"Sanitized" Engagement Deliverable: Data Governance Blueprint

General Ledger Data Governance Model

Created On: November 29, 2007Last Update On: November 29, 2007

Document Version 1.0

For Internal Distribution Only

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Contact InformationCompany Client CorporationDepartment Controller

Revision HistoryVersion Date Created/Modified by Summary of Changes or Remarks1.0 11/29/2007 Steven M. Morgen Creation of Initial Document2.0

Approval ListApprovers Name & Department Role Played Initials Date Remarks

Distribution ListRecipients Name & Department

Role Played Comments (If any)

Reviewer

Table of Contents

1. INTRODUCTION................................................................................................................................................4

1.1 Background of the G/L Data Governance Issue....................................................................................................41.2 The Purpose of This Solutions Document.............................................................................................................41.3 Summary.............................................................................................................................................................4

2. PROPOSED TECHNICAL APPROACH...........................................................................................................4

2.1 High Level Data Flows........................................................................................................................................4

1. Introduction

1.1 Background of the General Ledger (G/L) Data Governance Issue

Client has embarked on a multi-year effort to transform and update the technologies and processes supporting those core Financial Management and Accounting processes responsible for the Company’s management of its Books and reporting of its results. Its short term goal is to be compliant as an SEC (Regulation S-X and Release 33-8128 and SOX Sect. 409) accelerated filer while ensuring the complete, repeatable, trustworthiness of the information supplied through its required Financial Reporting (10Q, 10K, and 8K) . Its long term goal is to design and promulgate a global financial information management governance framework that will ensure consistent , Enterprise-level processes and controls across all its businesses that will both support, and link to, locally required practices

The starting point for this effort is the implementation of a new corporate-level General Ledger structure created in SAP that will replace the current, mostly manual, spreadsheet based compilation process. This process extends filing time upwards of 200% beyond the 20 day (10Q) and 45 day (10K) accelerated filer mandate. The information supplied is subject to constant revision resulting in fines, corporate emabarassment, mis-trust from the investment community, ultimately manifesting itself in a stock price that is not an accurate depiction of AIG’s current performance or future earnings potential.

1.2 The Purpose of This Solutions Document

This document provides a point of view (POV) on the role and value of Data Governance as a component of the Enterprise’s overall solution set whose focus is on dutiful compliance to the regulations which, as a publicly traded company it is obligated to observe, as well as the warranting of the information supplied in the course of meeting those obligations.

1.3 Summary

Data Governance is generally regarded as the combination of processes, procedures, activities, and controls which collectively safeguard information assets. Its Value Proposition includes:

Compliance and regulatory adherence accomplished by leveraging current technoliges to develop data management environments that meet specific mandated controls (eg, A.M.L.; Basel 2; KYC; Anti-Fraud, etc)

Enhanced Business Intelligence whose capabilities facilitate fact-based decision making that result in the identification of new and / or expanded opportunities for product and market growth, innovation, and increased customer satisfaction and loyalty.

Greater Alignment of I.T. to Business Objectives through accelerated information availability and enrichment that enables strategic planning and plan execution.

Improved platforms and tools for measuring, monitoring, and improving business performance by linking operational metrics to business performance measures and facilitating the reporting of critical business processes.

Reduced technical and operational complexity that improves efficiencies and reduces costs by strengthening business flexibility and agility through the deployment of comprehensive, predictable, and accurate information management environments

These central themes benefit the General Ledger, and , commensurately, support Management’s objectives (see “Background”) in the following ways:

Ensure consistent representation of financial activity that is the source data for internal and external reporting within the rules and standards for accounting as regulated and administered by local compliance Authorities or Bodies.

Safeguard the transaction activity data of the Company and preserve the General Ledger’s role as the Trusted Source for accounting information from elements capable of compromising or corrupting its integrity, accuracy, and quality.

Provide means and methods to validate the data and continuously audit the traceability of information to its source

Provide controls limiting proliferation within the Chart of Accounts that might result in creating ways that could accidentally, or intentionally, prevent the ability to clearly discern activity throughout the company’s organizational, geographic, line of business, or product segments.

Establish a policy base which supports and enables the Company’s continuous transformative and re-engineering initiatives

Links to the Company’s overall data management and governance strategy.

Facilitate and ensure compliance with all privacy and security standards

Satisfy all internal audit guidelines

Ensure that financial information that needs to be communicated can occur as proscribed and used as defined

Charters a “Center of Excellence” providing oversight and guidance on all matters related to continuity of the General Ledger and ownership for the trustworthiness of its information.

