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Getting your company to be proactive about data quality


In hindsight, it's often easy to see what went wrong with a project or a program, but it's much more difficult to anticipate what issues could arise in the future. This is particularly the case when it comes to data quality. Often, executives and employees struggle to understand why they should fix something that isn't broken - that is, that hasn't become such a problem that they can no longer ignore it.

David Harris wrote for his Obsessive-Compulsive Data Quality blog that it can be difficult for companies to take charge when it comes to making list management and other data quality undertakings a priority.

"Reactive data quality still remains the most common approach because 'let's wait and see if something bad happens' is typically much easier to sell strategically than 'let's try to predict the future by preventing something bad before it happens,'" Harris explains.

But knee-jerk reactions to any business problem often lead to haphazard solutions that treat the symptoms rather than the disease. Not only does a company need to clear out its databases of inaccurate or out-of-date entries, it also needs to adopt technology and establish policies and processes that will prevent the same mistakes from creating havoc in the future.

So how can you convince the decision makers in the organization that they need to invest time and money into being proactive about maintaining their information assets? What will sway an executive to prioritize management and governance of data?

In a web seminar hosted by Information Management, Jim Orr - the information management director at Information Builders and author of "Data Governance for the Executive" - explains that there has to be a philosophy that data holds a high importance in all aspects of business operations.

Orr notes that a common challenge is getting the attention of executives, and he advises building a business case by presenting new concepts about data and giving real examples of how dirty data negatively impacts the business.

The first step, he says, is to have a strategy about how you plan to achieve data governance in the organization. In this phase, it's necessary to frame the need for information policies in its relation to the business, not technology. Orr warns that the name of the program can also have an effect on its success - governance is a organization-wide undertaking, not a siloed project that is only launched in one department. Lay out a definition of the program so everyone has a firm understanding of what is involved, and also set realistic expectations for the decision makers.

From there, you will have to convey the value that data quality programs will offer in terms of improved productivity, efficiency and cost management. Come to the table with a data assessment - telling the business leaders where they are now will help everyone visualize where they could be in the future. Also outline the technology requirements. What contact data quality or other validation solutions will the company need to adopt as part of this governance strategy? These may be questions the decision makers ask.

Orr also points out that it will be vital to list the examples of success and risk if and when the program actually kicks off. These will factor into the leaders' thinking process as they determine whether this undertaking is worthwhile. The success criteria can be interpreted through a combination of several factors: business outcomes, management-by-objective focus, project-based analysis and data outcomes.

In terms of risk, access to adequate funding, the span of control, authority, the program's legitimacy and having the appropriate technology can all weigh on the success of the data governance initiative, Orr says. Be sure you consider these and other elements as you build up a case for why your organization needs to make data quality, management and governance a top priority.

 


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