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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 kelle@firstsanfranciscopartners.com 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!

When implementing data governance (DG), enterprise information management (EIM) or any similar business program, strong sponsorship is essential. Without it, a new program is all but guaranteed to fail.

But sponsorship can mean different things to different organizations. Often there is no clear understanding of what the role of Business Sponsor entails, and this may present a very real challenge. When success evades a new program, it may be because the sponsor has missed the mark and failed to progress the program to a point of sustainability. We have observed this in many organizations we have worked with.

Let's consider a different term: engagement. Engagement means there is leadership embracing accountability for success. Engaged leaders go beyond just buy-in and typical sponsorship activities in order to effect change. Programs and initiatives like DG and EIM are more likely to succeed--and the success more likely to be sustainable--when business leadership prevails. 

That's not to say that the role of Business Sponsor--or the concept of sponsorship in general--should necessarily be eliminated from the equation. Indeed, a very powerful Sponsor will be able to secure needed buy-in and engagement. But when that is not the case, you may need to shift from a sponsorship model to one of leadership to be effective.

This transition should occur in three stages.

Stage 1: Align the Program to the Business

Look at enterprise needs--not user wants--when setting up the program. An enterprise need, for example, might be "to increase brand awareness." The program should then be structured so that it supports these kinds of overall business needs.

Indeed, it is common for stakeholders to think alignment is about granting access to all transaction details. But that is not what is meant by alignment in this context.

Stage 2: Develop a Vision

Create a vision for the program and a purpose for why the program is being implemented and resourced. But be practical. The vision must support that of the enterprise where data assets are adding to business value.

Stage 3: Pivot and Operate the Program

Once program goals and objectives have been aligned with those of the enterprise, and armed with your vision, you can then start to engage the organization in rallying behind the plan. You'll want to be sure to identify who needs to participate, when and how. Be as specific as possible--details are important here.

There are multiple ways to approach this. You might hold an orientation or a series of meetings, or you might establish operational groups. You may also decide to hold a Pivot Workshop, which can help you pull together cross-functional leaders in the organization.

The key is to plan the pivoting from being developed to being operational--and from being business sponsored to truly business led.

And don't forget to follow through: Summarize results and activities and follow-up to ensure continued accountability and program sustainability.

This post was written jointly with First San Francisco Partners' John Ladley and drawn from our January 7, 2016 CDO Webinar, The Difference between Business Sponsored and Business Led. For an expanded discussion of this topic, watch the video replay or view the slide deck.

Posted February 22, 2016 10:00 PM
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Whether your data governance program has been recently deployed or has already matured into a going concern in your organization, consistent and impactful communication plays a critical role in translating data value into business value.

A communication plan lays out a strategy to help an organization achieve its awareness goals. It describes the What, When, Where, Why and How of a communication program and is meant to create a bi-directional conversation.

With a solid communication plan, you can keep stakeholders informed of your program's progress and accomplishments, fostering executive buy-in and ongoing commitment.

Your communication plan can also:

  • Give the working team a day-to-day work focus
  • Help stakeholders and the working team set priorities
  • Provide stakeholders with a sense of order and controls
  • Provide a demonstration of value to the stakeholders and other business folks
  • Help stakeholders support the data program
  • Help to protect the data program against last-minute demands from stakeholders

The key to communication that resonates is to ensure the metrics and measurements map to stakeholders' defined professional and personal goals.

Here are some starter questions to help as you develop your plan:

  • Who needs to be communicated to?
  • What information is important to them? 
  • How frequently should they be updated? 
  • What is the method of communication?
  • Who should be communicating the message?

For further inspiration, here are some components of a communication plan you'll want to tailor to specific stakeholder groups.

Components of Communication Plan

Posted November 22, 2015 4:15 AM
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Having previously identified five common pitfalls to data governance, let's now look at some best practices you'll want to adopt in each case to help you get back on track or avoid falling into that trap in the first place.

Pitfall #1: Governing data from within IT

Best Practice: Identify and recruit a change leader on the Business side

Though IT is often the first to identify the need for data governance, Business is generally the primary creator, fixer and user of that data. Not surprisingly, data governance tends to be much more successful when the control of data occurs from within Business.

When looking for an executive sponsor for your data governance program, you'll want to consider the following qualities:

  • Ability to lead cross-functional initiatives
  • Ability to manage multiple political functions simultaneously
  • High regard as a respected leader
  • Self-confidence and flexibility
  • Ability to communicate effectively and inspire others

You'll also want to consider the impact data has on the potential sponsor's business unit, ensuring there is a high level of interest. The right business-focused change leader can subsequently help drive implementation of other best practices.

Pitfall #2: Governing data in silos

Best Practice: Establish enterprise data governance so people "Think globally and act locally"

While a data governance program confined to an individual business unit or line of business will likely help that unit or line, problems arise because data is shared across different business groups--each group defines a given data element according to their own needs and perspectives and this can lead to inconsistencies in information and inefficient or suboptimal decisions.

Data governance can only be successful when an organization governs data as an enterprise asset. The cross-functional steering committee, for example, can ensure shared definitions are consistent across groups and that value is created across the enterprise. It is important for people to "think globally but act locally." Data governance existing in silos is not without value, however, as what is already in place can be leveraged and built out to the enterprise.

Pitfall #3: Assuming everyone understands and appreciates the value of data

Best Practice: Communicate early and often about the impact of inaccurate and inconsistent data and the benefit of data governance

While some stakeholders are involved in or at least aware of all that goes into cleaning data and appreciate the value of that data to the organization, others who only see the data after it has been cleaned may have little appreciation for its true value. Thus it is important to communicate this value and the benefit of data governance from the start and repeatedly after that. The change leader and sponsor identified in the first best practice should play a central role in this communication.

