data agility eim

Enterprise Information ManagementInsights and Strategies into the Direction of EIM

A WHITE PAPER

ENABLING THE DATA-DRIVEN ORGANISATION

JANUARY 2006

ENTERPRISE INFORMATION MANAGEMENT 2

Table of contents

Executive summary 3

1 Introduction 5

1.1 Background and purpose 5

1.2 Enterprise information management — A defi nition 5

2 Key trends in EIM 7

3 EIM — an Australian perspective 10

3.1 Background 10

3.2 Australian EIM requirements 10

3.3 Key trends in EIM in Australia 11

3.3.1 A maturing understanding of data 11

3.3.2 The role of information technology and business agility 13

3.3.3 New data-centric roles and responsibilities 13

3.3.4 Enabling genuine business KPIs through the disciplined use of data 14

3.3.5 Architecture for the enterprise 14

3.3.6 Data quality, governance and culture 15

3.3.7 Data integration and consolidation 15

3.3.8 The semantic layer 16

3.3.9 Data warehousing 16

Further information 17

Annex A Research materials 17

Annex B Endnotes 18

Corporate overview 19

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Executive summaryAustralian organisations have been undertaking enterprise information management (EIM) initiatives for fi 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 identifi es 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 effi ciency• 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

1. Information architecture High

Developing architectures that enable core group infrastructure and fl exibility within business lines. Redefi ning the relationship between IT and business for data management

2. Data quality and governance

HighImplementing data quality tools and technologiesInitiating data governance forumsDeveloping organisation-wide data quality cultures

3. Enterprise data integration Medium

Recognising that data is the challenge— not just systemsPlanning 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

Medium Identifying latency opportunitiesParking real-time initiatives and going back ʻup-streamʼ to address data quality issues

5. Semantic reconciliation

Medium Developing metadata and master data strategies and tacticsDeploying metadata/dimension data solutions

6. Infrastructure standardisation Medium

Consolidating systems and versions of systemsLeveraging existing enterprise licenses

7. Platform performance Low

Assuming that Mooreʼs law will continue to apply as data volumes increase leading to low levels of concern with storage issues/costs

8. Tool standardisation Medium Rationalising to two or three tools in a subject area such as data

warehousing or business intelligence

Table 1: EIM – key trends


There is signifi cant focus nationally and internationally on enterprise and data architecture. This is seen as core to EIMʼs 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 ʻsea of dataʼ 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 fi nancial performance. They are also seeking to derive signifi cant 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-streamʼ fi 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-defi ned technical and data architecture

• high calibre resources applied at the right time in the implementation

• effective enterprise/vendor relationships

• defi ned 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 offi cers are satisfi ed 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.

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1 Introduction

1.1 Background and purposeThe paper has been developed through international research and consultation with Australian executives and senior offi cers in data, technology, marketing and research. Industry coverage includes banking, fi nancial services, telecommunications, fast moving consumer goods, federal government, universities and information technology. The paper:

• provides a defi nition of EIM that focuses on structured data

• identifi es 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 defi nition Data Agility research has shown that while there is yet no commonly agreed defi nition of the term ʻenterprise information managementʼ 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 defi ned as “the processes, technologies and tools needed to turn data into information, information into knowledge, and knowledge into plans that drive profi table 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.

Figure 1: Field of view — The EIM ʻstackʼ

Enterprise Business Intelligence Tools

Subject Oriented Data Marts

Enterprise Data Warehouse

Data Integration Platform

Data Source


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

DATA WAREHOUSE

BUSINESSUSERS

TECHNICALTEAM

CUSTOMERS

PRODUCTS

ACCOUNTS

EXTRACT

CLEAN

MODEL

TRANSFORM

TRANSFER

LOAD

QUERY

REPORT

ANALYSE

MINE

VISUALISE

ACT

DATA WAREHOUSE ENVIRONMENT ANALYTICAL ENVIRONMENT

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2 Key trends in EIMKey trends shaping an organisationʼs 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 Agilityʼs national and international research identifi ed 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.

