2. “Data Management” Vision: We have defined & implemented a data management function that supports the data supply & data quality requirements of the Bank and external entities.
BUSINESS INTENT V0.2
A WHERE ARE WE NOW? B WHERE DO WE WANT TO BE?
Current State Future State
Data 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 C WHAT DO WE NEED TO GET THERE? Data Quality - Issues:
1. We have an ongoing data quality management program in place to e nsure minimal errors at data capture.
code because ‘it works’ not because it is correct]. 2. We have communicated & achieved a balance between customer servic e & data quality backed up by system validation
2. Business focus is on customer service not necessarily data quality. where appropriate.
3. Issue with completeness; accuracy; and consistency of data entered into systems. Dependencies 3. We have addressed data accuracy issues with standard data entry rules & system validation where appropriate.
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; 4. The organisation has a greater commitment to the resolution of d ata issues.
street addresses; etc). 1. Executive buy in and ongoing support of a data management function. 5. We have created roles for the detection of data issues and provision of feedback to staff.
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]. 2. Recognition at all levels of the organisation that this is not a trivial task – it is 6. Our data quality approach includes feedback & staff education to highlight/rectify data entry issues.
6. No ownership or responsibility for detecting errors and providing the necessary feedback to staff. a major undertaking that we have attempted previously and failed. 7. We have a Bank-wide glossary of agreed definitions to ensure consistent use of terminology.
7. No organisational focus on resolution of data errors. 3. Adequate resourcing of the data management function. 8. Data quality – we have ascribed a business cost to the entry & ongoing use of poor quality data.
8. No common terminology – no single glossary or data dictionary to facilitate data sharing. 4. Adequately skilled business staff to undertake the data management
9. No one could say who is ultimately accountable for data quality & data integrity. function. Data Management – Governance:
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 5. Adequately skilled IT staff to support the data management function. 9. We have a clearly defined ownership & accountability structure for data in the organisation.
data; etc]. 6. Bank has the willingness to accept the cultural change required to support 10. We have a data governance process in place to assist with ongoing data quality.
11. Not sure if the impacts of ‘bad’ data are really appreciated. the data management function. 11. We have implemented the data management function as an appropriately staffed organisational support unit [not a
7. Need to fit this initiative in with the organisational change process project].
Data Management - Governance: 8. Dependency - the Bank’s future data warehouse strategy. 12. We have established a single point of ownership of data and reduced the incidence of data redundancy.
12. Board & Executive do not appear to have an understanding of thesignificant issues, frustration & associated costs caused by lack of data governance. 9. Dependency – the Bank’s future GL strategy & approach. 13. There is an organisation wide commitment, from Board & Executive down, to the adherence to data management
13. No review of data management processes or requirements since integration. principles.
14. No owner of data management. 14. We have an understanding of the benefits & cost savings to be achieved from a sound data management approach.
15. Need for governance/management approach to be determined – centralised or decentralised? 15. We have established clear responsibility & accountability for da ta management [data quality & data integrity].
16.
17.
Definition of data rules required – who owns them [the organisation or each department]?
No single point of ownership for data – also have multiple stores and views for same piece of data.
D HOW DO WE MAKE IT HAPPEN? 16. The Data Management group has the authority to enforce data quality initiatives.
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 Data Management – Controls:
themselves and not take an organisational view. High -level project tasks 17. Data/information is readily accessible to those staff authorised to source it.
19. Data – no one at Executive level appears to want to own it or take accountability since the merger 18. Our reporting states the data sources used for the report and any data transformation or consolidations required to
20. Data management – is not a significant part of anyone’s role/responsibility – therefore is easily parked when other issues or priorities aris e. Short Term Activities: produce it.
1. Determine the scope & requirements for implementation of a data 19. We are confident in the data & information obtained from our DW and other certified sources.
Data Management - Controls: 20. We have a process of certification of trusted data sources.
management function.
21. Data accessibility – there is minimal control over sourcing and reusing data. 21. We have implemented system controls to assist with the input of quality data.
2. Determine how much detail is required by Executive to prove the worth of the
22. Short term approach – there is a need for additional controls [eg do not allow any more customer data extracts for new databases]. 22. We have implemented manual & systematic validation/reconciliation processes to ensure the ongoing integrity of the
initiative.
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]. Bank’s data resources.
