Data Governance & Stewardship Framework
Data Governance Defined
"Effective data governance can enhance the quality, availability and integrity of a company’s data by fostering cross-organizational collaboration and structured policy-making."
Benefits of Data Governance
- Repeatable & automated processes around data management
- Improved data quality and usability
- Improved data security and auditability
- Increased visibility into costs associated with data
- Increased accountability for use of data & appropriate use of data
- Standardized lifecycle management for data use
Data Governance Framework
A Data Governance Framework is created to provide a mechanism to govern company Data Assets through the use of authority and control over the management of these assets to ensure:
- Data Assets are strategically aligned with company goals and objectives
- Data Assets are compliant with company standards, policies & procedures and legislative requirements
- Confidence in the data used to make business decisions
- Effective control of data management processes
- Formalized roles and responsibilities in regards to data management
- Protection of Data Assets
The development of a Data Governance Framework will include Enterprise Data Governance for all Data Assets within the organization and data management functions as outlined in the DAMA Data Management Framework:
Enterprise Data Management Suite (EDMS)
Meridian will utilize our Enterprise Data Management Suite (EDMS), which is a comprehensive data management software application, to facilitate and ensure compliance with your Data Governance Framework in the following areas:
- Data Quality Management (DQM)
- Data Security Management (DSM)
- Master Data Management (MDM)
- Business Intelligence Management (BIM)
- Software Configuration Management (SCM)
- Data Integration Management (DIM)
- Copy Data Management (CDM)
- Data Retention Management (DRM)
Master Data Governance Policy
A Master Data Governance Policy will be developed, published and communicated to all relevant stakeholders. This policy will be reviewed and updated annually, or more frequently as needed. This policy will contain references to the individual data management functions specified in the DAMA Data Management Framework.
Functional Data Management Policies
Policies will be developed for each of the data management functions listed above. These policies will be utilized when reviewing, updating, or developing new data management procedures across the company. Each Policy shall be reviewed and updated at minimum on an annual basis.
Functional Data Management Procedures
Data Management Procedures will be developed to define the processes and procedures to be followed to meet the requirements for each Functional Data Management Policy. Existing Procedures will be reviewed and updated, and new Procedures will be created to meet Functional Data Management Policy requirements.
Data Asset Inventory
All Data Assets will be documented in a Data Asset Inventory. All new Data Assets will be added to this master inventory as required. Data inventories will include items for each Data Asset such as: Asset purpose, data owner, data custodians/stewards, data consumers, type of data, location of data, sensitivity level of data, data retention requirements, number of environments, size of each environment, refresh frequency, growth pattern, and backup strategy.
Data Stewardship & Custodianship
Data Asset owners, users, and other stakeholders will be identified and Data Stewards and Custodians will be appointed to each Data Asset. This information will be updated annually and maintained in the Data Asset Inventory.
Data Stewards are responsible for what is stored in a Data Asset, Data Custodians are responsible for Data Asset structure and technical considerations.
Data Governance Roles and Responsibilities
The following roles and responsibilities will be utilized to effectively implement and manage the Data Governance Framework:
- Data Sponsors – Effective owner of a Data Asset. Data sponsors are responsible for: identifying need for data collection, purpose for data collection, scope and coverage of data collection, appointing a custodian, resourcing data collection, directing data collection, and approving release of information
- Data Stewards & Custodians – Data Stewards have day to day responsibility for the Data Asset and are responsible for: data compliance, implementation of data collection, data security, data storage, data quality, and data access. Data Custodians are responsible for the Data Asset structure and technical environment
- Business Owners – Responsible for managing policies and procedures related to a Data Asset in accordance with Functional Data Management policies. Business owners are responsible for prioritizing data management requirements identified by data managers, ensuring data management activities are being monitored and improved, authorizing access to Data Assets alongside the Data Custodian, and overseeing staff that fulfills data management duties
- Data Managers - Data Managers work with Business Owners and Data Custodians to control the Data Assets. Responsibilities include: defining requirements, improving data management, resolving data issues, monitoring data management activities, maintaining data management processes
- Data Users – Data Users are authorized consumers of a Data Asset
Data Governance Monitoring
A mechanism will be established for measuring, monitoring, and communicating the Data Governance Program, to ensure effectiveness, quality, and constant value to the organization.