Pitfall #4: Using meaningless metrics

Best Practice: Measure impact as well as progress

Borrowing from the pitfall addressed above, we can understand how, for example, a process metric reflecting a decrease in data errors is meaningful to a group that actively scrubs the data and addresses problems with it, but that metric may mean little to another group that only sees the data in its clean state.

That's why it is important to measure (and, of course, communicate) both impact and progress and to translate metrics into business value. In the above example, we want to understand how the progress metric (reduction in data errors) translates into improvements in the business, a KPI.

Pitfall #5: Treating data governance as a project

Best Practice: Embed data governance into your operations

When implementing a data governance initiative, organizations will often approach it as they would a project, with a distinct beginning, middle and end, and funding allocated accordingly. However, data governance is an ongoing program that cannot be sustained without continued resources and support.

It is critical for organizations to ensure data governance is fully integrated into their operations and that it continues to receive necessary funding and attention. Ensuring from the start that your operating model fits the culture of your company facilitates embedding data governance into your operations and aligning your strategy for long-term sustainability.

Posted November 21, 2015 4:00 AM
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Maybe you are at the early stages of planning a data governance initiative at your company. Maybe you have already started implementing a program but progress has stalled. Perhaps you previously attempted to implement governance...but were ultimately unsuccessful. 

Whatever your situation, understanding the pitfalls that have typically plagued organizations pursuing data governance will help you plan ahead to avoid them. Here are five common pitfalls you'll want to be on the lookout for.

1. Governing Data from within IT

Often, the need for data governance is first identified by the IT organization. IT, however, is generally not the primary user of the data, nor are they the creator of the data. In most cases, Business is the primary creator and user of data - and they are the one who must to fix the data when there are errors, duplications, etc. Therefore, an IT-led data governance initiative is likely to see limited success.

2. Governing Data in Silos

When data issues arise within an individual business unit or line of business, the tendency may be to address them within that unit. Implementing a data governance program confined to the individual unit may, indeed, satisfy their own, internal governance needs. But the problem arises when - and because - data is shared across different business groups. Where one group defines a given data element according to their own perspective and needs, another group may define the same element differently leading to inconsistent information across the enterprise and the potential for even more data problems.

3. Assuming Everyone Understands (and Appreciates) the Value of Data

While some stakeholders in an organization are highly involved in or at least aware of all that goes into fixing data errors and other issues and appreciate the value of that data to the organization, others may be immune to it. Only seeing the cleaned data, but not the resources consumed to get it to that state, they may have less appreciation for its value.

4. Using Meaningless Metrics

Drawing from the scenario in the prior pitfall, where one unit declares a program that results in a 30% reduction in data errors a success, another unit, unaware of the time and other resources that typically go into fixing data issues and the impact if not fixed, may view such a metric as useless (along with the investment made in the program). A metric that is meaningful to the first group is meaningless to the second. (Learn more about creating meaningful metrics here.)

5. Treating Data Governance as a Project

Most companies are project-driven. They identify what they want to accomplish, plan the approach, acquire funding and resources and plot a timeline with milestones. Then they execute. There is a beginning, a middle and an end. When pursuing a data governance initiative, companies will often approach it as they would any project.

However, once the data governance program is rolled out, allocated funding is used up, people shift their focus to other projects and the established policies, practices and standards governing data reach a point where they are no longer maintained or adjusted in response to organizational and business changes, the program falls apart.

The issue with treating data governance as a project is that it creates the expectation that there is a finite timeline with an endpoint, finite funding and finite participation. But data governance cannot be sustained without ongoing resources and support.

Stay tuned for a follow-up post where I'll turn to the flip side and discuss best practices that you'll want to adopt in order to avoid or overcome each of these pitfalls.

Posted October 14, 2015 1:52 PM
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One of the biggest challenges in implementing and sustaining a data governance program is determining the true impact the program has made to the organization.

While it may be a relatively straightforward process to identify things like changes in data accountability, the creation of new standards and policies, improvement in data quality, etc., the real challenge is determining how all of this progress has improved the bottom line.

Metrics are a good start, as they are already necessary to ensure alignment, relevance and value of your data initiative. But to truly translate measurement into tangible business value, you must link progress metrics with impact metrics and align everything to key business goals.

To accomplish this, you'll want to take a step back from focusing on the metric (any standard of measurement) or key performance indicator (a quantifiable metric that the data governance program has chosen that will give an indication of program performance) in isolation.

Start by looking at the business challenge, and then create the measurement and metrics that address the business need.

Instead of asking "How do I measure data lineage?" ask, "What is the issue I'm trying to address?"

The point is to clarify the issue - what is meant by the issue, why that issue is important and what is the change you'd like to see, i.e. the goal. Many times, just by clarifying "what you mean" and "why you care," you can come up with a way to track a change over time or measure the result. 

Remember, also, that measurement is iterative. The more you know, the more you can adjust your metrics and measurements to be more precise or focus on different things to drive value.

Once you've identified meaningful metrics for your organization, don't forget to create a communication plan to disseminate the findings. Communication is key to maintaining commitment. 

Metrics have no value if they are not aligned to the interests of stakeholders, so ensure there is some way of measuring how improvements to data (and to the governance of that data) are helping them progress toward their goals and translate the value statement into their own language.

Posted October 13, 2015 10:27 PM
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