DISCIPLINE DATA GOVERNANCE GOVERN PLAN IMPLEMENT

Transcription

1 DATA GOVERNANCE DISCIPLINE Whenever the people are well-informed, they can be trusted with their own government. Thomas Jefferson PLAN GOVERN IMPLEMENT 1

2 DATA GOVERNANCE Plan Strategy & Approach Data Ownership Partnerships Goals and Objectives Governance Model Control Targets Compliance Privacy Data Access Management Data Maintenance Charter & Process Distinguishing the Charter Scope and Jurisdiction Roles & Responsibilities Engagement Process People Envolvement Governance Council Data Stewards and Analysts Business vs IT roles Data Access Quality Management Data Quality Requirements Quality Management Process Process Improvements Training & Awareness 2

3 Planning: Key Points Defining and planning a Data Governance (DG) model should start with an initial assessment and the ground work necessary to drive a sound charter and implementation proposal for your governance model. The assessment and proposal should predominantly be a business driven initiative with sponsorship at the VP level to ensure strong commitment and advocacy exists for establishing data governance. Within the sponsoring organization there should be an existing Director or Senior Manager appointed by the VP to lead the program and tactical aspects of the initiative. Data governance needs to be clearly distinguished from other types of governance or steering committee charters that typically exist in a company. 3

4 Planning: Key Points (continued) The value of data is highly dependent on how accurately the data is captured, how relevant that data is in context to its usage, and how well this is governed. A DG council needs to have sufficient influence to ensure that data standards, validation rules, and quality control expectations are actively involved in business process areas. There is nothing worse than a new DG council with little knowledge and influence. The council needs to be an effective team with deep knowledge related to data, processes, policies, and standards in order to recognize current and future state needs for data governance and quality control. You can t govern effectively if you don t know who is touching the data. A DG council needs to have a clear understanding of the data entry points and what processes have create, update, delete capability with this data. 4

5 Planning: The Data Governance Concept A Data Governance model must support the following concepts and constructs: Policies: Principles and guidelines that describe when data governance processes, compliance rules, and data standards must be followed. Standards: Rules and definition that support data governance policy, data management practices, data quality adherence, or other areas within data governance span of control. Organizational Structure: Structure and functions that define the model and operating process needed to support the data governance initiative. Decision Making: A responsive, authoritative decision making process supported by the data governance charter with active engagement from data management leaders and practitioners who can represent business, IT, and regulatory requirements. Action Assignment: Ability for the data governance team to assign actions to responsible persons or parties who can effectively address a data governance action item. Measurement and Monitoring: Ability to measure and monitor data activity, data quality, and adherence to governance policies and standards. Data Ownership and Stewardship: A framework of roles and responsibilities that give people the necessary accountability and authority to own and manage data in accordance to data governance policies and standards. Data Maintenance: A set of processes and supporting resources that are responsible for the updating or correction of data and the associated processes, logic, or metadata to ensure that data integrity and quality standards are met and maintained. 5

6 Planning: Design & Implementation Data Governance Process Design & Implementation Approach Planning & Design Phase Implementation Phase Establish The Charter Policies, Standards, & Controls Process Readiness Implement Maintain & Improve Distinguishing the Charter Agreement on Mission & Objectives Define Scope & Jurisdiction Identify Roles & Responsibilities Set Top Priorities Committed Resources & Budgeting Ratify Charter Define Key Policies, Big Rules & Quality Standards Establish Key Metrics, Monitors, & Improvement Targets Identify Data Entry Points & Team Leads Establish Quality & Service Level Agreements Define Metadata Management Plan Communication of Charter & Implementation Plan Readiness of Processes, Tools, & Baseline Measurements Completion of Training & Readiness Plans With Core Teams & Sub-Teams Launch the Process. Conduct Regular Complete Key Council Meetings Improvement Manage Projects Priorities, New Identify and Issues & Address Negative Requirements Quality Trends Review Key Monitor & Correct Metrics & Negative Data Performance Entry Process Indicators Behavior Communicate Manage New Data Status of Projects & Integration Improvements Requirements & Keep Sub-Teams Quality Impacts and Regional Teams Actively Engaged 6

