Data quality management Basic
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On this slides, we tried to give an overview of advanced Data quality management (ADQM). To understand about DQ why important, and all those steps of DQ management.
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Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail.
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Takeaways:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Data Quality guiding principles & best practices
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Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
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Bob will address the following in this webinar:
- A structure for delivery of a Data Strategy
- How to address people, process, and technology in a Data Strategy
- Why Data Governance is an important piece of a Data Strategy
- How to include Data Governance in the structure of the policy
- Examples of how governance has been included in a Data Strategy
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2. Commonly used data quality dimensions include completeness, conformity, consistency, duplication, integrity, and accuracy. Specific metrics are then defined within each dimension tied to business objectives and rules.
3. Targets and trends provide valuable insights, with traffic light targets highlighting priority areas in red and trends showing progress over time.
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2) Defining Data Quality - What, Why, Costs
3) Identifying problems early - using a simple semantic publishing process as an example
4) Assessing Linked (big) Data quality
5) Quality of LOD cloud datasets
References can be found at the end of the slides
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 (CC-BY-SA-40) International License.
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Join Bob Seiner for the webinar where he will discuss how glossaries, dictionaries, and catalogs can result in effective Data Governance. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must result in governed data. Learn how glossaries, dictionaries, and catalogs can result in Data Governance in this webinar.
Bob will discuss the following subjects in this webinar:
- Successful Data Governance relies on value from very important tools
- What it means to govern your data catalog, business glossary, and data dictionary
- Why governing the metadata in these tools is important
- The roles necessary to govern these tools
- Governance expected from metadata in catalogs, glossaries, and dictionaries
Implementing Effective Data Governance
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Seminar hosted by Embarcadero technologies, where Christopher Bradley presented a session on Data Governance.
Drivers for Data Governance & Benefits
Data Governance Framework
Organization & Structures
Roles & responsibilities
Policies & Processes
Programme & Implementation
Reporting & Assurance
Data Quality Presentation.ppt
Data Quality Presentation.ppthailemariam hailemariam The document discusses data quality in the context of monitoring and evaluating HIV and AIDS programs. It outlines six key elements of data quality - validity, reliability, completeness, precision, timeliness, and integrity. It emphasizes the importance of data quality for evidence-based programming, accountability, and data use. The document also describes South Africa's approach to ensuring data quality, which includes data quality assessments, training, a data warehouse system, and developing a data quality plan as part of the monitoring and evaluation plan.
Open data quality
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Takeaways:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Data Quality guiding principles & best practices
Steps for improving data quality at your organization
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Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
DMBOK 2.0 and other frameworks including TOGAF & COBIT - keynote from DAMA Au...
DMBOK 2.0 and other frameworks including TOGAF & COBIT - keynote from DAMA Au...Christopher Bradley This document provides biographical information about Christopher Bradley, an expert in information management. It outlines his 36 years of experience in the field working with major organizations. He is the president of DAMA UK and author of sections of the DAMA DMBoK 2. It also lists his recent presentations and publications, which cover topics such as data governance, master data management, and information strategy. The document promotes training courses he provides on information management fundamentals and data modeling.
Data Catalog for Better Data Discovery and Governance
Data Catalog for Better Data Discovery and GovernanceDenodo Watch full webinar here: https://buff.ly/2Vq9FR0
Data catalogs are en vogue answering critical data governance questions like “Where all does my data reside?” “What other entities are associated with my data?” “What are the definitions of the data fields?” and “Who accesses the data?” Data catalogs maintain the necessary business metadata to answer these questions and many more. But that’s not enough. For it to be useful, data catalogs need to deliver these answers to the business users right within the applications they use.
In this session, you will learn:
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*What key capability requirements should you expect in data catalogs
*How data virtualization combines dynamic data catalogs with delivery
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In this RWDG webinar, Bob Seiner will share where Data Governance fits into an effective Data Strategy. As part of the strategy, the program must focus on the governance of people, process, and technology fixated on treating and leveraging data as a valued asset. Join us to learn about the role of Data Governance in a Data Strategy.
Bob will address the following in this webinar:
- A structure for delivery of a Data Strategy
- How to address people, process, and technology in a Data Strategy
- Why Data Governance is an important piece of a Data Strategy
- How to include Data Governance in the structure of the policy
- Examples of how governance has been included in a Data Strategy
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Data Quality Management
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Data Quality Dashboards
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2. Commonly used data quality dimensions include completeness, conformity, consistency, duplication, integrity, and accuracy. Specific metrics are then defined within each dimension tied to business objectives and rules.
3. Targets and trends provide valuable insights, with traffic light targets highlighting priority areas in red and trends showing progress over time.
Data Governance
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Real-World Data Governance: What is a Data Steward and What Do They Do?
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- A Data Steward is someone who is responsible for data used in their job, including defining, producing, and ensuring quality of data.
- The role of a Data Steward depends on the organization's data governance approach. It should leverage existing responsibilities rather than assigning new roles.
- Different types of Data Stewards are discussed, including Operational Stewards, Domain Stewards, and Steward Coordinators.
- The responsibilities of Data Stewards include data definition, production
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1) Defining Quality
2) Defining Data Quality - What, Why, Costs
3) Identifying problems early - using a simple semantic publishing process as an example
4) Assessing Linked (big) Data quality
5) Quality of LOD cloud datasets
References can be found at the end of the slides
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 (CC-BY-SA-40) International License.
Data Governance Best Practices
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- 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.