Data Integrity: the Unisa Library experience 14-16 November 2011, North-west University...
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![Page 1: Data Integrity: the Unisa Library experience 14-16 November 2011, North-west University (Potchefstroom Campus) Modiehi Rammutloa IR Quality Reporting Unisa.](https://reader035.fdocuments.in/reader035/viewer/2022062722/56649f385503460f94c547e6/html5/thumbnails/1.jpg)
Data Integrity: the Unisa Library experience
14-16 November 2011, North-west University (Potchefstroom Campus)
Modiehi Rammutloa
IR Quality Reporting
Unisa Library
![Page 2: Data Integrity: the Unisa Library experience 14-16 November 2011, North-west University (Potchefstroom Campus) Modiehi Rammutloa IR Quality Reporting Unisa.](https://reader035.fdocuments.in/reader035/viewer/2022062722/56649f385503460f94c547e6/html5/thumbnails/2.jpg)
What is the fuss about Data Integrity?
![Page 3: Data Integrity: the Unisa Library experience 14-16 November 2011, North-west University (Potchefstroom Campus) Modiehi Rammutloa IR Quality Reporting Unisa.](https://reader035.fdocuments.in/reader035/viewer/2022062722/56649f385503460f94c547e6/html5/thumbnails/3.jpg)
What is data integrity
Data integrity implies that the data system, the process and the content of the data are reliable, consistent and accurate
Sen-Yoni Musingo (2008)
Data Integrity is essential in order for data to be considered credible
Data quality is a perception or an assessment of data’s fitness to serve its intended purpose in a given context.
www.searchdatamanagement.techtarget.com
![Page 4: Data Integrity: the Unisa Library experience 14-16 November 2011, North-west University (Potchefstroom Campus) Modiehi Rammutloa IR Quality Reporting Unisa.](https://reader035.fdocuments.in/reader035/viewer/2022062722/56649f385503460f94c547e6/html5/thumbnails/4.jpg)
Aspects of data integrity
![Page 5: Data Integrity: the Unisa Library experience 14-16 November 2011, North-west University (Potchefstroom Campus) Modiehi Rammutloa IR Quality Reporting Unisa.](https://reader035.fdocuments.in/reader035/viewer/2022062722/56649f385503460f94c547e6/html5/thumbnails/5.jpg)
Data integrity unpacked
o Accuracy - Closeness of measurement to the expected value.
Accuracy can be achieved if data is clean and precise– mechanisms to detect & correct (EDCS)– Business rules (eliminate duplication)– 24/7 approach in data maintenance??– Checks and balances– Default values (using 0 – no empty field)
![Page 6: Data Integrity: the Unisa Library experience 14-16 November 2011, North-west University (Potchefstroom Campus) Modiehi Rammutloa IR Quality Reporting Unisa.](https://reader035.fdocuments.in/reader035/viewer/2022062722/56649f385503460f94c547e6/html5/thumbnails/6.jpg)
Data integrity unpacked
o Consistency - Data as it is at any given point
How is that achieved?
- Standardization (agreement on processes)
- Automation of processes (Special membership)
- Back up systems?
![Page 7: Data Integrity: the Unisa Library experience 14-16 November 2011, North-west University (Potchefstroom Campus) Modiehi Rammutloa IR Quality Reporting Unisa.](https://reader035.fdocuments.in/reader035/viewer/2022062722/56649f385503460f94c547e6/html5/thumbnails/7.jpg)
Data integrity unpacked
Reliability - consistency of measurement.
Same results repeatedly. Can your data be trusted?
Can it be achieved?– Timely– Security– Completeness
![Page 8: Data Integrity: the Unisa Library experience 14-16 November 2011, North-west University (Potchefstroom Campus) Modiehi Rammutloa IR Quality Reporting Unisa.](https://reader035.fdocuments.in/reader035/viewer/2022062722/56649f385503460f94c547e6/html5/thumbnails/8.jpg)
Causes of bad data
o Lack of data clean upo Migration of systemso Walk over technologyo System generated (uploaded data from vendors)o Access rights - malicious modificationso Manual operationso Lack of standardization
![Page 9: Data Integrity: the Unisa Library experience 14-16 November 2011, North-west University (Potchefstroom Campus) Modiehi Rammutloa IR Quality Reporting Unisa.](https://reader035.fdocuments.in/reader035/viewer/2022062722/56649f385503460f94c547e6/html5/thumbnails/9.jpg)
Benefits of high quality data
o Easy retrievability of information resourceso Accessibility of the most relevant informationo Customer satisfactiono Cost reduction (staff time saved)o Image of the Institution (e.g. High quality
catalogue).
