Role of analytics in delivering
health information to help fight
cancer in Australia
Katerina Andronis
(Deloitte, Melbourn...
Background
 Challenges in Health Information Management is about appropriate

data governance that is; using, analysing a...
Health Sector
 The health sector is one of the most complex
organisations of any industry.

 The entire clinical overlay...
Big Data in Health
 Data is primarily created in the context of individual
service delivery processes and is often fragme...
Data Governance – Selected Definitions
The data management association
Data governance is the exercise of authority and co...
Data governance – common
themes
Data is an asset
Key point

There is nothing new here.
These statements are fairly
self-ev...
Data Governance
 What is data governance in this context?

 Is data governance being done?
 The business imperatives
 ...
Basis of informal research
(IAIDQ)

Strategy and governance

Definition

Strategy
We have defined and implemented an
infor...
Preliminary Results
Largely complete
Key point

Data governance
is not a
significant
capability within
health care
provide...
The information challenge for
health care providers
Key point

Much of the work in these
areas is process- and
system- rel...
Health service output functional footprint
Many areas are involved directly and indirectly in delivery of funded health ca...
Strategic
Alignment

Capacity planning

Service delivery
management

Capacity planning

Patient Care Services
utilisation ...
Health service output costing functional footprint
Many areas directly incur costs, or incur costs that are attributed to ...
Strategic
Alignment

Capacity planning

Service delivery
management

Capacity planning

Patient Care Services
utilisation ...
Compliance requirements and standards
Privacy Act (state based variations)
Regulates how personal information is handled (...
Data Governance framework - overview


An effective Data Governance framework
includes the following elements:

Principl...
Cascading dependencies across framework
Guiding principles
Foundational capabilities required to achieve excellence in Dat...
Organisation overview – Key Actions
• Establish functional organisation capability to manage, deliver,
and ensure quality ...
Policies overview – Key Actions
• Translate guiding principles into
pragmatic, actionable and measurable
organisational ob...
Standards & processes overview – Key Actions
• Define compliance and monitoring standards, including
frequency of auditing...
Technology overview
• An appropriate area of the organisation should provide Information
Architecture representation withi...
Conceptual data governance model

Managing Information
as a Strategic Asset
Governance Framework

Organisation

Policies

...
Conceptual data governance model - typical current status

Managing Information
as a Strategic Asset
Governance Framework
...
Organisational capabilities in more detail
Functional capability

Description

Basis

Executive sponsor(s)

• Facilitates ...
Operating view of the data governance organisation

Managing Information
as a Strategic Asset
Governance Framework

Organi...
Managing Information
as a Strategic Asset

Data governance organisation in health care provider

Governance Framework

Org...
Data governance RACI model
This table shows a view of functional responsibilities across the data governance areas

Master...
The $64,000 question – justifying the exercise
Attempting to justify a complete whole-of-enterprise data governance exerci...
Business value model
Consider using a value driver model of some kind that allows business functions to be viewed in
terms...
Some guidelines for tackling data governance
• There is no magic bullet for data governance (or the data quality that is a...
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Role of Analytics in Delivering Health Information to help fight Cancer in Australia

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Voices 2014

Role of Analytics in Delivering Health Information to help fight Cancer in Australia

Katerina Andronis,
Deloitte Consulting, Australia and Chandana Unnithan,
Deakin University, Australia


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  • Chandana – to introduce and open for Katerina to explain the sector – brief discussion to clarify.Explain the background more – Katerina – Australia sector, codification, not integrated
  • Katerina to talk through examples of health organisations.
  •    One major cancer organisation in the US are using a data concierge model to manage and create rich data assets for delivery of world class cancer research and treatments.  The data concierge model includes data custodians, data stewards, business users and application services all integrated into a process and facilitation environment that supports the creation of quality and useable data. Most of the people who are in these roles a clinical informatics professionals which is a key enabler to ensure the data that is created has meaning and usefulness.
  • Role of Analytics in Delivering Health Information to help fight Cancer in Australia