High-Level Steps to Creating a Data Governance Framework
1. Assess the current state of Data Governance within the company based on maturity level of each of the data management functions
2. Obtain Executive support for Data Governance based on the assessment
3. Create a Data Governance Charter
4. Form a Data Governance Council
5. Identify Data Stewards & Custodians
6. Develop Data Governance Principles, Policies and Procedures and Metrics
7. Incorporate into operations via training and communication
8. Start with a small Data Governance Project
Assess Current State of Data Governance
Interview Key Stakeholders from the business, as well as technology teams to assess the data management functions, based on the following CMMI Data Management Maturity Model. Components that should be assessed are:
- Organizational Structure
- Data Quality
- Data Policies & Procedures
- Data Security
- Data Cost
- Data Architecture
- Skills & Training
- Master Data Management
- Metadata Management
- Unstructured Data Management
Data Management Maturity Model
Obtain Executive Support for Data Governance
Executive support is needed from business units across the organization, as well as from IT. Interviewing potential executive sponsors during the assessment phase will help to gain executive buy-in for Data Governance efforts, as well as ensure that executives see data as a valuable corporate asset.
Create a Data Governance Charter
A Data Governance Charter will document the vision, mission and objectives for the Data Governance Framework. The Data Governance Charter is an executive-level business document that will:
- Identify opportunities and concerns with the current state of Data Governance
- State the desired future state and how Data Governance will support the desired future state
- Reinforce how Data Governance aligns with corporate strategy
- Outline clear action-oriented objectives for Data Governance
Form a Data Governance Council
The Data Governance Council will be responsible for carrying out the activities defined in he Data Governance Charter. The Data Governance Council consists of: data governance program manager, executive sponsors, senior business unit leaders and data stewards & custodians. The Data Governance Council will:
- Ensure Data Governance activities, standards, policies and procedures align with corporate strategies
- Prioritize and coordinate enterprise Data Governance efforts
- Determine and assign Data Governance related roles and responsibilities and authorities across the organization
- Keeps the organization aware of Data Governance initiatives and the ongoing value of Data Governance
- Facilitates training efforts for Data Governance
Identify Data Stewards & Custodians
Once the Data Governance Council is formed, data stewards should be selected from each subject area of the organization. Data stewards & Custodians from each subject are should be recognized subject matter experts from within each subject area. Data stewards & custodians are responsible for the following for their subject area:
- Data compliance with standards, policies and procedures
- Implementation of data collection
- Data security
- Data structure
- Data storage
- Data quality
- Data access
Develop Data Governance Principles, Policies and Procedures and Metrics
Data Governance Principles will be created by the Data Governance Council to articulate organizational values that will serve as a basis for creating policies and procedures.
Data Governance Policies will then provide the guidelines and standards for managing data assets, defining the lifecycle for data assets, ensuring integrity of data assets and securing data assets. Data Governance policies will align with the data management functional areas of Data Governance for each Data Asset.
Data Governance Procedures are step-by-step instructions that facilitate the guidelines defined in Data Governance Policies.
Metrics and KPIs should be developed to not only measure compliance with Data Governance policies, procedures and standards, but to illustrate the tangible value of Data Governance efforts such as cost savings and efficiency improvements.
Incorporate Data Governance Into Operations
Creating and executing a training and communication plan is critical to establishing buy-in and compliance for Data Governance at all levels of the organization. Ongoing communication and the sharing of success stories will ensure that the entire organization sees the value and benefits of Data Governance.
Start with a Small Data Governance Project
Once the Data Governance Framework is established, identify a small project within a business unit or subject area to begin practicing Data Governance. Defining a data-related that can be addressed with the Data Governance Framework will show the organization how the process works, as well as the tangible end result and benefits. Some examples are:
- Cost of storing redundant copies data due to no Copy Data Management strategy
- Performance issues due to lack of Data Retention Management strategy
- Data usability/integrity issues due to no Data Quality Management strategy
- Security concerns due to no Data Security Management strategy
Start Project Form
Our Storage footprint and cost was continually increasing, Quality of test data was insufficient in spite of higher volumes of accounts to provide more scenarios, refreshing test environments was time consuming due to higher account volumes, no method in place to provide purge functionality that would maintain data integrity. After implementing the Meridian EDMS platform the Immediate result was the elimination of 17TB of premium storage that supported test environments and one high end Unix server that hosted the databases, reductions in storage growth provided O&M savings of $2 M costs over 5 Years, avoided capital costs of $4.4 M from storage purchases no longer required, smaller test databases through EDMS functionality that provides representative samples of accounts specific to our installation, faster test database refreshes through EDMS representative samples and Improved test quality through an EDMS data extraction process which allows users to extract accounts that specifically address their test scenarios.
— (BGE) Baltimore Gas and Electric Company