Data quality management Basic
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The document provides an overview of data quality management, highlighting its definitions, dimensions, and the importance of data quality in organizations. It emphasizes issues such as data duplication and inconsistency while outlining the benefits of high-quality data, including improved customer satisfaction and decision-making. The presentation underscores the necessity for robust data quality practices as data volumes increase in today's diverse technological landscape.
Data quality management Basic
- 1. Advance Data Quality Management Basice Overview Khaled Mosharraf. Msc [email protected] A.K.M Bhalul Haque. M.Sc [email protected] FH Kiel, Germany 2016
- 2. Agenda • Motivation / Introduction • Data Quality Definitions • Foundation of Data Quality • Data Quality Assessments • Measuring Data Quality • DQ-Organisation • Data Policies • Data Governance • DQ Policies • Data Profiling Kiel University of Applied Sciences
- 3. Introduction Today is world of heterogeneity. We have different technologies. We operate on different platforms. We have large amount of data being generated everyday in all sorts of organizations and Enterprises. And we do have problems with data. Kiel University of Applied Sciences
- 4. What is data quality? • Data quality is a perception or an assessment of data’s fitness to serve its purpose in a given context. • It is described by several dimensions like • Correctness / Accuracy : Accuracy of data is the degree to which the captured data correctly describes the real world entity. • Consistency: This is about the single version of truth. Consistency means data throughout the enterprise should be sync with each other. Kiel University of Applied Sciences
- 5. • Completeness: It is the extent to which the expected attributes of data are provided. • Timeliness: Right data to the right person at the right time is important for business. • • Metadata: Data about data. Kiel University of Applied Sciences
- 6. Data Quality Definitions i. Intuitive definition ii. System definition iii. Information consumers’ definition iv. Objective and Subjective IQ dimensions v. Context independent and dependent IQ dimensions Kiel University of Applied Sciences
- 7. Data Quality Definitions ‘‘Data quality is measuring data to determine if its fit for the purpose or not. „ • Main problem of data quality Data duplication Data inconsistent Data incomlite Data Ambiguous Kiel University of Applied Sciences
- 8. Data Quality Kiel University of Applied Sciences Real World In the real world, activities are implemented in the field. These activities are designed to produce results that are quantifiable. Data Management System An information system represents these activities by collecting the results that were produced and mapping them to a recording system. Data Quality: How well the DMS represents the real world Real World Data Management System
- 9. Why data quality matters? • Good data is your most valuable asset, and bad data can seriously harm business and credibility… What have you missed? When things go wrong. Making confident decisions. Kiel University of Applied Sciences
- 10. Why data quality is important now a days ? • Improve customer satisfaction. • Reduce of time from empoly on manual process. • Improve Profit. • Improve product • Improve Reportaion Kiel University of Applied Sciences
- 11. Why we interested in data quality. • Day by day data quentity is increasing. So we need any data for use we cannot figureout it easely. So data quality is most important for future anylisis. • Waste of time and money • Labor cost increase if data quality not standerd. Kiel University of Applied Sciences
- 12. Next slide we will continue Kiel University of Applied Sciences
- 13. Thank You If you have any question please write email.