    1. 1. Role of analytics in delivering health information to help fight cancer in Australia Katerina Andronis (Deloitte, Melbourne) & Chandana unnithan (Deakin University)
    2. 2. Background  Challenges in Health Information Management is about appropriate data governance that is; using, analysing and understanding health data is managed properly so everyone is on the “same page” when accessing and using data (Only oranges, not apples and oranges!)  Robust management of an organization’s data assets is a mixed bag in the health environment - there are areas of good structured codified data and other unstructured valuable data that cannot be used for data mining, management and research opportunities.  Australian Health sector is complex and not well integrated. Healthcare funding, delivery and management are changing – these rely critically on information management.  Detailed asset utilization management and strong financial analysis are necessary to understand service costs and optimize revenue. This is critical for financial viability under an Activity Based Funding regime which is currently codified and provides accurate and rich information March 7, 2014 2 2
    3. 3. Health Sector  The health sector is one of the most complex organisations of any industry.  The entire clinical overlay with its obvious importance and potential for impact tends to overshadow other information governance perspectives.  This may support the creation and use of clinical data for clinical purposes but generally does not address nonclinical information domains or properly manage clinical information to support broader use.  Most health organisations have various degrees of data governance and are currently coming to grips of the importance and the impact of a lack of data governance March 7, 2014 3 3
    4. 4. Big Data in Health  Data is primarily created in the context of individual service delivery processes and is often fragmented and not sufficiently well formed to support the required analysis.  Data is a “lateral asset” spanning multiple functional areas. It is used for multiple purposes across the health care organisation and in ways that may not be known or seem important in the context of where the data is created.  Effective management is challenging, especially considering that this “lateral” characteristic does not necessarily align well with organisational management arrangements. March 7, 2014 4 4
    5. 5. Data Governance – Selected Definitions The data management association Data governance is the exercise of authority and control (planning, monitoring and enforcement) over the management of data assets. International association for information and data quality The management and control of data as an enterprise asset. “Governance” is what information management is mostly all about. Information management is the process by which those who set policy guide those who follow policy. Governance concerns power and applying an understanding of the distribution and sharing of power to the management of information technologies. (from Paul A. Strausmann’s work circa 2001). Providing management and control over enterprise information assets in order to harness maximum value. 5
    6. 6. Data governance – common themes Data is an asset Key point There is nothing new here. These statements are fairly self-evident and we believe they would be generally accepted as valid. So conceptually it is not difficult – the challenge is in giving form and substance to the concepts 6 So data is recognised as having value but the more common perspective is that poor data quality causes harm (rather than data being valued per se). Governance involves authority and control Governance therefore involves people with appropriate authority and there must be defined control processes through which governance can be exercised. It can be inferred that there should be appropriate standards which the controls are intended to achieve. There should be an understood and agreed purpose For data governance to be meaningful (and to help understand when enough has been done) there must be a known purpose. This should be well defined with a clear means of assessing compliance.
    7. 7. Data Governance  What is data governance in this context?  Is data governance being done?  The business imperatives  A data governance framework  Data governance in a health care provider  Valuing and tackling data governance 7
    8. 8. Basis of informal research (IAIDQ) Strategy and governance Definition Strategy We have defined and implemented an information quality strategy designed to manage information as an asset Standards We have defined information quality principles, policies, and standards that are used to guide decisions and actions affecting information quality Management We have implemented a data governance model covering key roles and responsibilities, formalised accountability, established decision rights, and identified channels for management actions related to managing our data Environment and culture Accountability We have defined accountabilities for information quality across all functions throughout the organisation Data governance is the definition and exercise of authority and control over data assets encompassing the entire data lifecycle(creation, storage, access, use, archiving, disposal). Measurement and monitoring Education We actively educate management and staff regarding data quality and our approach to managing it Measurement There are well defined data quality standards, and associated reporting Process Personnel can readily access the information they need to understand data quality requirements and processes related to their jobs Monitoring Achievement of data quality standards is actively monitored and compliance is an accountable element of job responsibilities Sustaining information quality Question responses Projects Data quality implications are actively and appropriately addressed during computer application implementation or upgrade projects 8 Operations Information quality is explicitly built into our business operations, processes and systems 1. Haven’t started to do this 2. Have made a start but it’s early days 3. Significant progress but not complete 4. Have largely completed and embedded Based loosely on IAIDQ framework
