understanding data governance and its role in your business

a Datamation® IT Management eBook

Understanding Data Governance

and Its Role in Your Business

2 A Data Governance Primer

5 The Silent Problem of Data

8 Data Governance: Learning Data Lessons

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Contents…

This content was adapted from the Baseline and Database Journal websites. Contributors: Jill Dyché and Keesa Bond.

Understanding Data Governance and Its Role in Your Business

2 Understanding Data Governance and Its Role in Your Business a Datamation IT Management eBook. © 2013 QuinStreet, Inc.Back to Contents


tandard industry intelligence says that corporate data volumes double every 18 months, but mobile and online data are growing at an even faster clip. From

corporate cost containment, regulatory compliance, strategic “voice of the customer” initiatives and beyond, the need for businesses to manage proliferating data has never been more urgent.

Enter data governance. The term has become as buzz-worthy with businesspeople as it is with their IT colleagues. Nevertheless, both sides continue to struggle with how to define data governance and how to formalize it as a sustainable set of practices.

The term “governance” has bounced from the political world to corporate boardrooms, landing squarely in the laps of IT executives, who have been busy formalizing their IT governance initiatives. Despite the term entering the lexicon, there is still some confusion about what data governance actually means, what work is involved with it and who should own it.

IT organizations have begun to recognize the need to define terminology and business rules around data that is increasingly shared across business processes and organizations. As part of efforts to rein in costs, CIOs are starting to understand the enormous and often-replicated efforts involved in finding, gathering, annotating, consolidating and deploying data to support a growing project portfolio.

Unaware that this work has already been performed for other projects, well-meaning developers roll up their sleeves and integrate the data yet again. The expense of this duplication is buried, but it can cost a large company millions of dollars in excess labor hours.

Increasingly, data governance is coming to the attention

of businesspeople, many of whom have begun to develop workarounds due to the lack of available data that’s meaningful, integrated and easily accessible. It’s a phenomenon that’s become rampant across industries, resulting in enormous costs.

“I’ve developed my own database to protect myself from other people’s databases,” one health care provider says wryly. “I can’t trust other people’s interpretation of data about my patients. If I have to stay late and maintain my own Access database, that’s what I have to do.”

A Data Governance PrimerBy Jill Dyché

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Multiply this clinician’s efforts by the number of clinicians in the hospital network, and you’ll get an idea of the costs—and the risks—involved.

What Is Data Governance?

Part of the general wariness about data governance is due to the lack of a clear definition.

“In health care, there’s a special urgency around information,” says Mike Nauman, corporate director and CIO at the Children’s Hospital and Health System of Wisconsin. “This is amplified in academic health care organizations that operate at the forefront of medicine.”

Nauman and his team recognized that data governance would be an organizational discipline, driven by the business but enabled by IT. “We knew that information was a shared asset, so we introduced a data management function in IT,” he explains. “The function encompasses new roles, new processes and specialized skills. As data governance evolves, we’ll be ready to support it.” Together, these two functions inform a workflow that defines, tracks, manages and deploys information across its life cycle at a company. After all, the extent to which data is shared across business processes and organizations is the extent to which it mandates formal management and policy decisions.

But how do you kick off a new data governance initiative? And how do you sustain it?

Launching Data Governance

A few years ago, when the early-adopter companies launched data governance, there were few examples to follow. Many of these organizations relied on a smattering of vendor and consultant advice, which admonished them to secure executive sponsorship and to “manage data as a corporate asset.”

That didn’t necessarily help deliver results. Often a visionary manager would convene like-minded people on both the business and IT sides to agree that data

was an asset, that data quality was poor and that someone needed to clean it up. The natural next step was to convene a data governance council, the de facto decision-making body for data governance.

Then things got really quiet.

The main hurdle facing these pioneers is translating the all-too-real phenomenon of meaningless, unavailable, duplicated, siloed data into an information-cleansing, integration and deployment strategy that serves the greater good.

