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Enterprise Information Management

Insights and Strategies into the Direction of EIM

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.

Figure 4: EIM - Key Trends

Figure 5 indicates where the categories line up and make significant impact on the EIM stack. Data Agility recommends that organizations consider this "whole of EIM" view in its data warehouse direction setting.

Figure 5: Key Trends and the EIM Stack

EIM - An Australian Perspective

Much of the research references overseas experiences and trends - particularly those of North America and Europe. This means that it is based in organizations and markets that are frequently much larger than those in which most Data Agility clients predominantly operate - Australia and New Zealand.

While it has many similarities to other geographies, Australia has its own market characteristics. Recognizing this, Data Agility has sought to identify key trends within Australia's market as well as those internationally. Accordingly Data Agility engaged with:

  • Senior officers at some of Australia's largest organizations (and greatest data users) to get their views on the direction of EIM.
  • Senior officers of the local operations of global organizations to see how they apply themselves to the Australian market and regulatory conditions in the context of their parent company's requirements and regulatory environments.
  • Representatives of the Australian and Asia Pacific research and university community.

Data Agility is extremely grateful to this group of high performers whose frankness and openness reflect both the reason for their achievements and the recognition of the challenges before them.

Australian EIM Requirements

Consultation identified a number of forces that are continuing to challenge Australian enterprises' requirements of EIM:

  • Business-to-business relationships are changing. For example, many Australian FMCG companies are being required by the largest supermarket chains to consolidate often brand-based operations into a single supply chain. This requires a corporate response, and enterprise-wide data consolidation is a feature of this change.
  • Retail operations are changing as banks, financial services providers and telecommunications organizations are seeking a genuine single view of the customer to enable effective service and sales.
  • In the Australian public sector, taxation, social security, health and policing are being radically transformed by issues such as security, safety, a need to improve the citizen's experience at lower cost and the opportunities presented by a "single view of client."
  • Emerging national and international regulatory reporting requirements are forcing many into new systems, new data and new processes providing consolidated reporting at group level. For some of the Australian operations of international organizations, this is a real challenge as they update their infrastructure, applications and data under the scrutiny of the U.S. Securities and Exchange Commission.

There are also forces within Australian organizations that continue to challenge enterprises' requirements of data:

  • Executive management continues to challenge whether they do in fact have the data to run the business - there is widespread frustration with data that is often filtered and untimely. There is demand at senior levels for data that is complete, transparent and presented when required.
  • At an execution level, front of house staff in both public organizations and private enterprise are continuing to be called out by customers who expect to know about all transactions in all channels. This is impacting operational execution in often time-critical client interactions.
  • The growth in the range and volume of data is applying pressure to improve data quality.

References:

  1. The Data Warehousing Institute, "The Rise of Analytic Applications: Build or Buy?"
  2. The Data Warehousing Institute, "Smart Companies in the 21st Century."
  3. Gartner, "Emergence of EIM Drives Semantic Reconciliation."
  4. IDC, "Oracle Builds Comprehensive SOA Platform." Grid computing involves the leveraging of a virtual pool of resources (servers, storage, devices, databases, network devices, etc) to support enterprise workloads. These resources can be allocated to different, and parallel, workloads depending on priorities. Grid computing is enabled by a collection of software services that automatically manages this workload supply and demand.
  5. Forrester, "Grading BI Reporting and Analysis Solutions."

This article is an excerpt from a white paper of the same name. Please register to read this white paper at http://www.dmreview.com/portals/portal.cfm?topicId=230638.


Iain Kiernan is a director of Australian services firm Data Agility where he leads the data management division of the company. Prior to joining Data Agility in 2003, Iain was an executive with responsibility for customer data for Australia's third largest bank.

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