    9. 9. Preliminary Results Largely complete Key point Data governance is not a significant capability within health care providers Significant progress Started Haven’t started 9
    10. 10. The information challenge for health care providers Key point Much of the work in these areas is process- and system- related, but sustainable capability and improvement requires the allied data governance to be in place to ensure that data is defined and captured correctly and can be reliably used for accounting, management and analysis purposes. Activity based funding pays for the health service outputs delivered at an established “ efficient price” which requires diligent counting/billing processes and an accurate knowledge of service cost. So, from a revenue perspective • Need to manage patients and services with a view to best revenue alignment and reduced revenue leakage within obligation framework and acceptable practices • Need to code promptly and accurately under appropriate guidelines • Need to identify and rectify individual DQ problems not “code around” them or rectify on best-efforts basis after the fact. … and from a cost perspective • Ideally need to understand cost per service instance • Need detailed current service profitability reporting and trended reporting • Need to understand cost driver and levers. Then there are the rules • Significant funding, regulatory and compliance requirements. 10
    11. 11. Health service output functional footprint Many areas are involved directly and indirectly in delivery of funded health care outputs. Specialty Clinical Service Delivery Cathlab & HDU & Cardiology ICU IPU Services Services Theatre Services Mgmt Medical & Rapid Women’s/ Surgical Assessment Children’s & Inpatient Medical Paediatric Services Unit Services Mgmt Services Mgmt Mental Health Community Inpatient Services Services Mgmt Mgmt Cancer IPU Services Inpatient Ambulatory Services Services Mgmt Mgmt Hotel Services Care Delivery Support Services Federal Government Research Networks Reporting Directories Pharmacy Services Pathology Services Patient Information & Health Records Management Specialist Consulting Services Imaging services Integration Patient ID PCEHR Payments Corporate Network Transport Services Allied Health Services Interpreter Services Transcription/ Typing Services Specialist Testing Services Transit Lounge Services Education & Research Services Document Management Service Library Services Data Extraction, Storage & Analysis Service State Dept. Health Network Finance system Clinical system Identity Resources & Appointment Management Research Collaboration Services Publishing Services Research Unit Specific Services Professional Development & Education Services Simulation Services External Clinical Knowledge Sources Customer Mgmt Clinical Care Management Clinical Knowledge Theatre Clinical Care Mgmt Risk Management & Continuous Improvement Patient Management Critical Care Management Care Planning & Management MediHotel Internal Clinical Knowledge Sources 3 Party Testing Services Sub-Acute Mental Health Patient & Client Management Home & Emergency Community Out-patient Management Based Care Mgmt Services (HIH) rd Inpatient Appointment & Access Management Satellite Site & Partner Access Remote Clinical Access Quality Services Emergency Clinical Services Management Real-time Tracking Critical Care Hospital Based Access Audit Services Ambulatory Services Public Health Access Incident Reporting & Management External & Onsite Access Home Access Real-time Data Access & Capture GPs, Specialists, MultiDisciplinary Teams & Tele-medicine Patients & Citizens Clinical Reporting (Audit, Risk & Performance) Remote monitoring Tele-medicine Operational Support Services Biomedical Engineering Services Cleaning Services Building & Engineering Maintenance Management Manage Material Distribution & Logistics Laundry Services GP/Specialist Procurement Patient & Staff Food Services Clinical Operations Support Equipment Distribution & Management Mortuary Services Mail Room & Courier Services Waste Services Reception & Switchboard Management Customer Service Management Business Support Services Internet Volunteer & Fundraising Services Manage Accounting & Financial Decision Support Manage Capital & Risk Human Resource Management Manage Capital Projects Manage Payroll Manage Staff Rostering Other Network Not health service output funded 11 Health service output funded Procure Materials & Services ICT Management Plan & Manage Business Business/ Statutory Reporting & Analysis Security Services Management Legal Services Management
