DATA GOVERNANCE BLACKICE INC. REFERENCE PRESENTATION FEBRUARY 2017

Transcription

1 DATA GOVERNANCE BLACKICE INC. REFERENCE PRESENTATION FEBRUARY 2017

2 DATA GOVERNANCE

3 RISKS AND CHALLENGES FACED BY INSTITUTIONS 3 Financial instittions are nder increasing pressre and obligation to nderstand the complexity of the Basel Committee for Banking Spervision (BCBS) and regional gidance to ensre the risk management environment meets these expectations and that of their Board. The primary challenges faced by most instittions relate to their inability to: - Calclate/Assess; - Aggregate; - Link; - Consolidate; and - Report/calclate all risk exposres across their instittion. Reporting across all bsiness lines and portfolios mst ensre:? - Accracy; - Integrity; - Comprehensiveness; and - Completeness of data in a timely manner.

4 RISK IS COMPLEX BASEL FUNCTIONAL DIAGRAM 4

5 MAIN DRIVERS OF THE PROBLEM FOR AN INSTITUTION 5 Undefined Data Ownership at the Enterprise Level Single View of Client and Relationship to Exposres Data Qality Data Aggregation End-to-end data element identification Inconsistent or Inaccrate Reporting Complex and Comprehensive Reglatory Reqirements Inadeqate Strctre or Framework for Data

6 GOALS OF RISK DATA AGGREGATION 6 Risk Data Governance Metadata Management Risk Data Qality Processes Implement Data Policies Update Data Standards Create Bsiness Data Ownership Prioritize Data Isses Implement Risk Data Taxonomies Inventory Risk Logical Data Models Docment Risk Data Bsiness Process Flows Docment & Display Lineage Create Design Standards & Gidelines Update/Create Risk Data Qality Rles and Policies Update & Implement Risk Data Controls Monitor Data Qality Rles People Data Stewards/Governance Teams Involve Enterprise Data Architects Data Modelers Risk Data Stewards & Data Analysts Soltion Architects Storage / Technical Architects DBAs Data Stewards/Governance Team Data Qality Services Teams Enterprise Architectre DBAs Technology Engage Data Modeling Tools Update Data Modeling Libraries Use Design / CASE Tools Create Risk Metadata Repository Update Archiving Tools Review Storage management and Hardware Update Risk Technical Architectre Employ DQ Tools & Reports/Dashboards Perform Data Profiling, Qality, Enrichment and Monitoring Tools Investigate ETL Tools Create Adit Reports Policies and Procedres Policies and Standards relate to Governance metrics Data Domain Ownership Change Management Policies and Standards relate to: Data modelling, inclding standards arond: Conceptal data models Logical data models Physical data models Policies and Standards relate to How best to define target qality for respective data How best to achieve Data Qality targets How best to sstain targeted Data Qality

7 10 PRINCIPLES OF SUSTAINABLE GOVERNANCE 7 1. Exective Mandate Adopting a sstainable data cltre and getting people to behave is impossible withot top-of-the-hose mandate. 6. Data Qality Data qality is the goal bt it is dependent on bsiness reqirements and measred against what sers have at stake in their decisions. 2. Serve Bsiness Captre, verify and re-verify bsiness reqirements to align strategy with operational goals. 3. Process Understanding Data is a manfactred prodct and we mst nderstand how data flows from process-to-process. 4. Core Data Elements Not all data elements are created eqal. Do the bsiness forensics to identify critical attribtes. Accontability is mandatory! 5. Standards Standards are essential for data harmonization. They are the key to integration. They will nify the indstry. They are the legacy of the financial crisis. 7. Data Meaning Unambigos shared meaning of the obligations of the contract is the key to everything. Learn to love the word ontology. 8. Athoritative Sorce Every reference data element mst have a single athoritative sorce and all copies mst be identical to the athoritative sorce. 9. Edcation is Essential Data content management is a new concept for everyone. It is not the same thing as data processing management. 10. Collaboration Data is fondational. It brings people from diverse areas together and promotes data collaborative sharing - a shared vision.

8 DATA IMPLICATIONS TO ICAAP 8 Instittion ICAAP Risk Appetite How confident are yo that yor data and reporting spports senior management expectations and reglatory reporting reqirements for ICAAP? Internal Controls Stress Testing Reglatory Oversight & Governance Capital Planning Risk Qant Data What governance strctre is in place to define data reqirements for accracy, timeliness, and completeness? Pillar II Risks Pillar I Risks What validation is performed against data for accracy, timeliness, and completeness?

