One of the biggest impediments to (and failures of) a new data governance program is the perceived level of “extras” required. Let’s enumerate some of the concerns that I hear consistently from our clients:

1. Extra people will be required to staff the implementation.
2. Extra budget money will be needed to fund the project.
3. Extra time will slow critical projects.
4. Additional documentation will be burdensome.
5. Too many people need to be involved in decision making.
6. Business ROI cannot be justified.
7. There will be additional levels of hierarchy and bureaucracy.
8. Consensus will not achievable.
9. Accountability will involve blame.
10. Extra work will be required for everyone involved.

When designing a data governance program, it is critical to anticipate these concerns, as they may reflect past experience in failed governance attempts. Unless these issues are addressed, another "extra" activity will include ongoing efforts to convince participants and end users of the true benefits of a well-executed data governance program.

First let’s examine where these concerns arise. Any discussion of new processes or standards will automatically raise concerns about additional work. But let’s consider some of the main drivers for data governance:

1. Data is being handled by different people in different ways, resulting in constant rework to fix recurring issues. We call this an "over-investment" in maintaining data because ultimately it is expensive, inefficient and wasteful.
2. Current lack of documentation means that each new project or analytic cycle can’t benefit from work already done. Constant one-off projects are inefficient.
3. Current lack of accountability for data forces people to fix data themselves or resort to informal personal networks that never result in standardized solutions. We call this a reliance on “tribal knowledge” or “urban legend.”
4. While existing decision making may be perceived to be quicker, decisions don’t "stick" — they never get documented or standardized. These same decisions are getting made inconsistently and repeatedly, again resulting in over-investments of people’s time.
5. Current investments in data aren’t quantified; nor are they tied to business value. If the true costs of inefficient data management practices were tabulated in terms of poor data quality, overinvestment in data maintenance and rework, a simple ROI calculation easily justifies investments in data governance standards and processes directly tied to better decision making.
6. Current lack of accountability for data means that everyone and no one is responsible. By assigning accountability to data stewards, a central go-to resource can drive out inconsistencies and more efficiently escalate issues needing resolution.

A well-designed and well-executed data governance program results in quicker decision making and better prioritization of issues that require enforceable data policies. The all-too-common situation most enterprises face today is "data management churn," whereby lack of documentation (including metadata, requirements and data standards) and lack of a formalized decision-making process results in extraordinary waste and a drain on the business value of data. The investments in people and processes for data governance will more than make up for existing inefficiencies in most of today’s data-dependent enterprises.