    12. 12. Strategic Alignment Capacity planning Service delivery management Capacity planning Patient Care Services utilisation analysis Patient care costing analysis Clinical Care Support utilisation analysis Clinical Care costing analysis Casemix funding Capacity planning Capacity planning Business and capital planning Service delivery management Service delivery management Service delivery management Non-clinical Care utilisation analysis Operation Support utilisation analysis Inventory management Research project management Rostering and workload analysis Incident reporting and management Cost analysis Costing analysis Costing and funding analysis Education service delivery Remuneration analysis Quality monitoring and analysis Revenue analysis Capital project management Training and accreditation Risk management Asset management Accreditation compliance Patient flow analysis Personnel management Human resources Audit and risk Financial management Costing analysis Casemix funding Education and research strategy Workforce capacity planning Audit/risk mgt planning Financing strategy Capital analysis Dimensions Decision Support Health service output revenue information footprint Information Groups and Subjects Patient care services Clinical care support Non-clinical care support Operational support Business support Education and research Demographic data Clinical results Appointments Operational support mgt data Volunteer and fundraising Education content Employees Risk data General Ledger Inpatient episodic data Medication data Care delivery support resources Operational support services Procurement Education events and records Recruitment and Terminations Incident data Revenue Billing data Medical images Care delivery support events Customer service data Capital projects Research projects Payroll Audit data Costing Ambulatory event data Clinical information Supply and inventory Security management data Research data Rostering Project Accounting Engineering and maintenance Legal services data Publications Credentialing Asset accounting Clinical coding data Employee performance Patient Administration Data sources Patient Admin System Patient billing ICD coding Client management Community care Emergency department management Clinical care delivery support Pathology Radiology Business support Audit & Risk Biomedical engineering Laundry Waste management Human Resources Security Services Financial Risk Building and engineering maintenance Supply Research Customer service management Rostering Procurement Capital Project Quality Library Cleaning services Mortuary Call Management Payroll Legal Services Asset Audit Food services Equipment management Credentialing Volunteer & Fundraising Publishing Theatre Mental Health Resource scheduling Anaesthetic Operational support Education & Research Transport management Medication management 12 Non-Clinical care delivery support Specialist clinical Dictation system Document management
    13. 13. Health service output costing functional footprint Many areas directly incur costs, or incur costs that are attributed to the delivery of health service outputs Specialty Clinical Service Delivery Cathlab & HDU & Cardiology ICU IPU Services Services Theatre Services Mgmt Medical & Rapid Women’s/ Surgical Assessment Children’s & Inpatient Medical Paediatric Services Unit Services Mgmt Services Mgmt Mental Health Community Inpatient Services Services Mgmt Mgmt Cancer IPU Services Inpatient Ambulatory Services Services Mgmt Mgmt Hotel Services Care Delivery Support Services Federal Government Research Networks Reporting Directories Pharmacy Services Pathology Services Specialist Consulting Services Imaging services Integration Patient ID PCEHR Payments Patient Information & Health Records Management Resources & Appointment Management Corporate Network Allied Health Services Interpreter Services Transcription/ Typing Services Specialist Testing Services Transit Lounge Services Education & Research Services Document Management Service Library Services Data Extraction, Storage & Analysis Service Research Collaboration Services State Dept. Health Network Finance system Clinical system Identity Transport Services Research Unit Specific Services Publishing Services Professional Development & Education Services Simulation Services External Clinical Knowledge Sources Customer Mgmt Clinical Care Management Clinical Knowledge Theatre Clinical Care Mgmt Internal Clinical Knowledge Sources Patient Management Critical Care Management Care Planning & Management MediHotel Risk Management & Continuous Improvement Sub-Acute Mental Health Patient & Client Management Home & Emergency Community Out-patient Based Care Management Services Mgmt (HIH) 3rd Party Testing Services Inpatient Appointment & Access Management Satellite Site & Partner Access Remote Clinical Access Quality Services Emergency Clinical Services Management Real-time Tracking Critical Care Hospital Based Access Audit Services Ambulatory Services Public Health Access Incident Reporting & Management External & Onsite Access Home Access Real-time Data Access & Capture GPs, Specialists, MultiDisciplinary Teams & Tele-medicine Patients & Citizens Clinical Reporting (Audit, Risk & Performance) Remote monitoring Tele-medicine Operational Support Services Biomedical Engineering Services Cleaning Services Building & Engineering Maintenance Management Manage Material Distribution & Logistics Laundry Services Patient & Staff Food Services GP/Specialist Procurement Clinical Operations Support Equipment Distribution & Management Mortuary Services Mail Room & Courier Services Waste Services Reception & Switchboard Management Customer Service Management Business Support Services Internet Volunteer & Fundraising Services Manage Accounting & Financial Decision Support Manage Capital & Risk Human Resource Management Manage Capital Projects Manage Payroll Manage Staff Rostering Procure Materials & Services Other Network Other cost element 13 Direct cost element Indirect cost element ICT Management Plan & Manage Business Business/ Statutory Reporting & Analysis Security Services Management Legal Services Management