7 Planning: Concept to Functional Model Policies: Principles and guidelines that describe when data governance processes, compliance rules, and data standards must be followed. Standards: Rules and definition that support data governance policy, data management practices, data quality adherence, or other areas within data governance span of control. Organizational Structure: Structure and functions that define the model and operating process needed to support the data governance initiative. Decision Making: A responsive, authoritative decision making process supported by the data governance charter with active engagement from data management leaders and practitioners who can represent business, IT, and regulatory requirements. Action Assignment: Ability for the data governance team to assign actions to responsible persons or parties who can effectively address a data governance action item. Measurement and Monitoring: Ability to measure and monitor data activity, data quality, and adherence to governance policies and standards. Data Ownership and Stewardship: A framework of roles and responsibilities that give people the necessary accountability and authority to own and manage data in accordance to data governance policies and standards. Data Maintenance: A set of processes and supporting resources that are responsible for the updating or correction of data and the associated processes, logic, or metadata to ensure that data integrity and quality standards are met and maintained. Data Governance Process Design & Implementation Approach Planning & Design Phase Implementation Phase Establish The Charter Policies, Standards, & Controls Process Readiness Implement Maintain & Improve Distinguishing the Charter Agreement on Mission & Objectives Define Scope & Jurisdiction Identify Roles & Responsibilities Set Top Priorities Committed Resources & Budgeting Ratify Charter Define Key Policies, Big Rules & Quality Standards Establish Key Metrics, Monitors, & Improvement Targets Identify Data Entry Points & Team Leads Establish Quality & Service Level Agreements Define Metadata Management Plan Communication of Charter & Implementation Plan Readiness of Processes, Tools, & Baseline Measurements Completion of Training & Readiness Plans With Core Teams & Sub-Teams Launch the Process. Conduct Regular Council Meetings Manage Priorities, New Issues & Requirements Review Key Metrics & Performance Indicators Communicate Status of Projects & Improvements Keep Sub-Teams and Regional Teams Actively Engaged Complete Key Improvement Projects Identify and Address Negative Quality Trends Monitor & Correct Negative Data Entry Process Behavior Manage New Data Integration Requirements & Quality Impacts

8 DATA GOVERNANCE Implementation Implementation Plan Charter & Process Approved Policies, Rules, and Standards Website & Metadata Roles & Resources Communication Charter, Process, Priorities Representatives Meetings and Minutes Positives & Negatives Priorities Immediate Needs Actionable & Achievable Measureable IT & Data Stewards Engaged Measurement Baseline Measures Key Process Monitors Quality Scorecards Improvement Targets Training & Readiness People Process Tools Go-Live Plan 8

9 Implementing: Key Points There should be a robust communication plan to sufficiently broadcast the purpose and launch of the data governance process. The approved DG charter should be internally posted and able to be summarized for general communication purposes. There should be sufficient distinction between a data governance council, a data quality management forum, and data maintenance teams. All should work collaboratively under governance oversight, but each should have specific roles. DG should not be an as needed process. Typically there should be more than enough challenges, need for policies, standards, and data or process improvement opportunities to keep a DG process continuously busy. Ensure that crossfunctional and regional interests are being well served. 9

10 Implementing: Key Points (continued) When considering items for prioritization, make sure these fit within a reasonable time frame and expectation of execution. Longer term objectives often need more vetting out and are likely to be dependent on the execution and status of the current and nearer term priorities and initiatives. A DG council needs to keep a watchful eye on key metrics and performance indicators from a trending and compliance perspective. The DG council should work closely with a data quality forum (or sub-team) to fully understand how people, process and system events impact data integrity. Establish a broad, ongoing cadence of DG communication to the sponsoring executives, core team, extended team, and various interest groups or impacted stakeholders 10

11 Implementing: Process Flow Example Data Governance Drivers Quality Policy Standards Process People Compliance Constraints Maintenance Governance Domain Team Metadata Management Quality Management Data Governance Charter Access Management Policies Standards Processes Structure No Qualified Request? Yes Cross- Domain issue? No Engage Steering Committee? No Decision Yes Yes Cross- Domain Governance Review Executive Steering Committee Presentation by Mark Allen and Dalton Cervo 11

12 DATA GOVERNANCE Govern Manage Resources, Budget, Priorities New Issues & Initiatives Compliance & Regulations Quality Improvement Mature Increasing Influence Quality Control Multi-Domain Practices Self Maintenance Maintain Data Maintenance Policies and Standards Desired Quality Levels Steady State Needs Improve Progress From Baseline Control from Monitoring Complete Quality Initiatives Minimize Risks Communicate Roadmap & Priorities Achievements Dashboards Decisions & Policies 12

13 Governing Key Points Data governance needs to be an active process with its members regularly engaged. It s this active network of people that creates the channeling and a community framework that enables data governance to thrive. A data governance process should be regularly revisiting existing priorities and putting them into context with any new issues or requirements that have emerge. Ensure that projects are well planned, funded, resourced, and executed in a timely manner. Completing key initiatives on-time and on-budget will demonstrate the data governance value. Communicate good and bad news quickly to ensure that awareness and opportunity for feedback is immediately available. 13

14 Governing Key Points (continued) Ensure that the stakeholders recognize that data governance is vital to the health and welfare of the business. An ongoing, caretaking approach is needed to maintain the health and integrity of the data assets. Over time data governance influence should become well embedded in the company s business model with various processes and teams operating in self-monitoring and selfcorrecting modes. This will reflect that data governance has reached a mature and steady state. Over time as data governance efforts mature, data quality management effort should decline. 14

