Kindling The Flames: The Future of Data Governance
Data Governance & Data Quality: Angels & Angles

Your Data Quality Wish List Answered

by Len Dubois, Sr. Vice President, Marketing & Sales Support, Trillium Software

WishlistDuring my career here at Harte Hanks Trillium Software I’ve spoken to thousands of clients. Many, if not all, have in the back of their mind a vision for what they are looking for in a data quality solution and often they discuss this vision in terms of a set of “wishes.” This being the beginning of the season for granting wishes, I thought I’d write about some of the more common requests that I’ve heard and attempt to begin to provide a roadmap for improving the value of information in order to deliver the assurance that your data meets the needs of your organization.

If you talk to any data management professional today they will tell you that data quality is imperative to the success of their specific data-centered project. From customer records to claims information, regional sales data to product specs, ensuring the integrity and accuracy of data is paramount as it feeds critical business systems and fuels company decisions. However, despite its importance, it is often difficult to know how to kick start a data quality initiative.

“I wish I knew how to get started,” is a common refrain we hear from data professionals. There are four pieces to consider when starting a data quality project:

1. Determine business goals: From the outset define the specific business benefits you expect from your project. In many cases, organizations fail to recognize the importance of setting one specific goal. For example, perhaps the objective is to reduce billing costs by consolidating customer lists, or reconcile different versions of the same consumer record from one department to the next, or identify customers in support of fraud detection. Many successful organizations begin by aligning success with a specific set of deliverable. In specific terms, business leaders will say, “With better data I will know for certain that my customer are segmented correcting by what purchases they made.” Or “By understanding what information I am missing, I can accurately report to federal regulators the status of our business.” While this sounds simplistic, establishing measurable goals is a fundamental first step that can mean the difference between success and a project in jeopardy of failure from the very beginning.

2. Identify your data sources: Keeping in mind the end goal, identify the data sources you need. As line of business owners and managers, you can work directly with your IT counterparts to identify which systems and processes have the right information and must be incorporated. Consider establishing a cross-functional team that is responsible for adequately which data sources meet your businesses needs and which will work with the design and implementation of your project. That way you can ensure all the relevant stakeholders, business needs and data sources are included.

3. Outline the methodology: Leverage best practices that are tied to the same type of problem or challenge that you are trying to overcome. Department level solutions may not support enterprise challenges, single domain and entity products may not suffice in a world of diverse and global data types. Business operations and regulatory requirements require specific subject matter expertise beyond indexed cleansing projects to satisfy the need for data assurance. Talk with data quality experts, not just vendors, who understand the nuances of global data quality and its associated complexities.

4. Don’t forget about the end goal: Poor information quality can manifest itself in many ways within an operational process. Organizations that see the greatest ROI from are those that measure not only the costs, but also the benefits of improved and enhanced data quality. Metrics that range from shorter processing time, reduced hardware costs, shorter sales cycles, more accurate analytics, reduced telemarketing costs, increased return on existing technology investment), and higher acceptance rates of business applications (CRM for example) all reveal benefits of improved data quality.

Next week we will address another common concern for data professionals, “I wish I knew how poor data quality was manifesting itself in my business”.

Len Dubois Len Dubois
Sr. Vice President, Marketing & Sales Support, Trillium Software

Len has been with Harte-Hanks for thirteen years and has more than fifteen years experience selling and marketing high-technology solutions. He is responsible for the strategic development and execution of worldwide marketing initiatives for Trillium Software. He created the Trillium Software System® brand that has been recognized as one of the top enterprise solutions in the data quality industry. Prior to coming to Harte-Hanks Trillium Software, Mr. Dubois was a Marketing Manager for Epsilon Data Management, Inc. He has spoken at Data Quality conferences in both the U.S. and UK. In addition, he has authored many articles on Data Quality and CRM.

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