    14. 14. Strategic Alignment Capacity planning Service delivery management Capacity planning Patient Care Services utilisation analysis Clinical Care Support utilisation analysis Patient care costing analysis Clinical Care costing analysis Casemix funding Capacity planning Capacity planning Business and capital planning Service delivery management Service delivery management Service delivery management Non-clinical Care utilisation analysis Operation Support utilisation analysis Inventory management Research project management Rostering and workload analysis Incident reporting and management Cost analysis Costing analysis Costing and funding analysis Education service delivery Remuneration analysis Quality monitoring and analysis Revenue analysis Capital project management Training and accreditation Risk management Asset management Accreditation compliance Patient flow analysis Personnel management Casemix funding Costing analysis Education and research strategy Workforce capacity planning Audit/risk mgt planning Financing strategy Capital analysis Dimensions Decision Support Service costing information footprint Information Groups and Subjects Patient care services Clinical care support Non-clinical care support Operational support Business support Education and research Human resources Audit and risk Financial management Demographic data Clinical results Appointments Operational support mgt data Volunteer and fundraising Education content Employees Risk data General Ledger Inpatient episodic data Medication data Care delivery support resources Operational support services Procurement Education events and records Recruitment and Terminations Incident data Revenue Billing data Medical images Care delivery support events Customer service data Capital projects Research projects Payroll Audit data Costing Ambulatory event data Clinical information Supply and inventory Security management data Research data Rostering Project Accounting Engineering and maintenance Legal services data Publications Credentialing Asset accounting Clinical coding data Employee performance Patient Administration Data sources Patient Admin System Patient billing ICD coding Client management Community care Emergency department management Clinical care delivery support Pathology Radiology Business support Audit & Risk Biomedical engineering Laundry Waste management Human Resources Security Services Financial Risk Building and engineering maintenance Supply Research Customer service management Rostering Procurement Capital Project Quality Library Cleaning services Mortuary Call Management Payroll Legal Services Asset Audit Food services Equipment management Credentialing Volunteer & Fundraising Publishing Theatre Mental Health Resource scheduling Anaesthetic Operational support Education & Research Transport management Medication management 14 Non-Clinical care delivery support Specialist clinical Dictation system Document management
    15. 15. Compliance requirements and standards Privacy Act (state based variations) Regulates how personal information is handled (captured, accurately maintained, stored, used, disclosed and disposed of) Freedom of Information Act 1982 Framework for access to information held by the state or institutions. Burden of proof falls on the information provider to explain non-provision. Australian Council on Healthcare Standards (ACHS) EQuIP5 Accreditation standard for private and public hospitals. The non-mandatory information (system) related standards are: Key point Data governance appears to be largely confined to a direct response to mandatory compliance areas 2.3 Information management systems enable the organisation’s goals to be met. 2.3.1 Health records management systems support the collection of information and meet the consumer / patient and organisation’s needs. 2.3.2 Corporate records management systems support the collection of information and meet the organisation’s needs. 2.3.3 Data and information are collected, stored and used for strategic, operational and service improvement purposes. 2.3.4 The organisation has an integrated approach to the planning, use and management of information and communication technology (I&CT). Health Level 7 (HL7) Messaging specification for clinical and administrative information to enable (system interoperability. Adopted by the Australian health sector and e-health community, and software providers as a data transfer standard. International Statistical Classification of Diseases and Related Health Problems (ICD-10 Standard for codifying diseases, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or diseases. Mandatory and fundamental to billing, costing and Activity Based Funding. 15