2. Proposed Technical ApproachThe tools and techniques to enable Data Governance’s contribution to the guardianship of the General Ledger are:

A Maturity Model-based approach lined to a defined Continuous Improvement process to evolve G/L management in a way that is predictable , measureable, and constantly monitored and refined

The design and maintenance of G/L coding and Chart of Accounts architectures that represent and support its use as a shared resource across the Company

Security/ Privacy Compliance controls that restrict access and provide adequate traceability on User activities within the G/L and conduct regular testing to ensure defensibility from unauthorized access or use

Information Lifecycle Management activities providing for classification and retention as part of the Company’s overall similar strategy and practices

Policy making that ensures continuous adoption , enforcement, and consensus on procedures and practices related to G/L management. Should have linkage to generally accepted I.T. governance methodologies (eg CoBIT) as well as accounting and financial rules and standards

Risk Management which, for the G/L would include operational risk related to the nature and structure of incoming data from the Integration Layer; outbound data to the Warehouse or reporting engines, and regulatory risk as may be related to specific accounts containing data maintained for legal or specific liability purposes as well as data managed through journal entry , accrual, or adjusted transactions made directly to the G/L

Quality and Profiling to ensure that incoming data has the same structure as its own metadata proscribes as well as that its enrichment, calculation, or aggregation through the integration layer has not altered its source-based or true transaction level value

Defined Stewardship that documents and proceduralizes roles and responsibilities as well as provides for their adequate separation and supervision

Data Validation via reconciliation , auditing, and reporting conducted on a frequency basis commensurate with account level volume, criticality, or handling

A Metadata methodology which addresses coding, Chart of Account structure, change control, and linkage to metadata and mater data management strategies at the source system and integration layers levels.

2.1 High Level Data Flows

Current State:

Not Available

Proposed Solution:

Legal Department

Policies

ReqPro Policy Repository

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Rational Data Architect

Business Rules Models

Policy Parameters

PolicyArtifacts

Data Classification Schema

Risk Classification Schema

Control Classification

Schema

Schema Elements

WBCR Self AssessmentPortal

Requirements

Compliance Office

BusinessProcessModel

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Model

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Incident Management

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ClearQuestPolicy Compliance

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Event Monitoring Code Generation

Data Stewardship Framework

Created On: December 17, 2007Last Update On: December 17, 2007









Reviewer

Table of Contents

1. INTRODUCTION................................................................................................................................................4

1.1 Background of the Data Stewardship Issue..........................................................................................................41.2 The Purpose of This Solutions Document.............................................................................................................41.3 Summary.............................................................................................................................................................4



3. Introduction Data Stewardship is cornerstone of Data Governance and the principle mechanism responsible for its management and administration. Data Stewardship begins with the the recognition that data is a shared resource that needs to be managed by the organization for the good of the organization. Data Stewardship engages key stakeholders from both Technology and Business interests in a collaborative effort directing and coordinating people, processes, and technology to ensure the continuous quality and improvement of data and the information into which it is transformed as the organization’s most precious essential, renewable and reusable resource

Data Stewardship refers to the “people” efforts expended to ensure a common understanding and acceptance of data guidelines, management practices, and maintenance of data quality

3.1 Background of the Data Stewardship Issue

The establishment of a General Ledger Data Governance framework to underpin and ensure the ability of the new Corporate G/L to meet its goals of supporting trustworthy financial reporting and the migration to an accelerated closing process requires the correct management model capable of executing Governance’s processes and controls while also contributing to Data Governance maturity, continuous improvement, and alignment with Enterprise-wide Data Management and Governance standards and protocols. .


This document provides a point of view (POV) on the role of Data Stewardship , and its principal vehicle, the Data Stewardship (or, Governance) Council, as the primary enabler and executor of the already proposed G/L Data Governance framework , and corresponding Maturity Model, .

3.3 Summary

Data shares four main characteristics with every other business product:

Understanding customer or user expectations. Training in how to produce a quality product. Measuring quality to assure process effectiveness. Placing accountability for all work products.

Data Stewardship involves establishing a Data Stewardship Council, which will ensure that data is viewed as a business product throughout the organization. This council will provide the means by which an organization can improve product service, and data quality. Data Stewardship is an integral part of enterprise data quality management. Long term data quality improvement can only be achieved by implementing management accountability for data, like accountability for any other product. This accountability must begin at the point of origin of the data, in the programs that capture the data, and flow upward. Each member of the organization should be made aware that there are real costs associated with poor quality data, and subsequently, each member must understand their role in improving or maintaining the quality of data.

The end state is for data, and the information into which it is transformed, to be accurate, reliable, secure, and available as, and whenever, needed

4. Proposed Technical Approach

4.1 Definition and Purpose of Data Stewardship

Data Stewardship has, as its main objective, the management of the enterprises’ data assets – to facilitate a common understanding and acceptance of the data. The purpose of doing this is to maximize the business return on the investment made in the data resources. The expected results are improved reusability, accessibility and quality of the data. Data Stewardship responsibilities include:

Document and implement business-naming standards. Develop consistent data definitions. Determine data aliases. Document standard calculations and derivations. Document the business rules related to the data – i.e. Master / Meta; Edit; Integration; Calculation, and Validation rules, etc.. Monitor development efforts for adherence to standards.