9 DOES YOUR INSTITUTION HAVE DATA ISSUES 9 Do yo nderstand the impact of IT projects across the entire organization, or only with systems with direct relationships (i.e., one-step removed)? Do yo know who owns the data, is there a central grop that will drive changes, or does each bsiness nit determine their own priorities? Do yo know how accrate the data is, are they confident that all reports reflect the same information? Is there a data strategy, is it at a enterprise or a bsiness-level strategy? How comprehensive is the data framework and data policies to spport the approach and to meet senior management expectations? Has the instittion identified Mandatory Risk Data from origination to reporting/calclation? Has the instittion identified controls to ensre accracy for Mandatory Risk Data? What validation/monitoring is performed on data qality? Cold yo potentially have these isses? ALCo reports being generated sing incorrect data. The data dictionary was incomplete, and the bsiness thoght the data was real-time/crrent and was the same vale as the book of record. Reports being generated by two different grops for different prposes, bt the vales for the same period did not match. Neither grop cold determine which was the correct vale.

10 BENEFITS OF HOLISTIC DATA 10 Improved Decision Making Improved speed at which information is available Improved qality of strategic planning Improved ability to manage risks Enhanced management of information across the instittion Redced probability of losses reslting from weak risk management

11 RISK DATA FRAMEWORK

12 GUIDING PRINCIPLES 12 Giding Principle Description Robst Governance and Clarity of Roles and Responsibilities Roles and responsibilities for risk data and risk reporting management and practices mst be clearly defined, established, and commnicated across the three lines of defense. Risk Data Viewed as a Strategic Asset of the Bank Strategic Investment in Risk Data and Risk Report Technology Independent Validation in Place for Risk Data Aggregation and Risk Reporting Practices Risk data and risk reporting practices mst be well defined and consistently implemented across the Bank Risk data management approach mst be aligned with the overall enterprise data strategy. Robst controls and governance to validate risk data and risk report integrity mst be in place. Investments in technologies and initiatives shold be bilt in a manner that it is easily scalable across mltiple bsiness areas (i.e. risk areas) Roles and responsibilities for validation fnctions mst be separate and independent from the bsinesses and be part of the second line of defense

13 FRAMEWORK OVERVIEW 13 As part of a robst risk data and reporting framework, the following key components shold be developed to provide effective governance and oversight for risk data aggregation and risk reporting practices: Governance and Organizational Strctre Risk Data Management Processes Risk Report Management Processes Independent Validation Infrastrctre & Tools Governance and Organization Strctre (Committees, Policies, Roles and Responsibilities) Risk Data Management Data Reconciliation Data Qality Service Level Agreements Data Processes and Docmentation Risk Report Management Report Processes and Docmentation Report Qality Design and Rationalization Service Level Agreements Independent Validation Infrastrctre & Tools Risk Data Dictionary Risk Data Hierarchy Risk Data Sorces Standards Risk Data and Reporting

14 RISK DATA MANAGEMENT PROCESSES 14 To ensre that risk data is accrate, complete, timely, and adaptable, an instittion shold develop key risk data management processes that need to be in place for all risk data. These processes are the responsibility of a Data Management Office, who will ensre the consistent implementation and exection of these core risk data practices throghot the risk niverse. The RDRG Framework and its spporting policies and standards otline the approach and reqirements for these key risk data processes that each risk area needs to meet. Key risk data management processes inclde: Risk data reconciliation processes Risk data qality processes inclding methodology and approach Service level agreements for risk data processes Risk data processes and docmentation

15 RISK REPORT MANAGEMENT 15 To spport effective and timely risk analysis and risk-based bsiness decisions, the instittion mst identify and develop an approach to risk report management that needs to be established. This approach is the responsibility of a specific grop, who will ensre the consistent implementation and exection of these core risk reporting practices throghot the risk niverse via a federated operating model. Key risk report management processes inclde: Risk report processes and docmentation Risk report qality processes Risk report design and rationalization Service level agreements for risk report processes

16 INDEPENDENT VALIDATION 16 To ensre that risk data aggregation and risk reporting practices are in alignment with defined reqirements, an independent validation fnction is necessary. As part of the independent validation fnction, the following shold be defined: Approach and process to provide independent validation of all risk data aggregation and risk reporting practices across the Bank against defined reqirements Process to assess and report non-compliance isses and deficiencies to the appropriate stakeholders Process to monitor and track all identified isses and deficiencies to ensre that all risk areas acknowledge and remediate their respective isses and deficiencies in a timely manner

17 ORGANIZATIONAL STRUCTURE AND GOVERNANCE 17 To establish robst risk data and risk reporting governance and oversight to spport effective risk data aggregation and risk reporting practices, a centralized organizational strctre arond risk data and risk reporting is necessary. The strctre helps to ensre that risk data and risk reporting practices are consistent and aligned with the reqirements in this framework and its spporting docments.

18 RISK DATA MANAGEMENT STANDARD APPROACH 18 Risk Data Lifecycle Risk Data Lifecycle Standards otline the detailed reqirements applicable to a risk data and the technology and processes spporting the risk data elements. Risk Data Service Level Agreements The Risk Data SLA Standard provides reqirements for the establishment and oversight risk data. Risk Data Process and Docmentation This incldes reqirements that ensre risk data processes are formally docmented and all process docmentation are kept crrent, commnicated efficiently and well nderstood. Risk Data Reconciliation The Risk Data Reconciliation Standard defines the reconciliation processes for all risk data processes.

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