SMU ACCT673 Data Governance and Quality Lecture 1 PDF

ACCT673 Data Governance and Quality Introductions Introductions to Irene Liu Contact : +65 9873 2234 Email : [email protected] Data Governance and Quality - SMU Accounting Masters Program What it means for you to get your course credit 1. Class Participation - 1...

ACCT673 Data Governance and Quality Introductions Introductions to Irene Liu Contact : +65 9873 2234 Email : [email protected] Data Governance and Quality - SMU Accounting Masters Program What it means for you to get your course credit 1. Class Participation - 15% Attendance monitoring is required. Students are encouraged to participate and proactively ask questions. You are also encouraged to participate in all quizzes. 1. Individual assessment - 45% 2 individual short essay assignments - 20% 5 MCQ pop quizzes - 25% 1. Group project - 40% We will deploy continuous assessments throughout the term. This includes class participation, progress assessment (including case discussions, presentations, individual assignment, quiz, mid-term exam, etc.), group projects, and final examination (this course has no final exam). Data Governance and Quality - SMU Accounting Masters Program Data Governance and Quality Lecture Series Business aspects of data management Regulations, industry guides, Lecture 1 : Overview of data management in practice best practices, roles, set up, Lecture 2 : Data management strategy org structure, data culture Lecture 3 : Data management organisation Data governance Components of data Lecture 4 : Data governance and what it entails governance, and all various Lecture 5 : All about data management documentation forms of data documentation Data quality Data quality dimensions, Lecture 6 : Data quality - Part 1 controls, rules, monitoring, Lecture 7 : Data quality - Part 2 remediation Data architecture & trends Data taxonomy, lineage, Lecture 8 : Data architecture & technology architecture tools, data ethics, data on Lecture 9 : Emerging trends on data management cloud, analytics Lecture 10 : Recap : What have you learnt? Lecture structure Reading Pop Individual assignment Lecture series materials quiz 1 - Overview - 25 Nov 2 - Strategy - 28 Nov 3 - Data organisation - 30 Nov 4 - Data governance - 2 Dec 5 - Data documentation - 5 Dec 6 - Data quality (1) - 7 Dec 7 - Data quality (2) - 9 Dec 8 - Data architecture - 12 Dec 9 - Emerging trends - 14 Dec 10 - Group presentation - 16 Dec Data Governance and Quality - SMU Accounting Masters Program Project work Structure : Each group to be between 4 to 5 students in a group, and a short personal reflection by each student. Questions : 1. How may you maximise the use of structured data currently collected within your organisation? In your response, please state the industry and geographies the organisation is in, the data collected, ways in which you propose to maximise the use of data and provide a cost-benefit analysis. Determine the data risks involved and any mitigating actions to be taken and by whom. 2. It is said that data is the key enabler of innovation and digitalisation, for example Generative AI. Critically examine the importance of good quality data in supporting these initiatives. Provide clear examples of the type of data that support the type of innovation and the financial values they bring. Additionally, determine the roles and responsibilities of various parties involved to ensure the quality of the data in your selected use case for innovation. Also, determine the pitfalls of poor quality data and the potential causes of these, and how they should be remediated. Data Governance and Quality - SMU Accounting Masters Program  Overview of data management in practice 1 Brief History of Data Management Data Governance and Quality - SMU Accounting Masters Program Data - the new currency of the universe… but... Data Governance and Quality - SMU Accounting Masters Program Data governance & quality - the miracle pill ★ Improves data quality risks against reputation loss, and reduce risk of regulatory fines ★ Understanding the data processing can lead to streamlining of processes ★ It improves the overall usability of the data ★ Improved insights and decision making ★ Recognising data is an asset Data Governance and Quality - SMU Accounting Masters Program  Class Discussion What are the benefits of having data governance? The Bakery Analogy The simple analogy of a bakery can help illustrate the key components of data management... Good quality ingredients and Data Definition and Quality clear specification of Standards, processes and policies all need to ingredients help drive established to support the manufacture of consistent consistency. data quality and trusted financial reporting. Notes : 1. All ingredients are defined in terms of exact specifications, quantity, quality. Reduced inconsistencies reduce likelihood of poor outputs. 2. In real world terms, we define the data in exactly the definition, format, quantum, timing needed. 