Ensure full time ownership , responsibility, and accountability for the maintenance of data quality standards.

More and more organizations are recognizing the critical role that the Data Stewardship function serves in providing for high-quality, available data. A common understanding of the data provides the foundation for the sharing of data across the organization. Data Stewardship is an ongoing process with a stewardship (a.k.a Governance) council as an ongoing part of the organization. A Data Stewardship / Governance Council consists of both technical and business specialists as permanent members. A Data Stewardship / Governance Council is responsible for overseeing conformity to generally agreed to and accepted standards (always aligned to those if the Enterprise) as changes occur to data collection and data management activities which affect business processes and the information systems they access.

4.2 Goals of Data Stewardship

The goal of Data Stewardship is to manage data as a strategic resource. Data can enable improvements to the profitability and competitive advantage of the organization, and business process effectiveness when the data is trusted, understood and accepted. For example, an insurance company that wants to understand customer or product profitability must be able to measure and monitor that profitability. If it is difficult to match claims to policies and identify the multiple types of transactions related to a policy it becomes even more difficult to measure the costs related to the policy; therefore, it also becomes quite challenging to measure profitability. When the quality of data is good, there exist issues of understanding the data across the organization. It is not uncommon for managers of multiple products to report a metric such as earned premium only to spend hours and days determining whether or not they all used the same calculation to arrive at their numbers. One of the costs associated with lack of stewardship is the time spent discussing and investigating how the numbers were created rather than acting upon the information.

There are two major streams of work that fall under Data Stewardship:

Creation and maintenance of the corporate glossary and meta data. Actively maintaining data quality.

Creating and maintaining the corporate glossary and meta data.

Data Stewardship must make “English a common language” as it relates to the data. The creation of the data glossary will facilitate communications in all directions within the organization. There is an up-front investment of time and effort to define and document terms; therefore, it is important to determine a phasing strategy for these efforts.

Take the example of Earned Premium. There are multiple ways to calculate earned premium. It is not the goal of the Data Stewardship / Governance Council to get a single definition and calculation of earned premium because there are often good business reasons to have multiple calculations. Rather than force a single calculation on all parties, the Data Stewardship/ Governance Council should come to agreement on different terms to be used for the different calculations and document each calculation under the agreed term. For instance, one product may calculate earned premium spread evenly throughout the policy term – recognizing equal risk during the term – and another product front-loads earnings to reflect an uneven spread of risk. Both calculations are valid and necessary, however, the use of the same name creates confusion. Renaming the calculations to be named “Evenly Earned Premium” and “ Front-Loaded Earned Premium”, for instance, would mean that the concepts are better understood. When earned premium is to be calculated across multiple products, there are at least 3 possibilities:

Using one of these calculations. Using a different calculation altogether. Using the results of each product’s calculation and adding them together.

The key is to come to an agreement that both calculations are valid, and to document how each calculation is derived. In this way, management can expend the effort once to decide how the metric should be calculated and publish the decision for use. Populating the meta data is a way to document the information so that it can be made available electronically and the means by which the accountable Data Steward can profile sampe data to be sure it maps to its Meta standard at any point in the Financial Management Architecture bw it Source System, Integration Layer, G/L, Data Warehouse, or Report

Actively maintaining data quality.

Data Stewardship also encompasses the process of ensuring the maintenance of data quality. A Data Quality Program will identify data problems from the past and means to resolve them on-going; however, the Data Stewardship/ Governance Council works to prevent future data quality problems. Before new applications go live, the Data Stewardship / Governance Council should test to make sure that procedures are in place to ensure that only when data meets quality standards will it be allowed to become a corporate resource.

It is not the goal of Data Stewardship to disallow transactions when all of the data is not perfect. Often, a system that is the point of capture for data does not require that data to be correct before a transaction can be completed. For instance, it is not imperative to have a correct VIN number before quoting, or even binding, a policy. The Transaction Data Store may, therefore, allow an override to an edit on VIN in the policy

admin system. However, a process should be in place to ensure that the VIN number is investigated and cleaned up prior to propagating it to other data stores and systems. The Data Stewardship Council should ensure that a process has been put in place, that it is tested to ensure that it meets the data quality standards set forth for VIN; and that a person/position is responsible for the process.

The other area of concern for maintaining data quality is to ensure that where an application is using a data element that already exists in the corporate data glossary, that the common data element is re-used rather than creating a new one.

4.3 Data Stewardship: A Component of Enterprise Data Quality Management

A discipline has emerged within information architecture development arena to address the need for managing data quality. This discipline, known as Enterprise Data Quality Management (EDQM), is intended to ensure the accuracy, timeliness, relevance and consistency of data throughout an organization, or across multiple business units within an organization, and therefore to ensure that decisions are made on consistent and accurate information.

