I've said it many times: data governance is one of the most important topics in IT. How effectively you manage the quality, consistency, usability, security and availability of your organization's data will play a large part in how successful your business ultimately is. Data is the lifeblood of any business, and if the data isn't healthy ... well, you know the rest.
The old sports aphorism applies: It's all about the fundamentals. Many of my clients have a data governance initiative underway or in place, but at many organizations, data governance is a back-burner, IT-driven project that matters only when some serious problem involving data quality arises (read: affects the bottom line). However, data governance should form the underpinning of the information management organization and strategy. As the rock upon which an information management strategy is built, any approach to data governance should be multidisciplinary and enterprise-wide. Successful data governance requires support from across the organization at all levels, from knowledge workers to the C-suite. In my experience, there are four critical principles to a successful data governance implementation effort.
The first principle of successful data governance is clear ownership. If no one owns the data governance effort, it will be adrift, with no clear purpose or compass. However, if IT alone owns the effort, the business may not feel that the data governance initiative meets their needs or requires their input. A data governance committee or council is needed. It should be composed of representatives from all business units. The data governance council should set data policies, procedures and standards for the company as a whole. These should be uniform throughout the company and updated with council consensus when appropriate.
The second principle of successful data governance is value recognition. It's difficult to quantify the value of data in dollar terms, but data is one of the most important assets of any business. Without data standards and quality, businesses don't function well. They can't serve their customers adequately, and dissatisfied customers tend to speak with their wallets. Therefore, in any data governance effort, appreciation for the true value of business data is critical, along with C-suite financial support for the time, effort and expense to effectively manage that data.
The third principle of successful data governance is effective data policies and procedures. To be maximally effective, data policies and procedures must cross business unit silos and apply to the business as a whole. It's essential to have a fundamental, common business information model that the entire business can rely on and follow. Otherwise, data chaos will likely reign, and data quality problems can mushroom to the point where they are often intractable.
The final principle of successful data governance is data quality. It's absolutely crucial for knowledge workers and management to be able to trust the source. However, few companies can absolutely trust their data. The amount of human middleware in place at some companies to fix and control data quality often astounds me. With the plethora of data quality tools and methodologies on the market, any reason for poor data quality is merely an excuse. Get a good data quality tool and an effective methodology to implement it. It's worth the capital outlay.
Speaking of capital outlays, most folks I talk to in the C-suite ask me to help them justify spending their scarce dollars on something as nebulous as a data governance initiative, especially when there are few hard benefits. Au contraire, I tell them. There are indeed tangible benefits.
An effective data governance framework can help organizations manage data more efficiently. It should provide consistent definition, establish enterprise data management, and measure and track the quality of transactional and analytical data used across the organization. It should also improve coordination between lines of business and provide broader insights into data across products and business units. The data steward groups, part of the data governance framework (usually organized by data subject areas such as customer, product and vendor), can help create, implement and establish measures of the standards across the business.
Information costs may also be lowered with an effective data governance framework. Duplicative data stores throughout the company can be eliminated, and data cleansing costs can be reduced via better quality source data. Substantial information cost reductions will likely be achieved through the application of standard processes across the business as well.
Finally, effective data governance helps improve compliance and control efforts. With effective data governance, data standards facilitate high quality data. The data standards should apply across business functions and lines of business, creating a uniform transactional and analytical environment for compliance monitoring. Also, with effective data governance, data stewardship should be an organization-wide effort, which reduces risk of noncompliance with regulatory and statutory requirements.
That's quite a bit for your hard-earned IT dollars, is it not? I know money is tight, and the outlook is not improving measurably for revenue increases, so the alternative is to make do with less. That alternative, by definition, means improving operational efficiency and squeezing the most out of every corporate asset. One of the best ways I've found to accomplish both those goals is to more effectively govern your corporate data. The benefits are high, and I believe the risk is worth the venture.
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