Information Management Journey…coordinating a group wide approach

September 2011

Future StateData Quality - Issues:1. We have an ongoing data quality management program in place to e nsure minimal errors at data capture.2. We have communicated & achieved a balance between customer service & data quality backed up by system validation

where appropriate.3. We have addressed data accuracy issues with standard data entry rules & system validation where appropriate.4. The organisation has a greater commitment to the resolution of d ata issues.5. We have created roles for the detection of data issues and provision of feedback to staff.6. Our data quality approach includes feedback & staff education to highlight/rectify data entry issues.7. We have a Bank-wide glossary of agreed definitions to ensure consistent use of terminology.8. Data quality – we have ascribed a business cost to the entry & ongoing use of poor quality data.

Data Management – Governance:9. We have a clearly defined ownership & accountability structure for data in the organisation.10. We have a data governance process in place to assist with ongoing data quality.11. We have implemented the data management function as an appropriately staffed organisational support unit [not a

project].12. We have established a single point of ownership of data and reduced the incidence of data redundancy.13. There is an organisation wide commitment, from Board & Executive down, to the adherence to data management

principles.14. We have an understanding of the benefits & cost savings to be achieved from a sound data management approach.15. We have established clear responsibility & accountability for da ta management [data quality & data integrity].16. The Data Management group has the authority to enforce data quality initiatives.

Data Management – Controls:17. Data/information is readily accessible to those staff authorised to source it.18. Our reporting states the data sources used for the report and any data transformation or consolidations required to

produce it.19. We are confident in the data & information obtained from our DW and other certified sources.20. We have a process of certification of trusted data sources.21. We have implemented system controls to assist with the input of quality data.22. We have implemented manual & systematic validation/reconciliation processes to ensure the ongoing integrity of the

Bank’s data resources.23. We have audit trails in place to assist with tracking changes to data of high importance.

Data Management – Impacts:24. We are confident that our existing data sources & interfaces are correct/validated.25. We are confident our business decisions are based on adequate & trusted data.26. We have clearly defined responsibility for managing data integrity on an ongoing basis.27. We have implemented an approach to reduce current levels of multiple data sources and associated data redundancy.28. Our data management approach has been developed taking into account the requirements of both internal and external

[eg APRA] stakeholders.29. Our data management approach has reduced the chance of the Bank providing customers with erroneous data.30. We have reduced the risk of data privacy issues through enhanced rules & controls on the use of data.31. We have established a data management regime that takes into account the complexity of future information requests.

Business Processes:32. We have defined/achieved a balance between a controlled data approach and the business units ’ needs for flexibility &

timely data.33. We have defined a range of data rules and their ownership [organisation wide or individual business unit].34. We have a clear understanding of the performance drivers/measures for each business unit and can provide the data

necessary to support the process.35. We have the capability to service these data requirements.36. We have developed a catalogue of trusted data resources for the business.37. Our trusted data resources are fully reconciled and validated.38. We have developed & communicated the role played by each part of the organisation in the ongoing data management

function.

Staffing:39. We have adequately trained staff involved with the data management function.40. Staff understand the value of our data resource and strive to maintain data quality.41. We have overcome the staff frustration through the introduction of more automated processes, etc.42. Staff focus on analysis of the data not compilation activities.

Technical Aspects:43. We utilise a corporate data model for all development initiative and view data as multipurpose not application specific.44. Our corporate data model is governed within the enterprise architecture framework to manage reusability & redundancy.45. We have a record of the Bank’s current physical data architecture [data sources; data bases; data flows; etc].46. We have established a single data dictionary.47. We are capable of easily integrating the data from 3rd party applications.

Existing DW Resources:48. We have a corporate data warehouse that satisfies a significant proportion of the Bank’s management reporting

requirements.49. A clear management strategy (including potential decommissioning) has been established for all existing data

warehouses.50. The ongoing management of the EDW has been resolved and suitably resourced.51. Our data sources have data structures that are less complex and easily adapted to change.

Reporting & Tools:52. We have suitable analytical/reporting tools to assist the business with self service reporting utilising the DW [including ad

hoc reporting requirements].53. We have a catalogue of current management reports used by the organisation.54. Our approach to recurrent reporting is largely automated.55. Bank users also have access to appropriate self service reporting capability.56. Our Board & Executive reporting are free of inconsistencies caus ed by the use of varied data sources.57. Our external reporting is sourced from the same data as for inte rnal management reporting.