Absent a tactical plan for addressing key data needs, meetings of the data governance council degenerated into complaint sessions: The data on the ERP system is unusable. Marketing won’t share its data. No one in the C-suite understands how bad the company’s information really is. Who will fund the project?

The result? Data governance never transcends ownership discussions and priority debates. It is, instead, relegated to one more intellectual exercise at the company, with plenty of people sharing opinions but no one claiming accountability for fixing the problem.

At many of these companies, data governance has become a dirty word. An insurance executive recently invited us to help relaunch a moribund data governance effort. “Just don’t call it ‘data governance,’” he warned us. “You’ll lose credibility.”

Best Practices to Consider

The good news is that companies embarking on data governance for the first time are learning from those that have gone before them. Here’s a list of best practices to consider before launching a data governance initiative:

• Find the need, pain or problem. Sure it sounds trite, but if data governance isn’t addressing an acknowledged business problem (such as fines for noncompliance or duplicate customer records eroding marketing ROI), it won’t stick.



• Know your road map. If you know your problem, you can scope your project. And that leads to definable mea-sures that can prove the value of data governance.

• Get an executive sponsor—if you need one. Com-panies—often large ones with consensus-driven cul-tures—require executive buy-in before launching cross-functional initiatives such as data governance. Others, however, need to show value quickly before enlisting senior-level support.

“We enlisted our CIO as the sponsor at the very beginning of data governance,” says Karen O’Dell, director of business systems for Station Casinos, based in Las Vegas. “He was a champion on behalf of corporate information and kept saying, ‘We really need to do this.’ Involving him ultimately got everyone else on board pretty quickly.”

• Start small. You can build a data governance charter and craft some guiding principles. Indeed, these are great mechanisms to support data governance. But make sure these apply to an actual project that addresses an iden-tified business problem.

And ensure that the project solves at least a portion of the business problem, while at the same time introducing new processes and job roles. This is the proof of concept that will give data governance the visibility it needs to have staying power.

• Proselytize your success. A successful project is the plat-form on which you’ll make the data governance pitch to a broader audience of business constituents and execu-tives. Show how implement-ing data policies, cleaning up a subset of data, and deploying it to support a busi-ness process helped lower costs or drive new revenues. People will be lining up to be next.

• Think transformation. Data governance involves more than buying a data-quality tool or hiring a data steward. Clear policies for the definition, access and use of cor-porate information raise the probability that data can be leveraged to streamline business processes, generate new revenue and even drive innovation.

When data governance works, it not only meets one or more business needs, it also cultivates an awareness of specialized skills, processes and tools needed to define, maintain and provision data across the enterprise. It removes needless manual rework by businesspeople who have day jobs, and it reduces the overreliance on human relationships that many businesspeople still fall back on.

Good data governance ensures consistent and meaningful information to support strategy and streamline operations. In short, it’s what the “data-driven organization” is all about.

“The good news is that companies embarking on data governance for the first time are learning from those that have gone before them.”



here is a silent problem in many organizations. That problem is data. More specifically, the problem is the lack of governance over data. Without

governance, data is much like a two-year-old child who is left alone for too long near the temptations of a cookie jar. Once the child empties the jar, she may experience momentary happiness, not realizing the inevitable price of a tummy ache that will arrive later.

In many respects, data is prone to suffer the same fate as that child. Data is received. It is housed in a database. It is foundational for running the business, so more data appears to be beneficial at first. It is only later that the ultimate lesson is evident because without data governance, there is an eventual price to pay. The penalty for lack of data governance however is more permanent and pervasive than a tummy ache. The negative consequences can vary the full gamut from merely annoying to facilitating the downfall of an organization.

If you are involved with data (and, in one way or another that is all of us), ask yourself if you have ever found an error in data (that will be almost everyone). Now imagine your singular negative data experience versus the overall potential of negative data experiences. Given the global volume of data out there, if even 10 percent of it is not being managed by a mature data governance approach, then how can data ever be trusted? In reality, that 10 percent figure is likely very low. There are so few organizations with effective data governance strategies; the overall percentage of data that is not being governed is probably high.

