
Effective Data Warehouse Testing Strategy
Preparing a data warehouse testing strategy can ensure the successful development and completion of end-to-end testing of any data warehouse, data mart, or analytical environment.
Preparing a data warehouse testing strategy can ensure the successful development and completion of end-to-end testing of any data warehouse, data mart, or analytical environment.
Data services support the emergence of data offices and data-centric architectures to allow organizations to develop opportunities for improved data management and usage Data Service
Data warehouse and data integration testing should focus on ETL processes, BI engines, and applications that rely on data from the data warehouse and data
A Data Office has become a reality as part of many organizations’ enterprise architecture frameworks. A data office can support moving the organization toward a
It is important to connect program-level agile frameworks with data and analytics delivery and the variety of application programs that will benefit from agile, flexible
The Business Analyst is confronted with the stakeholder’s desire to modify features, functions and requirements “just because” resulting in scope creep. Why are the stakeholders
Data scientists and other data management professionals need guidance from mentors who have a combination of education, practical experience, and soft skills to support successful
The pervasive and anonymous use of social media has increased the rate and frequency of online harassment and cyber-bullying, endangering the data, safety, reputation, and
Context is essential to the meaning of any text. The value of context in textual data analysis cannot be overlooked. We take context for granted.
There is no difference between requirements for agile projects and those for traditional projects. Therefore, the preferred phrase should be “requirements for agile projects” Agile
Data-oriented positions and professions may be less vulnerable to disruption by automation and new technologies, due to data management’s reliance on critical thinking, analysis, and
Creating and maintaining business and technical metadata definitions are two common data stewardship activities. Doing them well is essential to data stewardship success. Data stewards
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