3. Develop & document case for change [benefits/costs] and present this to
24. Ad hoc requests for reporting are often difficult to deliver as data is not available or accessible. 23. We have audit trails in place to assist with tracking changes to data of high importance.
Executive.
4. Identify a list of initiatives that can be undertaken in the short term to progress
[Lack of] Data Management- Impacts: the initiative. Data Management – Impacts:
25. General lack of confidence in the organisation’s d a t a . 5. Define management/performance reporting requirements for Board; Executive 24. We are confident that our existing data sources & interfaces are correct/validated.
26. APRA expectations regarding and ADI’s management of data. 25. We are confident our business decisions are based on adequate & trusted data.
and BU Heads.
27. There are occasions when the Bank has provided customers with incorrect data. 26. We have clearly defined responsibility for managing data integrity on an ongoing basis.
6. Determine the gaps between current data availability and the requirements &
28. Current situation a result of multiple mergers and inconsistent incorporation of data [mapping not always correct]. 27. We have implemented an approach to reduce current levels of multiple data sources and associated data redundancy.
and the approach
29. There is a cost associated with data issues [eg over compensating capital; inability to securitise loans; inability to identify appropriate risks]. 28. Our data management approach has been developed taking into account the requirements of both internal and external
to close these gaps.
30. Risk of data privacy issues as data is propagated and lack of audit trail. 7. Determine the required approach to get this initiative into the organisational [eg APRA] stakeholders.
31. Data security issues as data is propagated [including data on laptops, etc that can be lost, etc]. change process. 29. Our data management approach has reduced the chance of the Bank providing customers with erroneous data.
32. Simple data requirements are not defined. Potential that business decisions are being based on ‘no data ’ or simple trend data. 8. Define preferred options for the short term and longer term approach to 30. We have reduced the risk of data privacy issues through enhanced rules & controls on the use of 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. 31. We have established a data management regime that takes into account the complexity of future information requests.
establish the data
34. Complexity of data & reporting requests is growing – and eventually become unsustainable with current processes, etc .
management function.
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 Business Processes:
9. Analyse and document information on the various data bases/data
integrity. 32. We have defined/achieved a balance between a controlled data approach and the business units ’ needs for flexibility &
sources/interfaces from
timely data.
source systems.
Business Processes: 10. Review all current management reporting processes and define enhancements 33. We have defined a range of data rules and their ownership [organisation wide or individual business unit].
36. There is a history of creating new databases/spreadsheets each time we cannot access data or source it quickly enough. that can be 34. We have a clear understanding of the performance drivers/measures for each business unit and can provide the data
37. Multiple databases across the Bank containing the same data, leading to data redundancy. undertaken in the short term [eg Board; Executive]. necessary to support the process.
38. No central source or catalogue to assist with accessing data from the most appropriate source in a timely manner. 35. We have the capability to service these data requirements.
11. Define a road map of prioritised initiatives to develop a phased development &
39. The role played by each part of the organisation in data management is unclear [particularly since the merger]. No one knows who is 36. We have developed a catalogue of trusted data resources for the business.
implementation
responsible for providing different information. 37. Our trusted data resources are fully reconciled and validated.
strategy for the required reporting approach [including review o f current state of
38. We have developed & communicated the role played by each part of the organisation in the ongoing data management
EDW].
Staffing: 12. Determine & meet with all internal & external stakeholders to communicate the function.
40. Do we have the skills in-house to progress with the data management approach? Require data architects; data modellers; business SMEs ; etc. proposed
41. Data analysts – more focus on number crunching than on analysis and providing insights. approach and potential timeframe. Staffing:
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 39. We have adequately trained staff involved with the data management function.
13. Develop interim approach until DM team is established.
equipped and coordinated? 40. Staff understand the value of our data resource and strive to maintain data quality.
14. Discuss and consolidate lessons learned/experiences from data management
43. Risk of losing good staff due to their frustration in dealing wit h manual processes. Also attempts to empower staff are thwarted by absence 41. We have overcome the staff frustration through the introduction of more automated processes, etc.
initiatives
of tools for exception reporting, etc – therefore revert to a more command approach. 42. Staff focus on analysis of the data not compilation activities.
[business & technical] undertaken previously in the Bank as well as appropriate
external examples.
Technical Aspects: 15. Review the existing EDW and data models to determine capability to meet Technical Aspects:
44. Multiple third party/vendor systems is an issue for data mapping. current and future 43. We utilise a corporate data model for all development initiative and view data as multipurpose not application specific.
