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Kelle O'Neal

Thanks for joining our data conversation! This blog is an opportunity to share the real life challenges, opportunities and approaches to improving the quality and value of data in your organization. We will write about everything data related from translating "data" speak into "business" speak, to governance models, to the real differences among the myriad software tools available. But there's one catch: we all have to agree to toss out the fluff. That's right, no 30,000 foot, theoretical strategies that leave you wondering how to execute and actually improve performance. Visit regularly to learn from peers and partners on how they are managing and improving data, and we hope you'll also share your views and experiences.

About the author >

As Founder and Managing Partner of First San Francisco Partners, Kelle O’Neal manages specialist data governance and data management consulting services to complex organizations that deliver faster time to results. Kelle can be reached at or through the First San Francisco Partners website.

Follow First San Francisco Partners on Twitter at @1stSanFrancsico.

Editor's Note: Find more articles and resources in Kelle's BeyeNETWORK Expert Channel. Be sure to visit today!

There are a number of factors that have been shown to play key roles in the success of a data governance initiative. In this post, we will explore these four:
  • Executive sponsorship
  • Leadership alignment
  • Communication
  • Stakeholder engagement

Executive Sponsorship

As the factor most often identified as the greatest contributor to the success of a change management program*, having the right executive sponsorship ensures that stakeholders impacted by a data governance implementation receive the necessary guidance to transition efficiently and effectively through the change. The executive sponsor should be someone who understands the initiative and who believes in it and fully supports it. And he or she must be able to effectively communicate with and engage other leaders in support of the changes. 

Action: At the outset, gauge the sponsor's understanding of his or her role and level of comfort in leading the data governance change. Provide clear guidance about what you need from them and exactly what you want them to communicate to the organization. Chances are they will appreciate the explicit direction and coaching.

Leadership Alignment

Another critical success factor is ensuring that leaders - at all levels - are aligned. This is concerned with questions like:

  • Is there agreement on and unified support for the need for a data governance program in your organization?
  • Is there agreement about what defines success in a data governance implementation?

If your leadership is not aligned, they will end up sending mixed messages which can lead to resistance and eventually derail the forward progression of change.

Action: Assess - and regularly re-assess - your leaders to identify disconnects and then take steps to address them quickly. This, of course, presents a great opportunity for your expertly coached executive sponsor to step in and reinforce expectations for support and commitment.


Also identified as a leading contributor to change management success, communication must start early (the earlier the better) and should continue openly and often. Stakeholders need to have a clear understanding of and be actively engaged in the change process. Messages should be customized according to stakeholder group - certain groups will require breadth of information about the governance implementation while those on the frontlines of the changes will need depth of information in order to perform their jobs appropriately. 

Whatever the message and to whomever it is going, keep repeating it until it is heard.

Action: Create a story around your governance initiative. Then build key, repeatable messages around it. Finally, test your messaging over time to ensure its effectiveness and adjust as needed.

Stakeholder Engagement

  • Who will be impacted by the data governance program?
  • How will roles and responsibilities shift?
  • How might those affected respond to the changes affecting them?
  • What issues and concerns will people have?

Individuals as well as groups and departments impacted by your data governance initiative will react differently to needed changes. How you engage these stakeholders - how you communicate with, respond to and leverage them - will have a significant impact on the success of your data governance initiative.

Conduct a stakeholder analysis to answer questions like those above. Having a clear understanding of the impacted individuals and groups will help you determine the best approach to engaging them in the change. Do the analysis early - the more you can anticipate reactions to the change, the more you can plan for them.

Insight gained during this exercise will also help you determine how to best allocate time and other limited resources, and it will enable you to more effectively prioritize and manage your overall change efforts.

Action: One way to determine the best approach to engaging different stakeholders in the change process is to map each individual or group according to level of influence within the organization and level of interest in the data governance implementation.

Stakeholder Engagement Quadrant_revised lower left.JPG

In the above diagram, we can see that for an individual in a highly influential position, even though they may only be marginally impacted by the data governance change process, it will still be important to meet their needs. You want to be sure they support the program, so that when they talk about it, it is in a positive light. For an individual or group that is highly impacted by-- and therefore highly interested in -- the change but who may have a low level of organizational influence, you would want to show them a lot of consideration and support. They may not be a key player, but at least they will buy-in to the program and they won't be speaking negatively about it.