EDQM requires the development of standards, which ensure that data is entered and standardized in accordance with a set of rules, which adapt to changes in business needs, and also requires changes to business processes. The five components of an EDQM program are:

Data Management Standards Data Stewardship Data Quality Program (DQP) Balancing Auditing

The Data Stewardship / Governance Council is charged with the job of ensuring the sustainability and maintainability of EDQM through both system and organizational changes, and must be easy to use.

Enterprise data management standards are the standards that will be used throughout the organization to manage data as a corporate resource. These standards include both business and technical standards. Enterprise Data Management Standards sets the standards, while Data Stewardship is responsible for applying those standards throughout the organization through the creation of the Data Stewardship Council. A Data Quality Program is set in place to clean up existing problems within the data, while Data Stewardship is charged with preventing data problems in the future. The meta data defined by the data stewards helps easily identify the source of data to which data must balance.

It is important to note that, while the intent of this paper is not to cover the implementation of an Enterprise Data Quality Management (EDQM)

program; however, a view towards EDQM provides a context for better understanding the importance of Data Stewardship.

4.4 The Planner / Owner / Builder Based Framework and Data Stewardship

The Planner / Owner / Builder perspectives can be used for determining the organizational responsibilities for the Data Stewardship.

The Planner Perspective provides an enterprise view. In this view, the major subjects of the organization are identified. If an enterprise conceptual data model exists, then this document would be used as the basis for defining the major subject areas. The conceptual data model should be reviewed for completeness and accuracy. If the conceptual data model does not exist, the major subject areas would be defined by bringing business people from each of the major functional areas of the company together so that the major subjects can be identified. These definitions are the starting point for the creation of meta data. A list of major subject areas might include customers, products, competitors, claims, agents, billing, facilities, human resources, etc. The major subject areas, and the scope and definition of each, needs to be agreed upon at an enterprise level. The business area subject matter experts that defined the subject areas can form the nucleus of a Data Stewardship / Governance Council, which would provide an enterprise view of the data.

In the Owner Perspective, the entities and attributes within each subject area are identified and defined from a business perspective. The relationships between entities and the domain of valid business values are both defined in the owner perspective. These definitions become enhancements to the existing meta data. To a large extent, the work performed in the owner perspective is done within individual subject areas. For each subject area, a business subject matter expert might be assigned as a business area data steward to oversee the development of the owner perspective. The Data Stewardship / Governance Council would provide guidance for matters involving multiple subject areas or in areas where a consensus cannot be reached within the subject area that an entity belongs.

In the Builder Perspective, physical databases and tables are defined and created, and the meta data will be updated to reflect the physical information about the data. Data administrators need to ensure that the data definitions correspond to the business definition, while database administration needs to ensure that the referential integrity implied in the system model is preserved.

The Data Stewardship roles exist within the framework to ensure the integrity of the populated databases and tables, and to create the meta data. The integrity created through the Data Stewardship roles will increase the value of the data, and the meta data created throughout the process, which will reduce the amount of time, spent researching the same data elements time and time again. Throughout the process the meta data is updated to reflect more information about the data.

The Planner, Owner, and Builder perspectives can be used for determining the organizational responsibilities for the Data Stewardship. The final product is a set of meta data and procedures to ensure that data quality standards are being adhered to in development efforts. The Data Stewardship roles will provide the meta data, and the application or system will provide the actual data. Data Stewardship will be accountable for the meta data, while the application or system will be accountable for the data.

4.5 Meta Data and Data Stewardship

Data Stewardship will provide a means to accomplish a common enterprise strategy in the creation and maintenance of meta data. The corporate data glossary will be implemented via the creation of meta data for availability throughout the organization.

Meta data is the glue that enables users to have the confidence in the meaning and quality of the data. This confidence will allow the technical and business users to know that the right data is being used to achieve the desired results. The availability of this information will:

Help developers and programmers identify expected impacts of modifications. Help users identify the data source that will supply the information that they need. Ensure a common understanding of how measures have been calculated. Ensure a single source for meta data which makes it available to the entire organization.

The Data Stewardship program will be responsible for implementing the creation and maintenance of meta data for both business and technical definitions. The Meta Data Foundations document for any Company should have a comprehensive list of the types of information that should be captured in the form of meta data. This document should be reviewed for completeness and accuracy.

5. Forming a Data Stewardship CouncilThe Data Stewardship / Governance Council implements data management standards. These standards would address data creation, maintenance, dissemination and disposition from an enterprise viewpoint, with the responsibility for executing the policies for individual data elements or subject areas being under the control of the appropriate individuals.