1 Information architecture

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.

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 ʻdata as an assetʼ throughout the organisation.

2 Data quality and governance

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 remains a signifi cant challenge for EIM initiatives and is a major reason for the failure of these projects.

Response

Focus is turning to tackling what can seem like intractable data quality problems in nearly every business domain. The completeness, validity and accuracy of data is being widely addressed. Organisational accountability and governance mechanisms are key elements of the response.

3 Enterprise data integration

Challenge

A signifi cant component of the EIM effort is consumed by data integration issues. In many large organisations integration among disparate applications has been achieved via custom-developed interfaces.

Response

With the growing demand for integration services and the need to reduce latency in information delivery, organisations are moving to a standard set of integration services. In addition organisations are addressing architecture, standards, processes, and structures that will support an enterprise approach to their data integration efforts.


4 Information latency and real-time intelligence

Challenge

The distinction between transactional and analytical activities is diminishing. Improvements in technology have raised the bar on the performance of business processes moving decision support into the operational domain.

As more businesses strive for the ideal of the real-time enterprise, there is growing interest in reducing the latency of information delivery. Making faster decisions based on more real-time information can benefi t enterprises seeking faster and more effi cient operational processes.

Response

The advent of business activity monitoring (BAM) and real-time intelligence uses data warehouse and business intelligence capabilities to optimise transactional systems, and embed decision-support capabilities into operational processes.

Organisations will develop strategies to reduce the latency in their information delivery systems.

5 Semantic reconciliation

Challenge

Most large organisations have hundreds of separately developed and implemented applications, many of which contain information that must be shared among them. As data sources proliferate, a complex problem has emerged — achieving what is often referred to as ʻsemantic reconciliationʼ. This is the state in which users and applications that are involved with data have a persistent and consistent interpretation of that data3.

Response

Various technologies and directions around metadata, master data, and the ʻsemantic webʼ are each seeking to address aspects of this complex reconciliation challenge. The majority of successful data warehouse initiatives include formal metadata management facilities.

6 Infrastructure standardisation

Challenge

Recent innovations in database technology and hardware performance have effectively eliminated the need to fragment data to accommodate tradeoffs between size and speed.

Response

This new freedom enables organisations to consolidate their current EIM investments to remove duplication and ineffi ciencies introduced through a historically siloed approach to their decision support activities.

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7 Platform performance

Challenge

Advances in parallel processing and grid computing4 along with innovations in database design and performance are driving enterprise data marts.

Response

As performance challenges are removed from the EIM equation new opportunities exist to realise platform consolidation and integrated business intelligence, and to deliver a service-oriented approach to information acquisition and business analytics.

8 Tool standardisation

Challenge

Most large organisations have many similar tools delivering, at high cost, fragmented and often undisciplined usage of data.

Response

To achieve economies of scale and drive down IT support costs organisations are standardising reporting and analysis tools. While consolidating to a single suite can be challenging, organisations are adopting a standard for each tool category — for example analytic or reporting. Profi les of end-user communities are then developed to map the appropriate tools to their business needs.5

Table 2: EIM — Key Trends

Figure 3 below indicates where the categories line up and make signifi cant impact on the EIM stack. Data Agility recommends that organisations consider this ʻwhole of EIMʼ view in its data warehouse direction setting.

Figure 3: Key trends and the EIM stack

Enterprise Business Intelligence Tools

Subject Oriented Data Marts

Enterprise Data Warehouse

Data Integration Platform

Data Source

INFO

RM

ATIO

N A

RC

HIT

ECTU

RE

STANDARDISATION REAL-TIME INTELLIGENCE

CONSOLIDATIONPERFORMANCE

LATENCYSTANDARDISATION

QUALITY SEMANTICS


3 EIM — an Australian perspective

3.1 Background Much of the research references overseas experiences and trends and particularly those of North America and Europe. This means that it is based in organisations 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. Recognising this Data Agility has sought to identify key trends within Australiaʼs market as well as those internationally. Accordingly Data Agility engaged with:

• senior offi cers at some of Australiaʼs largest organisations (and greatest data users) to get their views on the direction of EIM

• senior offi cers of the local operations of global organisations to see how they apply themselves to the Australian market and regulatory conditions in the context of their parent companyʼs requirements and regulatory environments

• representatives of the Australian and Asia Pacifi c research and university community.

