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It used to be that oil fueled the means of production. Increasingly in our 21st century economy it is data, not oil that is the fuel that powers 21st century enterprises. Data is a strategic asset that presents both dire perils and unmatched opportunities unlike any other asset except an enterprise’s people. It is only with the highest quality fuel (data) that peak revenue performance can be achieved. As a result, it has become an absolute requirement for any enterprise pursuing revenue excellence to have a person in the role of the data quality manager. This role is often also referred to as a data steward.

In order to ensure the highest level of data quality in an enterprise’s customer and prospect data, it is not enough to rely solely on culture. Culture alone never works. Nor can you simply rely on technology solutions, until Jarvis (think Iron Man) is a reality, human intervention in the proper measure is a data quality necessity. It is the role of the data quality manager, whether self-appointed or designated in an enlightened organization who determines how to balance the automation enabled by data quality assessment tools, and data quality hygiene / enhancement tools and the human intervention needed to make sure the tools are performing optimally. It is also the data quality manager who through their actions and leadership inspires an enterprise to embrace a data quality driven culture.

Best Practices For A Data Quality Manager

Perform a data quality assessment

Like baking a cake or painting a room, quality data means putting first things first and in data’s case that means performing a data quality assessment first. A data quality manager can not fix the data unless its quality has been measured first. On the basis of data quality assessment and analysis they should develop a data quality management strategy and data governance policies that can be presented to stakeholders. First measure, then plan.

Create a data quality firewall

Experienced data quality managers will implement a data quality firewall around their CRM and marketing automation platforms to ensure that the data is right at the instant it is created, which is when its quality is set. A focus on prevention, which can be largely automated is the best practice because whatever it costs the data quality manager to see to it the data is created properly, it will cost 10x if the data quality manager’s strategy is to fix the data later.

The data quality manager role should reside in Marketing or Sales, not IT

It is the managers whose department’s activities are creating the new data and whose departments are also consuming the data who should take ownership of the data governance role to manage data quality improvement from end to end. They should take a leadership role in resolving data integrity issues and managing data governance policies and initiatives.

Creating an inter-departmental data governance board

In order for a culture of data quality to be institutionalized across an enterprise, the data quality manager should establish a data quality governance board encompassing Sales, Marketing, IT and C-level stakeholders. It is also essential to include stakeholders from the frontlines of data consumption which means to frontline salespeople and marketers, and not just leadership stakeholders.

The author:

RingLead offers a complete end-to-end suite of products to clean, protect and enhance company and contact information, leading to improved organizational efficiency, reliable business intelligence, and maximized ROI on CRM and marketing automation investments.Since 2003 RingLead has helped solve the dirty data problems of large enterprises, Fortune 500 companies and small businesses across the globe.