With representation from IT and the major business units, the Council’s charter is to provide corporate governance to strategic data decisions regarding both new and existing data. With respect to new data, the council will govern things such as new subject areas, new data sources, and new business problems solved with the data. With respect to existing data, the council will govern things such as data definitions and domains. The council will bring data definitions to a common language, and create new elements as necessary, all of which will be documented in a corporate data glossary.

The council will be responsible for:

Providing oversight to data stakeholders, such as customers, shareholders, employees and external regulatory entities with respect to the quality of data.

Providing oversight in the creation of processes and accountability within the application development teams surrounding data quality. Creating and maintaining meta data. Resolving data related issues. Establishing performance measures for data. Effecting culture change for data quality and data accountability as a management tool.

The implication of these responsibilities includes:

Appropriate Leadership level, support, direct involvement, and sponsorship exists Data Stewardship has been adopted at all required organizational levels The council represents the requisite multiple constituencies , has involvement of the right business personnel, and includes business and

technical specialists. Data Stewards, or Data “Champions”, are not merely Roles and Responsibilities, but are Full Time positions. The executive sponsors and the Data Stewardship Council have a real time commitment to these efforts.

The Council will be most effective when:

The Council has a defined and authoritative business charter with clear objectives and scope. The Council has a strong facilitator, who is results oriented and time driven. The Council has clear guidelines for data management across the organization. The Council has an effective process in place for conflict and issue resolution.

5.1 Data Stewardship / Governance Council Roles

Collaboration and communication is required to ensure that the data assets of the organization are used to provide the highest return on investment. In order to facilitate this communication and collaboration at least one full-time member must be assigned to the council. In addition, there are roles that can be covered by a number of members with part-time commitment. The full-time person is responsible for ensuring that the council is involved in all development efforts, ensuring that business and technical representation on the council for any given project is adequate, and for creating and maintaining the corporate glossary. The physical implementation and maintenance of the corporate date glossary within the meta data repository should be managed by a member of the Council. The definitions of the data elements within the corporate data glossary are facilitated by the Council, in coordination with one or more business units.

At any given point there may be several technical and business specialists involved in Data Stewardship Council activities. The skill set required of the council include:

Solid understanding of the business Excellent communication skills Objectivity Creativity Diplomacy Team Players Well-respected subject area expertise Well respected for knowledge and view of the organization’s overall mission, environment, and businesses

The member roles on the Data Stewardship / Governance Council follow. It should be recognized that these are roles, not individuals. The same person may fill one or more roles. One or more individuals may fill a single role.

5.1.1 Technical Roles

The technical roles for Data Stewardship include data architect, data base administrator, and data administrator. Their role on the Data Stewardship Council will be to insure that the data structure adheres to quality and integrity standards and to maintain the meta data. The skill set required to fill these roles include:

Experience with data modeling Understanding of the data architecture Understanding of any specifics unique to a business unit’s data architecture Understanding of DBMS Technical writing

5.1.1.1 Data Architect

The data architect will be accountable for the coordination of the technical roles on the council. The data architect will be accountable for providing input on the structural quality and integrity of the data. The architect will apply broad knowledge of the enterprise data architecture to ensure that data is designed according to corporate standards.

5.1.1.2 Data Administrator

The data administrator will be accountable for the structural quality of the data infrastructure, and data models. The data administrator will facilitate data definition, ensuring that existing definitions are reused, and that new definitions are developed where appropriate. The data administrator is responsible checking that data management standards are followed, especially as they concern audit and balance. The data administrator is responsible for the initial review of audit and balance procedures.

5.1.1.3 Database Administrator

The database administrator will be accountable for the creation of the physical meta data repository and tables.

5.1.2 Business Roles

Business roles on the Council will be responsible for defining or validating data definition, domain values, and business rules as they apply to a specific business process. The business roles for Data Stewardship are needed to ensure that data meets the needs of a specific business process, and also meets the requirements of any other business area that uses their data to perform a business processes.

5.1.2.1 Business Data Steward

The Business Data Stewards will be subject matter experts who understandsboth the business process, and the data being produced from that process. They will be accountable for the validity of the data definition, documenting data definitions, calculations, summarization, maintaining and updating business rules, and analyzing and improving data quality. They are also responsible to ensure on-going business unit support. There may be one or more business data stewards assigned to the Data Stewardship / Governance Council depending upon their expertise.

5.1.3 Data Stewardship / Governance Council Responsibilities

The Data Stewardship / Governance Council responsibilities include:

Standard Entity and attribute definitions Documentation of Business Rules applied to data Documentation and definition of standard calculations and summarization applied to data Analysis of data quality processes

6. Implementing Data Stewardship

6.1 Moving Forward

With no constraints on time and money, an organization would establish the Data Stewardship / Governance Council, and begin the process of applying the enterprise data management standards to both new and existing systems, creating the meta data . For most organizations this would be an overwhelming task. However, data exists, constraints on time and money exist, and we can’t stop to create all meta data before moving forward. A more realistic approach is to begin the creation of meta data in a phased approach proceeding down two paths. The first path is to choose a project where meta data has already been created, and to bring the data, and the meta data to corporate standards. The second path is the breakdown of development efforts into three main types; new custom systems, new packaged systems, and enhancing existing systems. This path will focus on creating meta data for new systems, and adding meta data for existing systems as enhancements are made.