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

3.2 Australian EIM requirementsConsultation identifi ed a number of forces that are continuing to challenge Australian enterprisesʼ 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, fi nancial services providers and telecommunications organisations 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 citizenʼs experience at lower cost and the opportunities presented by a ʻsingle view of clientʼ

• 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 organisations this is a real challenge as they update their infrastructure, applications and data under the scrutiny of the US Securities and Exchange Commission.

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There are also forces within Australian organisations that continue to challenge enterprisesʼ 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 fi ltered 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 organisations and private enterprise are continuing to be caught out by customers who expect then 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.

3.3 Key trends in EIM in AustraliaAs with the overseas research architecture, quality, integration and sematic reconciliation were important topic areas in Australia and these together with commentary on the Australian data warehouse experience are discussed below. There were also four local themes that came through strongly:

• a maturing understanding of data• the role of information technology and business agility• enabling genuine business KPIʼs through the disciplined use of data• new data-centric roles and responsibilities.

This section starts by presenting these topics.

3.3.1 A maturing understanding of dataThere is continuing growth in understanding that the data an organisation holds is the internal articulation of its customers, its products and its operations. Data and the ability to leverage it both singularly and collectively are now recognised as one of the greatest ʻassets ̓that it holds.

There is also a maturing understanding of data management and its principal components of data consolidation, the enablement of reporting and ʻrear viewʼ analysis. Additionally there is more interest in enabling the organisation through the application of predictive capability and the application of artifi cial intelligence. It is noted however that while interest is growing in artifi cial intelligence applications are not widely used and trust in ʻblack boxʼ applications is not yet at a high-level.

Using the example of Data Agilityʼs retail customer data maturity model the table following sets out an example of how many Australian organisations are approaching the data challenge.


Maturity levelLevel 1: Coordinating the current

Level 2: Understanding the past

Level 3: Predicting the Future

Theme Consistent customer management

Rearward-looking customer analysis

Forward-looking customer insight

Objective

Centralisation and coordination of customer data.

Delivery of valuable customer-related insight through analysis of historical data.

Key customer decisions driven by understanding of likely future behaviour.

Required business capability

Common organisational recognition of single customer system of record.

Cross-departmental commitment to maintain accuracy, validity and timeliness of customer data.

Specialist analytical team — responsible for collation and analysis of historical customer data and delivery of insights to operational business units.

Suitably skilled analytical personnel to drive basic data analytics tools and methods.

Specialist insight team with expertise in predictive modelling and scenario planning.

Organisational maturity and agility to adjust to predicted future threats and opportunities.

Required technology capability

Single, universally agreed and recognised system of record for customer information and interactions, with visibility of transaction history.

A well-architected data warehouse with appropriate tools to support ad-hoc reporting and slicing and dicing of information.

Predictive modelling, statistical analysis and pattern identifi cation technology to support advanced modelling and scenario planning.

Table 3: Example of a data maturity model — retail customer data

The Australian experience shows that while many of the organisations Data Agility engaged with have sought to achieve Level 3 these endeavours have only been partially successful. Major issues have emerged with the quality of the data, the tools and the discipline with which they are applied.

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3.3.2 The role of information technology and business agilityWhile technology is an important enabler many interviewees made it clear that the challenge in Australia was not solely about the technology.

Many in the Australian IT community recognise that their efforts to ʻsupport business agilityʼ, ʻenable the businessʼ or ̒ provide the data the business needsʼ have not been completely effective. Several senior IT offi cers stated that in their view the business regarded IT as too slow, too expensive and to unresponsive. In effect, they were not enabling the data-driven enterprise.

