This type of program typically comes into existence in conjunction with a major system acquisition, development effort, or update that requires new levels of cross-functional decision-making and accountabilities.

Focus on Architecture

What other types of groups and initiatives might want such a program focus? Enterprise initiatives such as

  • A move to Service Oriented Architecture (SOA), with its need for well-governed data
  • A new focus on Metadata
  • A Master Data Management (MDM) initiative
  • Enterprise Data Management (EDM)
  • Business Process Reengineering (BPR)
  • Standardization on platforms
  • Changes to systems due to new business focus or Merger and Acquisitions activity

A charter for this type of program may hold Data Governance and Stewardship participants accountable to:

  • Ensure consistent data definitions
  • Support architectural policies and standards
  • Support Metadata Programs, SOA, Master Data Management, Enterprise Data Management (EDM)
  • Bring cross-functional attention to integration challenges
  • Identify stakeholders, establish decision rights, clarify accountabilities

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Demonstrating Value

Everything an organization does should tie to one of three universal value drivers. Data Governance efforts MUST tie back to one or more of these drivers. And YOU must communicate how it does.

Data Governance Program Phases

As you perform the activities needed to gain support and funding, remember that your program may plan to address multiple focus areas. Each new effort should be introduced using the seven steps of the life cycle. Even specific governance-led projects, such as creating a set of data standards, will want to follow the Data Governance Life Cycle steps.

Governance and Decision-Making

Remember our (long) definition for Data Governance? “Data Governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and...

Governance and Issue Resolution

One of the three most important jobs of a Data Governance program is to help resolve data-related issues. These may be conflicting data definitions, data usage concerns, or problems with how data is sourced, how it is integrated, how it is protected, or a myriad of...

Assigning Data Ownership

One of the tenets of Data Governance is that enterprise data doesn’t “belong” to individuals. It is an asset that belongs to the enterprise. Still, it needs to be managed…

Working with Data Stewards

Approaches to Assigning Data Ownership and Stewardship Organizations can take multiple approaches to assigning Data Owners and Data Stewards for enterprise data. In doing so, they need to consider several factors and answer the following questions.Question #1:  Should...

Focus Areas for Data Governance: Privacy, Compliance, Security

This type of program typically comes into existence because of concerns about Data Information Security controls, or compliance. Compliance, in this context, may refer to regulatory compliance, contractual compliance, or compliance with internal requirements.This...

Focus Areas for Data Governance: Policy, Standards, Strategy

This type of program typically comes into existence because some group within the organization needs support from a cross-functional leadership body. For example, companies moving from silo development to enterprise systems may find their application development teams...

Setting Governance Roles and Responsibilities

Who does what in a Data Governance program? First, a group of individuals (or a hierarchy of groups) representing a cross-section of stakeholder groups makes a set of rules in the form of policies, standards, requirements, guidelines, or data definitions. (Or, they...

Dealing With Politics

It’s essential that Data Governance and Stewardship program facilitators avoid being “caught up” in politics. It’s our jobs to acknowledge the realities of the situations we work with, while avoiding taking sides or engaging in behaviors that could be perceived as favoring one set of data stakeholders at the expense of others.