Final Thoughts

When it comes to implementing a data governance program, it is essential to focus your efforts and limited resources on those factors that have been consistently shown to have the greatest impact on the program's success. While those discussed above represent only four of them, they underscore the critical role that people play in an undertaking often viewed as fundamentally technology-based.

For more information on organizational change management for data governance success, view these slides or watch this webinar (presented jointly with our partner, Pam Thomas, of IMCue Solutions).

*According to Prosci studies on best practices in change management

Posted August 24, 2015 12:03 PM
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In previous posts, we talked about how choosing the right operating model and employing organizational change management can impact the success and sustainability of your data governance program. Now we share our Top Eight Data Governance Design Principles (also published here) to help keep your program in the right direction.

1. Be clear on purpose
Build governance to guide and oversee the strategic and enterprise mission.

2. Use enterprise thinking
Provide consistency and coordination for cross-functional initiatives. Maintain an enterprise perspective on data.

3. Be flexible
If you make it too difficult, people will circumvent it. Make it customizable (within guidelines), and people will get a sense of ownership.

4. Simplicity and usability are the keys to acceptance
Adopt a simple governance model people can use. A complicated and inefficient governance structure will result in the business circumventing the process.

5. Be deliberate on participation and process
Select sponsors and participants. Do not apply governance bureaucracy solely to build consensus or to satisfy momentary political interest.

6. Align enterprise-wide goals
Maintain alignment with both enterprise and local business needs. Guide prioritization and alignment of initiatives to enterprise goals.

7. Establish policies with proper mandate and ensure compliance
Clearly define and publicize policies, processes and standards. Ensure compliance through tracking and audit.

8. Communicate, communicate, communicate!
Frequent, directed communication will provide a mechanism for gauging when to "course correct" managed stakeholders and effectiveness of the program.

Posted August 20, 2015 9:24 AM
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Does it feel like you're just spinning your wheels when it comes to implementing data governance at your organization? 

If you find your organization repeatedly going through the process of initiating a data governance program or your efforts just seem to fall flat, let me share with you why success may be eluding your organization and what can be done about it.

People: The Chief Obstacles to Data Governance Success

Companies often blame poor data management infrastructure when a data governance implementation fails. In reality, while that can make it more difficult, it isn't usually the biggest obstacle to success. The greatest obstacles tend to be things like:

  • Competing priorities
  • Lack of resources
  • Data ownership and other territorial issues
  • Lack of cross-business unit coordination
  • Lack of data governance understanding
  • Resistance to change, transformation or accountability
  • Lack of executive sponsorship and buy-in
  • Lack of business justification
  • Inexperience with cross-functional initiatives
  • Personnel changes

The common thread running through all of these? People.

Implementation means change. A data governance implementation, in particular, involves changing your information management culture, processes and policies. It means asking people to change the way they think and behave about how data is accessed and used. And more often than not, it means diving into the unfamiliar or unknown.

If there is to be any chance of success with a data governance implementation, you must plan for and manage that change.

Employing Organizational Change Management for Governance Success

Change management is typically viewed as having two sides:

  • A situational/business side, which focuses on the who, what, when, where and why of the change
  • A psychological/people side, which addresses the reorientation people go through as they come to terms with their new situation

While the situational side of change management tends to be relatively easy to anticipate and plan for, the psychological side is more complex. However, for change to be successful, BOTH sides must be addressed.

Focusing on the psychological side, we can look to organizational change management for an organized and systematic approach to address the people side of change.

Organizational change management involves helping people through change quickly and successfully so that business value is achieved. It involves anticipating and observing individuals' reactions, proactively identifying and addressing problems and needs, and closely monitoring and responding to feedback so that change adoption can proceed smoothly and efficiently.

An organizational alignment action plan can be prepared in advance of change to provide guidance. This tool helps ensure:

  • Processes, practices and procedures are updated to reflect the new way of doing things
  • Roles and responsibilities are redefined or created in support of the changes
  • Performance goals and recognition/reward structures are realigned to encourage and reinforce the new behaviors and ways of working.

Success metrics will need to be defined and accepted. Strong leadership and sponsorship from key executives will also be important to encourage acceptance and buy-in.

Once transition is under way, pay attention to feedback and how people are reacting to the new requirements and respond accordingly. It will be important to hold people accountable so they don't slip back into old behaviors. But, be sure they have or can easily access needed resources and support, including training. Also, listen closely to any pushback occurring - what is it telling you? The pushback may be a source of new ideas or approaches in addressing the data governance program.

