Data Management

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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just become yet another outdated artifact that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.

J) I.reeye In9

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