These remarks were often made by senior members of the IT community who would be widely viewed as exceptionally ʻcustomer-centricʼ in their behaviours and engagement with the business community. An underlying issue may be that the IT function in some organisations may have become so ʻcustomer-centricʼ that it has sacrifi ced some good IT management principles and good practice which in turn has lead to the greater complexity and cost and eventually a less responsive and effective IT operation. An effective Australian analogy may be Ansett Airlines, which collapsed under the burden of an aircraft fl eet that met every route and passenger need perfectly but couldnʼt be managed or maintained.

The more widespread appreciation of the need to focus on the data has for some clarifi ed that their business/technology relationship had become unbalanced and that much of the true business opportunity is still to be achieved at this interface.

It is also acknowledged that there are signifi cant opportunities to deliver better business outcomes by improving communication within an enterpriseʼs IT community. It was evident that there continues to be a described gap between ʻbusinessʼ and ʻtechnologyʼ communities in many organisations.

3.3.3 New data-centric roles and responsibilitiesAn organisational response to the developing understanding of data has been the creation of roles that have specifi c responsibility for data. At two of Australiaʼs largest organisations data responsibilities that sat with the CIO have now been moved to executives who are peers with the CIO but have specifi c responsibility for data. Telstra has a Chief Data Offi cer and the Australian Taxation Offi ce a Chief Knowledge Offi cer. Both have responsibility for data and its management.

The creation of these roles has to a signifi cant degree been driven by the developing understanding:

• of the value of data

• of the challenge of large growth in data volumes

• that data is not just an IT systems challenge

• that the value cannot be delivered solely by integrating systems. It is about integrating and consolidating data and having a coherent view of the customer, product, account, relationship, operations, fi nance and supply chain.

These roles have impacted the balance of power in the business/technology relationship adding a new component to the relationship.

For some of those on this path the IT function has been asked to focus on designing and delivering a cost effective architecture that supports ʻbusiness agilityʼ and responds in a consistently timely manner to business change request.


3.3.4 Enabling genuine business KPIs through the disciplined use of dataData Agility Australian research found a widespread desire within the data and technology community to enable the Good to Great6 perspective of confronting the brutal facts and focusing on genuine business KPIs.

Research also found the community senses there is widespread undisciplined use of data. One executive stated that in a recent audit he identifi ed that his organisation had over 1000 Microsoft Access databases and he had little understanding of their contents, usage or value. He observed that much of this activity appeared to be focused on maintaining data for individual rather than corporate agendas. Another executive spoke bluntly of Microsoft Excel as ʻthe enemyʼ due to its proliferation and undisciplined usage. While typically the language used was less provocative it does refl ect a widely held view. An extremely powerful tool Excelʼs usage amongst a burgeoning analyst community was uncontrolled leading to high analytical staff costs, and often questionable (and perhaps self-serving) ʻfactsʼ being used in business decision making. An issue that emerges is that data is often viewed as a free resource and therefore undervalued and applied without discipline.

While there are a wide variety of needs to be met, from controllable audit and regulatory functions to more nimble entrepreneurial activities, the data itself was on occasion becoming the ʻvillainʼ. Widespread access and undisciplined usage is in a number of instances undermining trust in the data.

It was acknowledged within the community that while ̒ data high-jacking ̓did occur some of the drivers of these business behaviours lie with ITʼs inability to be suffi ciently responsive to business needs.

The overall requirement was to clarify and enable the true metrics of the business. This will stem the ad hoc analysis cost blow out affl icting so many businesses and enable genuinely focused application of EIM.

3.3.5 Architecture for the enterpriseThe Australian research found there is a move across industries for enterprises to strengthen their architectural function and signifi cantly improve their data architecture capability. Key features of the current architectural discussion are:

• architecture for the enterprise underpinned by an IT organisation that is much more responsive to the needs of the agile business

• pressure to provide architectures that increasingly allow near real time responses to events

• the focus on business agility driving take-up of Service Oriented Architectures

• tool rationalisation. Many organisations have developed roadmaps that reduce the number of data warehouses or business intelligence tools as well as back-end systems. Few are seeking to get to one tool and most are comfortable rationalising to two or three tools

• signifi cantly greater rigor in the usage of enterprise licenses. This is aligned to tool rationalisation. For example, business cases premised on high spend for non-standard data warehouses are being rejected in favour of leveraging incumbent enterprise licensed solutions

• there is evidence that outside the very largest organisations a ̒ local council model ̓of architecture which allows business to make choices within prescribed standards is working effectively.