Implementation of Data Stewardship for a development effort will involve a three-step process.

1. The application or system area will approach the council with a proposed system or enhancement. 2. The information will be distributed to all appropriate members on the council for review.3. Once reviewed by each council member, consensus is reached as to the response back about corporate data reuse and additions as

well as a point of view around the proposed data quality processes. If required, a meeting is set to discuss the project. Once consensus has been reached, the council will respond back to the area that requested the change.

If the response back to requires changes to the proposed system or enhancement, the process will be iterative. In order to be effective the process must:

Be defined and documented. Be repeatable. Have an owner. Be flexible.

The role of the Data Stewardship / Governance Council will vary depending upon the type of development effort being undertaken. The responsibility of the council will remain the same for all project types, to bring data definitions to a common language, to create and maintain the corporate data glossary, and to ensure that accountability and processes for data quality are in place.

6.2 Methodology

6.2.1 What tasks happen at the Leadership level?

At the highest level, the first steps in implementing Data Stewardship are as follows:

Meta data strategy and data definition standards are first established to ensure that the Data Stewardship / Governance Council has the necessary means to accomplish its goals. This step has been accomplished in the organization through the publication of the Meta Data Definition and Goals paper.

Document and publish the process under which the Data Stewardship / Governance Council will be involved in related projects going forward including a means for issue resolution. The process will involve: Communication of proposed new custom solutions, new packaged solutions, and enhancements of existing systems, to the

Data Stewardship / Governance Council. Communication to all Business Data Stewards regarding changes, including a timeframe within which the data stewards must

respond to requests from the council. Response from the Data Stewardship / Governance Council regarding meta data, enterprise standards, and reusable data

elements where applicable. Identify and empower a leader to organize the Data Stewardship / Governance Council.

With the exception of identifying and empowering the leader, the above steps are considered complete when this document has been reviewed and accepted for publication. Once the leader is appointed and a Data Stewardship / Governance Council organized, their first tasks will include:

Define and document the process for the maintenance of data quality. Define and document the process for data acceptance testing. Create the meta data tables to store the corporate data glossary based upon the standards defined in the Meta Data Definition and

Goals paper.

6.2.2 Inputs from project teams to the Data Stewardship / Governance Council

At a project level, each project will be responsible for presenting the data requirements of their project to the Data Stewardship / Governance Council for approval. Below is a summary of tasks each project team operating within the arena covered by the Data Stewardship / Governance Council would be required to accomplish:

Assign a data steward to the project, and identify the individuals or team that will be responsible for maintaining the data definition deliverables updating information in the data dictionary. Define procedures for maintaining data definition deliverables. If an automated data dictionary is in use, enter any newly developed standards in the data dictionary (e.g., domain boundaries, entity prefixes).

Analyze the data. Prepare a data component inventory. Review the existing data naming standards and establish naming conventions in accordance with organization standards. Determine Business Rules. Determine the stored data needed to support each elementary process that was identified in the analysis of the current processes. Assess the Condition of Existing Data. Document data conditions, identify opportunities, and create a data analysis report. Create data models. Create a first cut business definition of data elements.

6.2.3 What tasks should the Data Stewardship / Governance Council complete?

6.2.3.1 Implementing new custom solutions

During the implementation of a new custom solution, the Data Stewardship / Governance Council role will include:

Reviewing the system data requirements against the existing corporate data glossary. Identifying potential data elements which already exist within the corporate data glossary, and determining if the definitions match, and if

the physical representations are the same. If there is a match then a new data element does not need to be created, but rather the existing element definition needs to updated to reflect the additional use of the data element.

Identifying new data elements and ensuring that the element is moved through the process of acceptance into the corporate data glossary.

Applying enterprise data standards with respect to naming conventions, calculations, size, etc. Facilitating common data definitions throughout the organization. Addressing the reusability of existing data. Maintaining the corporate data glossary with new entities, and updating existing entities where applicable. Ensuring that a process is in place to maintain data quality. Ensuring that a process is in place for data acceptance testing prior to productionalization of the system. Dissemination of meta data throughout the organization.

6.2.3.2 Implementing new packaged solutions

During the implementation of a new packaged solution, the Data Stewardship / Governance Council role will include:

Mapping data elements to the existing corporate data glossary. The data elements used in a package system have been created for use within the software, and therefore, from a maintenance perspective the names of data elements should not be changed, but rather the relationships documented.