    16. 16. Data Governance framework - overview  An effective Data Governance framework includes the following elements:  Principles: Define the high level objectives (mission statements) for Data Management  Organisation: Defined roles and responsibilities, defined accountability for meeting objectives, strong executive leadership & commitment from Business/IT stakeholders  Policies: Translation of the guiding principles into pragmatic, actionable and measurable organisational objectives including adherence to standards, monitoring and continuous improvement  Standards and processes: Standards promote common terminology and data definitions across the enterprise, including quantitative metrics for data quality Processes provide procedural direction over how the Governance organisation will operate  16 Technology: Technology tools and practice capability that enable definition, execution and compliance measurement of data governance policies, standards and processes.
    17. 17. Cascading dependencies across framework Guiding principles Foundational capabilities required to achieve excellence in Data Management Provides the high level objectives for DM Policies Provides traceable requirements to principles Policies The mechanism for translating guiding principles into pragmatic, actionable and measurable organisational objectives which will deliver upon the overarching objectives of DM Informs how & when policies will be executed and compliance measured Direct the formalisation of standards to achieve an outcome Standards Define the minimum requirements for data management, including data definition, quality and control standards to deliver upon the policies of the organisation. Provides the boundaries for the processes to achieve the standards Inform how the minimum standards can be met Processes Define the “how to” aspect of standards, detailing the logical sequence of tasks to achieve and measure the standards and policies set out by the organisation. 17
    18. 18. Organisation overview – Key Actions • Establish functional organisation capability to manage, deliver, and ensure quality data • Enable the business areas to move beyond a project or program centric approach to an enterprise data management capabilities • Establish Data Owners that are accountable for decision making, monitoring and continuous improvement of data assets • Establish Data Working Teams, comprised of information specialists within each data domain to drive development of enterprise policies, standards and processes for effective management of data Promote global enterprise stewardship of information in accordance with defined data policies, standards and processes. Managing Information as a Strategic Asset Governance Framework Organisation Policies Standards & Processes 18 Founded on Principles Technology
    19. 19. Policies overview – Key Actions • Translate guiding principles into pragmatic, actionable and measurable organisational objectives • Develop and document minimum expectations for the management of data, such as security, data quality and controls • Identify and document roles within the organisation which will be tasked to comply with these policies, in addition to those who will monitor the adherence to them • Provide the framework for the creation of standards and processes, ensuring clear traceability to guiding principles. Managing Information as a Strategic Asset Governance Framework Organisation Policies Standards & Processes Founded on Principles 19 Technology
    20. 20. Standards & processes overview – Key Actions • Define compliance and monitoring standards, including frequency of auditing • Develop enterprise terms and definitions for data, promoting common structures, codification rules and naming conventions • Define data quality standards • Define security, accessibility and control standards • Develop processes to effectively govern data throughout its lifecycle, including clear guidelines and accountability for change control, issue management, impact assessment and communications Managing Information as a Strategic Asset Governance Framework • Develop processes for formal compliance measurement of governance mechanisms. Organisation Policies Standards & Processes Founded on Principles 20 Technology
    21. 21. Technology overview • An appropriate area of the organisation should provide Information Architecture representation within the Data Governance Organisation. This representation is essential to ensure compliance with enterprise information management frameworks and standards • There is also a need to provide tools and technology practice capability to the Data Organisation to enable analysis, documentation, data quality assessment and compliance measurement • There are key enabling information management capabilities that are important to achieving and sustaining excellence in Data Management (pictured right). Some of these may fall outside of the domain of the Data Governance Organisation • Strategy development and robust governance functions around all enabling information management capabilities should be defined at the Enterprise level. Managing Information as a Strategic Asset Key point The data governance organisation and capabilities should integrate with, rather than overlay, business as usual processes and key functions required for broader information and technology management Governance Framework Organisation Policies Standards & Processes Founded on Principles 21 Technology
    22. 22. Conceptual data governance model Managing Information as a Strategic Asset Governance Framework Organisation Policies Standards & Processes Technology Founded on Principles Data governance Governance organisation capabilities Data owners Support functions Executive sponsor(s) Governance lead Technology and change/comms specialists Governance working teams “Information specialists” Business data stewardship 22
