved
in the data warehouse for sharing and complying with their
roles.
Enterprise
Data Quality Management
Clean, useful and accurate data represents the added revenues
that are realized when businesses correctly model and track
their customer relationships, product or service preferences.
This translate into huge savings in processing time, storage and
mailing costs, in the confidence users have in their own data,
analysis and conclusions, but most importantly in the cost of
contacting customers and managing ongoing customer
relationships. The information is of value only if it is
accurate, and more so in today’s more complex information
technology. The internal and external data are blended together
in data warehouses and more advanced on-line analytical
processing applications. Today, specially, it is imperative to
deal with the data quality issue from a point of prevention as
well as distillation of on hand data storage. Whereas a lot of
organizations comprehend the value of clean data, most
organizations are still using the old and risky methods of data
ownership and handling the information. Converting the data is
now possible from one configuration to another with the
development of procedures to convert the contaminated data and
standardize it on an enterprise-wide scale. The new data
management solutions ensure the data re-engineering and process
the use of tools to assist companies in implementing enterprise
data quality management efforts.
It is essential to know as to why Enterprise Data Quality
Management is valuable and what problems are controlled through
its use. Conceivably the most excellent way to exemplify the
value of data quality is to review the pedigree of bad data, the
ways corrupt data can impact an organization and the quality
data that can be achieved by eliminating the errors including
misspellings, typographical errors, out of range values and
incorrect data. When data entry responsibilities are extended
among different people and business units, variations are bound
to arise. Often data that is present may contain the proper
structure and values, and in fact may appear to be correct but
data that has unintentionally been misplaced may cause poor
quality data through identification and linkage mechanisms. This
problem usually occurs when an organization’s knowledge is not
up to the standard. In many applications, phony data is also
used to indicate that there is no valid data for a particular
field. At this stage data entry confirmation is the first line
of protection against bad data, with validation schedule of
checking data ranges. Validation checks are routine activity in
many systems. The new solutions frequently have more complicated
conditional reason that may taper the range of suitable data
based on entries to preceding fields. These solutions enable
organizations to develop data reengineering procedure free of
exact projects. These procedures are carried out either on-line
at the point of entry or in batch mode within legacy systems,
which are close to achieving enterprise-wide data quality
management. The advantages of scrutiny of data quality at the
data entry stage are literally understandable. Mistakes are
caught at the beginning while the information is still new. In
this manner the requirement for rework performed by someone
strange with the source data is evaded. Managing data quality
throughout an organization therefore requires an enterprise
approach. Such an approach, which focuses on prevention and
standards, as well as error correction, can provide significant
benefits to users. (Leonard Dubois, 2002)
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Data
Ownership
The ownership and access to data generated for commerce and
research purposes are intended to serve the public by making
such data available to them on need to know basis. With the
conflicting considerations in mind, the organizations establish
categories of data and enact their rules concerning its
ownership, control and use. The Data Administration generally
addresses questions regarding electronic access to data owned by
the organizations, which are stored on the mainframe systems.
The copyright law is the principle under which the ideas and
facts, or data, are protected for ownership that cannot be
protected otherwise by a copyright. The copyright protection is
extended to the expression of ideas and compilation of facts, or
data as well. All persons and entities having a rightful claim
to ownership or control of data enter legal and binding
contracts.
Access and use of the computing resources and services is
presumed consistent with organization’s rules and regulations on
the matter, including other policies, codes of conduct,
departmental policies and procedures, and other Information
Services policies on computer use. To persuade the professional
development, under the ownership system of information
management the organizations may grant opportunities to the
users for access and use resources. In these situations, the
users may be collecting and generating compilations of data. The
user owns this compilation of data, unless the user and the
organizations have agreed it upon otherwise, in writing. There
is always obscure philosophy with respect to data ownership. The
mode of dissemination, collecting data from the public sphere of
influence, as well as acquiring data from data providers perplex
the subject. Therefore, a set of ownership paradigm is
introduced, including decision makers, sellers, influential,
protectors and workers. These paradigms bound the limits
adjoining data ownership. There are also added complications
related the general aspects of ownership problems, including
metadata ownership, control of storage and responsibility for
data policies. (Loyola University Chicago)
Because of the complications of the ownership of the data, the
corporations are considering to decide on attaining the better
sources of data. The challenging growth of data storage and
difficulty of the access tools are verification of the fact that
data is one of the most critical resources any company
possesses. Data, in the form of information, must be conveyed to
decision-makers swiftly, in brief and, precisely in an
integrated layout. The data warehouse is an excellent mechanism
for providing the quality information into the hands of
decision-makers. Problems do take place when an endeavor to
obtain and deliver this information is made. Therefore
significant effort is needed in defining, integrating, cleansing
and synchronizing the data coming from the innumerable
operational systems. There is some one required to undertake the
huge task and for a mounting number of companies is a new
business role called Data Stewardship.
