Data Integration Helps Achieve Business Goals

by Suganthi Shivkumar    Aug 03, 2009

Imagine a world where IT is perfectly aligned with the business. Accurate, relevant information flows freely throughout the enterprise, driving timely decisions and actions. The IT infrastructure is flexible and designed for reuse ensuring that companies don t just respond to changing business requirements and competitive pressures, but stay ahead of them. And in an ideal world, IT delivers real, measurable value to the business, supporting key goals such as:

Growing revenue. Companies have a comprehensive understanding of their customers. A 360-degree view of customers provides the insight and knowledge needed to drive sales, marketing, and customer service. By accurately anticipating customer needs, companies increase their cross-sell rates and improve customer retention.
Streamlining business operations. Business operations run smoothly and efficiently, and noncore functions are outsourced. Reliable, timely information is readily available to support business processes, no matter which system or organizational boundaries those processes traverse. And the data across processes are synchronized, ensuring consistency in business operations and visibility across the enterprise.
Ensuring regulatory compliance. Organizations have a single version of the truth to report to regulators and auditors, reducing the compliance burden. Information sources and changes are automatically documented to provide an audit trail, ensuring absolute confidence in the quality and accuracy of the information.

In an ideal world, IT organizations are agile and highly responsive to changing business requirements. They provide solutions that align with strategic business goals, delivering measurable business impact. They rationalize their IT infrastructure. With increased efficiency, they coordinate efforts across the enterprise to reduce overall costs and deliver more with less.

Battling Data Fragmentation & its impact

But this is the real world. Strategic business initiatives often trigger one or more major IT projects implementing a single view of customer, synchronizing multiple operational systems to support an end-to-end business process, or consolidating multiple applications to reduce costs.

IT organizations struggle to deliver against the requirements of the business. They’re inhibited by the brittleness and complexity of their existing IT systems and infrastructure. And they don t have resources to spare most efforts are dedicated to keeping the lights on.

What’s a key cause of these problems?
Data fragmentation.
Data resides in disparate silos throughout the enterprise. For example, customer data may be scattered over dozens if not hundreds of different applications, databases, and legacy systems.

These silos may have sprung up organically, as different business units implemented their own projects independently, or they may have been acquired via a merger. Either way, organizations of all sizes combat the proliferation of data silos. The content, quality, structure, and definitions of the data in these silos are as variable as the silos themselves. Users lack confidence that the data they need to run the business is comprehensive and accurate. Without a single view of relevant data and a consistent understanding of its meaning enterprise-wide, different groups or systems may produce different answers to the same questions based on their own versions of data. Moreover, as business complexity grows, timelines accelerate, and data volumes increase, the problem of data fragmentation becomes more difficult to solve.

Data fragmentation impedes efforts to grow revenue, increase operational efficiency, or ensure regulatory compliance. Putting enterprise data to work to achieve business goals means addressing the data fragmentation problem. To ensure that business decisions and operations are based on trustworthy, timely, holistic information, almost every initiative calls upon IT to access, integrate, and deliver data.

Accessing data alone is no easy trick. Data can be found throughout the enterprise, in multiple disparate systems and in many different formats. Data is scattered everywhere on the mainframe, in databases, in obscure legacy systems, in spreadsheets on desktops, in enterprise resource planning (ERP) applications, on message queues, in files.

Data then has to be cleansed, aggregated, and validated, and put into a form that is meaningful to the applications and users who need it. In today s stringent regulatory environment, data also needs to be governed properly. Organizations must track and document where their data came from, how it has changed, and who has changed it to meet audit requirements of such regulations as Sarbanes-Oxley. And the security of data must be ensured throughout the whole process.

So what’s the solution? Data integration.

Data integration allows organizations to access all their fragmented data, create an accurate and consistent view of their core information assets, and easily leverage these assets across the enterprise to drive business decisions and operations.

Unlike application integration, which is focused on transaction management and process integration, data integration resolves the complex issues that arise out of data fragmentation. These issues include poor data quality, inconsistencies in the structure and meaning of data, and inadequate data governance.

Organizations are using data integration in many different ways to drive business value. They are implementing real-time reporting and analysis to optimize minute-by-minute operational, as well as strategic, decisions. They are using data integration to migrate data into new applications, or implement master data management. They are also using data integration to synchronize data across operational processes and systems, and to create flexible, reusable data services.

Managing Data Integration Across an Enterprise

The Costs of Complexity:
Organizations should look to data integration technologies that can be used across a broad range of initiatives, including data migration, data consolidation, data synchronization, data warehousing, and the establishment of data hubs and data services. But functionality alone is insufficient to address the full scope of data fragmentation in most enterprises.

Historically, IT organizations have approached data integration on a project-by-project basis. Depending on the needs of their specific project, one IT team might use an extract transform- load (ETL) tool. Another team might hand-code scripts, in conjunction with enterprise application integration (EAI). Yet another might use an application vendor s tools.

Over time, the result is a proliferation of one-off data integration technologies. The wide range of data integration approaches results in a complex, brittle IT infrastructure that s extremely costly to manage. As IT organizations take on new initiatives such as outsourcing a business function the complexity only increases.

Today, many organizations are adopting a common, enterprise approach to data integration one that leverages a unified platform, is built on shared services, and supports competency centers.

Such shifts are often part of a larger movement to rationalize the IT infrastructure, with the goal of reducing costs and increasing agility. Also, today s increasingly strict regulatory environment demands a more consistent approach to data integration and data governance. Organizations need to ensure they provide consistent answers to questions from regulators, and they need to properly document all data to certify its validity.

Approach: Integration Competency Center
Every data integration project is different and includes many variables - such as data volumes, latency requirements, IT infrastructure, and methodologies.

Integration Competency Centers (ICCs) have emerged as a best practice for enterprise data integration. ICCs are an organizational approach designed to increase agility and reduce implementation costs by promoting reuse, sharing best practices and resources, and establishing common processes and standards for integration. ICCs facilitate cross-enterprise collaboration and coordination for global IT teams, including both internal and external resources such as systems integrators and outsourcers.

Conclusion
While many organizations are migrating to an enterprise-wide approach to data integration, almost no one can afford a high risk, big bang implementation. It is critical that data integration evolves over time, with an incremental rollout focused on delivering urgent business requirements now, while laying the foundation for an enterprise-wide data integration infrastructure for the long term.

The author is MD, South Asia of Informatica