Data quality assessment (DQA) is the process of scientifically and statistically evaluating data in order to determine whether they meet the quality required for projects or business processes and are of the right type and quantity to be able to actually support their intended use. The Data Quality Assessment Report is intended to be a stand-alone report documenting the drivers, process, observations, and recommendations from the data profiling process. The report includes recommendations relating to any discovered or verified anomalies that have critical business impact, including tasks for identifying and eliminating the root cause of the anomaly. With the importance of this report, this should be considered, a must. In making this, it might be a burden if you are going to do it from the very scratch. To help you ease this dilemma, our site offers you free, available and ready-made templates that you can choose from. Just browse among these templates and choose the one that satisfies your needs the most.

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10+ Data Quality Assessment Report Samples

1. Data Quality Assessment Report

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  • PDF

Size: 80 KB

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2. Sample Data Quality Assessment Report

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  • PDF

Size: 866 KB

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3. Data Quality Assessment Final Evaluation Report

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  • PDF

Size: 127 KB

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4. Standard Data Quality Assessment Report

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  • PDF

Size: 311 KB

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5. Data Quality Management Assessment Report

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  • PDF

Size: 407 KB

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6. Data Files Quality Assessment Report

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  • PDF

Size: 1 MB

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7. Data Quality Assessment Recommendation Report

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  • PDF

Size: 580 KB

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8. Data Quality Assessment Initial Report

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  • PDF

Size: 606 KB

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9. Data Quality Assessment Maintenance Report

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  • PDF

Size: 738 KB

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10. Data Set Quality Assessment Report

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  • PDF

Size: 439 KB

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11. Data Quality Assessment Training Report

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  • DOC

Size: 70 KB

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Data Quality Assessment

Data Quality assessment is not an option. Even if you say it will not happen, it will happen. You cannot get very far into managing information in any way and not encounter data quality issues. The only considerations for the approach is the level of detail to be done now vs. later, and the techniques that will be used to conduct the actual assessments. Connecting potential data issues to business impacts and considering the scope of those relationships allows the team to prioritize discovered issues and document a “fitness score” based on the feedback from the business data consumers. In turn, an additional step is needed to consider the options for addressing the most critical issues and providing recommendations for issue remediation, data quality improvements, and/or ongoing data quality inspection.

Data Quality Assessment Report Includes:

A rough outline of a Data Quality Assessment Report contains the following sections:

  • Executive summary, providing high-level overview of the task and the results
  • Introduction, describing how data profiling and additional analyses were used to assess the quality of selected data sets
  • Goals, enumerating the specific goals of the analysis, such as “reviewing the quality of data prior to integration in a data warehouse”
  • Scope, detailing the results of the scope assessment and the identified business impacts
  • Approach, describing the details of how the assessment was performed, namely, profiling and other analyses performed, the identified critical data elements, proposed measurements, and the techniques applied
  • Data analysis results, providing the listed observations of potential anomalies and reasons for consideration
  • Recommendations, detailing the suggestions resulting from the synthesis
  • Open issues, listing any unresolved questions
  • Next steps, providing the action items resulting from the recommendations review and any requirements to resolve any of the open issues.
  • Additional supporting material, including such information as raw statistics from the column, table, and cross-table templates and any other (non profiling) analyses to support the recommendations

FAQs

What is the purpose of Data Quality Assessment?

The aim of a data quality assessment is to identify incorrect data, to estimate the impact on the business processes, and to implement corrective action. Ideally, organizations have no errors in their databases.

What is routine data quality assessment?

The RDQA is designed to assess the quality of data and underlying systems related to indicators that are reported to programs or donors to measure success in program areas related to specific diseases during specific reporting periods.

What is Data Quality Framework?

The Data Quality Framework (DQF) provides an industry-developed best practices guide for the improvement of data quality and allows companies to better leverage their data quality programmes and to ensure a continuously-improving cycle for the generation of master data.

You might find yourself getting caught up in the report that you are making especially that you’ve got to do it from the very scratch. With this, it is highly encouraged of you to make available all the resources you can find online. What are you waiting for? Avail our templates in SampleTemplates now!

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