Even in organizations where a data governance approach is mature, there may be aspects of data that have been

ignored. Some organizations, for example, ensure that their day-to-day transactional business data always meets quality standards, but fail to consider a governance approach for data that has been manipulated or aggregated for reporting purposes. This means that while the original data in the transactional systems is reliable, the data being used for reporting and analytics may suffer from inaccuracies. Since many organizations use analytics to determine future strategies, inaccuracies from reported data can cause some serious and unexpected complications.

The question becomes “How can a voice be given to this silent data problem?” The answer to that question leads directly to the doors of a Data Governance methodology. Following the thought process, the next question is

The Silent Problem of DataBy Keesa Bond

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probably “What is Data Governance?” In seeking definitions, it is clear that the meaning of Data Governance is not succinctly defined. According to Wikipedia:

“Data governance is an emerging discipline with an evolving definition. The discipline embodies a convergence of data quality, data management, data policies, business process management, and risk management surrounding the handling of data in an organization. Through data governance, organizations are looking to exercise positive control over the processes and methods used by their data stewards and data custodians to handle data.”

Gwen Thomas, President of DataGovernance.com also provides several definitions. My preferred choice of those definitions says that, “Data Governance is the exercise of decision-making and authority for data-related matters.”

In considering these definitions, some very important concepts begin to surface. Obviously the asset, “data,” needs to be leveraged, secured and aligned to allow appropriate protections and decision-making. Adding to these concepts, I would suggest that by the completion of a successful data governance implementation, data will be fully and completely identified, standardized across the full organizational structure, appropriately available and verified as accurate.

On some level, most businesses recognize the foundational needs for data governance, but recognition alone is not enough to solve the silent problem of data. Data Governance needs a champion, an enabler or perhaps, in the worst case, a crisis that makes the silence intolerable and allows the necessary effort to effect change. Without organizational buy-in Data Governance approaches seldom succeed. If you consider that data governance initiatives appear to involve an expense with few monetary rewards in return, you can see that the challenge of championing a Data Governance program is not trivial.

Timelines for a Data Governance implementation will usually be lengthy and the rewards achieved are not always evident. The protections that Data Governance programs provide involve the prevention of negative consequences and since negative consequences that are not realized are invisible to most of the corporation, proving value in order to garner funds for a Data Governance approach may be challenging. When seeking funding, it is easy to explain to management the benefit of a new data warehouse, for example, but much more difficult to explain the benefits of preventing invalid conclusions that could arise from data that wasn’t effectively managed. Examples of data integrity problems are difficult to explain unless they have already been shown to be occurring, but data integrity issues may occur without any realization long before they are discovered. The perception of the absence of data integrity issues may be inaccurate. The reality of the absence of data integrity issues is the goal of a mature Data Governance program.

Consider a scenario of using a database to store customer information. Perhaps you have a table like this:

First Name Last Name Number Street City State ZIPJane Doe 800 Pine St. Ashland Kentucky 41163

J Doe Pine St. Flatwoods Kentucky 41167

JD Marsh 800 Pine St. Flatwoods KY 99999

http://www.datagovernance.com/adg_data_governance_definition.html



Could this represent a data integrity issue? It is possible that all three rows in this table really only represent the same individual, Jane Doe Marsh. Alternatively, the rows in this table could represent three different individuals. If this organization had a mature data governance policy in effect, we would know the reality.

Perhaps you can visualize many considerations for Data Governance. Once you identify one data concern and start to think about solving it, you will likely begin to think of others. While there are standards, approaches and tools that can be used to launch and support a Data Governance initiative, the better the understanding of the data as it exists today, the easier the ability to begin the effort.

Consider metadata. If your organization has a comprehensive data dictionary, listing all the metadata, with universally understood corporate meanings (a business glossary) for each piece of metadata, you have already achieved one step towards Data Governance. If you have a thorough inventory that identifies the location of all the data (including flat files and other quasi-database locations), then you have another

piece of the data governance information available. Is there an organizational structure that oversees (owns) data? Is information about data shared throughout the organization in a timely manner? Is data tested for integrity? Are data relationships understood and documented? Are data audits being done to verify that the data is being used appropriate by all stakeholders? Is data change management effective and complete? All these are facets of a data governance program.