45. We don’t understand how data flows from source systems to reporting output – there is no system overview or architecture detailing this. 44. Our corporate data model is governed within the enterprise architecture framework to manage reusability & redundancy.
data/reporting requirements.
46. There is no common definition of data fields/terminology – makes it difficult to consolidate data. 45. We have a record of the Bank’s current physical data architecture [data sources; data bases; data flows; etc].
16. Determine how the data management function fits in with the ente rprise
47. Multiple data dictionaries – no ‘official’ data dictionary understood and communicated. Leads to inconsistency. 46. We have established a single data dictionary.
architecture approach.
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 47. We are capable of easily integrating the data from 3rd party applications.
17. Determine what reconciliations can be set up in the short term to enhance
systems; etc].
financial data
49. Current position is multiple heritage data warehouses and minimal activity on the Enable EDW. quality. Existing DW Resources:
18. Analyse current Board & Executive management reports and remove 48. We have a corporate data warehouse that satisfies a significant proportion of the Bank’s management reporting
Existing DW Resources: inconsistencies. requirements.
50. ERIS – future? At this time there is no clear direction or strategy – depends on the future role of the Enable EDW. 49. A clear management strategy (including potential decommissioning) has been established for all existing data
19. Align data sources used for management reporting and external reporting.
51. Enable EDW – initial focus was capital reporting and there is a need to revie w current state and determine and gaps that impact future uses. warehouses.
20. Analyse current reconciliation processes and ensure source syste ms are
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 50. The ongoing management of the EDW has been resolved and suitably resourced.
reconciled to the data
aware of these sources. 51. Our data sources have data structures that are less complex and easily adapted to change.
warehouse/s.
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 Longer Term Activities: Reporting & Tools:
21. Develop initial functions required to be undertaken by the DM te a m . 52. We have suitable analytical/reporting tools to assist the business with self service reporting utilising the DW [including ad
Current Reporting [& Tools] Situation: hoc reporting requirements].
22. Recruited/appoint staff to cover these DM functions.
55. Executive reporting – are data sources fully understood? 53. We have a catalogue of current management reports used by the organisation.
23. Clearly define the scope and agreed objectives pf the DM team.
56. Executive reporting – inconsistencies within reports [eg different figures for what is supposed to be the same data]. 54. Our approach to recurrent reporting is largely automated.
24. Define organisational position of the new DM function.
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 55. Bank users also have access to appropriate self service reporting capability.
25. Define critical success factors for the new DM function.
as it should be. 26. Establish appropriate metrics to measure data quality [if it is not measured it 56. Our Board & Executive reporting are free of inconsistencies caus ed by the use of varied data sources.
58. External reporting [eg by Will Rayner] – details put together by a different process and there are inconsistencies compared to Executive reporting. will not be managed]. 57. Our external reporting is sourced from the same data as for inte rnal management reporting.
59. Bank has no consistent set of reporting tools – issues with balance between self service and flexibility & contr o l . 27. Determine approaches undertaken by other organisations and define best
practices to feed to Data Management Requirements:
Data Management - Potential Requirements: 58. The organisation can readily source the required information and data critical for business decisions
feed into Bank approac h.
60. Doubts that we actually know what data is critical for each busin ess decision. 59. We have a clear definition of performance management for each business area and can support the required level of
28. Map & document existing data flows; data stores and data bases.
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. reporting.
29. Define the potential benefits that can be achieved through imple mentation of
62. No clear definition of performance measurement for business areas therefore cannot drive reporting in required direction. 60. We have defined a short term & longer term approach to implementing a data management function.
the advanced
63. MIRC – becoming business area data/reporting SMEs as the business does not have time to get into the required level of detail. Basel II capital management approach. 61. We have determined a range of data management initiatives aligned with organisational requirements & priorities.
64. Business areas do not have the tools and skills to support a degree of self help – therefore greater expectations of MIRC. 30. Define strategy for existing data warehouses [including potential 62. Our data management framework is flexible enough to cater for fu ture data & reporting requirements.
