As companies are turning to analytics for business insights and a competitive advantages they often overlook one of the most important aspects of information delivery. That is, Data Quality and Data Quality Management. The old saying, GIGO “Garbage In Garbage Out” is more true today than ever before when it comes to data. The average organization surveyed by Gartner said it loses $8.2 million annually through poor data quality. Further, of the 140 companies surveyed, 22% estimated their annual losses resulting from bad data at $20 million. Four percent put that figure as high as an astounding $100 million. We (DataHub) are starting to have numerous conversations with companies around information delivery. Our definition of information delivery is, delivering the right data at the right time to make better and more informed business decisions.
It all starts with Data Quality Management. So what is data quality management? Simply put, data quality management entails the establishment and deployment of roles, responsibilities, policies, and procedures concerning the acquisition, maintenance, dissemination, and deletion of data. In todays competitive landscape, data quality management is essential for success; for example, it helps provide a clear understanding of customers, partners and suppliers, which can make the difference between growing a business and failing to compete.
When trying to implement a Data Quality initiative, there are a few things you should be aware of. Data quality issues come in many shapes and sizes and can have a negative impact on your business results; trying to address all of an organization’s data quality challenges can be overwhelming and inefficient. By addressing data quality issues, organizations can avoid unfortunate consequences and improve business outcomes. Besides human error, most data quality problems arise from a lack of enterprise-wide information standards on how data is stored and uniquely identified.
Consider the following questions to establish return on investment (ROI) for each potential data quality initiative in the organization:
• What are the most critical business processes that rely on information?
• What information is most important to those processes?
• What is the cost of poor information to the effectiveness of those processes?
• What is the cost of maintaining high-quality information for those processes?
• What is the net benet to the organization for maintaining data quality for those processes?
To help adress Data Quality issues, DataHub has teamed up with IBM to provide a Data Quality Assessment. The assessment is design to help companies address current data quality issues but also provide a roadmap for Data Quality Management with in an organization. Some outcome you could expect from the assesment are listed below.
1) Identificationcation of and approach to the data sources in question
2) Advantages of automated data content-driven functions
3) Usage of data classificationcations to focus analysis
4) Validation of data formats and domains
5) Reporting and delivery of findings and results
The DataHub Team