Governance and Issue Resolution

Processes and Alignments

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 other issues.

Governance and Issue Resolution

Data Governance programs are generally structured to allow an orderly and predictable issue escalation path for data issues that pulls together the right stakeholders at the right time with the right research and analysis, so they can make a decision that is right for the enterprise.

The DGI Data Governance Framework includes “Resolving Issues” as a key Governance process. Of course, every organization will decide how much structure and formality to bring to the process of resolving data-related issues.

Ideally, this process should be standardized, documented, and repeatable. It should be crafted in such a way to support regulatory and compliance requirements for Data Management, Privacy, Security, and Access Management. Record-keeping should provide a clear history of which stakeholders were involved in resolving an issue, what resources/analysis were used, and what alternatives were considered.

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Goals and Principles for Data Governance

What do you want Data Governance to accomplish?  Regardless of the focus of your program, chances are you hope to accomplish the following universal goals for Data Governance programs: Goal – Enable better decision-making Goal – Reduce operational friction Goal –...
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Governance Communications

At a Data Governance Conference in Orlando, Florida (USA), a group of managers of successful Data Governance programs reached a startling consensus: They agreed that Data Governance is actually somewhere between 80 and 95% communications!How can this be? They said...
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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...
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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...
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Defining Data Governance

How you define your program will influence your ability to manage it — to keep all participants on focus, in sync, and striving toward the same goals.

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Focus Areas for Data Governance

All Data Governance programs are not alike. Quite the contrary: programs can use the same framework, employ the same processes, and still appear very different. Why is this? It’s because of what the organization is trying to make decisions about or enforce rules for....
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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...
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Focus Areas for Data Governance: Data Quality

This type of program typically comes into existence because of issues around the quality, integrity, or usability of data. It may be sponsored by a Data Quality group or a business team that needs better quality data. (For example: Data Acquisition or  Mergers &...
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Choosing Governance Models

It’s important to define the organizational structure of your Data Governance program. But before you can do that you have to define your governance model at a higher level. You need to consider what types of decisions your governance bodies will be called upon to...
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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.

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