Data governance. It's not exactly the most scintillating topic. However, exciting or not, data governance - the process by which you manage the quality, consistency, usability, security and availability of your organization's data - is a critical subject that you should address sooner rather than later.

If your organization is like most, your information architecture consists of many applications and databases that may or may not be integrated properly according to your ever-changing information needs. In this scenario, data governance is even more important. Unfortunately, many organizations don't have a data governance strategy in place. Therefore, knowledge-workers within the organization often don't have the data they need when they need it to do their jobs. What's more, when they do have the data, it's often of poor quality.

In addition to the critical need for knowledge-workers to have good data, data quality standards are now mandated by the government. The 2002 Sarbanes-Oxley Act requires, among other things, that organizations be able to attest to the quality of the data contained in their financial reports. Obviously, to obtain high quality financial data, most, if not all, of the organization's data must also be high quality.

Data governance methodologies are springing up all over the marketplace. Most of these methodologies will probably produce decent results. Therefore, there's no need to spend a lot of time discussing the technical details of any particular methodology. However, I do want to give you a strategic blueprint that you can apply to any methodology you use to build your data governance program.

As with any building project, you need a foundation. Commitment is the foundation of any data governance strategy. Data governance should be a part of the fabric of the organization's culture. Developed standards must be applied equally across the organization. No information system should be excused from any data governance standards that exist. Data governance objectives should also be a part of any current and future IT project plans.

Commitment includes funds as well as time. Data governance, unfortunately, is not free. Therefore, you must have appropriate funding to execute your data governance strategy. Probably the easiest way to obtain funding is to align data governance with your organization's ability to turn a profit. For example, better data governance could be tied to better customer service, which would result in less churn and more profit from customer relationship management (CRM) initiatives.

Technology is the next component of a successful data governance strategy. Two of the most important aspects of any data governance methodology are data management and delivery. Therefore, it's absolutely critical that you employ best-of-breed technologies to move your data through the organization's information systems. This means purchasing the best databases, EAI (enterprise application integration) and ETL (extract, transform and load) tools, data quality products and BI (business intelligence) suites available. It also means developing a plan to integrate these tools properly to support data governance objectives.

The next component of the successful data governance strategy is process. It's imperative that you have a process in place to monitor and reconcile data at critical points along data's path through the organization's IT systems - initial data entry points and data aggregation points. The best way to ensure initial data quality is to have front-line user applications perform data quality checks at the point of entry (initial data entry points). For example, the application could check for valid value entries or required field entries. At data aggregation points, the process should enable the elimination of duplicate entries, check for valid cross references and look for points where individuals may roll up into a single household or company. The process should also allow for manual corrections to exceptions and errors that cannot be handled automatically.

The final component of a good data governance strategy is accountability. Accountability begins and ends with leadership. The first step to achieve accountability is to appoint a data governance leader or ownership group. My preference would be for a lead data governance steward with a council composed of business and IT people. Give these people the authority to implement, consolidate and manage all enterprise-wide data governance efforts. The second step to accountability is to tie performance to incentives or compensation. In other words, give bonuses or merit increases to people who make outstanding efforts to support your data governance efforts. Alternatively, make it clear that lack of attention to detail will have undesirable results.

Again, this is not a data governance methodology - this is a strategic blueprint. Data governance is not a "one-size-fits-all" proposition. What I'm giving you here is a set of strategic principles that you can follow with the methodology that you choose to help ensure its ultimate success. However, remember, strategy and execution go hand in hand - so choose wisely and execute carefully!

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