Data Management Requirements:58. The organisation can readily source the required information and data critical for business decisions59. We have a clear definition of performance management for each business area and can support the required level of

reporting.60. We have defined a short term & longer term approach to implementing a data management function.61. We have determined a range of data management initiatives aligned with organisational requirements & priorities.62. Our data management framework is flexible enough to cater for fu ture data & reporting requirements.63. We have a data management framework in place that is accessible to all staff [eg solution architects].64. We have a framework that will facilitate the incorporation of additional systems [eg from M&A activities].65. We have established a continuous improvement environment that leverages each additional initiative. 66. We have a clear understanding of the management reporting/information requirements of the organisation.

Learnings – Previous DW Initiatives:

Current StateData Quality - Issues:1. Front line staff have an impact on data quality but most likely do not realise the down stream impact of work arounds or short cuts [eg always entering a reason

code because ‘it works’ not because it is correct].2. Business focus is on customer service not necessarily data quality.3. Issue with completeness; accuracy; and consistency of data entered into systems.4. Lack of system rules or checks to prevent the entry of incorrectdata at source system front ends. Data is entered in numerous formats ( eg hyphenated names;

street addresses; etc).5. No education or feedback process for staff who enter data [eg when recognised – staff are not told of errors and the impacts they may have].6. No ownership or responsibility for detecting errors and providing the necessary feedback to staff.7. No organisational focus on resolution of data errors.8. No common terminology – no single glossary or data dictionary to facilitate data sharing.9. No one could say who is ultimately accountable for data quality & data integrity.10. Not convinced that the Executive know the basis of their reporting [eg how many people involved; what sources; what manipulations of data; true sources of

data; etc].11. Not sure if the impacts of ‘bad’ data are really appreciated.

Data Management - Governance:12. Board & Executive do not appear to have an understanding of the significant issues, frustration & associated costs caused by lack of data governance.13. No review of data management processes or requirements since integration.14. No owner of data management.15. Need for governance/management approach to be determined – centralised or decentralised?16. Definition of data rules required – who owns them [the organisation or each department]?17. No single point of ownership for data – also have multiple stores and views for same piece of data.18. Lack of commitment by all levels of the business to adhere to da ta management principles – for expediency, business areas will source and manipulate data

themselves and not take an organisational view.19. Data – no one at Executive level appears to want to own it or take accountability since the merger20. Data management – is not a significant part of anyone’s role/responsibility – therefore is easily parked when other issues or priorities aris e.

Data Management - Controls:21. Data accessibility – there is minimal control over sourcing and reusing data. 22. Short term approach – there is a need for additional controls [eg do not allow any more customer data extracts for new databases].23. There are no details on what data fields are important/required to the run the business [eg what is it that makes a branch/channel successful].24. Ad hoc requests for reporting are often difficult to deliver as data is not available or accessible.

[Lack of] Data Management - Impacts:25. General lack of confidence in the organisation’s data.26. APRA expectations regarding and ADI’s management of data.27. There are occasions when the Bank has provided customers with incorrect data.28. Current situation a result of multiple mergers and inconsistent incorporation of data [mapping not always correct].29. There is a cost associated with data issues [eg over compensating capital; inability to securitise loans; inability to identify appropriate risks].30. Risk of data privacy issues as data is propagated and lack of audit trail.31. Data security issues as data is propagated [including data on laptops, etc that can be lost, etc].32. Simple data requirements are not defined. Potential that business decisions are being based on ‘no data ’ or simple trend data.33. Question as to what value is lost when acquiring data [eg from a M&A] as there is no framework to determine what is best data source.34. Complexity of data & reporting requests is growing – and eventually become unsustainable with current processes, etc .35. No one appears to be responsible for the ongoing management of the data resource [eg after it has been collected] – needs to be done to ensureongoing data

integrity.

Business Processes:36. There is a history of creating new databases/spreadsheets each time we cannot access data or source it quickly enough.37. Multiple databases across the Bank containing the same data, leading to data redundancy.38. No central source or catalogue to assist with accessing data from the most appropriate source in a timely manner.39. The role played by each part of the organisation in data management is unclear [particularly since the merger]. No one knows who is

responsible for providing different information.

Staffing:40. Do we have the skills in-house to progress with the data management approach? Require data architects; data modellers; business SMEs ; etc.41. Data analysts – more focus on number crunching than on analysis and providing insights.42. There appear to be quite a number of data analyst roles in the b usiness areas – are they reinventing the wheel and is there scope to have them better

equipped and coordinated?43. Risk of losing good staff due to their frustration in dealing with manual processes. Also attempts to empower staff are thwarted by absence

of tools for exception reporting, etc – therefore revert to a more command approach.