    23. 23. Conceptual data governance model - typical current status Managing Information as a Strategic Asset Governance Framework Organisation Policies Standards & Processes Technology Founded on Principles Data governance Governance organisation capabilities Usually missing Skills challenged (outside technology) Data owners Business as usual Support functions Executive sponsor(s) Governance lead Governance working team “Information specialists” Business data stewardship 23 Technology and change/comms specialists
    24. 24. Organisational capabilities in more detail Functional capability Description Basis Executive sponsor(s) • Facilitates the setting of strategic direction for data management • Provide visible executive and senior management support for the Data Governance. Demand driven Data owners • Data owners from across process and business unit lines - leaders for each data subject area • Reviews and approves required policies, standards and processes • Clear accountability for all aspects of data governance for their subject area. Demand driven Governance lead • Responsible for the day to day operation of the data organisation, across subject area, process and business unit boundaries • Custodian of all data governance processes from definition through to execution and continuous improvement. Full time Governance working team • Serves as information specialists for their data subject • Group that defines and executes corporate data policies, standards and processes • Provides recommendations for continuous improvement and monitoring. Demand driven Support functions Business stewardship 24 • • • • Supports in creating data management policies, standards and processes Ensures consistency and quality across different types of data. Create and administrate the Issue Tracking and Resolution process Establish a program to effectively communicate data governance to the business and technology community, including supporting training activities • Designs and implements regular compliance reviews against policies and standards. • Business personnel who maintain the data in the systems • Enters and maintains the data following established data standards, polices, and procedures • Manage operational based issues and conduct initial impact assessment. Part/full time shared Part/full time shared
    25. 25. Operating view of the data governance organisation Managing Information as a Strategic Asset Governance Framework Organisation Provides executive support and budget approval Data Ownership functions are expected to have infrequent day-to-day involvement however will be accountable for their data subjects Data owner Data owner Governance lead Escalation of issues that cannot be resolved by the Working Team Issue resolution and endorsement of developed governance mechanisms Provides guidelines and task direction Facilitate impact assessment and issues management processes Support functions Governance working teams Data quality mgt Subject area Subject area Subject area Proposes new ideas and policies, standards and processes Work together to develop standards, analyse issues and manage data issues Metadata mgt Change management Issue management Measure and communication compliance with standards Support issue management process Raises Issues and concerns Business data stewardship Data steward community Data Stewards Data Stewards Data Stewards 25 Contributes to a body of knowledge Technology Request for budget and status reports Data owners Data owner Standards & Processes Founded on Principles Executive sponsor(s) The domain data management function have delegated authority to make agreed decisions as authorised by the data owners Policies Subject processes Subject processes Subject processes Provide tools and technology capabilities Information technology
    26. 26. Managing Information as a Strategic Asset Data governance organisation in health care provider Governance Framework Organisation Policies Standards & Processes Technology Founded on Principles CXX Governance working teams relate to the information groups from the information model shown earlier. Seek to keep core teams smaller and engage other people as required Provides executive support and budget approval Request for budget and status reports Data owners should be selected on the basis of relevance to information groups and subjects within them as well as having a suitable level of authority to make and ratify related decisions Data owners Health information manager Business manager(s) Operations managers Quality manager The domain data management function have delegated authority to make agreed decisions as authorised by the data owners Finance manager Education & research manager HR manager Governance lead Escalation of issues that cannot be resolved by the Working Team Governance working teams Issue resolution and endorsement of developed governance mechanisms Facilitate impact assessment and issues management processes Provides guidelines and task direction Data Ownership functions are expected to have infrequent day-to-day involvement however will be accountable for their data subjects Support functions Patient and clinical care Non-clinical care support Operational support Business support Education & research Human resources Audit and risk Finance Data quality mgt Work together to develop standards, analyse issues and manage data issues Proposes new ideas and policies, standards and processes Existing roles operating with Raises Issues suitable principles, and concerns standards and training supported by systems and Data steward community issue resolution processes Data Stewards Data Stewards Data Stewards 26 Metadata mgt Change management Issue management Measure and communication compliance with standards Support issue management process Business data stewardship Patient care processes HR processes Finance processes Contributes to a body of knowledge etc Provide tools and technology capabilities Information technology
