Corporate governance is a wide framework of systems, rules, interfaces and principles that form the basis of fiduciary corporate culture and values. With strategic data as its backbone, these tactical mechanisms help spell out the rules and procedures for making decisions on corporate affairs, enabling you to conform to the internal rules of an organization as well as the larger law of the land. Enterprise data assets must support the rules of governance, not the other way around (with governance initiatives always catching up to or restricted by firm-wide data). Many times, not enough up-front diligence is exercised to bring IT into the forefront of governance discussions. Senior management may neglect to realize how important data assets really are until a mission-critical compliance or regulatory crisis rears its head. While a business can limp around on bad customer and product data, it cannot afford to ignore corporate statesmanship. Poor business intelligence (BI) will put your company at a competitive disadvantage; poor governance could put you out of business. With the harsh regulatory realities of the 21st century, directors are feeling the heat of increased accountability for all corporate actions, large and small.
A value proposition for the data supply chain needs to be thoughtfully crafted by IT and communicated to the entire enterprise so that individuals are motivated to align their data integrity and quality behaviors with the overall corporate good. Accountability for data assets and enforcement of data integrity standards - with palpable leading indicators of quality - must be made part of the formal governance agenda and communicated throughout the enterprise and beyond. Just as a flawed part on a factory assembly line will render defective its larger constituent products, the data supply chain is only as strong as its weakest link. Managers must make it a continued priority to pay extra attention to all data sources and feeds that are "external" to enterprise system boundaries. In contrast to your operation, third-party data producers and suppliers do not always have the same values or high standards for data cleanliness, redundancy and consistency of semantics. In fact, service-level agreements with data vendors for all stages of data apportionment (production, augmentation and distribution) are becoming increasingly important as much of the data-providing industry consolidates or picks up new standards. All data needs to be carefully profiled before it persists anywhere in your IT infrastructure. The cost of processing and mining information that supports governance (or any other strategic undertaking) goes up exponentially when the data credibility is mediocre or compromised.
Benchmarks, metrics and calculations for measuring the quality and success of governance rules should be stored as administrative business metadata. In this way, it will be possible to monitor and track the progress of all policy-driven mandates and objectives. Empirical measurements can be taken throughout the data lifecycle to see if the operation of the business is deviating from the governance mission statement. Furthermore, exceptions can be quickly raised and distributed to the appropriate interested parties (such as compliance or legal resources), reducing a multitude of potential risks and exposures across all business processes and transactions. It is better to make data quality and stewardship part of your current governance plan than to wait until you are in the midst of an external audit, subpoena or regulatory action to discover that the caliber of your current and archived data is of questionable virtue.
Data can be one of the biggest limiting factors to achieving robust enterprise statesmanship. Imperfections and faults in the data supply chain must be consciously addressed in all such initiatives in order to keep a company's internal and competitive risks to a bare minimum. Shareholders are demanding that business entities take greater accountability and responsibility for being good corporate citizens. Selling the value proposition for your organization's data supply chain should not be too difficult a task!
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