Successful transition will only be possible, however, if all of those involved in or impacted by the planned change have a full and clear understanding of exactly what's changing and what that means in terms of required behavior changes. It will be up to the organization in what may be one of its greatest challenges to effectively communicate that change and help people successfully navigate through it.

William Bridges, in his book, Managing Transitions, identifies "Four P's" to help guide companies in effectively communicating through transition. Adapting those to a data governance implementation, we have: 

  • Purpose: Why the company is implementing data governance why it is important to the business and why it is important to the individuals in the organization
  • Picture:  What the future state will be once data governance is implemented
  • Plan: Actual steps and a timeline to get to the future state
  • Participation: What each individual's role will be both during the transition and once at the future state

Communication should start early and continue openly and frequently throughout the transition. Messages will need to be customized to stakeholder group impacted executives and managers will want a to have a broad understanding of governance changes and will require a different message than data stewards, owners and custodians who will need a deeper understanding of the changes to perform their jobs effectively.

Embedding Organizational Change Management for Governance Sustainability

As you progress in your data governance implementation, it is essential to go beyond just employing change management practices to get people from point A to point B - you must embed them into your company culture and operations. This will give your organization the greatest chance of achieving data governance success over the long haul.

Organizational Change Management.JPG

To sustain governance change, there must be alignment of:

  • Organizational structure/Sponsorship
  • People/Jobs/Accountability
  • Policies/Practices/Procedures
  • Incentives/Reward structures
  • Performance management

You'll want to assess whether the changes have become an integral part of the way in which your organization works and whether previously defined success metrics are being achieved. Then, as needed, identify and implement actions to reinforce changes.

Ultimately, the success of your data governance is in the hands of the people in your organization. But it is up to you to provide the guidance and support to enable them to navigate past the resistance and fear of the unknown and through the change to a successful program. 

Organizational change management provides an effective framework for managing all aspects of this change, and it allows your organization to have the greatest chance for data governance success and sustainability.

For more information on organizational change management for DG success, view these slides or watch this on-demand webinar (presented jointly with our partner, Pam Thomas, of IMCue Solutions).

Posted August 14, 2015 5:50 PM
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The Framework for Data Governance

Whether it stems from a desire to understand customers at any time from any channel, a need to balance old mainframe systems with new technologies, a requirement to comply with numerous and emerging regulations or some other initiative...we all want to connect data to business value.

Amidst these and other data challenges, successful and sustainable Data Governance (DG) is becoming a top priority for organizations that want to ensure the right people are involved in determining standards, usage and integration of data across projects, subject areas and lines of business (LOBs).

A critical step in DG design is identifying the best-fit operating model for your organization. The operating model is a framework articulating the roles, responsibilities and decision making process for governance. 

It ensures the right people are represented and it helps to create accountability. It also facilitates communication and provides a process to resolve issues. While it does help form the basis for the DG organizational structure, it is NOT an org chart. 

Here's a high level overview of the pros and cons of the Centralized, Decentralized, Hybrid and Federated model types to aid in assessing your own organization.

Centralized Operating Model

Similar to a project model with an executive sponsor, here everything is owned by the DG Lead, and those involved in governing and managing data report directly to this person.

A rule of thumb to determine the best sponsor is to identify where there is political leverage beyond a title (such as CFO or COO) because cross-functional involvement and support are fundamental to success in this role.

  • Establishes a formal DG executive position
  • DG Steering Committee reports directly to executive
  • Data Czar/Lead means there is one person at the top; easier decision making
  • A one-stop shop
  • Easier to manage by data type
  • When implementing, there is generally a large organizational impact
  • New roles will most likely require Human Resources approval
  • Formal separation of business and technical architectural roles

Decentralized Operating Model

The antithesis to the Centralized model, here everything is committee-based so there is no single DG owner. This is how most data governance programs start and is a very grassroots structure.

  • Relatively flat structure
  • Informal Data Governance bodies
  • Relatively quick to establish and implement
  • Consensus discussions tend to take longer than centralized edicts
  • Many participants compromise governance bodies (making it potentially unruly)
  • May be difficult to sustain over time because of its informality
  • Provides least value since it tends to be the least productive
  • Difficult coordination
  • Business as usual
  • Issues around co-owners of data and accountability

Hybrid Operating Model

Like its name implies, this encompasses benefits of both models - here there is a centralized DG office with a decentralized (virtual) working group. The Steering Committee is centralized in that there is only one for the whole company, and it represents all the key lines of business or business units.

