effective data models data management
DESCRIPTION
Everyone knows that data volumes are increasing at enormous rates but isknowledge of the data, and more importantly knowledge within the business,increasing with it? Jason Tiret examines the best practices on how data modelsare used to improve service enterprise data management.TRANSCRIPT
Data Management
•uSlness
•ec Ive
successEveryone knows that data volumes are increasing at enormous rates but isknowledge of the data, and more importantly knowledge within the business,increasing with it? Jason Tiret examines the best practices on how data modelsare used to improve service enterprise data management.
IN TODAY'S WORLD OF DATA
The latest technological trendsBoth web services and service-oriented
architecture (SOA) are two hot technologies
that are currently on most business's radars.
SOA enables the integration and reuse of data
throughout the enterprise, which can, amongst
other thIngs, speed up processes and increase
data sharing across the organisation.This can
often improve efficiency across various business
areas such as customer service and technical
support call centres or sales, order management
and accounting, A big component of SOA and
web services is XML and XML schemas that
represent the data and structure in a message.
These, like everything else representing the
structure of data, need to be governed.
Many organisations are actually using data
models as the origin of XML schemas.This
makes sense because they can use the same set
of standards that are applied to physical data
level of the data it represents. the use of the
data on an enterprise or departmental level,
the last time the represented data was checked
for accuracy, or the last time the structure was
changed in the database. Most organisations
are just happy that an entity has a definition at
all. Nevertheless, this infor"mation needs to be
incorporated into the models, otherwise it will
just become yet another outdated artifact that
IT needs to manage, with no tangible benefits to
demonstrate to management.
business feel if you told them 85 per cent of
your data was unusable and just taking up disk
space?
Data governance entails many things but the
basic premise is a set of standards or guidelines
for managing data on an enterprise-wide scale
with the goal of making it more useful, more
secure and more valuable - i.e. turning a storage
cost into an asset for the business. By ensuring
best practice around data within a company you
can automatically drive down data centre costs
and gradually begin to utilise that 'missing' 85
per cent.
The scope of data governance extends
beyond the data architecture team and it
is very important that both the architects
and modellers are involved with the data
governance initiatives to ensure the business is
aligned correctly.This means creating standards•
for how your data is secured and documenting
what, if any, sensitivity to compliance laws it may
have. It means defining the stewards of your
data as it relates to responsibilities of managing
it, such as the quality, design and business rules.
It also means creating standards for database
development as new databases are built and
existing databases are re-architected. It is critical
that these standards be integrated into the
models to service the data governance initiatives
of your business,
A general definition of an entity in a typical
data model very rarely documents the sensitivity
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The importance of datagovernanceData governance is becoming ever important
to businesses as they strive to meet the needs
of regulatory compliance measures such as
Sarbanes-Oxley, Basel II and most recently
MiFID.
Howevel~ very little of the data that is stored
within a corporation is actually used to its
benefit. Gartner estimates that only 15 per cent
of data is actually used for the benefit of the
organisation - nobody knows what the other
85 per cent of data is, where it is or what to do
with it. How happy would executives in your
governance, web services, regulatory compliance
and heightened information security, data
architects are asked to build much more than
the classic data dictionary.The importance
of well-documented models, both data and
process, are at a premium.The traditional entity
and attribute definitions are not cutting it when
it comes to truly documenting the data and the
processes surrounding it.
As new projects are undertaken, ensuring
that business requirements are adequately
addressed and accurately implemented can be
a challenge unless the metadata (i.e. the data
about the data) has kept apace with the growing
needs of the business, continually evolving
alongside it.
,
Data Management
models and databases and leverage them for
creating the XML schema structure.This often
starts with creating logical models that represent
the canonical form of the XML messages. A
canonical model will typically be somewhere
In between a conceptual and a logical model
but will be fully attributed and enforce stricter
vocabulary and stronger typing for the attributes.
The benefit is that the same vocabulary and
naming standards can be used for the XM L as
it is to create databases that are typically where
the data originates anyway.
A safe repositoryThe importance of storing the data models in
a repository, as opposed to a network drive,
cannot be understated. Models represent a
large part ofthe intellectual property of a
business.The worst thing that can happen is to
have them stored on a personal hard drive or
network drives with no process for backup and
recovery, no ability to analyse what the sum
of the parts is, and no way of knowing what is
truly out there. It gets very difficult to align the
information about IT assets with the knowledge
and rules of the business. Getting them into one
central container maximises the benefit they
can provide to an organisation.This can help
isolate areas of redundant data and reduce the
overall cost of storage for the data. In addition,
most repositories have the ability to reuse
information across various models to promote
reuse and further- drive down the cost of
managing common data in systems throughout
the organisation.
Reporting is also an integral piece of any
repository.This allows searching and reporting
to audiences who may not be leveraging the
repository for active development but need
access to gain information about the data's use
and whereabouts across the enterprise.
ConclusionIn summary, how data is used underpins the
success of an organisation. Data models play an
integral role in managing data on an enterprise
level but that is only the initial step. Data models
need to be well-documented and tell the entire
story of the data, who can access what, when,
where and why.The data mocfels must also
explain the policies and use of the data acros.s
the enterprise to ensure governance, security
and best practice.•
Jason Tiret has over seven years of
experience in data modelling, metadata
and database management and currently
manages Embarcadero's award-winning data
modelling solutions.
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