In today’s digital economy where all aspects of a business are online and perceived value can often be gauged by mouse clicks, what is arguably an organization’s most valuable asset is the data they possess within their own four walls. Business data has become the new gold standard.

Today’s corporate vaults are called data warehouses, inventory and procurement management systems, business intelligence and customer relationship management (CRM) systems. The data they capture and protect is key to purchasing and sourcing trends, inventory and shipping options, customer preferences and expansion opportunities. That business data is the foundation of decision making at all levels of an enterprise.

Despite all of this, a surprising majority of executives do not pay adequate attention to the integrity of the data that is fed into those digital repositories. As a result, the data outputs they produce – the raw material for crucial decision-making – are all too often polluted by inaccurate and unreliable information, leaving the future of the business to chance.

A crucial IT weapon used by an expanding number of companies to alleviate this problem has been the implementation of an enterprise wide data quality solution. With such a solution and process in place, executives can feel confident in the creation of a centralized source of relevant data on which they can build a unified, accurate view of their enterprise.

In dealing with many of these companies and their leaders over the past decade, a composite list of “Best Practices” has developed. The following 10 guidelines supply a roadmap for a generic data quality process, regardless of the industry, the size of an organization or the current business challenges they may face.

1. Establish measurable business goals.