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Research also found that the fundamental architectural considerations such as centralised or federated models were being driven by changing business models. For example:

• A number of Australian fi nancial services organisations were moving to a federated architecture that largely empowers business within the context of baseline standards and a ʻcaveat emptorʼ philosophy

• Australian FMCG companies are being driven by the major supermarket chains to a more corporate data and IT position as centralised supply chain dominates over store based relationships.

3.3.6 Data quality, governance and cultureOne of the most frequently repeated stories in Australian data management is that attempts at Level 3 predictive analysis had driven out fundamental problems with the data. Incomplete, invalid or inaccurate data has been exposed and many organisations are now going back ʻup-streamʼ to clean-up their data.

The reasons for the data quality issues were many involving business processes, ETL process, and data integration and consolidation issues. Establishing a data quality culture throughout the enterprise is widely regarded as a major challenge requiring signifi cant changes in behaviour in business and technology. Australian research confi rmed this as a major element in building trust in data and improving the discipline within which it is applied.

Improving data governance was picked by many as a signifi cant issue and as an important component in an effective data culture. Many organisations are engaging business people through governance boards and forums and in the development of data quality policy and procedures.

Many have also found that data skills at senior levels are often lacking — not just on the business side. Data is often not well understood on the technology side. Currently data understanding is on the agenda of the ʻrightʼ people but they often donʼt have the skills need to execute the responsibilities. This intellectual depth is being built.

3.3.7 Data integration and consolidationAs noted above date warehouse/mart and application consolidation is widespread and relatively well understood. Many of these integration activities are positioned as a systems integration activity with data integration as a benefi t. The Australian experience shows that these initiatives will become signifi cant data integration endeavours, delivering material improvements in data availability, quality, timeliness and uniformity. However current approaches mean that cost and time on these initiatives often blow out as the data integration aspect is not suffi ciently well understood, planned or acted upon early enough.

A point that was made on a number of occasions was to beware of over consolidation. It is also understood that to genuinely support business agility, consolidation should not be undertaken for consolidationʼs sake and consolidation should not take place above a decision point.

It was also noted that while data storage costs are falling this should not be used as an excuse to store unwanted or unused data.


3.3.8 The semantic layerThe growth in understanding of data, the recognition that much data goes unused and that the benefi ts of aggregated data are enormous is driving much greater understanding of metadata, master data management, dimension management and the discussion of the semantic layer. This is driving a strong desire for enterprise data models and tools to enable metadata management. However in many organisations the level of understanding is uneven and there is still signifi cant discussion of basic defi nitions such as ʻwhat is a customer?ʼ.

In some organisations discussion is being lead by the data and technology leadership. However there is also evidence that in other organisations the agenda is being driven by sophisticated business users who understand the business benefi t.

Experience within Australian enterprise also indicates that the semantic discussion is well understood by consultants who operate at a project level but application and impact falls away as initiatives move into an operational arena where core staff do not have the same understanding and commitment.

In summary, it is a competence that many Australian organisations recognise they need to develop.

3.3.9 Data warehousingIt is well documented that data warehouses often under deliver against expectations and can be very costly. Data warehousing and data marts are in place in all the organisations Data Agility spoke to and are a critical element in doing business. However selecting, implementing and using warehouses has often been challenging in Australia.

Many organisations data warehouses have been provided by the leading vendors such as Oracle, IBM and NCR Teradata. The experiences within the Australian community vary widely. Two approaches to data warehouse implementation were described: a ʻbig bangʼ, which replaces existing capability, and an organic growth approach, which migrates capability over a longer time. Success with either approach was largely a refl ection of the clarity of vision for the warehouse and level of executive leadership support.

