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Enterprise Information Management

Insights and Strategies into the Direction of EIM


Table of contentsExecutive summary 1 Introduction 1.1 1.2 Background and purpose Enterprise information management A denition 3 5 5 5 7 10 10 10 11 11 13 13

2 Key trends in EIM 3 EIM an Australian perspective 3.1 3.2 3.3 Background Australian EIM requirements Key trends in EIM in Australia 3.3.1 3.3.2 3.3.3 3.3.4 3.3.5 3.3.6 3.3.7 3.3.8 3.3.9 A maturing understanding of data The role of information technology and business agility New data-centric roles and responsibilities

Enabling genuine business KPIs through the disciplined use of data 14 Architecture for the enterprise Data quality, governance and culture Data integration and consolidation The semantic layer Data warehousing 14 15 15 16 16 17 17 18 19

Further information Annex A Research materials Annex B Endnotes Corporate overview



Executive summaryAustralian organisations have been undertaking enterprise information management (EIM) initiatives for ve to ten years. There is growing corporate attention to EIM: what it means for the organisation, how it should be managed and what activities should be on the agenda next. This Data Agility paper identies the key international trends in EIM and reports on the Australian EIM experience. Organisations 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 efciency operational cost reduction security/identity capability regulatory reporting effectiveness.

The table below summarises what is important in EIM internationally and nationally and the actions being undertaken by leading Australian organisations in this area.Top international focus areas Importance in Australia Australian market status and actions Developing architectures that enable core group infrastructure and exibility within business lines. Redening the relationship between IT and business for data management Implementing data quality tools and technologies High Initiating data governance forums Developing organisation-wide data quality cultures Recognising that data is the challenge not just systems 3. Enterprise data integration Medium Planning and executing system and data integration Creating roles responsible for data as peers to the CIO Building data and data architecture skills and capabilities 4. Information latency and real-time intelligence 5. Semantic reconciliation 6. Infrastructure standardisation 7. Platform performance 8. Tool standardisationTable 1: EIM key trends 3

1. Information architecture


2. Data quality and governance


Identifying latency opportunities Parking real-time initiatives and going back up-stream to address data quality issues Developing metadata and master data strategies and tactics Deploying metadata/dimension data solutions



Consolidating systems and versions of systems Leveraging existing enterprise licenses Assuming that Moores law will continue to apply as data volumes increase leading to low levels of concern with storage issues/costs Rationalising to two or three tools in a subject area such as data warehousing or business intelligence



There is signicant focus nationally and internationally on enterprise and data architecture. This is seen as core to EIMs ability to respond to market and organisational change. Organisations 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 nancial performance. They are also seeking to derive signicant returns from their business intelligence capabilities to devise better tactics and plans, respond more effectively to emergencies and capitalise 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 organisations in Australia are currently back up-stream xing 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 organisation and a well-dened technical and data architecture high calibre resources applied at the right time in the implementation effective enterprise/vendor relationships dened and agreed internal charging model for the data warehouse governance framework to manage data quality, usage, access and security. In Australia few executives and senior ofcers are satised they have achieved the highest standards in data management and recognise the need to improve performance in quality, validity of use, security and privacy. Many have or are developing plans to bring all these threads together and thereby enable superior business performance.





1.1 Background and purposeThe paper has been developed through international research and consultation with Australian executives and senior ofcers in data, technology, marketing and research. Industry coverage includes banking, nancial services, telecommunications, fast moving consumer goods, federal government, universities and information technology. The paper: provides a denition of EIM that focuses on structured data identies and categorises the key global EIM trends setting out the major challenges and organisational responses in each area presents commentary on the Australian perspective on the direction of EIM covering the key international trends and on-shore organisational issues.

1.2 Enterprise information management A denitionData Agility research has shown that while there is yet no commonly agreed denition 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 report EIM is dened as the processes, technologies and tools needed to turn data into information, information into knowledge, and knowledge into plans that drive protable 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 inter-organisational collaboration. As depicted in Figure 1 below EIM encompasses data integration, data warehousing, business reporting and analytic tools.

Enterprise Business Intelligence Tools Subject Oriented Data Marts Enterprise Data Warehouse Data Integration Platform

Data Source

Figure 1: Field of view The EIM stack


As set out in the EIM component framework below, EIM responsibilities encompass the full information delivery lifecycle 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 2: EIM component framework2




Key trends in EIM

Key trends shaping an organisations 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 Agilitys national and international research identied eight key trends in EIM. These trends, a summary of the challenge they present and indicative response, are set out below. While presented in order of importance there are strong links between each element and Data Agility recommends that organisations consider each element in its direction setting.Challenge The increase in data sources and volumes, the need to integrate data across disparate systems, and the move to squeeze latency out of decision-making processes demand a more strategic approach to information management. 1 Information architecture Response Responding to these challenges requires a compelling vision for information architecture that sets some basic standards for information management and governance, as well as promoting the idea of data as an asset throughout the organisation. Challenge Poor data quality practices result in lost productivity, poor customer service, faulty business decisions, and an inability to effectively manage compliance and risk or capitalise on new opportunities. At a project level data quality re