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.

1. Advance Data Quality Management
Basice Overview
Khaled Mosharraf. Msc
mosharrafkhaled@gmx.de
A.K.M Bhalul Haque. M.Sc
b.haque@gmx.de
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
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13. Thank You
If you have any question please
write email.