Data Governance has been complicated by mergers and acquisitions, new initiatives, regulations and simple lack of interest. However, each new piece of data that arrives may contribute to moving the organization toward additional data integrity problems and concerns. Every report or analysis that involves data could be at risk.

The silent problem of data continues to exist. Data Governance can give voice to the problem and provide the foundation for a solution. Even initiating a Data Governance program moves the organization one step closer to ensuring that the valuable asset of data continues to be just that, a valuable asset.

“Data Governance has been complicated by mergers and acquisitions, new initiatives, regulations and simple lack of interest.”



n the macro viewpoint, data governance is data governance, regardless. The micro view, however, should celebrate the nuances of data governance. One of those nuances

is Data Governance for Education, which includes some unique challenges that are not always obvious.

Whether the notepaper being used is kindergarten blue or college ruled, the approaches to managing data in academia have been hampered by lack of funds and resources. The end result is that although educational environments could definitely benefit from the knowledge that could be found in the data they store, they are frequently prevented from effectively learning important data lessons. Challenging distinctions for education include the storage of massive amounts of unstructured and/or historical data that has not been computerized, the necessity of a robust privacy approach to protect student data, maintenance of the syllabi and course materials, faculty information that must be maintained to ensure quality instructors, certification and re-certification preparation information and human resources data, just to name a small sample.

Despite the complexities, it is easy to understand that education environments receive overwhelming amounts of data that must become data assets. The question is, “what is the best approach to bring universal meaning and order to that data?”

The Voice of Authority

Just as sports teams need a coach to help the team excel, data governance initiatives need authoritative sponsorship. The data governance problem as a whole would be overwhelming and the effort would likely never move beyond the outline stage without a sponsor to

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Data Governance: Learning Data LessonsBy Keesa Bond

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facilitate, mitigate and provide appropriate resources. Once a sponsor is identified, the scope of the Data Governance initiative can be determined and a plan of action can be designed.

Defining the Problem

Before the problem can be solved, it needs to be understood. An example of a problem statement might be, “Data is being stored in a manner that precludes its usefulness both for day-to-day operations and planning.” From there, the problem statement can be further defined:

• There is no road map for defining the business terms, definitions, appropriate data uses or metadata.

• There is no universal understanding of the chaining of data assets.

• There are no roles and responsibilities directly tied to data quality, data protection or data governance.



defined in the first discussions may not be those that are listed in the final meeting. That is a benefit of bringing this team together since the initial assumptions may not match the reality.

What are the Data Assets?

Now that the silos have been identified, the composition of the Data Governance team may need to change. The members now need to begin the detailed discovery phase of the initiative. The goal for the Discovery Phase is to discover business and technical subject matter experts who can help identify and explain data that is important to their particular silo.

Identification should include all facets of the data. In addition to obvious sources of data (such as databases) data assets should be considered in pseudo-databases which may include information stored in spreadsheets, hard copy and even small local databases. If accurate metadata is available, it will be valuable during this phase. Code reviews may be helpful to determine usage patterns. Reporting and statistical analysis requirements should also be reviewed since they may point to data assets that are critical to ongoing operations.

What is the Source of Those Assets?

After identifying the data assets, it becomes important to know the source of those assets. How is the data obtained? Is it entered via an online application, uploaded to a database from a source list, or generated by a batch process?

An ancillary question that can provide value toward further data governance steps is how often the data is updated and/or accessed. Knowing whether data assets are historical in nature or used frequently can help with later prioritization considerations.

Take a Check Point

After the data assets and their sources of data have been identified, a compilation of the gathered information

Working the Problem

With the problem statement defined, the data governance focus should begin with the plan to transform the “As Is” to the “To Be.”

Breaking the process into logical sections, incrementally moving forward, establishing checkpoints, re-assessing when necessary and publishing successes to appropriate officials will help ensure that the work effort remains on track.