There is evidence that business users have in some cases lacked confi dence that bold projects would succeed and some users have resisted losing direct control of ʻtheirʼ data. This has led to users seeking to retain existing reports and data fi elds rather than seize the opportunity to review and revise business objectives and KPIs. This means that some have found it very diffi cult to switch off existing capability thereby diminishing the value delivered by warehouse implementations.

Several organisations saw lack of depth of technical implementation resources in the local market as a particular concern. A number also spoke of local implementation teams being weakly supported by overseas headquartered vendors and expensive, expert resources only being engaged when deployments hit crisis point.

Sound technical and data architectures are a platform for success though it was noted that vendor industry data models were not always a good fi t with Australian organisations. Technical clarity of the upgrade path for the warehouse was viewed as very important for a full understanding of the total cost of ownership.

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An interesting feature is the methods by which businesses distribute the costs of major data warehouse projects and their ongoing usage. Most organisations are seeking to develop a strong internal sense of the value of data and balance three objectives: maximising usage of the data warehouse, covering operational cost and minimising cost to business user. These objectives are often being pursued in organisational environment that experiences frequent change.

Annex A Research materialsAMR Research — Developing a Winning Strategy for

Transforming Data into Information – 2003

Ascential — Master Data Management – 2005

Ascential — On Demand Data Warehousing – 2004

BearingPoint — Charting the Path to Enterprise Agility – 2005

BearingPoint — Enabling the Holistic Enterprise with Web Services and SOA – 2004

BearingPoint — Making IT Systems Development Agile and Adaptive – 2005

BearingPoint — The Agile IT Architecture – 2005

BearingPoint — The Process Driven Enterprise – 2005

BearingPoint — The ROI Behind a Converged Approach to Data – 2005

Business Objects — Metadata Management Solution – 2004

Cerebra — Enterprise Information Management – 2005

Cerebra — The CIOs Guide to Semantics – 2004

Data Agility — Forecasting and prediction: Data-driven insight – 2003

Data Agility — Customer relation management assessment – 2004

Data Agility — Data management maturity – 2005

Data Agility — Metadata concepts, benefi ts, applications and tools – 2005

Delphi — Insight for Business and Technology Leaders – 2004

Delphi — Intelligent Classifi cation and the Enterprise Taxonomy Practice – 2004

Delphi — Taxonomy and Content Classifi cation – 2002

Delphi — The xEnterprise Architecture – 2003

FirstLogic — Implementing Data Quality as a Corporate Service – 2004

Forrester — Change Data Capture Gives Data Integration Greater Scale and Speed – 2004

Forrester — Creating the Information Architecture Function – 2004

Forrester — Grading BI Reporting and Analysis Solutions – 2004

Forrester — How to Evaluate Enterprise ETL – 2004

Forrester — IT Consulting and Integration Services Continue to Commoditise – 2004

Forrester — Real-Time Datawarehousing - The Hype and the Reality – 2004

Forrester — The ETL Tool Market is Back and Growing – 2004

Gartner — Business Activity Monitoring BAM Architecture – 2003

Gartner — Data Integration forms the Technology Foundation of EIM – 2005

Gartner — Data Quality Firewall Enhances Value of the Data Warehouse – 2004

Gartner — EIM is a Core Element of Your IT Architecture – 2005

Further informationThe information provided here summarises Data Agilityʼs research.

For further information on any of the topic areas please contact us at:

L17 / 60 Albert Road L22/201 Miller StreetSouth Melbourne North SydneyVictoria 3205 New South Wales 2060Australia Australia

