Enterprise Information Management

Introduction

This article has been developed through international research and consultation with Australian executives and senior officers in data, technology, marketing and research. Industry coverage includes banking, financial services, telecommunications, fast moving consumer goods, federal government, universities and information technology. This article:

Provides a definition of EIM that focuses on structured data.
Identifies and categorizes the key global EIM trends setting out the major challenges and organizational responses in each area.
Presents commentary on the Australian perspective on the direction of EIM covering the key international trends and on-shore organizational issues.

Australian organizations have been undertaking enterprise information management (EIM) initiatives for five to 10 years. There is growing corporate attention to EIM: what it means for the organization, how it should be managed and what activities should be on the agenda next. This article identifies the key international trends in EIM and reports on the Australian EIM experience.

Organizations in the public and private sectors have rapidly growing data on customers, products and activities. The key business drivers for the EIM agenda are:

Sales and marketing growth,
Supply chain efficiency,
Operational cost reduction,
Security/identity capability, and
Regulatory reporting effectiveness.

Figure 1 summarizes what is important in EIM internationally and nationally and the actions being undertaken by leading Australian organizations in this area.

Figure 1: EIM - Key Trends

There is significant focus nationally and internationally on enterprise and data architecture. This is seen as core to EIM's ability to respond to market and organizational change. Organizations are developing architectures for their enterprises that are much more responsive to the needs of the agile business, enable the true metrics of the business and allow near real-time responses to events.

Often described as operating in a sea of data but with little information, enterprises are seeking to leverage their data assets to gain a clear and accurate picture of their operations, customers, supply chain and financial performance. They are also seeking to derive significant returns from their business intelligence capabilities to devise better tactics and plans, respond more effectively to emergencies and capitalize more quickly on new opportunities and threats.

Ineffective data management practices have lead to poor data quality undermining execution of marketing, sales and operations. There is now substantial focus on data quality and many organizations in Australia are currently back up-stream fixing their data before extending complex analytical and data-driven insight capabilities.

Australian experience with data warehousing is similar to that overseas. While the data warehouse has become a critical business tool, implementation and usage have been challenging. The Australian experience in the deployment of a major data warehouse initiative shows that the critical factors for success are:

Clarity of vision and clear articulation of the purpose of the data warehouse.
Executive leadership commitment to what is often a lengthy delivery cycle.
Knowledgeable and committed business users who drive and embrace the new capabilities provided.
Technical competence within the organization and a well-defined technical and data architecture.
High caliber resources applied at the right time in the implementation.
Effective enterprise/vendor relationships.
Defined and agreed-upon internal charging model for the data warehouse.
Governance framework to manage data quality, usage, access and security.

In Australia, few executives and senior officers are satisfied they have achieved the highest standards in data management and recognize the need to improve performance in quality, validity of use, security and privacy. Many have developed or are developing plans to bring all these threads together and thereby enable superior business performance.

Enterprise Information Management - A Definition

Research has shown that while there is yet no commonly agreed-upon definition of the term "enterprise information management," there is agreement about the challenges and opportunities presented by changes in the management and application of structured data.

For the purposes of this article, EIM is defined as "the processes, technologies and tools needed to turn data into information, information into knowledge and knowledge into plans that drive profitable business action."¹ The focus is on structured data within an enterprise - that which is typically created and captured in systems and includes customer, product, account and activity data. It does not include unstructured data such as email and electronic documents or models for interorganizational collaboration. As depicted in Figure 2, EIM encompasses data integration, data warehousing, business reporting and analytic tools.

Figure 2: Field of View - The EIM "Stack"

As set out in the EIM component framework in Figure 3, EIM responsibilities encompass the full information delivery life cycle from data acquisition and integration, transformation and consolidation, through to the provision of business intelligence and analytical capabilities to an end consumer as a tool or a service.

Figure 3: EIM Component Framework²

Key Trends in EIM

Key trends shaping an organization's approach to EIM incorporate both the improvements companies are making to their existing strategies and infrastructures, as well as the new technologies and initiatives that are moving EIM forward.

Data Agility's national and international research identified eight key trends in EIM. These trends, a summary of the challenge they present and indicative response, are described in Figure 4. While presented in order of importance, there are strong links between each element and Data Agility recommends that organizations consider each element in its direction setting.