Services-based architecture (SBA) is emerging as a way to provide immediate (if not completely accurate or complete) data, as well as update a complete, accurate historical data set, using the …
What is Big Data: A Comprehensive Overview
Definition of Big DataIf someone says Big Data, it means huge volume, complex, and diversified datasets that are generated at high rate from distinct sources. These datasets are too big …
Data Science – Process and Iterative Phases
The Data Science process follows the scientific method of refining knowledge by making observations, formulating and testing hypotheses, observing results, and formulating general theories that explain results. Within Data Science, …
Data Science – Dependency
Developing Data Science solutions involves the iterative inclusion of data sources into models that develop insights. Data Science depends on: Rich Data Sources: Data with the potential to show otherwise …
Big Data and Data Science – A Glance
Big Data is produced through email, social media, online orders, and even online video games. Data is generated not only by phones and point-of-sale devices, but also by surveillance systems, …
Data Quality – Policy and Metrics
Data Quality – Policy Data Quality efforts should be supported by and should support data governance policies. For example, governance policies can authorize periodic quality audits and mandate compliance to …
Data Quality – As a Part of Data Governance
A Data Quality program is more effective when part of a data governance program. Often data quality issues are the reason for establishing enterprise-wide data governance. Incorporating data quality efforts …
Data Quality Program – Readiness Assessment / Risk Assessment
Findings from a readiness assessment will help determine where to start and how quickly to proceed. Findings can also provide the basis for roadmapping program goals. If there is strong …
Data Quality Program – Implementation Guidelines
Typically, a hybrid approach works best – top-down for sponsorship, consistency, and resources, but bottom-up to discover what is actually broken and to achieve incremental successes. Improving data quality requires …