15 Governing: Interaction example Data Governance interaction with IT and Business projects IT & Finance Project: Implementation of New Tax Calculation Engine Data Governance Requirements Requirement to convert US Postal Codes to Zip+4 format Identify scope & impacts. Approve actions needed. Design Implementation Verification Changes Implemented Assist IT with zip code clean-up and verification process. Implement new data entry standards for Zip+4 format. Maintenance Quality Management Monitor and maintain zip codes to Zip+4 standard 15

16 Governing: Maturity Data Governance & Data Management Maturity Example Undisciplined Disciplined Unfocused Initiating Managed Controlled Data Governance: Data Stewardship: Data Quality Management: Data Access Management: Green Green Yellow Red 16

17 Governing: Multi-Domain Examine how to best coordinate and prioritize multi-domain activity and focus, particularly in regards to technology needs, quality improvement priorities, and demand for budget and IT resources. Although much of the data context will be unique within each data domain, there can be similar elements of governance that can begin to compete and cause unnecessary redundancy across the domains if they are not effectively coordinated. Various IT-oriented services such as metadata management, data analysis, data integration, data cleanup, or development of reports can create competing demand across multiple domain practices. Ensure that these services are as extensible and scalable as possible in order to manage the demand as economically and efficiently as possible. 17

18 Governing: Multi-Domain (continued) A well-conceived enterprise data governance program office should always be cognizant of how to continually coordinate and enable domain specific governance needs and avoid overmanaging where control and conformance is unnecessary. If one domain already has a successful data governance practice underway, an enterprise level program office should continue to keep that runway open and as clear as possible. A program office needs to cultivate an environment where a maturing governance practice can lead by example and develop best practices that the other domain areas can leverage to accelerate their governance implementations. 18

19 Governing: Multi-Domain Program Model Executive Steering Committee Enterprise Data Governance Program Office Technology Services Compliance Facilitation Customer Domain Product Domain Location Domain Domain Governance Domain Governance Domain Governance 19

20 Governing: Enterprise Data Governance People Process Enterprise Data Governance Standards Technology Data Integration Data Quality Data Domains Compliance Metadata 20

21 Appendix Section 21

22 About the Authors Mark Allen and Dalton Cervo are co-authors of the book Master Data Management in Practice: Achieving True Customer MDM (John Wiley & Sons, 2011). For more reference please visit Mark Allen has over 20 years of data management and project management experience including extensive planning and deployment experience with customer master initiatives, customer data integration projects, and leading data quality management practices. Mark is a senior consultant and enterprise data governance lead at WellPoint, Inc. Prior to WellPoint, Mark was a senior program manager in customer operations groups at both Sun Microsystems and Oracle Corporation. At Sun Microsystems, Mark served as the lead data steward for the customer data domain throughout the planning and implementation of Sun s enterprise customer data hub. Mark has led implementation of various customer MDM-orientated programs including customer data governance, data quality management, data stewardship, and change management. Mark has championed many efforts to improve customer data integration practices, improve quality measurement techniques, reduce data duplication and fragmentation problems, and has created hierarchy management practices that have effectively managed customer entity structure and corporate linkage. Mark has served on various customer advisory boards and user groups focused on sharing and enhancing MDM and data governance practices. Dalton Cervo has over 20 years experience in software development, project management, and data management areas, including architecture design and implementation of an analytical MDM, and management of a data quality program for an enterprise MDM implementation. Dalton is a senior solutions consultant at DataFlux, helping organizations in the areas of data governance, data quality, data integration, and MDM. Prior to DataFlux, Dalton served as the data quality lead for the customer data domain throughout the planning and implementation of Sun Microsystems enterprise customer data hub. Dalton has extensive hands-on experience in designing and implementing data integration, data quality, and hierarchy management solutions to migrate disparate information; perform data cleansing, standardization, enrichment, and consolidation; and hierarchically organize customer data. Dalton contributed a chapter on MDM to Phil Simon s book, The Next Wave of Technologies Opportunity in Chaos. Dalton is a member of the Data Quality Pro expert panel, has served on customer advisory boards, and is an active contributor to the MDM community through conferences and social media vehicles. Dalton has BSCS and MBA degrees, and is PM certified. 22

23 Helpful References Publications: The Information Difference Company Ltd. How Data Governance Links Master Data and Data Quality. August 2010 Dyche, Jill; Nevala. Kimberly. Ten Mistakes to Avoid when Launching Your Data Governance Program. Baseline Consulting Group, White Paper, 2009 Web Sites: The Data Governance & Stewardship Community of Practice at The MDM Community at MDM in Practice: Follow Data Governance topics at 23