Data Services

Gap analysis:
The case for daTa services
Data services gather and integrate data from many different sources—relational and non-relational—in
real time, bridging gaps to create consolidated information views—while hiding data source complexity
from applications. Addressing data virtualization with a service-oriented solution, an enterprise data
services platform brings the many benefits ofSOA to the problem of data access, integration, and
governance across the enterprise. Data services streamline development and maintenance by making it
easier for organizations to turn the data they have into the information they need. The result? A more
agile enterprise that can realize a faster return on investments in data and applications.

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Gap Analysis: The Case for DataServices

Table of conTenTs
3

Overview

4

The need fOr dATA ServiceS
4 The Growth of SOA
5 SOA Meets the Reality of Enterprise Data
6 Bridging the Gap
8 Working at a Higher Level: The New Data Tier

9

AnATOmy Of A dATA Service
9 What is a Data Service
11 Application Access to Data
11 Data Integration on Demand
12 Reuse of Data Services
12 Models and Layers of AbstractionEase Integration and Reuse
12 Data Services Complement Other SOA Technologies
13 Controlling Data Services : The Data Service Lifecycle

10 whAT iS A dATA Service mAnAgemenT SySTem
11 Creating Data Services: The Design Environment
12 Managing Metadata: From Data Source
13 The Runtime Environment: Control and Efficiency
18 cOncluSiOn

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Gap Analysis: The Case for DataServices

overview
The broad, cross-functional scope of modern business applications, changing regulatory and security
requirements, and the demanding, real-time nature of today’s business interactions are making the roll-out
of new SOA-based applications exceptionally challenging. Web services protocols directly address the need
for component-based applications, but what about the data? Mostcorporate data is currently stored in
departmental sources, commonly relational databases. Database schemas have been optimized for lines of
business, but many new applications require data across business lines. Meeting these challenging information requirements requires not only streamlining access to data and ensuring efficient delivery of bits
over the network, but also transforming andintegrating data from multiple, diverse sources to create new
consolidated views of information meaningful for today’s applications and decision-makers—views that drive
key initiatives in customer service, marketing, financial reporting, and compliance. Creating this critical
information requires gathering data from many sources. On each project, work must be done to bridge many
differentgaps—between relational or legacy application data sources and newer XML-based data structures,
between databases and non-relational data sources with different, overlapping semantics, and between
these existing data sources and the data structures, semantics, vocabularies, and formats needed by
new applications.
Use of many different data sources can place an undue burden on applications—and thepeople who develop
or integrate them. Adoption of SOA and Web services, even when accompanied by industry-standard XML
schemas, does not in itself address the need for uniform data access or the means by which mediation
between different semantics and vocabularies should occur. As a result, many projects incorporate extensive
custom coding efforts to transform and integrate data. The high volumeof work required leads to extended
development cycles, and the complexity of data transformation and integration requirements leads, in spite
of the high skill level of developers, to code that is necessarily complex and difficult to maintain. This laborintensive integration effort exacerbates the organization’s perpetual struggle to standardize data—to create
and manage the consistent,...