Strength from Weakness

Silos of data are typically one of the weaknesses identified during the majority of Data Governance initiatives. While silos lead to data inconsistencies and often prevent effective communication those same silos can become a strength during an initial Data Governance effort. Since most education environments have developed logical data silos (departments, schools, etc.) simply by the nature of their mission, one approach might be to use those silos to initially subset the task into manageable modules.

Silos, Experts and Objectives

A Data Governance team is now needed. It should be composed of members who are familiar with the full spectrum of academic operations and data flows. These are not typically the information technology professionals, but instead they are the ‘business’ experts. Depending on the type and size of the educational environment, these members could include employees of the administrative branches, academic advisors or possibly representatives of the offices of the Dean or Provost. These should be the individuals who understand the data, how it is sourced and how it is used. Employees who are familiar with problems or issues due to lack of data quality can be instrumental in driving positive changes, so including those individuals in the team is optimal.

This stage begins with some detective work and may uncover some unexpected outcomes. The silos that were



is needed. For each silo, a list of the data assets, the sources of those assets and their definitions should be prepared. An overarching analysis of these lists will likely indicate that some data assets are used by more than one silo. This is possibly the first opportunity for the Data Governance team to see realizable opportunities for improvements for data issues such as duplication, ambiguity, incompleteness or other data concerns.

How are Those Assets Currently Being Used?

With the overarching list of data assets, a determination of how those assets are being used can begin. The team will undoubtedly confront some obstacles in this phase, but without this information, the foundation of the Data Governance program will be unstable and each successive step may cause re-work.

Definition scenarios can be especially challenging to decipher. Consider data assets that are ‘named’ differently but which represent the same meaning, both within silos and spanning different silos. For example, what do the terms ‘admission’ and ‘enrollment’ mean? Do they mean the same thing, but are just known by different data naming conventions? If so, imagine the confusion of the new employee who sees these terms as presenting two different concepts because they are named differently. Perhaps these two terms do mean different things, but each department ascribes their own individually distinct meaning and there is no single holistic definition for each term. Consider too that historically, these terms may have had completely different meanings than they do now and perhaps some

of these older meanings are still housed in currently used information systems.

How Should Those Assets be Used?

Now that the current use of the data is understood, it is time to determine how the data ‘should’ be used. Often the ‘currently used’ answer is different than the ‘should be used’ answer. Duplication of data, data inconsistencies, and misleading data definitions are all prime candidates for review.

Predominant data quality standards and validations will logically begin to be defined during this step. At this point, the team may want to consider beginning the effort to build an initial ‘business glossary’ which can provide meaning and definition to the data asset terms and set standards to facilitate clear communication for the rest of the Data Governance process.

Who is the Owner?

One of the most critical parts of any data governance approach is the identification of data stewards. Data stewards are considered the ‘owner’ of the data asset. They hold responsibility for ensuring that the data within their purview meets quality standards, answers a business need and that it is appropriately available to authorized users. Data stewards are the champions of the data. They are the ultimate layer of quality control. Typically their job function will depend upon the data that they own and therefore, they will have a vested interest in maintaining it properly.

“One of the most critical parts of any data governance approach is the identification of data stewards.”



Applying a Data Governance Maturity Approach

All the data lessons learned have been leading up to this point. This is when the true Data Governance maturity phase can begin. Depending on the outcome of the previous investigations, a decision can be made about the nature of the appropriate Data Governance model and approach. With the approach defined, now is the time to begin the synchronization efforts that will bring the silos into the whole.

Learning the Right Lesson

Data Governance is not a onetime event. Data must be consistently viewed as an asset and the culture must recognize and support the continuing protection of the Data Governance processes. With the right sponsor, the evolutionary process to enable an ongoing governed approach to data quality and protection will provide significant benefits both for the present and the future. To enable that future, however, the culture must change and adapt to one that recognizes the value of the data and as a result, embraces a Data Governance focused mindset.

Before academia can learn the lessons that the data assets provide, they have to build the foundational knowledge required. Data Governance provides that foundation.

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