Addressing the reusability of existing data. Maintaining the corporate data glossary with new entities, and updating existing entities where applicable. Ensuring that a process is in place to maintain data quality. Ensuring that a process is in place for data acceptance testing prior to productionalization of the system. Dissemination of meta data throughout the organization.

6.2.3.3 Enhancing an existing solution

During the enhancement of an existing system, the Data Stewardship / Governance Council role will include:

Reviewing the system data requirements against the existing corporate data glossary. Identifying potential data elements, which already exist within the corporate data glossary, and determining if the definitions match, and if

the physical representations are the same. If there is a match then a new data element does not need to be created, but rather the existing element definition needs to updated to reflect the additional use of the data element.

Identifying new data elements and ensuring that the element is moved through the process of acceptance into the corporate data glossary.

Applying enterprise data standards with respect to naming conventions, calculations, size, etc for new data items, and weighing the cost benefit of applying these standards to changed items.

Addressing the reusability of existing data. Maintaining the corporate data glossary with new entities, and updating existing entities where applicable. Ensuring that a process is in place to maintain data quality. Ensuring that a process is in place for data acceptance testing prior to productionalization of the system. Dissemination of meta data throughout the organization.

6.2.3.4 Data Stewardship / Governance Council Acceptance Process

The project team will initiate the first step in this process. The project team will provide the necessary documentation to the Data Stewardship Council. Next, the Council will determine which Business Area Stewards should participate in the review process. The proposed changes and a list of tasks are then distributed to the business data stewards. The Business Data Stewards must respond in a predetermined time frame. They must determine what the impact is to their area, if the data elements as defined are acceptable, and any other relevant information. Finally, the Data Stewardship/ Governance Council will respond to the project team regarding meta data, enterprise standards, and reusable data elements where applicable.

6.2.3.5 Process of Maintaining Data Quality

The process of maintaining data quality is necessarily application specific. Where data capture is involved in a system, the ideal would be to build the edits and validations that would prevent non-quality data from entering the system. For names and addresses, it would be the ideal to ensure that the system had to first ensure that the person or company did not already exist in the customer file and then validate to the greatest degree possible any new customers added. The reality is that these types of edits and validations may impact the ability to do business. Each project will need to determine:

What data must be edited/validated at the point of capture What can be edited/validated without impacting the ability to do business What is left to edit/validate and how they will ensure that it occurs prior to distribution of that data to the enterprise.The Data Stewardship / Governance Council should be seen as a partner in the process of making these determinations and designing the process. The council will be the group that has a view of how it is done across multiple applications and as their experience grows, they should be viewed as a source of knowledge on this topic. Additionally, the council will approve and be involved as users in the acceptance testing of these processes.

6.2.3.6 Process of Data Acceptance Testing

The Data Stewardship / Governance Council should have a process in place that requires final approval of all data prior to that data being moved into a production environment. This process should involve verification of all processes that are affected by the new or changed data element. Balancing and audit should be performed where applicable.High Level Data Flows

Current State:

Not Available

Proposed Solution:

:

Information Governance

Steward –Led

Working Committees

Data Stewardship / Governance

Council

A Data Stewardship / Governance Organization Flow

Senior Governance Lead(Executive in Charge)

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Targeted Data Quality Improvement

Coordinated Execution of the Matrix of Responsibilities

Coordinated Systems and Process Change

Committee-Led Daily

Governance Management

Responsibilities

Data Stewardship II:Data Governance Council Charter Framework

Created On: January 8, 2008Last Update On: January 8, 2008









Reviewer

Solutions Doc <Title of Issue> Page: 1 of 6 Version: 1.0 Last updated date: April 13, 2023 7:36 AM

Table of Contents

1. INTRODUCTION................................................................................................................................................4

1.1 Background of ....................................................................................................................................................41.2 The Purpose of This Solutions Document.............................................................................................................41.3 Summary.............................................................................................................................................................4




7. Introduction The first document dealing with the subject of Data Stewardship discussed the integral and fundamental existence of a Data Stewardship council as a core ingredient to the success , maintenance, and sustainability for any Data Governance implementation initiative. The document also proposed the design and purpose of this linchpin body.

This follow-up presents a model outline for a Council’s Charter which operationalizes the guiding principles presented in Part I.

7.1 Background

A Data Stewardship / Governance council is the central point of contact for mobilizing the processes, Resources, and technology ensuring Data quality and conformity to related standards underpinning and assuring its consistency. No Data Governance effort can succeed with this collaborative body which brings together technical and business stakeholders in a joint effort to create a long term, continuously improving environment for the most renewable and reusable asset owned by an Enterprise; its information and the source data which manufactures it.


