By Joe DeSiena, President Consulting Services

Finding Success

The seven major steps that must be taken to achieve Data Quality are:

  1. Acknowledge the problem, and identify the root causes;
  2. Determine the scope of the problem by prioritizing data importance and performing the necessary data assessments;
  3. Estimate the anticipated ROI, focusing on the difference between the cost of improving Data Quality vs. the cost of doing nothing;
  4. Establish a single owner of Data Quality with accountability (e.g., make it a senior management role, such as a Data Officer/DQ COE);
  5. Create a Data Quality vision and strategy, and identify the key change drivers;
  6. Develop a formal Data Quality improvement program based on specific tools wherever possible, and use a value-driven approach for large projects;
  7. Make it a priority to move your organization up through the levels of the Data Maturity model!

Achieving Data Quality is critical, but getting there is often a complex process. Data Quality requires commitments from all business functions, as well as from the top-down. Quick fixes typically do not work and generally only end up creating frustration. For many organizations, it may have taken years to create and foster a culture of data denial, and it will require rigorous processes to:

  • First, identify the problem before it can be fixed;
  • Second, recognize – and accept – the full extent of the potential benefits that can ultimately be realized.

For many business enterprises, the numbers speak for themselves, where the implementation of a Data Quality initiative ultimately leads to:

Reductions ranging from:

  • 10 – 20% of corporate budgets
  • 40 – 50% of the IT budget
  • 40% of operating costs

And increases of:

  • 15 – 20 % in revenues
  • 20 – 40% in sales

The application of Data Quality provides an organization with the opportunity to capitalize on its cumulative information and knowledge assets. Knowledge that was previously unknown, or unavailable, such as cross-referenced customer buying patterns, profiles of potential buyers, or specific patterns of product/service usage may be uncovered and put into practical use for the first time. The end result can lead to anything ranging from improvements in operational efficiency, accurate sales forecasting, effective target marketing, and improved levels of customer service and support – all based on a strong foundation of Data Quality.

 

About the Author
Joe DeSiena is President of Consulting Services at Bardess Group, Ltd., a Management Consulting firm specializing in data revitalization, business process design, and information technology for services-related businesses.   He is currently a board member of the Society for Information Management in New Jersey.

He is an experienced management consultant with over 20 years of professional experience assisting Fortune 500 clients in resolving business issues related to the Triangle Relationship between business data, processes and systems functions for services and sales organizations. More specifically, he has directed engagements in services marketing and delivery, business planning, data revitalization, data migration, process design and reengineering among others. He has shared his experience and insights in presentations before numerous senior client and association groups.

Joe DeSiena’s industry exposure includes data networking, telecommunications, manufacturing, pharmaceuticals, financial services, utilities, travel and entertainment among others. He has corporate management experience in major companies such as American Express, Chase, Bristol Meyers-Squibb, Coopers & Lybrand (PWC), Deloitte Touche, and Pan Am.  Joe DeSiena is a graduate of the Stern School of Business at NYU with an MBA in Finance. He received his B.A. in Mathematics and Economics from the State University of New York at Stony Brook graduating Magna Cum Laude with Phi Beta Kappa honors.