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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!

October 2015 Archives

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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