T +61 3 8646 3333 T +61 2 8923 2655

F +61 3 8646 3399 F +61 2 8923 2525

www.dataagility.com


Gartner — Emergence of EIM Drives Semantic Reconciliation – 2004

Gartner — ETL and Application Integration Suites Convergence Continues – 2004

Gartner — Magic Quadrant for Customer Data Integration Hubs – 2005

Gartner — Service Oriented Business Applications Require EIM Strategy – 2005

Gile — Business Processes are Important to BI Applications – 2005

Human Inference — Compliance and Data Quality – 2005

IBM — Banking Data Warehouse - General Information Manual – 2004

IBM — Banking Data Warehouse Brochure – 2001

IBM — Information FrameWork - Critical Business Process Models – 2002

IBM— Information FrameWork Objects Model – 2002

IBM — Introduction to BI Architecture Framework and Methods – 2004

IDC — Managing Master Data for Business Performance Management – 2005

IDC — Oracle Builds Comprehensive SOA Platform – 2005

IDC — The State of Business Analytics – 2005

IDC — Why Consider Oracle for Business Intelligence – 2004

Informatica — How to Design and Implement an Integration Competency Center – 2004

Informatica — Integration Competency Centers WP – 2004

Intel — Metadata Management - The Foundation for Enterprise Information Integration – 2004

Kaledo — An Architected Approach to Information Integration – 2004

Knightsbridge — Top 10 Trends in BI and DWH – 2005

OMG — Common Warehouse Metamodel 03-03-02 – 2003

OMG — Oracle 10g Integration - Business Activity Monitoring – 2004

Stratature — The Case for Enterprise Dimension Management – 2005

TDWI — Best Practice in Business Performance Management – 2004

TDWI — Building the Real-Time Enterprise – 2003

TDWI — Development Techniques for Creating Analytic Applications – 2005

TDWI — Evaluating ETL & Data Integration Platforms – 2003

TDWI — Strategies for Consolidating Analytic Silos – 2004

TDWI — The Rise of Analytic Applications – 2002

TDWI — The Secrets of Creating Successful Business Intelligence Solutions – 2003

Teradata — Enterprise Event Management in Banks – 2005

The CDI Insititute — Customer Data Integration - Market Review & Forecast – 2004

The Nucleus of Enterprise Integration – 2005

Wipro — Federeated Data Warehouse Architecture – 2004

Annex B Endnotes1 The Data Warehouse Institute, The Rise of Analytic Applications: Build or Buy?

2 The Data Warehouse 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

6 Jim Collins, Good to Great, Random House 2001, Chapter 4: Confront the Brutal Facts

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Corporate overviewData Agility is a vendor independent specialist data organisation that works with clients to apply their data effectively. Work is focused in four services lines.

Services

Data managementMany organisations now have a proliferation of data together with warehouses, marts and tools to manage it. Data Agilityʼs approach and methods enable organisations to achieve their business goals by leveraging these often complex data assets.

Data-driven systemsData Agility deploys an end-to-end structured framework for developing and enabling systems that focus on access and application of data. The framework is focused on developing service and system components for a common business function across multiple business processes such as single view of price or single view of customer.

Data-driven insightThe Insight Service consists of tools and process that drive insight from an organisationʼs data. It provides executive and business management with unfi ltered access to data supporting/enabling strategic and operational decisions. It is deployed as a solution within an organisation or provided as a bureau service.

Project servicesData Agilityʼs project managers have long experience in managing business and IT projects that deliver real business benefi t. This experience means they apply solid project management disciplines — risk management, cost control, resource management, change control and communication — within Data Agilityʼs methodology.

ClientsData Agilityʼs clients Include Amgen, AAMI, ANZ Banking Group, Australian Tax Offi ce, British American Tobacco, Charles Sturt University, Coates, Department of Employment and Workplace Relations, Epworth Hospital Group, GlaxoSmithKline, Insurance Australia Group, InsuranceLine, NEC, News Limited, PMI and Telstra.

PartnersWhile vendor independent Data Agility has relationships with many of the worlds leading technology providers. Further details are provided at www.dataagility.com.

L17 / 60 Albert RoadSouth MelbourneVictoria 3205Australia

T +61 3 8646 3333

F +61 3 8646 3399

L22 / 201 Miller StreetNorth SydneyNew South Wales 2060Australia

T +61 2 8923 2655

F +61 2 8923 2525

www.dataagility.com

ENABLING THE DATA-DRIVEN ORGANISATION

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