    27. 27. Data governance RACI model This table shows a view of functional responsibilities across the data governance areas Master data subjects Executive sponsor(s) Policies Standards Processes Executive sponsor is accountable for mandating the MDM Governance Organisation charter and ensuring its effective execution Governance lead C R R A Data ownership A A A R Domain data management R R R C Data quality management C C C C Metadata management C C C C Comms and change management I C C C Issues management I I I C Data stewardship R C C I R: Responsible | A: Accountable | C: Consulted | I: Informed 27
    28. 28. The $64,000 question – justifying the exercise Attempting to justify a complete whole-of-enterprise data governance exercise on the basis of principles will fail (at least experience shows that this is very unlikely to be approved or to succeed). Opportunities to consider include: Key point Attach data governance initiatives to something that already matters and is on the executive agenda • Responding to a problem that has occurred. The danger here is that this has a strong tendency to remain narrowly focused on a one-time approach to addressing the effect of the problem rather than fixing the cause and preventing further occurrences by institutionalising the changes required • Focusing on a single important data subject area and deliberately establishing only a basic capability to contain scope, cost and risk. However, it may still be difficult to justify in terms of benefits unless there significant known issues so focus in information imperative areas • Leveraging a systems project that will require data migration to justify the effort required to handle data related work soundly and transition the management capability into business as usual. In other words, incubate the capability in the context of a project and sustain beyond the project • Searching through the benefits in business cases of substantial approved projects for benefits that are strongly data-linked and unlikely to deliver the planned benefit unless there is an appropriate effort put into managing associated data quality. This can expose “business value at risk” or benefit shortfall. Generally the cost of addressing the required data governance is much less than the project and can be positioned as a worthwhile supplementary activity • Align with process improvement initiatives which almost always have a significant relationship with data. Process improvement is usually short lived or limited if the associated data governance is not also addressed as part of the change. 28
    29. 29. Business value model Consider using a value driver model of some kind that allows business functions to be viewed in terms of the business value they produce and therefore confers a corresponding level of importance on the data that these functions need to operate effectively and efficiently. Data value can then be linked to the business value delivered by the function. 29
    30. 30. Some guidelines for tackling data governance • There is no magic bullet for data governance (or the data quality that is a common goal) • Have a framework (such as the one we have presented) to provide some context for whatever activity is undertaken so that there is some leverage and convergence over time Key point Build to a framework but deploy in small focused steps with an emphasis on ensuring changes, monitoring and management are integral to business as usual. It is better to have modest complete portions than a broad based initiative that remains incomplete. • Build the data governance arrangements into the business as usual organisation or they will evaporate • Execute modestly scoped activities in areas that are a clear priority • While it is reasonable to have a plan in mind, recognise that you should only move forward at a pace and to an extent where there is support and relevance. Expansion is likely to cease when enough has been done which is certain to be well short of implementing a completely comprehensive approach to data governance across the enterprise • Take steps with permanence in mind. A once-of improvement in data quality achieves little over the longer term if the required governance is not assimilated going forward • Data governance is not a spectator sport – it is a team participation sport. It can only take place effectively when people understand the purpose, know that they are on the team and know what there role is • The adage that “what gets measured gets managed” is true when bit comes to data quality. Even very basic data quality reporting helps instil it as a relevant business activity • Keep it simple – for example just go for a basic data dictionary in the first instance • Implement the governance organisation as data subjects are addressed • Implement data quality reporting as subject areas are addressed. 30

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