3. Must have common taxonomies and definitions (i.e. gross vs. net exposure) Data Governance and Quality - SMU Accounting Masters Program The Bakery Analogy The simple analogy of a bakery can help illustrate the key components of data management... A standard cookbook that Policies, Processes documents which ingredients Common data models, golden sources and well- are required, in what quantities, documented processes and assumptions are required with what tolerances and the for consistency, in both BAU and stress and crisis steps to combine, mix, bake reporting. etc. Notes : 1. Business process and rules, for example, the SOPs to obtain reporting numbers for a regular reporting period vs SOPs to obtain ad hoc reporting numbers on a stress / crisis situation 2. Clear assumptions, limitations recorded 3. Repeatable process to ensure consistency, comparability and sustainability of reporting Data Governance and Quality - SMU Accounting Masters Program The Bakery Analogy The simple analogy of a bakery can help illustrate the key components of data management... In a bakery, you would need Monitoring Controls and Oversight to ensure that the bakers sift, Formal processes and controls need to be established mix and churn ingredients in to ensure that data is extracted, transformed, the same way for consistent, aggregated and reported in the right way. Even with good-quality products clear data definitions and business rules, you run the risk of obtaining inconsistent results unless the processes are adhered to. Notes : 1. Clear process (with checks and reviews) is necessary to ensure consistency and quality of desired outcomes 2. Independent validations (and internal and external audit checks) to ensure adherence to policies and guidelines, and model validation teams to ensure no unauthorised changes to data models Data Governance and Quality - SMU Accounting Masters Program The Bakery Analogy The simple analogy of a bakery can help illustrate the key components of data management... Doesn’t matter how good the Technology Architecture ingredients are or how good Even with established standards, common definitions the recipe is – if the oven and high-quality data, differences in the technology doesn’t work, the bread will infrastructure could impact the ability to produce the not be of high quality. right outputs in a timely fashion. Notes : 1. Which of the processes are manual? 2. Are calculations performed under a controlled environment? 3. Data must be extracted and reconciled for each system hop 4. Technology capabilities to serve requirements - eg real-time data, ability to integrate (...think legacy systems...) Data Governance and Quality - SMU Accounting Masters Program  Class If you are asked to Discussion govern Singapore’s data governance, what would you do? From the regulators’ desk - BCBS 239 Principles Risk data aggregation and reporting practices subject to strong governance arrangements 1. Governance Processes fully documented Overarching Board and senior management should be fully aware governance & Integrated data taxonomies and architecture should be established across the bank infrastructure 2. Data architecture Data Dictionaries and Golden sources should be in place such as Single identifiers and/or & IT infrastructure unified naming conventions Data ownership roles and responsibilities clearly defined Controls on Risk data should be as robust as those applied to Finance data 3. Accuracy & Integrity Risk data should be reconciled with bank’s sources, including accounting data Appropriate balance of automated and manual processes All material risk exposures included (including off-balance sheet) 4. Completeness Risk Data Data should be available by business line, legal entity, asset type, industry, region, etc. Aggregation Aggregated Risk information should be produced on a timely basis 5. Timeliness Risk systems should be capable of producing critical risk data rapidly during times of stress/crisis Capability to produce a range of on-demand, ad hoc risk reporting, including: stress/crisis 6. Adaptability situations, requests due to changing internal needs and requests to meet supervisory queries Data Governance and Quality - SMU Accounting Masters Program From the regulators’ desk - BCBS 239 Principles Automated, manual edit and reasonable checks in place 7. Reporting Integrated procedures for identifying, reporting and explaining data errors via exceptions Accuracy reports 8. Reports should identify emerging risk concentrations and provide a forward looking Comprehensiveness assessment of risk Risk information reported in a clear and concise manner Risk 9. Clarity and Meaningful information tailored to the needs of the recipients Reporting Usefulness An appropriate balance between qualitative and quantitative Required frequency of reporting defined and routinely tested 10. Frequency In stress/crisis situations all relevant and critical reports are available within a very short period of time 11. Distribution Reports distributed to relevant parties while ensuring confidentiality is maintained Supervisory review, tools 13. Remedial actions and supervisory 12. Review 14. Home/host cooperation & measures cooperation Data Governance and Quality - SMU Accounting Masters Program 19  Class What is the difference Discussion between data management and data governance? Introducing DAMA-BOK and EDM