65. Current approachon for new systems to create new databases with out an overarching architecture/roadmapP Zeitz; A Woods; B Spears; M Bamford; Rthese
Based is Meetings held 17.11.2009 & 18.11.2009 - Attendees [at one or both sessions]: R Fennell [part]; – therefore lose potential to leverage Tolladay; W Robertson; P Bancroft; D Boromeo; D Look; T Camporeale; M Wickett; L Groom; A Wat ts; S Lai [ S Brooks & K Bond63. We have a data management framework in place that is accessible to all staff [eg solution architects].
decommissioning]. - facilitators]
solutions for other users. 64. We have a framework that will facilitate the incorporation of additional systems [eg from M&A activities].
31. Define approach to establish end user self service reporting & d ata sourcing
66. Cannot simply focus on data warehouses – take into account state of source systems and data capture; dat stored in off the shelf systems and alignment to
a 65. We have established a continuous improvement environment that leverages each additional initiative.
function.
the Bank’s definitions. 66. We have a clear understanding of the management reporting/information requirements of the organisation.
32. Define approach to automate standard or recurring reporting processes.
33. Investigate approach to control ongoing creation of Access databases.
Learnings – Previous DW Initiatives: Learnings – Previous DW Initiatives:
3. In a nutshell …..
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
Page 3
4. Management Information Framework
Internal and External
Customer Information Needs
•Efficient decision making •Improved Performance Management •Timely production of results •Accurate regulatory reporting
•Consistency of information at all levels •Branch level financial performance •Customer/Product/Service contribution
Change and
Infrastructure Management Information Framework Risk
Consistent Making Common
Obligation and Engagement
analysis and information language
motivation to report (spread the
reporting accurate and for data
How
information issues word)
framework accessible miners
Bank wide
Business Intelligence
•Resources Data Warehouse
•Processes Adl DW MRS •Resources
•Frameworks •Processes
What
•Methodologies •Frameworks
Rationalisation of Feed
•Support agreements EIS Mkt.D’base ERIS •Methodologies
Data Analysts Corporate Data back
•Support agreements
Linx? ALM EDW
Control
Bank wide
Data Governance
Corporate Data Governance Engagement KPI’s /
Glossary
Model Policy (spread the word) Measures
Page 4
5. Information Management Functional Alignment
Providing:
•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
Information Management Committee
Other DW Support and Enhancement BI Centre
(dedicated IT resources)
SS A Business Rules
Str
Related (Metadata)
ate
SS B
gic
Landing Staging (DDS)
Area Area Corporate
SS C (pulled) (pulled) Future
Data Store Cap Ad.
Cube?
SS D corporate data model Future Future
Dir
Cube? Cube?
ect
SS E
ion
Analysis tool set
GL
Linx Norkom SAS
Static Adhoc
Reports Reports
Data Governance
Version 3
6. Information Information Management Committee (IMC)
? Executive Committee
Management ?
?
Set strategic direction
Monitor progress
Functional ? Monitor effectiveness of data management components
? Set development priorities
Alignment ? Manage Information Management Risks
Resources continue to report to functional Head of
managers in IT and Change however their
daily tasks and direction is set by dotted line
Information
manager Management
DW Support and Business Intelligence Centre (11) Data Governance (2)
Development (15) Functions: ? Data Governance
Roles: Managers
? Manager Business Intelligence
? Deployed to support BU or
? DWSD Manager ? Front Desk/Project Office
Data Groupings
? DW Support ? Portal
? Data Steward structure to
? ETL Developers ? Essbase leverage off Risk Team
? Data Architect ? Consultancy/Business Partnering/Training
? Data Modeller ? Data Experts
Data Governance Function to utilise
? DBA’ s ? Report Development Business Risk Partners Model for
support.
? DW Architect
? Source System Experts
Risk Business
Partner Support
![“Data Management” Vision: We have defined & implemented a data management function that supports the data supply & data quality requirements of the Bank and external entities.
BUSINESS INTENT V0.2
A WHERE ARE WE NOW? B WHERE DO WE WANT TO BE?
Current State Future State
Data 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 C WHAT DO WE NEED TO GET THERE? Data Quality - Issues:
1. We have an ongoing data quality management program in place to e nsure minimal errors at data capture.
code because ‘it works’ not because it is correct]. 2. We have communicated & achieved a balance between customer servic e & data quality backed up by system validation
2. Business focus is on customer service not necessarily data quality. where appropriate.
3. Issue with completeness; accuracy; and consistency of data entered into systems. Dependencies 3. We have addressed data accuracy issues with standard data entry rules & system validation where appropriate.
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; 4. The organisation has a greater commitment to the resolution of d ata issues.
street addresses; etc). 1. Executive buy in and ongoing support of a data management function. 5. We have created roles for the detection of data issues and provision of feedback to staff.
