Q&A: Expanding the Definition of Data Governance
Data governance is more than just managing data in order to optimize outcomes.
- By James E. Powell
- April 17, 2012
Data governance is more than just managing data in order to optimize outcomes. According to Jim Orr, Information Builder's director of corporate marketing, the discipline is becoming more about information asset management. We spoke to Orr, author of Data Governance for the Executive, about why executives must understand data governance, the benefits of a data governance program, how to measure data governance performance, and other ideas in his book.
BI This Week: For those readers that aren't familiar with the term, what exactly is data governance?
Jim Orr: To be honest with you, I actually despise the term data governance because it does not accurately reflect what the discipline is trying to accomplish nor does it resonate in the business community. Data governance is about managing far more than the data in order to optimize data outcomes. It now involves cross-functional business process, people, technology, business alignment, and more.
For many, the word data reflects IT-centric responsibilities and governance comes across as a bureaucratic term. Collectively, the term and the program struggle to gain acceptance in many organizations, certainly at a corporate level. Rather than data governance, this discipline is becoming more about "information asset management."
The title of your book implies that executives need to be involved in data governance. Is that really necessary?
No. In fact, a well-run data governance program does not need executive participation, certainly not in the daily process. The book is intended to raise executive awareness on how data governance drives overall business performance. By elevating the understanding among business leaders, organizations can better position themselves to manage (govern) their data assets to take advantage of business opportunities.
In your book there seems to be an emerging theme around gaining holistic visibility into an organization's data assets. Can you expand on what you mean by this?
Historically, organizations and the industry in general, have focused their attention on individual projects. These point solutions have created a fragmented, silo effect that inhibits us from looking at the entire picture when it comes to information management. This disjointed environment shields organizations from understanding data interdependencies and how it actually drives business outcomes. The data governance process brings visibility to the problem and divulges opportunities and risk that organizations are simply unaware of in a normal course of business.
You use the word "asset" throughout your book in reference to data and information. How do you get executives and business leaders to treat data as an asset?
I find that getting executives to view data as an asset is the easy part. Getting them to treat data as an asset is the challenge. One effective way I have found to address this problem is to draw the analogy between financial assets and data assets. Both are similar assets in that they are influenced and managed by numerous people and processes across the organization. The difference between the two is that one is formally governed by a central body that establishes and enforces policy and procedures. The other is typically void of any ownership or controls. In other words, organizations need to treat data like a financial asset.
In addition, demonstrating the holistic value data assets bring to an organization raises executive awareness and their interest in treating data like an asset.
You mention several benefits of a data governance program. Which one do you feel is the most important?
Transparency, without question. The data governance process unveils visibility into an organization's business and data operations that are simply not possible in a business-as-usual environment. What flows from the transparency are unimaginable opportunities to reduce cost, drive revenue, and mitigate risk. I share several examples in the book and these scenarios are replayed over and over again at every organization that goes through this process.
In your book you talk about the three operational components to data governance (business, IT, administrative) and how they need to work together. What is the most important aspect of this model for executives to understand?
The administrative component is the most important. While business and IT clearly need to work together it requires administrative oversight for an organization to effectively implement data governance and reap the benefits from it. The administrative piece is the glue. For example, grass root efforts seldom do well in this arena because of the lack of administrative oversight needed to allow the program to grow and sustain itself.
You provide some unique ways to measure data governance performance. Is this a big challenge for organizations?
Yes. Like any program, data governance needs to be measured, and for three primary reasons. One is to support the initial business case and measure progress against expectations. Another is to make sure performance is known and visible so that the program is not cast aside when there is a revolving door at the top. The third is to provide non-tradition yet reasonable measurements for what is a very unique discipline. In other words, the full benefit of a data governance program cannot be measured solely against the data.
If a company has invested in data quality tools why do they need data governance? Are they both one and the same?
Data quality and data governance are two separate disciplines, though both share similar goals; to improve data quality or data integrity. The difference between the two is that the practice of data quality is mostly tactical and involves automating data governance policy within a specific project and technology. Furthermore, it is focused on the act of actually fixing the data.
On the other hand, data governance is about creating data policy and enforcing it across technology, business processes, and business units. Many organizations have silo data quality solutions and need governance to expand the data quality implementations as well as the consistency of the business rules and standards being applied within them. Data quality is a sub-domain of data governance as are stewardship, metadata management, data modeling, security, privacy, and data lineage.
What would your advice be for executives as they contemplate launching a data governance program?
I would recommend that executives take the time to understand what this discipline actually is, the holistic value it brings, potential scope, and the fundamental requirements for successful data governance. Gaining insight into what the discipline is will open their eyes to what's possible. By understanding the value, it level sets the expectations and anticipated investment needed to support the program. Establishing scope allows the organization to capitalize on its biggest opportunities. Building a program on the fundamental requirements for success will ensure growth, sustainability, and maximize the return on investment.
I would also encourage executives to consider engaging a third party to help provide best practices and navigate the political waters that are sure to exist. Many organizations struggle to launch data governance programs on their own. In many cases, this is as easy as bringing in a mentor and advisor for 4-6 months to help organize and implement the initiative.