Technical Aspects:44. Multiple third party/vendor systems is an issue for data mapping.45. We don’t understand how data flows from source systems to reporting output – there is no system overview or architecture detailing this.46. There is no common definition of data fields/terminology – makes it difficult to consolidate data.47. Multiple data dictionaries – no ‘official’ data dictionary understood and communicated. Leads to inconsistency.48. The longer the Bank runs two banking systems – the problems will grow [eg impacts on capital measurement and c osts; inability to match customers between

systems; etc].49. Current position is multiple heritage data warehouses and minimal activity on the Enable EDW.

Existing DW Resources:50. ERIS – future? At this time there is no clear direction or strategy – depends on the future role of the Enable EDW.51. Enable EDW – initial focus was capital reporting and there is a need to review current state and determine and gaps that impact future uses.52. Various data warehouses are still in operation and these have not been supported for a number of years and some business areas that use the data are not

aware of these sources.53. No plans to decommission these unsupported data sources – should be built in as part of future initiative.54. These data sources have complicated data structures built up over time – therefore if change is required it becomes too difficult and work-arounds are in place

Current Reporting [& Tools] Situation:55. Executive reporting – are data sources fully understood?56. Executive reporting – inconsistencies within reports [eg different figures for what is supposed to be the same data].57. Executive reporting – currently not a report produced by a roll up – therefore cannot drill down to source data and reconciliation to source systems not as easy

as it should be.58. External reporting [eg by Will Rayner] – details put together by a different process and there are inconsistencies compared to Executive reporting.59. Bank has no consistent set of reporting tools – issues with balance between self service and flexibility & contro l .

Data Management - Potential Requirements:60. Doubts that we actually know what data is critical for each business decision.61. Need to have a balance between the disciplines imposed by a data architecture approach and the flexibility that business require for sourcing & using data.62. No clear definition of performance measurement for business areas therefore cannot drive reporting in required direction.63. MIRC – becoming business area data/reporting SMEs as the business does not have time to get into the required level of detail.64. Business areas do not have the tools and skills to support a degree of self help – therefore greater expectations of MIRC.65. Current approach is for new systems to create new databases with out an overarching architecture/roadmap – therefore lose potential to leverage these

solutions for other users.66. Cannot simply focus on data warehouses – take into account state of source systems and data capture; data stored in off the shelf systems and alignment to

the Bank’s definitions.

Learnings – Previous DW Initiatives:

High-level project tasksShort Term Activities:1. Determine the scope & requirements for implementation of a data

management function.2. Determine how much detail is required by Executive to prove the worth of the

initiative.3. Develop & document case for change [benefits/costs] and present this to

Executive.4. Identify a list of initiatives that can be undertaken in the short term to progress

the initiative.5. Define management/performance reporting requirements for Board; Executive

and BU Heads.6. Determine the gaps between current data availability and the requirements &

and the approachto close these gaps.

7. Determine the required approach to get this initiative into the organisational change process.

8. Define preferred options for the short term and longer term approach to establish the datamanagement function.

9. Analyse and document information on the various data bases/data sources/interfaces fromsource systems.

10. Review all current management reporting processes and define enhancements that can beundertaken in the short term [eg Board; Executive].

11. Define a road map of prioritised initiatives to develop a phased development & implementationstrategy for the required reporting approach [including review o f current state of EDW].

12. Determine & meet with all internal & external stakeholders to communicate the proposedapproach and potential timeframe.

13. Develop interim approach until DM team is established.14. Discuss and consolidate lessons learned/experiences from data management

initiatives [business & technical] undertaken previously in the Bank as well as appropriate external examples.

15. Review the existing EDW and data models to determine capability to meet current and futuredata/reporting requirements.

16. Determine how the data management function fits in with the ente rprise architecture approach.

17. Determine what reconciliations can be set up in the short term to enhance financial dataquality.

18. Analyse current Board & Executive management reports and remove inconsistencies.

19. Align data sources used for management reporting and external reporting.20. Analyse current reconciliation processes and ensure source syste ms are

reconciled to the datawarehouse/s.

Longer Term Activities:21. Develop initial functions required to be undertaken by the DM te am.22. Recruited/appoint staff to cover these DM functions.23. Clearly define the scope and agreed objectives pf the DM team.24. Define organisational position of the new DM function.25. Define critical success factors for the new DM function.26. Establish appropriate metrics to measure data quality [if it is not measured it

will not be managed].27. Determine approaches undertaken by other organisations and define best

practices to feed tofeed into Bank approach.

28. Map & document existing data flows; data stores and data bases.29. Define the potential benefits that can be achieved through imple mentation of

the advancedBasel II capital management approach.