Council’s DCAM DAMA International is dedicated to advancing the The EDM Council is the Global Association created to concepts and practices of information and data elevate the practice of Data Management as a business and management and … to address information and data operational priority… advocate for the development and management needs. The Data Management Body implementation of Data Standards, Best Practices. The of Knowledge (DAMA-DMBOK2) presents a DCAM is the industry standard framework for data comprehensive view of the challenges, complexities, management. DCAM defines the scope of capabilities and value of effective data management. required to establish, enable and sustain a mature data management discipline. Data Governance and Quality - SMU Accounting Masters Program The Data management Capability Assessment Model 1. Data Strategy & Business Case 5. Data Quality Management Capabilities to define, prioritize, organize, Data profiling, DQ measurement, defect fund, and govern DM and how it is embedded management, root cause analysis, and data into the operations of the organization in remediation. alignment with the objectives and priorities 6. Data Governance 2. Data Management Program and Structure, lines of authority, roles & Funding Model responsibilities, escalation protocol, policy & Execution of program management, standards, compliance, and routines. stakeholder management, funding management, communications, training, 7. Data Control Environment performance measurement. Data operations, supply-chain management, cross-control function alignment, and 3. Business & Data Architecture collaborative technology architecture. Data models such as taxonomies and ontologies, as well as data domains, 8. Analytics Management metadata, and business-critical data. culture, skills, platform, and governance Business Architecture defines the business required to enable the organization to obtain processes. business value from analytics. Source : https://edmcouncil.org/page/aboutdcamreview 4. Data & Technology Architecture Architectural requirements of the business, Data Governance and Quality - SMU Accounting Masters Program data, and technology across the organization. The building blocks of good data governance Independent Assessment Continuous Improvement Independent assessment of data quality to provide reasonable assurance of data quality checks performed Monitoring & Reporting Remediation Ongoing Controls Data remediation plans (strategic & DQ metrics are captured, reported tactical) are being developed, and used to drive remediation prioritised and actioned upon Framework DQ Identification Controls & Policies & Assessment Environment Fundamentals DQ framework to publish enterprise Discipline over data management DQ for CDEs are profiled, DQ dimensions, roles & processes to be auditable and analysed, graded and catalogued. responsibilities over maintaining consistency over control points, Root cause analysis is performed. DQ. review, escalation and approval. Data Governance and Quality - SMU Accounting Masters Program The three lines of defence …. The data quality governance framework mirrors the three lines of defence. The Businesses are accountable for the quality of their data. The Group Data Management Office defines the overall structure of the program and reports status with a network of Data Governance Officers for Global Functions and Businesses overseeing implementation and monitoring adherence. Internal Audit has an independent validation role. Three Lines of Defence 1. Direct responsibility Business Management including Operations & Technology Responsible for day-to-day implementation and The line of business, operational and technology teams who must fulfil their monitoring of data standards. assigned data related functions through direct execution Preventive CDEs Remediation Key Indicators Controls 2. Governance & oversight Data Management Office Responsible for ensuring compliance, setting Accountable for ensuring compliance with Policy, aligning resources to organisational priorities, and aligning resources drive execution of data management activities to drive execution of data management Policy Policy and DQ Data activities. Compliance Standards Assessment Review Repository 3. Independent assurance Internal Audit Objective review and evaluation of the Provide objective assessment of the requirements effectiveness of the processes, internal outlined in the Data Management Policy controls and governance. Governance Process Controls Data Governance and Quality - SMU Accounting Masters Program Recommended reading materials 1. EDM Council 2023 Data Management Benchmarking Report 2. CBIRC guidelines : https://www.nortonrosefulbright.com/en/knowledge/publications/40650e51/guidelines-on- data-governance-for-banking-financial-institutions 3. Data management capability assessment model : https://edmcouncil.org/page/aboutdcamreview 4. DAMA-DMBOK Functional Framework: https://damadach.org/dama-dmbok-functional- framework/ 5. BCBS 239 Principles for effective risk data aggregation and risk reporting: https://www.bis.org/publ/bcbs239.pdf Data Governance and Quality - SMU Accounting Masters Program