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]. 2. Recognition at all levels of the organisation that this is not a trivial task – it is 6. Our data quality approach includes feedback & staff education to highlight/rectify data entry issues.
6. No ownership or responsibility for detecting errors and providing the necessary feedback to staff. a major undertaking that we have attempted previously and failed. 7. We have a Bank-wide glossary of agreed definitions to ensure consistent use of terminology.
7. No organisational focus on resolution of data errors. 3. Adequate resourcing of the data management function. 8. Data quality – we have ascribed a business cost to the entry & ongoing use of poor quality data.
8. No common terminology – no single glossary or data dictionary to facilitate data sharing. 4. Adequately skilled business staff to undertake the data management
9. No one could say who is ultimately accountable for data quality & data integrity. function. Data Management – Governance:
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 5. Adequately skilled IT staff to support the data management function. 9. We have a clearly defined ownership & accountability structure for data in the organisation.
data; etc]. 6. Bank has the willingness to accept the cultural change required to support 10. We have a data governance process in place to assist with ongoing data quality.
11. Not sure if the impacts of ‘bad’ data are really appreciated. the data management function. 11. We have implemented the data management function as an appropriately staffed organisational support unit [not a
7. Need to fit this initiative in with the organisational change process project].
Data Management - Governance: 8. Dependency - the Bank’s future data warehouse strategy. 12. We have established a single point of ownership of data and reduced the incidence of data redundancy.
12. Board & Executive do not appear to have an understanding of thesignificant issues, frustration & associated costs caused by lack of data governance. 9. Dependency – the Bank’s future GL strategy & approach. 13. There is an organisation wide commitment, from Board & Executive down, to the adherence to data management
13. No review of data management processes or requirements since integration. principles.
14. No owner of data management. 14. We have an understanding of the benefits & cost savings to be achieved from a sound data management approach.
15. Need for governance/management approach to be determined – centralised or decentralised? 15. We have established clear responsibility & accountability for da ta management [data quality & data integrity].
16.
17.
Definition of data rules required – who owns them [the organisation or each department]?
No single point of ownership for data – also have multiple stores and views for same piece of data.
D HOW DO WE MAKE IT HAPPEN? 16. The Data Management group has the authority to enforce data quality initiatives.
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 Data Management – Controls:
themselves and not take an organisational view. High -level project tasks 17. Data/information is readily accessible to those staff authorised to source it.
19. Data – no one at Executive level appears to want to own it or take accountability since the merger 18. Our reporting states the data sources used for the report and any data transformation or consolidations required to
20. Data management – is not a significant part of anyone’s role/responsibility – therefore is easily parked when other issues or priorities aris e. Short Term Activities: produce it.
1. Determine the scope & requirements for implementation of a data 19. We are confident in the data & information obtained from our DW and other certified sources.
Data Management - Controls: 20. We have a process of certification of trusted data sources.
management function.
21. Data accessibility – there is minimal control over sourcing and reusing data. 21. We have implemented system controls to assist with the input of quality data.
2. Determine how much detail is required by Executive to prove the worth of the
22. Short term approach – there is a need for additional controls [eg do not allow any more customer data extracts for new databases]. 22. We have implemented manual & systematic validation/reconciliation processes to ensure the ongoing integrity of the
initiative.
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]. Bank’s data resources.
3. Develop & document case for change [benefits/costs] and present this to
24. Ad hoc requests for reporting are often difficult to deliver as data is not available or accessible. 23. We have audit trails in place to assist with tracking changes to data of high importance.
Executive.
4. Identify a list of initiatives that can be undertaken in the short term to progress
[Lack of] Data Management- Impacts: the initiative. Data Management – Impacts:
25. General lack of confidence in the organisation’s d a t a . 5. Define management/performance reporting requirements for Board; Executive 24. We are confident that our existing data sources & interfaces are correct/validated.
26. APRA expectations regarding and ADI’s management of data. 25. We are confident our business decisions are based on adequate & trusted data.
and BU Heads.
27. There are occasions when the Bank has provided customers with incorrect data. 26. We have clearly defined responsibility for managing data integrity on an ongoing basis.