30. Define strategy for existing data warehouses [including potential decommissioning].

31. Define approach to establish end user self service reporting & d ata sourcing function.

32. Define approach to automate standard or recurring reporting processes.33. Investigate approach to control ongoing creation of Access databases.

Dependencies1. Executive buy in and ongoing support of a data management function.2. Recognition at all levels of the organisation that this is not a trivial task – it is

a major undertaking that we have attempted previously and failed.3. Adequate resourcing of the data management function.4. Adequately skilled business staff to undertake the data management

function.5. Adequately skilled IT staff to support the data management function.6. Bank has the willingness to accept the cultural change required to support

the data management function.7. Need to fit this initiative in with the organisational change process8. Dependency - the Bank’s future data warehouse strategy.9. Dependency – the Bank’s future GL strategy & approach.

Vision: We have defined & implemented a data management function that supports the data supply & data quality requirements of the Bank and external entities.“Data Management”BUSINESS INTENT

WHERE DO WE WANT TO BE?B

WHAT DO WE NEED TO GET THERE?C

HOW DO WE MAKE IT HAPPEN?D

V0.2

Based on Meetings held 17.11.2009 & 18.11.2009 - Attendees [at one or both sessions]: R Fennell [part]; P Zeitz; A Woods; B Spears; M Bamford; R Tolladay; W Robertson; P Bancroft; D Boromeo; D Look; T Camporeale; M Wickett; L Groom; A Wat ts; S Lai [ S Brooks & K Bond - facilitators]

WHERE ARE WE NOW?A

In a nutshell …..

Page 3

The magic ingredients

•We are not IT•This is not a project this is a function (IM is not just for Christmas it’s for life)•We were an integral part if the F&T transformation program•Created a awareness of the issues and galvanised commitment to the cause

•Resources•Processes•Frameworks•Methodologies•Support agreements Data Analysts

Management Information Framework

Corporate Data Model

GovernancePolicy Glossary Engagement

(spread the word)KPI’s /

Measures

Consistent analysis and

reporting framework

Making information

accurate and accessible

Common language for data miners

Obligation and motivation to reportinformation issues

Bank wideData Governance

Bank wideBusiness Intelligence

Data WarehouseMRS

EDW

ERISEIS Mkt.D’base

Management Information Framework

Internal and External Customer Information Needs

Linx? ALM

Engagement(spread the

word)

•Resources•Processes•Frameworks•Methodologies•Support agreements

Risk

Rationalisation ofCorporate Data

Adl DW

Feedback

Change and Infrastructure

•Efficient decision making•Consistency of information at all levels

•Accurate regulatory reporting•Improved Performance Management •Timely production of results•Branch level financial performance •Customer/Product/Service contribution

How

Wha

tC

ontr

ol

Page 4

Information Management Functional AlignmentProviding: •strategic direction - transform our information management capability to improve all corporate internal and external customer information needs•clear functional responsibility and accountability•clarity of milestones•clarity of Interrelationship within and outside

SS D

SS C

SS B

SS E

LandingArea

(pulled)

Staging Area

(pulled)

(DDS)

Business Rules(Metadata)

Cap Ad.

SS A

Linx Norkom

corporate data model FutureCube?

FutureCube?

FutureCube?

AdhocReports

Static Reports

StrategicDirection

Analysis tool setGL

Other

CorporateData Store

Related

SAS

Version 3Version 3

Information Management CommitteeInformation Management Committee

Data GovernanceData Governance

BI CentreBI CentreDW Support and EnhancementDW Support and Enhancement(dedicated IT resources)(dedicated IT resources)

Business Intelligence Centre (11)Functions:

? Manager Business Intelligence

? Front Desk/Project Office

? Portal

? Essbase

? Consultancy/Business Partnering/Training

? Data Experts

? Report Development

Head of Information

Management

DW Support and Development (15)

Roles:

? DWSD Manager

? DW Support

? ETL Developers

? Data Architect

? Data Modeller

? DBA’s

? DW Architect

? Source System Experts

Data Governance (2)? Data Governance

Managers

? Deployed to support BU or Data Groupings

? Data Steward structure to leverage off Risk Team

Information Management Committee (IMC)? Executive Committee? Set strategic direction? Monitor progress? Monitor effectiveness of data management components? Set development priorities? Manage Information Management Risks

Resources continue to report to functional managers in IT and Change however their

daily tasks and direction is set by dotted line manager

Risk Business Partner Support

Data Governance Function to utilise Business Risk Partners Model for

support.

Information Management Functional Alignment