This document presents a concise outline identifying the mission critical elements which comprise the Council’s reason for existence, primary roles and responsibilities, and their central activities and accountabilities

7.3 Summary

The overarching objective of a Data Stewardship / Governance council is to improve both the quality and integrity of data . To accomplish this it must balance codification and enforcement of standards, continuous improvement, performance reporting, and the dissemination of information and training to the organization to ensure the growth and perpetuation of a culture of data stewardship, all of which must take place in a highly transparent and collaborative manner cognizant and respectful of Data’s role as a core shared resource to the Enterprise.

8. Proposed Technical Approach The principle objectives of a Data Stewardship / Governance Council are:

Develop a commonly understood and utilized data taxonomy consisting of definitions, terms, quality mappings, validation criteria, and quality guidelines.

Provide and enable the processes, procedures, policies, training, and organizational support components for building and sustaining improved Data Quality, Integrity, and overall Governance capabilities.

Assemble, document, warehouse, and manage all standards related to the Data , architecture, and infrastructure which the Council is responsible to safeguard

Ensure continuous data improvement by:

a. Conducting regular Data quality and integrity checks

b. Testing data profiles to assure conformity to meta and master standards

c. Manage the Data Dictionary

d. Manage Data Issues; perform root cause analysis and identification; implement remediation

e. Adopt and employ a continuous improvement methodology capable of providing meaningful and actionable metrics and measurements suitable for both repeatable improvement efforts and results as well as Enterprise reporting via dashboards and scorecards

Define the integrated architectures, methods, and standards required to support Enterprise data quality reporting activities.

Actively promote the conversion of Data Governance roles and responsibilities into full time positions


Employ a project management approach which will;

a. Minimize decisions made on incorrect or insufficient data

b. Accelerate the investigation and identification of sources of bad data

c. Provide tools, training, and procedures for data quality issue identification and remediation

d. Measure compliance with internal Data Governance procedures and policies as well as benchmark to leading practice standards

e. Implement best of breed methodologies (i.e CoBIT) to guarantee perpetual standards conformance

f. Enable organization-wide communication through standardized definitions and terms

g. Enforce data quality standards especially within the System Architecture’s Integration layer.

The principle roles and responsibilities include:

Data Stewardship Council:

The purpose of this body is to provide a forum for managing and maintaining continuity between and among business and technology stakeholders and subject experts to develop and ensure the general understanding of common and / or shared data, related management processes, and management systems. Primary responsibilities include:

Establish organizational and process frameworks for improved data quality and integrity by establishing quality standards and metrics

Provide the organization with a foundation to address poor data quality

Support the organization’s efforts to create common data quality goals

Support the definition, implementation, and enforcement of data domains, data quality rules, and processes

Enable all stakeholders to gain better insight and make more informed decisions around data and its usefulness

Highlight areas of concern around data tat does not conform to established data quality standards and target metrics

Facilitate the ongoing examination and commonality of data quality issues and implement ongoing data quality validation and monitoring

Data Stewards:

These full time resources are the front line implementers of the processes, procedures, and practices adopted or promulgated by the Council. Stewards manage the continuous improvement efforts, monitor and assess adherence to standards, and own activity-level quality control tasks. Their primary responsibilities include:

Ensure organization-wide adoption and consistent use of data definitions and standards approved by the Council

Partner with source system owners to create and adopt data standards which reflect, or even duplicate, organization level criteria

Develop and manage data quality dashboards and scorecards

Perform issue and defect management; identify deviations from quality standards and develop strategies to resolve these issues

Collectively maintain the Council’s Master Governance Repository


Data Quality Reporting:

Data Quality Reporting, owned by the Council but realized through the Stewards, focuses on the policies, structures, tools , processes, and methods need to establish a borad based approach to data quality. The specific responsibilities associated include:

Assist business stakeholders in defining and implementing the processes and standards which the business needs to observe in order to align with data quality standards

Work with application management stakeholders to program data quality processes and standards as rules within the application architecture

Work with Data Operations to develop and implement the standards applicable to raw data (file structure, databases, business models, data models, document files, mappings, and metadata) needed in order to efficiently operate the data quality program and the organization’s data environment

Establish , implement, and manage a data analysis framework which tests

Domain Values (content and distribution)

Completeness, Correctness, and Validity

Relational Integrity

Business Rule Compliance

Transformation Rule Compliance to ensure that ETL / Integration transforms were applied properly to data in DW


8.1 High Level Data Flows

Proposed Solution:The general template guiding daily activities related to Data Validation, Quality and Integrity Assessment, and Reporting can be summarized in the exhibit below:


1 Policy is defined, agreed, provisioned, and communicated

2 Data is classified, valued, and secured

3 Policies are re-composed into rules and integrated into processes and practices 4 Risks are quantified

5 Incidents are managed; impacts recorded; lessons learned drive continuous improvement

6 Results are dashboarded, communicated, and measured over time 6 Results are dashboarded, communicated, and measured over time

Documents

"Sanitized" Engagement Deliverable: Data Governance Blueprint