Topics: Data Quality
1. What areas do companies typically focus on when they want to create a pilot program to demonstrate ROI? Are there specific areas that may have more business relevance?
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Sally McCormack
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Topics: Data Quality
Recently, we outlined the top five data quality problems in enterprise data management and offered best practices to solve those challenges. This post explores the topic further to highlight five additional roadblocks associated with managing the critical data of an organization. Organizations that take time to decipher the root cause behind data challenges will run more successful enterprise data programs. They’ll also have a foundation in place for sustainable growth with an enterprise-wide view of customers, manufacturing, supply chains, sales, and operations.
Topics: Data Quality
Top Ten Data Quality Problems: Part I
By: Sally McCormack, Data Quality Competency Director, Datasource Consulting, LLC
Problems with data quality are costly to an enterprise. When facing the potential for missed opportunities, uninformed decision-making, non-compliance sanctions, and low customer satisfaction, today’s business leaders are making data quality a priority in their organizations’ data management programs. An Experian report found that 88 percent of companies see a direct effect of inaccurate data on their bottom line, losing an average of 12 percent of their revenue. In a similar study by Database Marketing, organizations estimate that they could increase sales by nearly a third (29%) with corrected customer data. (Source: Internal Results)
Topics: Informatica Data Quality, Blog, Data Quality
CONSISTENT NAMING AND CODING STANDARDS
When designing rules in Informatica Data Quality, the developers and data stewards will see the same rules. Therefore, it is important to develop consistent naming and coding standards. For example, both the data steward and the developer will understand what “rule_” means while not everyone will understand what “mplt_” means. Therefore, mapplets should be named rule_ if they are used in both the Analyst and Developer tools.
Topics: Data Quality, Blog
When Comprehensive Capital Analysis and Review (CCAR) was mandated by the Federal Reserve in response to the financial meltdown of 2008, it provided a framework for assessing banking organizations with consolidated assets over $10 billion. Designed to help prevent future turmoil in the financial services industry, this stress testing ensures large institutions are able to withstand changing economic conditions, providing uniform and consistent service.
Data quality is a critical component in CCAR compliance. The Federal Reserve Board (FRB) provides detailed rules, called schedule instructions, which define the specific checks that must be performed against a financial institution’s data. Called edit checks, this testing focuses on a wide variety of issues related to overall data quality.
Topics: Data Profiling, Informatica Data Quality, Blog
One of the first steps in solving a data quality problem is to perform data profiling. As seen in Jason Hover’s article, Data Profiling: What, Why and How?, data profiling allows you to analyze your data to determine what it looks like and what problems exist in the data. Manual data profiling can be performed; however, using software such as Informatica Data Quality allows both data stewards and developers to collaboratively profile the data in a common repository more quickly, often yielding a more thorough analysis.
Topics: Informatica PowerCenter, Blog
PowerCenter Tips
As an Informatica PowerCenter administrator, you may often have the need to obtain a list of users and associated groups, workflows that have last run, mappings in a folder, default values within a mapping, etc. This information can be queried in the PowerCenter tools, however, a more efficient way of collecting this data is to query the repository metadata tables directly in the database. This method proves to be very helpful when performing a large repository upgrade or decommissioning an environment.
Topics: Informatica Data Quality, Blog, Data Quality
CONSISTENT NAMING AND CODING STANDARDS
When designing rules in Informatica Data Quality, the developers and data stewards will see the same rules. Therefore, it is important to develop consistent naming and coding standards. For example, both the data steward and the developer will understand what “rule_” means while not everyone will understand what “mplt_” means. Therefore, mapplets should be named rule_ if they are used in both the Analyst and Developer tools.