6. Determine the gaps between current data availability and the requirements &
28. Current situation a result of multiple mergers and inconsistent incorporation of data [mapping not always correct]. 27. We have implemented an approach to reduce current levels of multiple data sources and associated data redundancy.
and the approach
29. There is a cost associated with data issues [eg over compensating capital; inability to securitise loans; inability to identify appropriate risks]. 28. Our data management approach has been developed taking into account the requirements of both internal and external
to close these gaps.
30. Risk of data privacy issues as data is propagated and lack of audit trail. 7. Determine the required approach to get this initiative into the organisational [eg APRA] stakeholders.
31. Data security issues as data is propagated [including data on laptops, etc that can be lost, etc]. change process. 29. Our data management approach has reduced the chance of the Bank providing customers with erroneous data.
32. Simple data requirements are not defined. Potential that business decisions are being based on ‘no data ’ or simple trend data. 8. Define preferred options for the short term and longer term approach to 30. We have reduced the risk of data privacy issues through enhanced rules & controls on the use of 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. 31. We have established a data management regime that takes into account the complexity of future information requests.
establish the data
34. Complexity of data & reporting requests is growing – and eventually become unsustainable with current processes, etc .
management function.
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 Business Processes:
9. Analyse and document information on the various data bases/data
integrity. 32. We have defined/achieved a balance between a controlled data approach and the business units ’ needs for flexibility &
sources/interfaces from
timely data.
source systems.
Business Processes: 10. Review all current management reporting processes and define enhancements 33. We have defined a range of data rules and their ownership [organisation wide or individual business unit].
36. There is a history of creating new databases/spreadsheets each time we cannot access data or source it quickly enough. that can be 34. We have a clear understanding of the performance drivers/measures for each business unit and can provide the data
37. Multiple databases across the Bank containing the same data, leading to data redundancy. undertaken in the short term [eg Board; Executive]. necessary to support the process.
38. No central source or catalogue to assist with accessing data from the most appropriate source in a timely manner. 35. We have the capability to service these data requirements.
11. Define a road map of prioritised initiatives to develop a phased development &
39. The role played by each part of the organisation in data management is unclear [particularly since the merger]. No one knows who is 36. We have developed a catalogue of trusted data resources for the business.
implementation
responsible for providing different information. 37. Our trusted data resources are fully reconciled and validated.
strategy for the required reporting approach [including review o f current state of
38. We have developed & communicated the role played by each part of the organisation in the ongoing data management
EDW].
Staffing: 12. Determine & meet with all internal & external stakeholders to communicate the function.
40. Do we have the skills in-house to progress with the data management approach? Require data architects; data modellers; business SMEs ; etc. proposed
41. Data analysts – more focus on number crunching than on analysis and providing insights. approach and potential timeframe. Staffing:
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 39. We have adequately trained staff involved with the data management function.
13. Develop interim approach until DM team is established.
equipped and coordinated? 40. Staff understand the value of our data resource and strive to maintain data quality.
14. Discuss and consolidate lessons learned/experiences from data management
43. Risk of losing good staff due to their frustration in dealing wit h manual processes. Also attempts to empower staff are thwarted by absence 41. We have overcome the staff frustration through the introduction of more automated processes, etc.
initiatives
of tools for exception reporting, etc – therefore revert to a more command approach. 42. Staff focus on analysis of the data not compilation activities.
[business & technical] undertaken previously in the Bank as well as appropriate
external examples.
Technical Aspects: 15. Review the existing EDW and data models to determine capability to meet Technical Aspects:
44. Multiple third party/vendor systems is an issue for data mapping. current and future 43. We utilise a corporate data model for all development initiative and view data as multipurpose not application specific.
45. We don’t understand how data flows from source systems to reporting output – there is no system overview or architecture detailing this. 44. Our corporate data model is governed within the enterprise architecture framework to manage reusability & redundancy.
data/reporting requirements.
46. There is no common definition of data fields/terminology – makes it difficult to consolidate data. 45. We have a record of the Bank’s current physical data architecture [data sources; data bases; data flows; etc].
16. Determine how the data management function fits in with the ente rprise
47. Multiple data dictionaries – no ‘official’ data dictionary understood and communicated. Leads to inconsistency. 46. We have established a single data dictionary.
architecture approach.
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 47. We are capable of easily integrating the data from 3rd party applications.
17. Determine what reconciliations can be set up in the short term to enhance
systems; etc].
financial data
49. Current position is multiple heritage data warehouses and minimal activity on the Enable EDW. quality. Existing DW Resources:
18. Analyse current Board & Executive management reports and remove 48. We have a corporate data warehouse that satisfies a significant proportion of the Bank’s management reporting
Existing DW Resources: inconsistencies. requirements.