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Data
Stewardship
Data Stewardship has as its key aim as the management of the
data assets of the enterprise in order to develop their
usability, ease of access and quality. Data stewards are valued
by the end-user because of their comprehensive knowledge about
the business. It is understood that they do not create Meta data
and business rules that cannot be put into practice. It is the
responsibility of data Steward to endorse business standards,
develop reliable data definitions, establish data pseudonym,
develop customary calculations and derivation, write down the
business rules of the corporation, monitor the quality of the
data in the data warehouse, define security requirements, and so
on. The corporation’s staffs in their daily scrutiny to
establish comparisons and trends that are significant can
subsequently use the Meta data created by data stewards. With
the rising demand for a quality data warehouse, the need for a
data stewardship function has similarly developed.
Progressively, companies are becoming aware of the significant
role this task for the overall pursuit for high quality,
existing data. A quality-integrated data provides the basis for
the shared data values in an enterprise. (Claudia Imhoff, 2002)
Data stewardship can have many functions to ensure quality data
of an enterprise but some of the most vital ones can be: -
• Adjudicating and Conversions of Set of laws
In the course of data collection it may be essential to
arbitrate divergence of opinion and diverse explanations of
value. The data steward, in affiliation with IT, will be able to
make the initial policy and ensuing changes.
• Validation of the Data
Validation of the data after the data store mass involves
confirmation of the data was loaded after application of
transformation rules. The confirmation therefore turns out to be
critical specifically when operational systems are changed on
short notice or without notice to the data warehouse.
• Contribution Towards The Business Metadata
Metadata is includes technical and business mechanism. Through
this data stewards contribute towards the business components
such as business definition, where as IT contributes towards the
technical components such as data sources and transformation of
rules concerned.
• Endorsing new users
The data steward is required to posse’s proficiency both in the
data and in the workload limitations of the data warehouse
system. This knowledge affords the data steward with the
capability to successfully endorse new users and their influence
levels.
• Sustaining the Population Using the Data
The new users
to the data warehouse shall always require training before
getting access to the data. This training is more of a business
focus than a technical oriented. Data steward should be able to
handle it well and should prove to be a good mentor as well.
(William McKnight, 2002) The stewardship also ensures that Data
users are expected to access the Information only in their
conduct of corporate business. This is done to respect the
confidentiality and privacy of records accessed, in order to
observe any ethical restrictions that apply to data to which
they have access. This also helps in ensuring the abidance of
the applicable laws, rules, regulations, or policies by the end
users. Data stewards are therefore to be responsible for
defining and documenting a solitary set of procedures by which
users may ask for authorization to access susceptible data.
Certification of the data elements and their appropriate use
should also be the responsibility of the data stewards. Data
administration staff on the other hand should be responsible for
working along with the Data Stewards in educating the enterprise
community about company Information, its use, restrictions, and
consistency. As far as the Functional Data Classifications is
concerned, Data stewards of the organization should be the
liable senior management of the functional areas of the
organization. The functional areas are to be clearly defined as
the primary organizational purpose served by the data. Due to
widespread dealings across functional units, functional
classification may not essentially follow organizational lines
of authority. A functional unit may have to be given authority
for data that is shared by many other organizational entities.
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Conclusion
The fact that stewardship is extremely needed in the enterprises
of today it is also established that stewardship is going to be
very fruitful for the developing enterprises, Certainly the
simplest method to slide into a data stewardship role is to
handing over one of the critical end users, the first data
warehouse giving the role of maintaining the subject area most
known to them. As every developing data warehouse project is
concluded, a new data steward is added to the increasing list.
For instance, if the first venture is dealt profoundly with
client profiling and demographics, the end user concerned with
that region could turn out to be the casual steward of the
customer focus region. The most important aspect is to how to
differentiate the responsibilities of data stewards, data
administrators and database administrators? Each one of these
functions must retain their own functions and responsibilities
described visibly to evade any uncertainty. There may be some
overlap in terms of each group's errands; still, there is a
great amount of teamwork and communication that must have effect
to ensure that the data assets of the enterprise are utilized to
give the highest return on investment.
References
Dubois Leonard, Achieving Enterprise Data Quality, Trillium
Software, Inc. (2002) http://www.tdan.com, Issue 22.0 - 4th
Quarter
Loyola University Chicago, Ownership and Use of Data- web:
http://www.luc.edu/
Imhoff Claudia, Ensuring Data Quality through Data Stewardship
(Oct 3, 2002) http://www.dmreview.com
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