![Page 10: Data Integrity: the Unisa Library experience 14-16 November 2011, North-west University (Potchefstroom Campus) Modiehi Rammutloa IR Quality Reporting Unisa.](https://reader035.fdocuments.in/reader035/viewer/2022062722/56649f385503460f94c547e6/html5/thumbnails/10.jpg)
Data Governance structures
o Millennium Working Groupo Data Stewards (External Departments)o Data Integrity Steering Committees
(Management Level)
![Page 11: Data Integrity: the Unisa Library experience 14-16 November 2011, North-west University (Potchefstroom Campus) Modiehi Rammutloa IR Quality Reporting Unisa.](https://reader035.fdocuments.in/reader035/viewer/2022062722/56649f385503460f94c547e6/html5/thumbnails/11.jpg)
Types of data at Unisa
Patron data Procurement
Financial
data
Item & Bib data
course reserve
Unisa Library
![Page 12: Data Integrity: the Unisa Library experience 14-16 November 2011, North-west University (Potchefstroom Campus) Modiehi Rammutloa IR Quality Reporting Unisa.](https://reader035.fdocuments.in/reader035/viewer/2022062722/56649f385503460f94c547e6/html5/thumbnails/12.jpg)
Where do we get data from?
o HR Oracleo Student systemo Millenniumo OCLCo 3rd party information providers and publishers
![Page 13: Data Integrity: the Unisa Library experience 14-16 November 2011, North-west University (Potchefstroom Campus) Modiehi Rammutloa IR Quality Reporting Unisa.](https://reader035.fdocuments.in/reader035/viewer/2022062722/56649f385503460f94c547e6/html5/thumbnails/13.jpg)
Database - Application level
Student system –1.Applications2.Registration3.Study Material4.Assignments5.Examinations6.Graduations
Finance
HR
Uniflow - routing
LibraryExternal databases
Academic exam - XMO
My UnIsa
AD
University estates
Hemis
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Data
Data correction flow
Data
Data
Data
access
ICT’s domainBusiness domain
Data
Data
Data marts
Staging area
Library
users
systems
Data and Information management model
- Data cleansing projects-Data integrity ID actions- Data correction initiatives- Report to DISC
Student, finance, HRExaminations, assignments
![Page 15: Data Integrity: the Unisa Library experience 14-16 November 2011, North-west University (Potchefstroom Campus) Modiehi Rammutloa IR Quality Reporting Unisa.](https://reader035.fdocuments.in/reader035/viewer/2022062722/56649f385503460f94c547e6/html5/thumbnails/15.jpg)
Challenges
o Importance of data integrityo Lack of training and ignoranceo Commitment from data ownerso Data ownership (Branches - Patron)o Access rights (re-deployment of staff in different
sections)o No real time feedback (24 hours)o Data corrected on Millennium is overridden o Commitment from external departmentso Silos – databases all over the show
![Page 16: Data Integrity: the Unisa Library experience 14-16 November 2011, North-west University (Potchefstroom Campus) Modiehi Rammutloa IR Quality Reporting Unisa.](https://reader035.fdocuments.in/reader035/viewer/2022062722/56649f385503460f94c547e6/html5/thumbnails/16.jpg)
What have we got in place?
o Headings reporto URL checkero Database of non-compliances
- Inventory Team & Cataloguerso ED Data Integrity Management Forumo Data Stewards Forum
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Into the future
o Solid monitoring and evaluation processeso Identity management (University initiative)o Standardization of data o Validity checking systemo Data Audit trails and controlso Data quality into Manager’s IPMS