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 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 …
Data Quality – Audit Code Module and Metrics
Quality Check and Audit Code Modules Create shareable, linkable, and re-usable code modules that execute repeated data quality checks and audit processes that developers can get from a library. If …
Data Quality – Preventive and Corrective Actions
Preventive Actions The best way to create high quality data is to prevent poor quality data from entering an organization. Preventive actions stop known errors from occurring. Inspecting data after …
Data Quality – Tools
Tools should be selected and tool architectures should be set in the.planning phase of the enterprise Data Quality program. Tools provide a.partial rule set starter kit but organizations need to …
Data Quality – Incident Tracking System
The incident tracking system will collect performance data relating to issue resolution, work assignments, volume of issues, frequency of occurrence, as well as the time to respond, diagnose, plan a …
Data Quality – SLA – Service Level Agreements
A data quality Service Level Agreement (SLA) specifies an organization’s expectations for response and remediation for data quality issues in each system. Data quality inspections as scheduled in the SLA …