50. ERIS – future? At this time there is no clear direction or strategy – depends on the future role of the Enable EDW. 49. A clear management strategy (including potential decommissioning) has been established for all existing data
19. Align data sources used for management reporting and external reporting.
51. Enable EDW – initial focus was capital reporting and there is a need to revie w current state and determine and gaps that impact future uses. warehouses.
20. Analyse current reconciliation processes and ensure source syste ms are
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 50. The ongoing management of the EDW has been resolved and suitably resourced.
reconciled to the data
aware of these sources. 51. Our data sources have data structures that are less complex and easily adapted to change.
warehouse/s.
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 Longer Term Activities: Reporting & Tools:
21. Develop initial functions required to be undertaken by the DM te a m . 52. We have suitable analytical/reporting tools to assist the business with self service reporting utilising the DW [including ad
Current Reporting [& Tools] Situation: hoc reporting requirements].
22. Recruited/appoint staff to cover these DM functions.
55. Executive reporting – are data sources fully understood? 53. We have a catalogue of current management reports used by the organisation.
23. Clearly define the scope and agreed objectives pf the DM team.
56. Executive reporting – inconsistencies within reports [eg different figures for what is supposed to be the same data]. 54. Our approach to recurrent reporting is largely automated.
24. Define organisational position of the new DM function.
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 55. Bank users also have access to appropriate self service reporting capability.
25. Define critical success factors for the new DM function.
as it should be. 26. Establish appropriate metrics to measure data quality [if it is not measured it 56. Our Board & Executive reporting are free of inconsistencies caus ed by the use of varied data sources.
58. External reporting [eg by Will Rayner] – details put together by a different process and there are inconsistencies compared to Executive reporting. will not be managed]. 57. Our external reporting is sourced from the same data as for inte rnal management reporting.
59. Bank has no consistent set of reporting tools – issues with balance between self service and flexibility & contr o l . 27. Determine approaches undertaken by other organisations and define best
practices to feed to Data Management Requirements:
Data Management - Potential Requirements: 58. The organisation can readily source the required information and data critical for business decisions
feed into Bank approac h.
60. Doubts that we actually know what data is critical for each busin ess decision. 59. We have a clear definition of performance management for each business area and can support the required level of
28. Map & document existing data flows; data stores and data bases.
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. reporting.
29. Define the potential benefits that can be achieved through imple mentation of
62. No clear definition of performance measurement for business areas therefore cannot drive reporting in required direction. 60. We have defined a short term & longer term approach to implementing a data management function.
the advanced
63. MIRC – becoming business area data/reporting SMEs as the business does not have time to get into the required level of detail. Basel II capital management approach. 61. We have determined a range of data management initiatives aligned with organisational requirements & priorities.
64. Business areas do not have the tools and skills to support a degree of self help – therefore greater expectations of MIRC. 30. Define strategy for existing data warehouses [including potential 62. Our data management framework is flexible enough to cater for fu ture data & reporting requirements.
65. Current approachon for new systems to create new databases with out an overarching architecture/roadmapP Zeitz; A Woods; B Spears; M Bamford; Rthese
Based is Meetings held 17.11.2009 & 18.11.2009 - Attendees [at one or both sessions]: R Fennell [part]; – therefore lose potential to leverage Tolladay; W Robertson; P Bancroft; D Boromeo; D Look; T Camporeale; M Wickett; L Groom; A Wat ts; S Lai [ S Brooks & K Bond63. We have a data management framework in place that is accessible to all staff [eg solution architects].
decommissioning]. - facilitators]
solutions for other users. 64. We have a framework that will facilitate the incorporation of additional systems [eg from M&A activities].
31. Define approach to establish end user self service reporting & d ata sourcing
66. Cannot simply focus on data warehouses – take into account state of source systems and data capture; dat stored in off the shelf systems and alignment to
a 65. We have established a continuous improvement environment that leverages each additional initiative.
function.
the Bank’s definitions. 66. We have a clear understanding of the management reporting/information requirements of the organisation.
32. Define approach to automate standard or recurring reporting processes.
33. Investigate approach to control ongoing creation of Access databases.
Learnings – Previous DW Initiatives: Learnings – Previous DW Initiatives:](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)