Proposing Simplified Architecture For A Complex Age
Posted in Business/IT Collaboration, Data Governance, Data Integration, Data Migration, Data Quality, Data Services, Data Warehousing, Enterprise Data Management by Joe McKendrick | No Comments
Good architecture is the foundation required for agile systems that are responsive to business needs. We see that with SOA, which has been missing a vital piece of its architectural approach – from the data. Since it came about in its current form in the early-to-mid 2000s, service-orientation (mainly focused on applications) has existed in a separate world from data management.
Problem is, an SOA-enabled infrastructure with bad data flowing through it can be useless and even dangerous. One commentator even compared SOA to a mosquito that can deliver payloads of bad data (”viral data”) at lightning speed all across the enterprise — pandemic style — before it can be stopped. [Read more]
While the market is showing signs of recovery from the "Great Recession" most state budgets have been feeling the squeeze from the lag in recovery. In a recent article titled
Business modernization programs typically focus on process standardization to gain the benefits of efficient repeatable, measurable processes. Enterprise resource planning (ERP) technologies fulfill the process standardization requirements and have now become a central point for management of business processes. However, ERP systems do not prevent low quality data from entering the systems nor do they measure its impact on the efficiency of a business process. Most organizations today are using the same ERP systems (SAP or Oracle) that were configured by the same consultancies. Therefore, the uniqueness and the scope for competitive advantage of any organization are defined by the people and the data.
Las Vegas, as we all know, is the city of conventions, fantastic shows, great restaurants, and of course casinos.
According to a recent 
This is the last posting in a 5 part series on the non-traditional challenges to achieving data quality. In Part 4, I reviewed the Data Quality Perception Gap. In this post, I will conclude with the Delivery Gap.