  • Centralized structure for establishing appropriate direction and tone at the top
  • Formal DG Lead role serving as a single point of contact and accountability
  • DG Lead position is a full-time, dedicated role so DG gets the attention it deserves
  • Working groups have broad membership for facilitating collaboration and consensus building
  • Potentially an easier model to implement initially and sustain over time
  • Pushes down decision making
  • Provides ability to focus on specific data entities
  • Issues resolution without pulling in the whole team
  • DG Lead position is a full-time, dedicated role (therefore may require new headcount)
  • Working group dynamics may require prioritization of conflicting business requirements
  • Too many layers

Federated Operating Model

Very closely related to the Hybrid Model, this model provides for additional layers of centralization/decentralization as is often required in large global enterprises. 

  • Centralized Enterprise strategy with decentralized execution and implementation
  • Enterprise DG Lead role serving as a single point of contact and accountability
  • "Federated" DG practices per LOB to empower divisions with differing requirements
  • Potentially an easier model to implement initially and sustain over time
  • Pushes down decision making
  • Provides ability to focus on specific data entities, divisional challenges or regional priorities
  • Issues resolution without pulling in the whole team
  • Too many layers
  • Autonomy at the LOB level can be challenging to coordinate across the enterprise
  • Difficult to find balance between LOB priorities and Enterprise priorities

The Key to Successful Data Governance

Whichever model you choose; remember that simplicity and usability are essential for acceptance. When you ensure the operating model fits the culture of your company, you're embedding Data Governance into your operations and aligning your strategy for long-term sustainability.

Posted July 27, 2015 4:39 PM
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A client recently asked me this, and I thought it was a great question!

Data Architecture and Data Governance support each other in a variety of ways, with the mutual goal of creating standards and guidelines to support the enterprise in increasing operational efficiency, decreasing costs and mitigating risk.

Data Governance Supports Data Architecture

There are policies/principles that are designed and enforced by the overall architecture group to ensure best practices are followed for new technology implementations. The Data Governance Team (DGT) can "translate" those policies into business requirements and guidelines and help to enforce them from a business perspective. Education sessions will help the business folks to understand the importance of data architecture and the impact when its guidelines aren't followed. This should help the business in its project planning process as well as set its expectations for what is possible. Without the DGT as a forum, this sort of knowledge transfer only happens on a project-by-project basis and usually only when a request is made that is denied because it doesn't adhere to the data architecture policies/principles.
Data Modeling--an important component of Data Architecture--is also critical to Data Governance. It is rare that the business cares to be involved in data modeling, and the DGT has an important role to play in educating the business on data modeling and translating why data modeling needs to be a reflection of the operational use of data (as well as a reflection of customer engagement, product positioning and other key business operations). The hierarchies and data relationships that the DGT creates to reflect the view of the customer, for example, need to be instantiated in the appropriate data model(s). Without a high-level understanding of data modeling, business people will lack the ability to understand how to translate the operational use of data into the systems that house the data. Essentially, the data model needs to reflect the business model, and the DGT can act as both a translator and a facilitator to ensure that this happens.

Data Architecture Supports Data Governance

Data Architecture provides an understanding of what data exists where and how it travels throughout the organizations and systems. It highlights changes and transformations made as data moves from one system to the next. These data inventory and data flow diagrams provide the information and the tools that the DGT needs in order to properly make decisions regarding data policies and standards. These artifacts also help the DGT perform root cause analysis when data issues are raised by business people, and they help to solve those issues.
The data inventory and data flow diagrams also help to determine what can be measured, when and how. They can help to identify the possible business impacts associated with improving data quality in the systems by understanding who uses the systems and for what purpose--this also helps in creating metrics and measurements. As well, they can help to determine how to measure adherence to standards based on who creates and updates the data in which systems.
Overlaying the data inventory and data flow diagrams with data accountability and ownership is a helpful artifact for everyone in the organization and also helps to determine where gaps in accountability and ownership may lie.
Participants in the DGT may also find these artifacts very useful to take back to their groups and educate their teams about the source and usage of data that they may consider "theirs." In fact, in many instances, key business stakeholders say it would be helpful to understand the data landscape and how data moves across the organization. The DGT has a role in educating the rest of the company on this information and in overlaying that information with the policies and standards that are implemented along the way--all of which is important in ensuring that data is accurate and maintains its integrity throughout its lifecycle.

Do your Data Governance and Data Architecture groups work together? How do you see them supporting one another? I'd love to hear how this works in your organization!

Posted March 26, 2014 5:00 PM
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